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- Image-Based Recommendations on Styles and Substitutes3,040
The approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within.
- Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments2,014
This work provides the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset and presents the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery.
- Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection1,828
The proposed memory-augmented autoencoder called MemAE is free of assumptions on the data type and thus general to be applied to different tasks and proves the excellent generalization and high effectiveness of the proposed MemAE.
- Wider or Deeper: Revisiting the ResNet Model for Visual Recognition1,757
This paper starts from a group of relatively shallow networks, which perform as well or even better than the current state-of-the-art models on the ImageNet classification dataset, and initialize fully convolutional networks (FCNs) using pre-trained models, and tune them for semantic image segmentation.
- Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation941
This work shows how to improve semantic segmentation through the use of contextual information, specifically, ' patch-patch' context between image regions, and 'patch-background' context, and formulate Conditional Random Fields with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.
- REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs918
The Retinal Fundus Glaucoma Challenge, REFUGE, was held in conjunction with MICCAI 2018 and it was observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task.
- A survey of appearance models in visual object tracking771
This survey provides a detailed review of the existing 2D appearance models for visual object tracking and takes a module-based architecture that enables readers to easily grasp the key points ofVisual object tracking.
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- FVQA: Fact-Based Visual Question Answering566
A conventional visual question answering dataset is extended, which contains image-question-answer triplets, through additional image- question-answer-supporting fact tuples, and a novel model is described which is capable of reasoning about an image on the basis of supporting-facts.
- REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments551
A dataset of varied and complex robot tasks, described in natural language, in terms of objects visible in a large set of real images, and a novel Interactive Navigator-Pointer model is proposed that provides a strong baseline on the task.
- Deep Anomaly Detection with Deviation Networks490
A novel anomaly detection framework and its instantiation that can be trained substantially more data-efficiently and achieves significantly better anomaly scoring than state-of-the-art competing methods is introduced.
- Learning to rank in person re-identification with metric ensembles481
This work proposes an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated and formulates two optimization algorithms, which directly optimize evaluation measures commonly used in person re -identification.
- Visual question answering: A survey of methods and datasets476
The state of the art by comparing modern approaches to VQA, and the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space are examined.
- What Value Do Explicit High Level Concepts Have in Vision to Language Problems?464
A method of incorporating high-level concepts into the successful CNN-RNN approach is proposed, and it is shown that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering.
- Graph-Structured Representations for Visual Question Answering459
This paper proposes to build graphs over the scene objects and over the question words, and describes a deep neural network that exploits the structure in these representations, and achieves significant improvements over the state-of-the-art.
- From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur439
This work directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow and is the first universal end-to-end mapping from the blur image to the dense motion flow.
- Fast Supervised Hashing with Decision Trees for High-Dimensional Data430
Experiments demonstrate that the proposed method significantly outperforms most state-of-the-art methods in retrieval precision and training time, and is orders of magnitude faster than many methods in terms of training time.
- Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge403
This work presents a massive exploration of the effects of the myriad architectural and hyperparameter choices that must be made in generating a state-of-the-art model and provides a detailed analysis of the impact of each choice on model performance.
- Image Captioning and Visual Question Answering Based on Attributes and External Knowledge401
A visual question answering model that combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions and allows questions to be asked where the image alone does not contain the information required to select the appropriate answer.
- Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources392
A method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions and is specifically able to answer questions posed in natural language, that refer to information not contained in the image.
- Attention-Guided Network for Ghost-Free High Dynamic Range Imaging313
The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error, and can achieve state-of-the-art quantitative and qualitative results.
- Explicit Knowledge-based Reasoning for Visual Question Answering295
A method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base is described, addressing one of the key issues in general visual answering.
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- Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Networks281
A graph-based, language-guided attention mechanism that represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches, and enables the comprehension decision to be visualizable and explainable.
- Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection257
This work devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data and outperforms state-of-the-art methods that require no labeled training data.
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- Fast machine-learning online optimization of ultra-cold-atom experiments220
It is demonstrated that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique.
- Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification213
A hybrid deep architecture which combines Fisher vectors and deep neural networks to learn non-linear transformations of pedestrian images to a deep space where data can be linearly separable.
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- VideoTrace209
The combination of automated and manual reconstruction allows VideoTrace to model parts of the scene not visible, and to succeed in cases where purely automated approaches would fail.
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- On the fitting of surfaces to data with covariances205
This work considers the problem of estimating parameters of a model described by an equation of special form, and generates a Newton-like iterative scheme that has as its theoretical limit the minimizer of the cost function.
- The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification201
This paper advocates that if used appropriately, convolutional layer activations constitute a powerful image representation by adopting a new technique proposed in this paper called cross-convolutional-layer pooling, and can achieve comparable or in some cases significantly better performance than existing fully-connected layer based image representations.
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- Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression194
An l2,1-norm based objective function is proposed which can simultaneously suppress the noisy signal in the text and learn a function to match the text document and visual features and develops an optimization algorithm to efficiently solve the resulting problem.
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- A General Two-Step Approach to Learning-Based Hashing188
This framework decomposes the hashing learning problem into two steps: hash bit learning and hash function learning based on the learned bits, and can typically be formulated as binary quadratic problems, and the second step can be accomplished by training standard binary classifiers.
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- Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features184
This work proposes a simple yet effective approach to the problem of pedestrian detection which outperforms the current state-of-the-art and directly optimises the partial area under the ROC curve (pAUC) measure, which concentrates detection performance in the range of most practical importance.
- Retrieval Augmented Classification for Long-Tail Visual Recognition157
Retrieval Augmented Classification is introduced, a generic approach to augmenting standard image classification pipelines with an explicit retrieval module that learns a high level of accuracy on tail classes and is applied to the problem of long-tail classification.
- Hashing on Nonlinear Manifolds157
It is shown that hashing on the basis of t-distributed stochastic neighbor embedding outperforms state-of-the-art hashing methods on large-scale benchmark data sets, and is very effective for image classification with very short code lengths, and the proposed framework can be further improved.
- Deep Weakly-supervised Anomaly Detection152
Empirical results on 12 real-world datasets show that PReNet significantly outperforms nine competing methods in detecting seen and unseen anomalies, and theoretically and empirically justify the robustness of the model w.r.t. anomaly contamination in the unlabeled data.
- Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries152
A unified framework, the ParalleL AttentioN (PLAN) network, to discover the object in an image that is being referred to in variable length natural expression descriptions, from short phrases query to long multi-round dialogs is proposed.
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- Object-and-Action Aware Model for Visual Language Navigation142
An Object-and-Action Aware Model (OAAM) is proposed that processes these two different forms of natural language based instruction separately and enables each process to match object-centered/action-centered instruction to their own counterpart visual perception/action orientation flexibly.
- Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning140
A novel ensemble learning method is proposed which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning and a new approach to extracting low-level visual features based on spatial pooling.
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- Poseur: Direct Human Pose Regression with Transformers135
This work proposes an attention mechanism that adaptively attends to the features that are most relevant to the target keypoints, considerably improving the accuracy of this regression-based approach to 2D human pose estimation.
- Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation135
This work introduces a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations.
- Active Learning by Feature Mixing135
This paper proposes a novel method for batch AL called ALFA-Mix, which identifies unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions resulting from interventions on their representations, and derives an efficient implementation based on a closed-form solution to the optimal interpolation causing changes in predictions.
- DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution133
A dynamic, proposal-free, data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances, which achieves promising results on both ScanetNetV2 and S3DIS, and improves inference speed by more than 25% over the current state-of-the-art.
- Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data133
A deep reinforcement learning-based approach that enables an end-to-end optimization of the detection of both labeled and unlabeled anomalies and significantly outperforms five state-of-the-art competing methods.
- Are You Talking to Me? Reasoned Visual Dialog Generation Through Adversarial Learning133
A novel approach that combines Reinforcement Learning and Generative Adversarial Networks (GANS) to generate more human-like responses to questions to overcome the relative paucity of training data, and the tendency of the typical MLE-based approach to generate overly terse answers.
- Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision132
This work proposes an auxiliary training objective that improves the generalization capabilities of neural networks by leveraging an overlooked supervisory signal found in existing datasets, a.k.a counterfactual examples, which provide a signal indicative of the underlying causal structure of the task.
- On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering132
A dataset that takes a step towards addressing the VQA problem in that it contains questions expressed in two languages, and an evaluation process that co-opts a well understood image-based metric to reflect the method’s ability to reason.
- Exploring Context with Deep Structured Models for Semantic Segmentation132
This work formulate deep structured models by combining CNNs and Conditional Random Fields for learning the patch-patch context between image regions, and formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.
- Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework122
This paper proposes to detect all the three important objects in a single learning-based detection framework, consisting of a dense feature extractor and detectors of three important classes, and proposes an object subcategorization method as a means of capturing the intraclass variation of objects.
- Learning Deep Gradient Descent Optimization for Image Deconvolution120
A recurrent gradient descent network (RGDN) is proposed by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme and learns an implicit image prior and a universal update rule through recursive supervision.
- Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization116
This work demonstrates that the simplicity bias can be mitigated and OOD generalization improved, and obtains SOTA re-sults in visual recognition on biased data and generalization across visual domains, the first to evade the simplicity biases.
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- Mid-level Deep Pattern Mining∗114
This work finds that for an image patch, activation extracted from the first fully-connected layer of a CNN have two appealing properties which enable its seamless integration with pattern mining, and applies this approach to scene and object classification tasks, demonstrating that it outperforms all previous works on mid-level visual element discovery by a sizeable margin.
- Visual Question Answering with Memory-Augmented Networks112
It is shown that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting.
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- Pushing the Limits of Deep CNNs for Pedestrian Detection97
By reusing the convolutional feature maps of a DCNN model as image features to train an ensemble of boosted decision models, this paper is able to achieve the best reported accuracy without using specially designed learning algorithms.
- The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation95
This work proposes an object-informed sequential BERT to encode visual perceptions and linguistic instructions at the same fine-grained level, namely objects and words, and enables the visual-textual clues to be interpreted in light of the temporal context, which is crucial to multi-round VLN tasks.
- Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking95
This paper constructs an appearance model using the 3D discrete cosine transform (3D-DCT) and designs a discriminative criterion to evaluate the likelihood of a test sample belonging to the foreground object that is embedded into a particle filtering framework for object state inference over time.
- Towards Effective Deep Embedding for Zero-Shot Learning92
This paper proposes a pseudo labeling strategy to progressively incorporate the testing samples into the training process and thus balance the model between seen and unseen classes and extends the method to a transductive setting to better handle the model bias problem in ZSL.
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- The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions90
The core of the proposed method is a new co-attention model that learns how to exploit a set of external off-the-shelf algorithms to achieve its goal, an approach that has something in common with the Neural Turing Machine.
- Cross-Convolutional-Layer Pooling for Image Recognition90
A novel way to extract image representations from two consecutive convolutional layers: one layer is used for local feature extraction and the other serves as guidance to pool the extracted features, achieving superior performance over existing approaches for extracting image representation from a DCNN.
- Infinite Variational Autoencoder for Semi-Supervised Learning88
An infinite variational autoencoder (VAE) whose capacity adapts to suit the input data is presented, using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference.
- Characterness: An Indicator of Text in the Wild88
This work proposes a bottom-up approach to the problem of detecting general text in images, which reflects the characterness of an image region, and develops three novel cues that are tailored for character detection and a Bayesian method for their integration.
- Unshuffling Data for Improved Generalization in Visual Question Answering87
This work describes a training procedure to capture the patterns that are stable across environments while discarding spurious ones, and demonstrates multiple use cases with the task of visual question answering, which is notorious for dataset biases.
- Supervised Hashing Using Graph Cuts and Boosted Decision Trees87
The proposed framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods, and decomposes the hashing learning problem into two steps: binary code (hash bit) learning and hash function learning.
- Deep learning for hate speech detection: a comparative study85
A large-scale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets is presented, to illuminate progress in the area, and identify strengths and weaknesses in the current state of the art.
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- Probabilistic Multiple Cue Integration for Particle Filter Based Tracking85
This paper demonstrates how the combination of multiple cue integration and particle filtering results in a robust track- ing method and demonstrates how each cue's weight can be adapted on-line during the tracking procedure.
- Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction84
A novel cluster sparsity field based HSI reconstruction framework which explicitly models both the intrinsic correlation between measurements within the spectrum for a particular pixel, and the similarity between pixels due to the spatial structure of the HSI, thus combating the effects of noise corruption or undersampling.
- When Unsupervised Domain Adaptation Meets Tensor Representations82
This work proposes a learning-based method to adapt the source and target tensor representations directly, without vectorization, and shows that this method outperforms the state-of-the-art on the task of cross-domain visual recognition in both efficacy and efficiency.
- Non-sparse linear representations for visual tracking with online reservoir metric learning80
This work proposes a visual tracker based on non-sparse linear representations, which admit an efficient closed-form solution without sacrificing accuracy, and shows that online metric learning using proximity comparison significantly improves the robustness of the tracking.
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- Revisiting hartley's normalized eight-point algorithm76
A novel explanation is given for the improvement in performance of the eight-point algorithm that results from using normalized data, and it is shown that this cost function is statistically better founded than the cost function associated with the nonnormalized algorithm.
- Structured Multimodal Attentions for TextVQA73
This paper proposes an end-to-end structured multimodal attention (SMA) neural network that outperforms the SoTA models on TextVQA dataset and two tasks of ST-VQ a dataset among all models except pre-training based TAP.
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- From Speaker to Dubber: Movie Dubbing with Prosody and Duration Consistency Learning71
This work proposes a two-stage dubbing method that allows the model to first learn pronunciation knowledge before practicing it in movie dubbing, and outperforms several state-of-the-art methods on two primary benchmarks.
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- Reinforcement Learning with Attention that Works: A Self-Supervised Approach67
This work proposes the first combination of self attention and reinforcement learning that is capable of producing significant improvements, including new state of the art results in the Arcade Learning Environment.
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- Compositional Model Based Fisher Vector Coding for Image Classification62
This work breaks the convention which assumes that a local feature is drawn from a Gaussian distribution whose mean vector is composed as a linear combination of multiple key components, and the combination weight is a latent random variable and greatly enhance the representative power of the generative model underlying FVC.
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- Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition60
This work shows that the discriminative information of a skeleton sequence usually resides in a short temporal interval and proposes a simple-but-effective local descriptor called trajectorylet to capture the static and kinematic information within this interval and obtains superior results over existing approaches under various experimental setups.
- Large-Scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications60
This work presents a semidefinite programming (SDP) formulation for BQPs, with two desirable properties, and proposes two solvers, namely, quasi-Newton and smoothing Newton methods, for the simplified dual problem.
- Rationalising the Renormalisation Method of Kanatani60
An alternative derivation of the renormalisation technique of Kanatani is presented, and it is shown that the minimiser of the cost function must satisfy a special variational equation and not have as their theoretical limit the desired minimiser.
- Image Co-localization by Mimicking a Good Detector’s Confidence Score Distribution59
An entropy-based objective function is devised to enforce the above property when learning the common object detector, and it is shown that despite its simplicity, the approach outperforms state-of-the-arts.
- Mining Mid-level Visual Patterns with Deep CNN Activations58
It is shown that Convolutional Neural Network activations extracted from image patches typical possess two appealing properties that enable seamless integration with pattern mining techniques, and it is demonstrated that this approach outperforms or matches the performance of the state-of-the-arts on these tasks.
- Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning56
A Sparse neural Network for Continual Learning (SNCL), which employs variational Bayesian sparsity priors on the activations of the neurons in all layers and achieves state-of-the-art result for mitigating forgetting.
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- Guaranteed Ellipse Fitting with the Sampson Distance54
This work proposes a fast stable algorithm for fitting a guaranteed ellipse to data using the Sampson distance as a data-parameter discrepancy measure and shows that the algorithm is a fast and accurate approximation of the computationally more expensive orthogonal-distance-based ellipso fitting method.
- Dual-Attention-Guided Network for Ghost-Free High Dynamic Range Imaging51
This work proposes a novel dual-attention-guided end-to-end deep neural network, called DAHDRNet, which produces high-quality ghost-free HDR images and employs a hybrid loss function, which consists of a perceptual loss, a total variation loss, and a content loss to recover photo-realistic images.
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- HCVRD: A Benchmark for Large-Scale Human-Centered Visual Relationship Detection48
A webly-supervised approach to these problems is proposed and it is demonstrated that the proposed model provides a strong baseline on the authors' HCVRD dataset.
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- Visual Question Answering as Reading Comprehension47
This paper proposes to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem, which is a step towards being able to exploit large volumes of text and natural language processing techniques to address V QA problem.
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- Visual Question Answering as a Meta Learning Task45
This work adapts a state-of-the-art VQA model with two techniques from the recent meta learning literature, namely prototypical networks and meta networks, and produces qualitatively distinct results with higher recall of rare answers, and a better sample efficiency that allows training with little initial data.
- Efficient Pedestrian Detection by Directly Optimizing the Partial Area under the ROC Curve45
A novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning is proposed.
- Robust Tracking with Weighted Online Structured Learning45
This work develops a novel weighted form of online risk which allows more subtlety in its representation and proposes a principled approach to weighted online learning using weighted reservoir sampling and a weighted regret bound as a theoretical guarantee of performance.
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- A new constrained parameter estimator for computer vision applications45
A salient feature of the method is that it handles the ancillary constraint in an integrated fashion, not by means of a correction process operating upon results of unconstrained minimisation.
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- Adaptive Importance Learning for Improving Lightweight Image Super-Resolution Network42
This study presents an adaptive importance learning scheme for SISR that trains the network with an easy-to-complex paradigm by dynamically updating the importance of image pixels on the basis of the training loss, and enables smaller, lightweight, networks to achieve better performance than has previously been possible.
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- Image Captioning and Visual Question Answering Based on Attributes and Their Related External Knowledge.41
A visual question answering model is designed that combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions.
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- High dynamic range imaging via gradient-aware context aggregation network38
The proposed HDR imaging approach that aggregates the information from multiple LDR images with guidance from image gradient domain generates artifact-free images by integrating the image gradient information and the image context information in the pixel domain.
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- V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices38
A new large-scale benchmark to evaluates abstract reasoning over real visual data is proposed, and it is found that existing models, including those popular for vision-and-language tasks, are unable to solve seemingly-simple instances.
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- Goal-Oriented Visual Question Generation via Intermediate Rewards37
A Deep Reinforcement Learning framework based on three new intermediate rewards, namely goal-achieved, progressive and informativeness that encourage the generation of succinct questions, which in turn uncover valuable information towards the overall goal is proposed.
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- Domain Generalization via Rationale Invariance36
This paper proposes treating the element-wise contributions to the final results as the rationale for making a decision and representing the rationale for each sample as a matrix, indicating the model relies on domain-invariant clues to make decisions, thereby ensuring robust results.
- Learning to Zoom-In via Learning to Zoom-Out: Real-World Super-Resolution by Generating and Adapting Degradation35
A framework to learn SR from an arbitrary set of unpaired LR and HR images and to see how far a step can go in such a realistic and “unsupervised” setting is proposed.
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- FNS, CFNS and HEIV: A Unifying Approach35
This work extends the approach to more general cost functions of FNS, CFNS and HEIV to be placed within a common framework, and finds unconstrained (FNS, HEIV) and constrained (CFNS) minimisers of cost functions.
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- Multi-way backpropagation for training compact deep neural networks33
This paper first investigates the supervision vanishing issue in existing backpropagation (BP) methods and proposes to address it via an effective method, called Multi-way BP (MW-BP), which relies on multiple auxiliary losses added to the intermediate layers of the network.
- Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People33
A movement monitoring approach for the recognition of bed and chair exits based on a machine learning activity predictor that has potential for real-time monitoring but further research in the final target population of older people is necessary.
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- Training Effective Node Classifiers for Cascade Classification31
This work shows that a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of Wu et al. (linear asymmetricclassifier for cascade detectors, 2005), and designs a new boosting algorithm that directly optimizes the cost function of LAC.
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- Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems29
This paper shows that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process.
- Sequential Person Recognition in Photo Albums with a Recurrent Network29
This work proposes a novel recurrent network architecture, in which relational information between instances labels and appearance are modeled jointly, and demonstrates that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset.
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- Deep Reinforcement Learning for Unknown Anomaly Detection.20
A deep reinforcement-learning-based approach that actively seeks novel classes of anomaly that lie beyond the scope of the labeled training data to improve detection accuracy, without limiting the set of anomalies sought to those given anomaly examples.
- What's to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions20
A Bayesian model of the uncertainty in the implicit model maintained by the visual dialogue agent, and in the function used to select an appropriate output, enables a principled method for identifying when enough information has been acquired, and an action should be selected.
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- Image Captioning with an Intermediate Attributes Layer.18
This work shows that an intermediate image-to-attributes layer can dramatically improve captioning results over the current approach which directly connects an RNN to a CNN.
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- Online unsupervised feature learning for visual tracking17
It is shown that a small dictionary, learned and updated online is as effective and more efficient than a huge dictionary learned offline, which facilitates the advantages of both feature learning and structured output prediction.
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- MPGL: An Efficient Matching Pursuit Method for Generalized LASSO15
This work formulate the GL problem as a convex quadratic constrained linear programming (QCLP) problem and tailor-make a cutting plane method, and proposes an efficient Matching Pursuit Generalized LASSO (MPGL) method, which overcomes issues, and is guaranteed to converge to a global optimum.
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- Building Models of Regular Scenes from Structure and Motion15
The method uses the camera models and point cloud typically generated by a structure-and-motion process as a starting point for developing a higher level model of the scene to improve the accuracy of fit.
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- Weakly-supervised Deep Anomaly Detection with Pairwise Relation Learning.10
This paper addresses weakly-supervised anomaly detection with limited labeled anomalies and a large unlabeled data set by formulating the problem as a pairwise relation learning task and significantly outperforms state-of-the-art competing methods in detecting both seen and unseen anomalies.
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- An Active Information Seeking Model for Goal-oriented Vision-and-Language Tasks.9
This work proposes an reinforcement-learning approach that maintains an distribution over its internal information, thus explicitly representing the ambiguity in what it knows, and needs to know, towards achieving its goal.
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- Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)8
This workshop is focused on Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA) and will gather researchers and practitioners from data mining, machine learning, and computer vision communities and diverse knowledge background to promote the development of fundamental theories, effective algorithms, and novel applications of anomaly and novelty detection, characterization, and adaptation.
- GADE: A Generative Adversarial Approach to Density Estimation and its Applications8
A generative adversarial density estimator (GADE) is proposed, a density estimation approach that bridges the gap between the two likelihood-free models and a Bayesian model where the predictive variance can be leveraged to measure the confidence in the likelihood.
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- What value high level concepts in vision to language problems8
A method of incorporating high-level concepts into the very successful CNN-RNN approach is proposed, and it is shown that it achieves a significant improvement on the state-of-the-art performance in both image captioning and visual question answering.
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- RERERE: Remote Embodied Referring Expressions in Real indoor Environments.7
This work proposes the first Remote Embodied Referring Expression dataset of natural language references to remote objects in real images, which represents a complex challenge, but one that closely reflects one of the core visual problems in robotics.
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- A shape hierarchy for 3D modelling from video7
The method provides a modelling process which requires the user to provide only high level scene information, the remaining detail being provided through geometric analysis of the image set, and allows a non-expert user to rapidly and intuitively create a visually convincing 3D model of a real world scene from an image set.
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- Gold Seeker: Information Gain From Policy Distributions for Goal-Oriented Vision-and-Langauge Reasoning6
This work proposes a reinforcement-learning approach that maintains a distribution over its internal information, thus explicitly representing the ambiguity in what it knows, and needs to know, towards achieving its goal.
- Watch, Reason and Code6
A Watch-Reason-Code (WRC) model to synthesise programs that describe the process carried out in a set of video sequences and can achieve the state-of-the-art under a variety of settings is proposed.
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- Learning Sparse Confidence-Weighted Classifier on Very High Dimensional Data6
This paper presents an online-batch CW learning scheme, and then presents a novel paradigm to learn sparse CW classifiers, which essentially identifies feature groups and naturally builds a block diagonal covariance structure, making it very suitable for CW learning over very high-dimensional data.
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- MuseBarControl: Enhancing Fine-Grained Control in Symbolic Music Generation through Pre-Training and Counterfactual Loss5
A pre-training task designed to link control signals directly with corresponding musical tokens helps in achieving a more effective initialization for subsequent fine-tuning, and a novel counterfactual loss is implemented that promotes better alignment between the generated music and the control prompts.
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- Fitting multiple models to multiple images with minimal user interaction5
A method for modelling compound structures which intelligently divides this prior information into that which might be applied by the system and that which may not, in a manner which minimises the human input required.
- Rapid Interactive Modelling from Video with Graph Cuts5
The method is novel in that it uses information from several sources—video, sparse 3D points and user input—to fit models to a scene, and avoids common pitfalls of both automatic structure and motion algorithms, and image-based modelling pack ages.
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- Let Your Video Listen to Your Music! -- Beat-Aligned, Content-Preserving Video Editing with Arbitrary Music4
A novel and efficient framework-termed MVAA (Music-Video Auto-Alignment)-that automatically edits video to align with the rhythm of a given music track while preserving the original visual content while preserving the original visual content is proposed.
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- Deep Depression Prediction on Longitudinal Data via Joint Anomaly Ranking and Classification4
It is shown that data such as housing status, and the details of the family environment, can provide cues for predicting future psychiatric disorders, and a novel deep multi-task recurrent neural network is introduced to learn time-dependent depression cues.
- ANDEA: Anomaly and Novelty Detection, Explanation, and Accommodation4
This workshop will gather researchers and practitioners from data mining, machine learning, and computer vision communities and diverse knowledge background to promote the development of fundamental theories, effective algorithms, and novel applications of anomaly and novelty detection, characterization, and adaptation.
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- Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale3
This paper proposes a novel approach that considers multiple prediction hypotheses for each sample and investigates the rationale behind each hypothesis, and identifies the most likely correct hypotheses, which are used as a pseudo-labeled set to support a semi-supervised learning procedure for model adaptation.
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- Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference3
Experiments show that the proposed algorithm achieves better approximation than the state-of-the-art methods within a variety of time budgets on challenging non-submodular MAP-MRF inference problems.
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- Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data.2
A novel deep multi-task recurrent neural network is introduced to tackle depression classification, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking.
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- Cross-convolutional-layer Pooling for Generic Visual Recognition.1
A novel way to extract image representations from two consecutive convolutional layers: one layer is utilized for local feature extraction and the other serves as guidance to pool the extracted features.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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