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Works440 from public data
- BERT-ATTACK: Adversarial Attack Against BERT Using BERT936
This paper proposes a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT against its fine-tuned models and other deep neural models for downstream tasks and successfully misleads the target models to predict incorrectly.
- DSOD: Learning Deeply Supervised Object Detectors from Scratch646
Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch following the single-shot detection (SSD) framework, and one of the key findings is that deep supervision, enabled by dense layer-wise connections, plays a critical role in learning a good detector.
- Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach618
A weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure to regularize the 3D pose prediction, which is effective in the absence of ground truth depth labels.
- ExFuse: Enhancing Feature Fusion for Semantic Segmentation572
It is found that introducing semantic information into low-level features and high-resolution details into high-level features is more effective for the later fusion, and a new framework, named ExFuse, is proposed to bridge the gap between low-level and high-level features.
- Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification479
This work proposes a hybrid deep learning framework for video classification, which is able to model static spatial information, short-term motion, as well as long-term temporal clues in the videos, and achieves very competitive performance on two popular and challenging benchmarks.
- Pose-Normalized Image Generation for Person Re-identification476
This work proposes a novel deep person image generation model for synthesizing realistic person images conditional on the pose based on a generative adversarial network designed specifically for pose normalization in re-id, thus termed pose-normalization GAN (PN-GAN).
- Metaverse: Perspectives from graphics, interactions and visualization396
This paper proposes a framework that summarizes how graphics, interaction, and visualization techniques support the visual construction of the metaverse and user-centric exploration and proposes a taxonomy of interaction technologies based on interaction tasks, user actions, feedback and various sensory channels.
- Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks385
Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed regularized DNN (rDNN) is more suitable for modeling video semantics.
- Transfer learning for collaborative filtering via a rating-matrix generative model385
The proposed rating-matrix generative model (RMGM) can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap, and can outperform the individual models trained separately.
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- Multi-scale Deep Learning Architectures for Person Re-identification319
A novel multi-scale deep learning model is proposed that is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching.
- Star-Transformer296
Star-Transformer replaces the fully-connected structure with a star-shaped topology, in which every two non-adjacent nodes are connected through a shared relay node, and complexity is reduced from quadratic to linear, while preserving the capacity to capture both local composition and long-range dependency.
- Long-Term Cloth-Changing Person Re-identification248
This work introduces a shape embedding module as well as a cloth-elimination shape-distillation module aiming to eliminate the now unreliable clothing appearance features and focus on the body shape information.
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- Trajectory-Based Modeling of Human Actions with Motion Reference Points224
This paper proposes a simple representation specifically aimed at the modeling of human action recognition in videos that operates on top of visual codewords derived from local patch trajectories, and therefore does not require accurate foreground-background separation, which is typically a necessary step to model object relationships.
- Multi-Stream Multi-Class Fusion of Deep Networks for Video Classification188
A multi-stream framework that is able to exploit multimodal features that are more comprehensive than those previously attempted and produces significantly better results than the state of the arts on two popular benchmarks, 92.2% on UCF-101 (without using audio) and 84.9% on Columbia Consumer Videos.
- Addressing cold start in recommender systems: a semi-supervised co-training algorithm188
This paper uses a factorization model to capture fine-grained user-item context and proposes a semi-supervised ensemble learning algorithm that constructs different (weak) prediction models using examples with different contexts and employs the co-training strategy to allow each ( weak) prediction model to learn from the other prediction models.
- Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content186
This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, data sets, and evaluation settings and highlights the limitations of existing approaches.
- Learning to Score Figure Skating Sport Videos181
A deep architecture that includes two complementary components, i.e., Self-Attentive L STM and Multi-scale Convolutional Skip LSTM can efficiently learn the local and global sequential information in each video.
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- Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection170
A depth-conditioned dynamic message propagation (DDMP) network to effectively integrate the multi-scale depth information with the image context and dynamically predicting hybrid depth-dependent filter weights and affinity matrices for propagating information is proposed.
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- MEAL: Multi-Model Ensemble via Adversarial Learning157
This paper proposes an adversarial-based learning strategy where a block-wise training loss is defined to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously.
- Exploring Inter-feature and Inter-class Relationships with Deep Neural Networks for Video Classification154
A novel unified framework that jointly learns feature relationships and exploits the class relationships for improved video classification performance is proposed and demonstrates that the proposed framework exhibits superior performance over several state-of-the-art approaches.
- DeepSFM: Structure from Motion via Deep Bundle Adjustment142
This work designs a physical driven architecture, namely DeepSFM, inspired by traditional Bundle Adjustment, which consists of two cost volume based architectures for depth and pose estimation respectively, iteratively running to improve both.
- Weakly Supervised Dense Video Captioning142
This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences, and proposes lexical fully convolutional neural networks with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels.
- Predicting Emotions in User-Generated Videos141
Results of a comprehensive set of experiments indicate that combining multiple types of features---such as the joint use of the audio and visual clues---is important, and attribute features such as those containing sentiment-level semantics are very effective.
- Multi-Class Support Vector Machine139
This chapter exhibits a new Simplified Multi-class SVM (SimMSVM) that reduces the size of the resulting dual problem from l × k to l by introducing a relaxed classification error bound.
- Learning Dynamic Alignment via Meta-filter for Few-shot Learning134
This paper proposes to learn a dynamic alignment, which can effectively highlight both query regions and channels according to different local support information and facilitates dynamical knowledge adaptation for few-shot learning.
- Evaluating Two-Stream CNN for Video Classification126
An in-depth study is conducted to investigate important implementation options that may affect the performance of deep nets on video classification, and very competitive results are attained on two popular video classification benchmarks.
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- Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification123
A novel hybrid deep learning framework is introduced that integrates useful clues from multiple modalities, including static spatial appearance information, motion patterns within a short time window, audio information, as well as long-range temporal dynamics.
- Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers114
A hierarchical boosting algorithm is proposed by incorporating feature hierarchy and boosting to scale up SVM image classifier training in high-dimensional feature space and its experiments on a specific domain of natural images have obtained very positive results.
- SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation111
This paper proposes a method of Category-level 6D Object Pose and Size Estimation (COPSE) from the point cloud of the target object, without external real pose-annotated training data, and deploys the approach to a physical Baxter robot to perform grasping tasks on unseen but category-known instances.
- Towards Instance-Level Image-To-Image Translation109
This paper presents a simple yet effective instance-aware image-to-image translation approach (INIT), which employs the fine-grained local (instance) and global styles to the target image spatially and collects a large-scale benchmark for the new instance-level translation task.
- Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions108
The method first interpolates the low-res point cloud according to a given upsampling rate, and then refine the positions of the interpolated points with an iterative optimization process, guided by a trained model estimating the difference between the current point cloud and the high-res target.
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- Object Detection from Scratch with Deep Supervision103
Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch, is proposed based on the single-shot detection framework (SSD), and achieves consistently better results than the state-of-the-art methods with much more compact models.
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- Density-preserving Deep Point Cloud Compression93
A novel deep point cloud compression method that preserves local density information by encoding local geometry and density with three embeddings: density embedding, local position embedding and ancestor embedding.
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- Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study86
Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists' evaluation) and radiologists’ workload.
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- Sketch-BERT: Learning Sketch Bidirectional Encoder Representation From Transformers by Self-Supervised Learning of Sketch Gestalt85
This work presents a model of learning Sketch Bidirectional Encoder Representation from Transformer (Sketch-BERT), and generalizes BERT to sketch domain, with the novel proposed components and pre-training algorithms, including the newly designed sketch embedding networks, and the self-supervised learning of sketch gestalt.
- Understanding and Predicting Interestingness of Videos85
A pilot study on the understanding of human perception of video interestingness, and demonstrates a simple computational method to identify more interesting videos.
- Cross-domain collaborative filtering over time85
This paper adopts the cross-domain CF framework to share the static group-level rating matrix across temporal domains, and lets user-interest distribution over item groups drift slightly between successive temporal domains and based on a Bayesian latent factor model.
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- FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird’s-Eye View and Perspective View79
A new method, dubbed FastOcc, is proposed that achieves state-of-the-art results with a fast inference speed by carefully analyzing the network effect and latency from four parts, including the input image resolution, image backbone, view transformation, and occupancy prediction head.
- Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition76
A stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters is proposed, which can be easily generalized to other languages whose characters can be decomposed into strokes.
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- Adaptively Weighted Multi-task Deep Network for Person Attribute Classification75
A novel validation loss trend algorithm is proposed to dynamically and adaptively update the weight for learning each task in the training process to solve the problems of multi-task deep convolutional neural network for person attribute analysis.
- Delving into Data: Effectively Substitute Training for Black-box Attack73
A novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process and the combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack.
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- Multi-Scale Self-Attention for Text Classification72
A Multi-Scale Transformer which uses multi-scale multi-head self-attention to capture features from different scales and a strategy to control the scale distribution for each layer is designed.
- M$^3$Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging71
The quantitative results with plenty of metrics indicate the superiority of the proposed model on both slice- and patient-level classification tasks, and the generated lesion location maps make the system interpretable and more valuable to clinicians.
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- The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection67
This paper digs into the 3D object detection task and reformulate it as the sub-tasks of object localization and appearance perception, which benefits to a deep excavation of reciprocal information underlying the entire task.
- Neural Pose Transfer by Spatially Adaptive Instance Normalization67
This paper proposes the first neural pose transfer model that solves the pose transfer via the latest technique for image style transfer, leveraging the newly proposed component -- spatially adaptive instance normalization.
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- Discriminant neighborhood embedding for classification64
A novel subspace learning method called discriminant neighborhood embedding (DNE) is proposed for pattern classification Suppose that multi-class data points in high-dimensional space tend to move due to local intra-class attraction or inter-class repulsion and the optimal embedding from the point of view of classification is discovered consequently.
- Cost-Sensitive GNN-Based Imbalanced Learning for Mobile Social Network Fraud Detection61
Extensive experiments are carried out on two publicly accessible real-world mobile network fraud datasets, revealing that the proposed CSGNN effectively addresses the graph imbalance issue while outperforming state-of-the-art algorithms in detection performance.
- Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction61
This paper for the first time, advocate a multi-task deep neural network for jointly learning face recognition and facial attribute prediction tasks and can achieve the best performance on the small protocol among all the publicly released methods.
- Semantic Feature Augmentation in Few-shot Learning.60
A novel auto-encoder network dual TriNet, which projects multi-layer visual features of deep CNNs into the semantic space, and the augmented instance representation is projected back into the image feature spaces by the decoder TriNet.
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- Chinese Text Recognition with A Pre-Trained CLIP-Like Model Through Image-IDS Aligning55
Inspired by the way humans recognize Chinese texts, a two-stage framework for Chinese Text Recognition is proposed, which can recognize zero-shot Chinese characters in text images without fine-tuning, whereas previous methods require fine- Tuning when new classes appear.
- TC-Net for iSBIR55
A Triplet Classification Network (TC-Net) for iSBIR is presented which is composed of two major components: triplet Siamese network, and auxiliary classification loss which can break the limitations existed in previous works.
- BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training52
A new large-scale benchmark termed BigDetection is constructed, which has 600 object categories and contains over 3.4M training images with 36M bounding boxes and a new taxonomy is generated which unifies the heterogeneous label spaces from different sources.
- Weakly-supervised Transfer for 3D Human Pose Estimation in the Wild.52
A weakly-supervised transfer learning method that learns an end-to-end network using training data with mixed 2D and 3D labels that produces high quality 3D human poses in the wild, without supervision of in-the-wild 3D data.
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- Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification47
A novel FIne-grained Representation and Recomposition (FIRe2) framework to tackle both limitations without any auxiliary annotation or data is proposed and can achieve state-of-the-art performance on five widely-used cloth-changing person Re-ID benchmarks.
- One-shot Federated Learning without server-side training47
This work proposes an effective algorithm for Model Aggregation via Exploring Common Harmonized Optima (MA-Echo), which iteratively updates the parameters of all local models to bring them close to a common low-loss area on the loss surface, without harming performance on their own data sets at the same time.
- Learning Hybrid Part Filters for Scene Recognition47
It is demonstrated that Hybrid-Parts outperforms recent state-of-the-arts benchmarks, and combining it with standard low-level features such as the GIST descriptor can lead to further improvements.
- A simplified multi-class support vector machine with reduced dual optimization47
This paper presents a simplified multi-class SVM (SimMSVM) that reduces the size of the resulting dual problem from lxk to l by introducing a relaxed classification error bound.
- Harnessing Synthesized Abstraction Images to Improve Facial Attribute Recognition46
This paper builds a dual-path facial attribute recognition network to utilize features from the original face images and facial abstraction images to improve facial attributes recognition, especially the attributes located on small face regions.
- Simulation and assimilation of the digital human brain45
The Digital Brain platform is capable of simulating spiking neuronal networks at the neuronal scale of the human brain and can reproduce blood-oxygen-level-dependent signals in both the resting state and action, thereby predicting the visual evaluation scores.
- AGO-Net: Association-Guided 3D Point Cloud Object Detection Network45
This work introduces an attention-based re-weighting module that adaptively strengthens the feature adaptation of more informative regions and achieves new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
- Embodied One-Shot Video Recognition45
A novel setting is introduced, termed as embodied agents based one- shot learning, which leverages synthetic videos produced in a virtual environment to understand realistic videos of target classes and proposes a general video segment augmentation method, which significantly facilitates a variety of one-shot learning tasks.
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- Ensemble multi-instance multi-label learning approach for video annotation task43
The En-MIMLSVM approach for the video annotation task considers the class imbalance and long time training problems of most video annotation tasks, and a temporally consistent weighted multi-instance kernel is developed to take into account both the temporal consistency in video data and the significance of instances of different levels in pyramid representation.
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- SGM3D: Stereo Guided Monocular 3D Object Detection42
This paper proposes a stereo-guided monocular 3D object detection framework, dubbed SGM3D, which aligns the monocular feature to the stereo feature and exploits the network's ability of generating the cross-modal feature for accurate 3Dobject detection.
- Support cluster machine42
The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies, and provides a promising approach to dealing with privacy-preserving data mining problems.
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- GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection40
A Graph ATtention network with COst-sensitive BOosting (GAT-COBO) for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors and is also helpful for solving the widespread over-smoothing problem in GNNs.
- Pixel2Mesh++: 3D Mesh Generation and Refinement From Multi-View Images39
This work adopts to further improve the shape quality by leveraging cross-view information with a graph convolution network, and learns to predict series of deformations to improve a coarse shape iteratively.
- A novel audio fingerprinting method robust to time scale modification and pitch shifting39
A novel audio fingerprinting method that is highly robust to Time Scale Modification (TSM) and pitch shifting is proposed, based on computer-vision techniques that transforms each 1-D audio signal into a 2-D image and treats TSM and pitch shift of the audio signal as stretch and translation of the corresponding image.
- Incorporating feature hierarchy and boosting to achieve more effective classifier training and concept-oriented video summarization and skimming39
A novel scheme to incorporate the results of semantic video classification to select the most representative video shots for generating concept-oriented summarization and skimming of surgery education videos is developed and a novel multi-modal boosting algorithm is developed to achieve more reliable video classifier training.
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- Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids.38
This study proposes a recurrent feature-pyramid structure to squeeze rich spatial and semantic features into a single prediction layer that further reduces the number of parameters to learn, and is the best performed model of learning object detection from scratch.
- Learning Compositional Representation for 4D Captures with Neural ODE37
A compositional representation for 4D captures, i.e. a deforming 3D object over a temporal span, that disentangles shape, initial state, and motion respectively is introduced and an Identity Exchange Training (IET) strategy is proposed to encourage the network to learn effectively decoupling each component.
- Optimal dimensionality of metric space for classification37
This paper proposes a new method, called Discriminant Neighborhood Embedding (DNE), to learn an appropriate metric space for classification given finite training samples, and defines a discriminant adjacent matrix in favor of classification task.
- A Multi-Task Neural Approach for Emotion Attribution, Classification, and Summarization35
A new neural approach is proposed, Bi-stream Emotion Attribution-Classification Network (BEAC-Net), to solve three related emotion analysis tasks: emotion recognition, emotion attribution, and emotion-oriented summarization, in a single integrated framework.
- Chinese character recognition with radical-structured stroke trees34
This paper proposes a two-stage decomposition framework, where a Feature-to-Radical Decoder decomposes each character into a radical sequence and a Radical-to-Stroke Decoder further decomposes each radical into the corresponding stroke sequence.
- Chinese Character Recognition with Augmented Character Profile Matching34
This paper proposes a Chinese character recognition method named Augmented Character Profile Matching (ACPM), which utilizes a collection of character knowledge from three decomposition levels to recognize Chinese characters and introduces a radical-stroke counting module (RSC) to help produce augmented character profiles.
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- Syntax-guided text generation via graph neural network32
This paper proposes the word graph model, a graph generation problem exploiting both syntactic and word-ordering relationships that builds a sentence incrementally and maintains syntactic integrity via a syntax-driven, top-down, breadth-first generation process.
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- Joint Parsing and Generation for Abstractive Summarization31
A novel neural architecture for abstractive summarization that combines a sequential decoder with a tree-based decoder in a synchronized manner to generate a summary sentence and its syntactic parse and describes a novel human evaluation protocol to assess if, and to what extent, a summary remains true to its original meanings.
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- Learning to Generate and Edit Hairstyles28
A new large-scale hairstyle dataset called Hairstyle30k is contributed, which is composed of 30k images containing 64 different types of hairstyles and a novel GAN model termed Hairstyles GAN (H-GAN) which can be learned efficiently is proposed.
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- Exploring Efficient Few-shot Adaptation for Vision Transformers27
A novel efficient Transformer Tuning (eTT) method that facilitates finetuning ViTs in the FSL tasks and proposes a novel prototypical regularization, which maximizes the similarity between the projected distribution of prefix and initial prototypes, regularizing the update procedure.
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- H4D: Human 4D Modeling by Learning Neural Compositional Representation26
Extensive experiments demonstrate the method presented is not only efficacy in recovering dynamic human with accurate motion and detailed geometry, but also amenable to various 4D human related tasks, including motion retargeting, motion completion and future prediction.
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- Vocabulary-Informed Zero-Shot and Open-Set Learning24
A weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms that ensures that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others.
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- DST: Dynamic Substitute Training for Data-free Black-box Attack23
A dynamic substitute structure learning strategy is proposed to adaptively generate optimal substitute model structure via a dy-namic gate according to different target models and tasks, which can achieve better performance compared with the state-of-the-art competitors on several datasets.
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- PourIt!: Weakly-supervised Liquid Perception from a Single Image for Visual Closed-Loop Robotic Pouring20
A simple yet effective framework PourIt!, to serve as a tool for robotic pouring tasks by designing a simple data collection pipeline that only needs image-level labels to reduce the reliance on tedious pixel-wise annotations.
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- Multi-view Shape Generation for a 3D Human-like Body18
This article proposes a multi-view shape generation network for a 3D human-like body reconstruction via a single RGB image using a coarse-to-fine learning model that gradually deforms a template body toward the ground truth body.
- Frame-Transformer Emotion Classification Network18
A new architecture--Frame-Transformer Emotion Classification Network (FT-EC-net) is proposed to solve three highly correlated emotion analysis tasks: emotion recognition, emotion attribution and emotion-oriented summarization.
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- Comp-GAN17
A novel end-to-end Compositional Generative Adversarial Network that is able to synthesize new face images with specified poses and desired facial expressions and which can dynamically change the expression and pose of faces according to the input images while keeping the identity information.
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- Dynamic Graph Message Passing Networks for Visual Recognition16
This paper proposes a dynamic graph message passing network, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph, and outperforms fully- connected graphs while using substantially fewer floating-point operations and parameters.
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- Feature Deformation Meta-Networks in Image Captioning of Novel Objects14
The feature deformation meta-networks (FDM-net), which is trained on source data, and learn to adapt to the novel object features detected by the auxiliary detection model, and the results show the effectiveness of the FDM-nets.
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- Image-Text-Image Knowledge Transfer for Lifelong Person Re-Identification With Hybrid Clothing States13
This paper proposes a novel framework, dubbed Teata, to effectively align, transfer, and accumulate knowledge in an “image-text-image” closed loop, and introduces a Knowledge Adaptation and Projection strategy, which tunes text knowledge via a slow-paced learner to adapt to different tasks without catastrophic forgetting.
- Rethinking Person Re-identification from a Projection-on-Prototypes Perspective13
This paper proposes a new baseline ProNet, which innovatively reserves the function of the classifier at the inference stage, and describes the identity of input images as similarities to all prototypes, which are then utilized as more discriminative features to perform person Re-ID.
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- Spatial-Temporal Aware Visuomotor Diffusion Policy Learning12
This work proposes 4D Diffusion Policy (DP4), a novel visual imitation learning method that incorporates spatiotemporal awareness into diffusion-based policies and leverages a dynamic Gaussian world model to guide the learning of 3D spatial and 4D spatiotemporal perceptions from interactive environments.
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- WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model12
The potential of integrating the most recent Large Language Models (LLMs) and existing visual grounding and robotic grasping system to enhance the effectiveness of the human-robot interaction is investigated.
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- Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability11
This paper extends auto-regressive models to 3D domains, and introduces discrete representation learning based on a latent vector instead of volumetric grids, which not only reduces computational costs but also preserves essential geometric details by learning the joint distributions in a more tractable order.
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- Neural Symbolic Representation Learning for Image Captioning11
The first attempt to investigate the ability of neural symbolic representation of the image for the image captioning task is made and it is demonstrated to better generalize to other domains with significant performance improvements compared with existing methods on the cross domain image captioned task.
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- The Shanghai-Hongkong Team at MediaEval2012: Violent Scene Detection Using Trajectory-based Features10
The central component of the system is a set of features derived from the appearance and motion of local patch trajectories, which use these features and SVM classier as the baseline approach and add in a few other components to further improve the performance.
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- A Neural Representation Framework with LLM-Driven Spatial Reasoning for Open-Vocabulary 3D Visual Grounding8
This work proposes SpatialReasoner, a novel neural representation-based framework with large language model (LLM)-driven spatial reasoning that constructs a visual properties-enhanced hierarchical feature field for open-vocabulary 3D visual grounding.
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- CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image8
This paper proposes a single-stage Network, CAP-Net, for estimating the 6D poses and sizes of Categorical Articulated Parts, and introduces the RGBD-Art dataset, the largest RGB-D articulated dataset to date, featuring photorealistic RGB images and depth noise simulated from real sensors.
- ImpDet: Exploring Implicit Fields for 3D Object Detection8
This work introduces a new perspective that views bounding box regression as an implicit function, which leverages implicit field learning for 3D object detection and presents a simple yet efficient virtual sampling strategy to solve the problem of sparsity on the object surface.
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- Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective8
A probabilistic way of directly estimating and fine-tuning the decision boundary between seen and novel/unseen classes is proposed and the uncertain domain is newly introduced in this framework to adopt those instances whose domain cannot be predicted confidently.
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- LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion7
This paper introduces a new framework that explores amodal segmentation for robotic grasping in cluttered scenes, thus greatly enhancing robotic grasping abilities and proposing a Linear-fusion Attention-guided Convolutional Network (LAC-Net).
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- SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images6
Experimental results show that SparseGrasp significantly outperforms state-of-the-art methods in terms of both speed and adaptability, providing a robust solution for multi-turn grasping in changeable environment.
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- You Only Estimate Once: Unified, One-stage, Real-Time Category-Level Articulated Object 6D Pose Estimation for Robotic Grasping5
YOEO is proposed, a single-stage method that simultaneously outputs instance segmentation and NPCS representations in an end-to-end manner and deploys a synthetically-trained model in a real-world setting, providing real-time visual feedback at 200Hz, enabling a physical Kinova robot to interact with unseen articulated objects.
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- Joint fMRI Decoding and Encoding with Latent Embedding Alignment5
This work proposes an end-to-end framework, SemanSig, which directly encodes fMRI signals and extracts semantic information, and uses the ImageNet class prototype space as the internal representation space of fMRI signals, thereby reducing signal redundancy and learning difficulty.
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- Content-Based Image and Video Indexing and Retrieval5
This paper surveys some of the existing techniques and systems for content-based indexing and retrieval of two main types of multimedia data - images and videos and proposes multi-scale color histograms by incorporating color and spatial information.
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- Improving Neural Surface Reconstruction with Feature Priors from Multi-View Image4
This study comprehensively explore multi-view feature priors from seven pretext visual tasks, revealing that feature priors from image matching and multi-view stereo outperform other pretext tasks and discovering that extending patch-wise photometric consistency to the feature level surpasses the performance of pixel-wise approaches.
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- Stacked multichannel autoencoder – an efficient way of learning from synthetic data4
This paper investigates and formalizes a general framework – Stacked Multichannel Autoencoder (SMCAE) that enables bridging the synthetic gap and learning from synthetic data more efficiently and shows that this SMCAE can not only transform and use synthetic data on a challenging face-sketch recognition task, but that it can also help simulate real images which can be used for training classifiers for recognition.
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- DecoFuse: Decomposing and Fusing the "What", "Where", and "How" for Brain-Inspired fMRI-to-Video Decoding3
DecoFuse is proposed, a novel brain-inspired framework for decoding videos from fMRI signals that first decomposes the video into three components - semantic, spatial, and motion - then decodes each component separately before fusing them to reconstruct the video.
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- Learning Versatile 3D Shape Generation with Improved AR Models3
The Improved Auto-regressive Model (ImAM) is proposed for 3D shape generation, which applies discrete representation learning based on a latent vector instead of volumetric grids and can synthesize diverse and faithful shapes of multiple categories, achieving state-of-the-art performance.
- HandO: a hybrid 3D hand–object reconstruction model for unknown objects3
A novel hybrid model—hand–object Model (HandO) enabling the hand–object 3D reconstruction with smooth surfaces and accurate positions is presented, for the first time making the hybrid 3D representation for this task by bringing meshes, SDFs, and parametric models together.
- Local Slot Attention for Vision and Language Navigation3
This work proposes a slot-attention based module to incorporate information from segmentation of the same object in the VLN task and a local attention mask mechanism to limit the visual attention span.
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- A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools3
A new screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19 with very high accuracy and a system implementing such an algorithm should assist the triage management or the clinical diagnosis.
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- Unsupervised Learning of Global Object-Centric Representations for Compositional Scene Understanding2
- Learning a Mixture of Conditional Gating Blocks for Visual Question Answering2
A novel model mixture of conditional gating blocks (McG) is proposed for VQA, which keeps the best of the Transformer, convolutional neural network (CNN), and dynamic networks.
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- Mining Large-Scale News Video Database Via Knowledge Visualization2
A novel framework to enable intuitive mining and exploration of large-scale video news databases via knowledge visualization and a statistical video analysis algorithm is proposed to extract the semantics from the video reports.
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- Video Shot Boundary Detection and Scene Segmentation2
This chapter presents a new and efficient method for shot boundary detection (SBD) and scene segmentation based on sequential change detection to achieve improved detection accuracy and to segment videos into scenes.
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- Ali-UI: Enhancing Complex Vision-Language Navigation with Alignment of Unified Map and Instruction Parsing1
Ali-UI is introduced, a novel vision-language navigation approach that enables agents to navigate from random starting points in unvisited scenes and handle complex multi-step instructions by incorporating continuously accumulating global grid maps and local semantic maps as scene memory by employing frontier-based exploration.
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- Learning to score and summarize figure skating sport videos.1
A reinforcement learning based video summarization algorithm is proposed here; and the experiments show better performance than the other baseline video summarizing algorithms.
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- Left-Right Skip-DenseNets for Coarse-to-Fine Object Categorization.1
The left-right skip-densenets for coarse-to-fine object categorization can enable both coarse and fine-grained classification in a single framework and the layer-skipping mechanism which learns a gating network to predict whether skip some layers in the testing stage is proposed.
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- Shot boundary classification by temporal pattern discovery from Laplacian eigenmap1
Experimental results are presented to show the effectiveness of the proposed shot boundary classification method by exploiting the characteristics of the four shot boundary types and discovering the inherent temporal pattern from the frames around each detected shot boundary.
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- From Sky to Site: A Unified Framework for Static and Dynamic 3D Reconstruction in Construction Sites–
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- Rethinking the Multi-view Stereo from the Perspective of Rendering-based Augmentation–
This work applies one of the state-of-the-art learning-based MVS methods, --MVSFormer, to overcome intractable scenarios such as textureless and reflections regions suffered by traditional PatchMatch methods, and finds that the MVS method could produce much better predictions through rendered images due to the coincident illumination, which is significant for the M VS community.
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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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