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Works269 from public data
- Supervised Discrete Hashing1,231
This work proposes a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification, and introduces an auxiliary variable to reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm.
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- Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval458
A novel cross- modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints, and an effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes.
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- Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions352
This paper proposes an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery and forms it as a network min-cost flow problem and proposes various pruning techniques that can dramatically reduce the network size.
- Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval302
This paper introduces a novel binary coding method, named Deep Sketch Hashing (DSH), where a semi-heterogeneous deep architecture is proposed and incorporated into an end-to-end binary coding framework, and is the first hashing work specifically designed for category-level SBIR with an end to end deep architecture.
- Discrete Collaborative Filtering289
This paper proposes a principled CF hashing framework called Discrete Collaborative Filtering (DCF), which directly tackles the challenging discrete optimization that should have been treated adequately in hashing, and devise a computationally efficient algorithm with a rigorous convergence proof of DCF.
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- Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation223
A graph-based global reasoning unit is introduced to strengthen the classification network’s ability to capture global relations among disjoint and distant regions and a potential object mining module is proposed to reduce the false-negative rate in pseudo labels.
- Jo-SRC: A Contrastive Approach for Combating Noisy Labels206
A noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) that trains the network in a contrastive learning manner to advance the model generalization performance by introducing consistency regularization.
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- Zero-Shot Sketch-Image Hashing175
ZSIH is the first zero- shot hashing work suitable for SBIR and cross-modal search and forms a generative hashing scheme in reconstructing semantic knowledge representations for zero-shot retrieval.
- Zero-Shot Hashing via Transferring Supervised Knowledge162
A novel hashing scheme, termed zero-shot hashing (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of " seen" categories is proposed and an efficient alternating algorithm is developed to solve the ZSH model.
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- From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis159
A new Zero-shot learning framework that can synthesise visual features for unseen classes without acquiring real images is proposed and extensive experimental results manifest that the proposed approach significantly improve the state-of-the-art results.
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- Auto-Encoding Twin-Bottleneck Hashing133
This paper proposes an efficient and adaptive code-driven graph, which is updated by decoding in the context of an auto-encoder, and introduces into the framework twin bottlenecks that exchange crucial information collaboratively.
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- Deep Asymmetric Pairwise Hashing124
This work proposes a novel Deep Asymmetric Pairwise Hashing approach (DAPH) for supervised hashing, and devise an efficient alternating algorithm to optimize the asymmetric deep hash functions and high-quality binary code jointly.
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- MSFlow: Multiscale Flow-Based Framework for Unsupervised Anomaly Detection118
A novel multiscale flow-based framework (MSFlow) composed of asymmetrical parallel flows followed by a fusion flow to exchange multiscale perceptions is proposed, significantly outperforming existing methods for anomaly detection and localization.
- Robust discrete code modeling for supervised hashing114
This work proposes a novel supervised hashing approach, termed as Robust Discrete Code Modeling (RDCM), which directly learns high-quality discretebinary codes and hash functions by effectively suppressing the influence of unreliable binary codes and potentially noisily-labeled samples.
- Learning robust and discriminative low-rank representations for face recognition with occlusion114
This paper proposes a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured.
- Zero-Shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation110
A novel embedding algorithm named Unseen Visual Data Synthesis (UVDS) that projects semantic features to the high-dimensional visual feature space and introduces a latent embedding space which aims to reconcile the structural difference between the visual and semantic spaces, meanwhile preserve the local structure.
- TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights105
This work proposes a Ternary-Binary Network (TBN), which provides an efficient approximation to standard CNNs, and demonstrates its consistent effectiveness when applied to various CNN architectures, and provides an optimal tradeoff between memory, efficiency and performance.
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- Multiview Discrete Hashing for Scalable Multimedia Search100
This article proposes a novel unsupervised hashing method, dubbed multiview discrete hashing (MvDH), which performs matrix factorization to generate the hash codes as the latent representations shared by multiple views, during which spectral clustering is performed simultaneously.
- A Large-scale RGB-D Database for Arbitrary-view Human Action Recognition97
A large-scale RGB-D action database for arbitrary-view action analysis, including RGB videos, depth and skeleton sequences, and it is the first database containing the entire 360?
- Generative Domain-Migration Hashing for Sketch-to-Image Retrieval95
A Generative Domain-migration Hashing approach, which for the first time generates hashing codes from synthetic natural images that are migrated from sketches that can migrate sketches to their indistinguishable image counterparts while preserving the domain-invariant information of sketches.
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- Discrete Nonnegative Spectral Clustering91
A novel spectral clustering scheme is proposed which deeply explores cluster label properties, including discreteness, nonnegativity, and discrimination, as well as learns robust out-of-sample prediction functions and preserves the natural nonnegative characteristic of the clustering labels.
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- A Survey of Human Action Analysis in HRI Applications88
This work reviews the existing HRI related references involving the action recognition, prediction, and the robot imitation of the human action, and gives a summary of robot platforms and action datasets that are frequently used in the study of HRI.
- Cross-domain facial expression recognition via an intra-category common feature and inter-category Distinction feature fusion network85
A novel feature fusion network for facial expression recognition in a cross-domain manner in order to realize the facial expression Recognition in extensive scenarios and achieves the state-of-the-art performances.
- Multi-view Latent Hashing for Efficient Multimedia Search83
This paper proposes a novel unsupervised hashing approach, dubbed multi-view latent hashing (MVLH), to effectively incorporate multi- view data into hash code learning, and provides a novel scheme to directly learn the codes without resorting to continuous relaxations.
- Towards Automatic Construction of Diverse, High-Quality Image Datasets82
This work proposes a novel image dataset construction framework by employing multiple textual queries and formulate noisy textual queries removing and noisy images filtering as a multi-view and multi-instance learning problem separately.
- Make a Face: Towards Arbitrary High Fidelity Face Manipulation78
This work proposes Additive Focal Variational Auto-encoder (AF-VAE), a novel approach that can arbitrarily manipulate high-resolution face images using a simple yet effective model and only weak supervision of reconstruction and KL divergence losses.
- Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach76
Comprehensive experimental results and analyses on two new benchmark datasets demonstrate that the proposed method achieves superior performance over the competing baseline models and states-of-the-art.
- Compressed K-Means for Large-Scale Clustering74
Extensive experimental results demonstrate that CKM outperforms the state-of-the-art large-scale clustering methods in terms of both computation and memory cost, while achieving comparable clustering accuracy.
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- Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation71
A saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes and an object guided label refinement module to take a full use of both the segmentation prediction and the initial labels for obtaining superior pseudo-labels.
- Temporal Reasoning Graph for Activity Recognition71
This paper proposes an efficient temporal reasoning graph (TRG) to simultaneously capture the appearance features and temporal relation between video sequences at multiple time scales, and constructs learnable temporal relation graphs to explore temporal relation on the multi-scale range.
- Bidirectional Long-Short Term Memory for Video Description71
A novel video captioning framework, termed as BiLSTM, which deeply captures bidirectional global temporal structure in video, and which is comprehensively preserving sequential and visual information and adaptively learning dense visual features and sparse semantic representations for videos and sentences.
- Adaptive Uncertainty-Based Learning for Text-Based Person Retrieval70
This work proposes a novel framework termed Adaptive Uncertainty-based Learning (AUL) for text-based person retrieval from the uncertainty perspective that consistently achieves state-of-the-art performance on three benchmark datasets in supervised, weakly supervised, and domain generalization settings.
- BatchNorm-Based Weakly Supervised Video Anomaly Detection70
This work proposes a novel method, BN-WVAD, which incorporates BatchNorm into WVAD, and uses the Divergence of Feature from the Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos.
- Deep Self-taught Hashing for Image Retrieval68
A deep self-taught hashing (DSTH) framework to combine deep structures with hashing to improve the retrieval performance by automatically learning robust visual features and hash functions and applies CNNs and Multi-layer Perceptron under deep learning scheme.
- Scene Text Image Super-Resolution via Parallelly Contextual Attention Network66
A Parallelly Contextual Attention Network (PCAN), which effectively learns sequence-dependent features and focuses more on high-frequency information of the reconstruction in text images, and outperforms eleven state-of-the-art (SOTA) SR methods in terms of boosting text recognition performance.
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- Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification66
A novel approach to remove irrelevant samples from the real-world web images during training, and only utilize useful images for updating the networks is proposed, which can alleviate the harmful effects caused by irrelevant noisy web images to achieve better performance.
- Chat More: Deepening and Widening the Chatting Topic via A Deep Model63
A novel deep scheme consisting of three channels, namely global, wide, and deep, which trains a Multi-layer Perceptron model to select some keywords for an in-depth discussion to generate desired responses in open-domain multi-turn dialog systems.
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- DCEL: Deep Cross-modal Evidential Learning for Text-Based Person Retrieval61
This work proposes a novel framework termed Deep Cross-modal Evidential Learning (DCEL), which deploys evidential deep learning to consider the cross- modal alignment uncertainty and consistently outperforms more than ten state-of-the-art methods in supervised, weakly supervised, and domain generalization settings on three benchmarks.
- Exact Adversarial Attack to Image Captioning via Structured Output Learning With Latent Variables61
This work proposes to fool an image captioning system to generate some targeted partial captions for an image polluted by adversarial noises, even the targeted captions are totally irrelevant to the image content.
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- TVT: Three-Way Vision Transformer through Multi-Modal Hypersphere Learning for Zero-Shot Sketch-Based Image Retrieval60
This paper proposes a Transformer-based approach called Three-Way Vision Transformer (TVT) to leverage the ability of ViT to model global contexts due to the global self-attention mechanism to bridge the modal gap between modalities of sketch and image and avoid the collapse in dimensions.
- Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation60
This paper proposes to leverage the multi-class label information during the episodic training to encourage the network to generate more semantically meaningful features for each category and proposes a fused prototype guidance branch for the segmentation of the query image.
- Similarity learning with joint transfer constraints for person re-identification59
The issue of person re-identification under inconsistent data distributions can be transformed into a consistent iterative convex optimization problem, and solved via the inexact augmented Lagrange multiplier (IALM) algorithm.
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- Relationship-Preserving Knowledge Distillation for Zero-Shot Sketch Based Image Retrieval58
RPKD firstly distill the instance-level knowledge to preserve inter-class relationships without semantic similarities that require extra effort to collect and reconcile the contrastive relationships among instances between different embedding spaces, which is complementary to instance- level relationships.
- Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing57
A novel prototype-supervised adversarial network (ProS-GAN), which formulates a flexible generative architecture for efficient and effective targeted hashing attack and is the first generation-based method to attack deep hashing networks.
- Face image classification by pooling raw features57
The first face feature extraction method by directly pooling raw patches is proposed, which can capture strong structural information of individual faces and achieves state-of-the-art results on several face recognition datasets.
- GraphCAR: Content-aware Multimedia Recommendation with Graph Autoencoder56
A Content-aware Multimedia Recommendation Model with Graph Autoencoder (GraphCAR) is proposed, combining informative multimedia content with user-item interaction, which significantly outperforms state-of-the-art techniques of both collaborative filtering and content-based methods.
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- Highly-Economized Multi-view Binary Compression for Scalable Image Clustering54
This work introduces a novel approach named Highly-economized Scalable Image Clustering (HSIC) that radically surpasses conventional image clustering methods via binary compression and intuitively unify the binary representation learning and efficient binary cluster structure learning into a joint framework.
- MetaMixUp: Learning Adaptive Interpolation Policy of MixUp With Metalearning52
A metalearning-based online optimization approach to dynamically learn the interpolation policy in a data-adaptive way (learning to learn better) and extensive experiments show that the MetaMixUp adapted SSL greatly outperforms MixUp and many state-of-the-art methods on CIFAR-10 and SVHN benchmarks under the SSL configuration.
- Modal-adversarial Semantic Learning Network for Extendable Cross-modal Retrieval52
A novel framework, termed Modal-adversarial Semantic Learning Network (MASLN), is proposed, to tackle the limitation of existing methods on this practical task of extendable cross-modal retrieval task where instances in source and target set have disjoint classes.
- Robust Web Image Annotation via Exploring Multi-Facet and Structural Knowledge51
This paper proposes an effective and robust scheme, termed robust multi-view semi-supervised learning (RMSL), for facilitating image annotation task, and exploits both labeled images and unlabeled images to uncover the intrinsic data structural information.
- DHHN: Dual Hierarchical Hybrid Network for Weakly-Supervised Audio-Visual Video Parsing49
This paper proposes a novel AVVP framework termedDual Hierarchical Hybrid Network (DHHN), a hierarchical context modeling network for extracting different semantics in multiple temporal lengths that maintains the best adaptions on different modalities, further boosting the video parsing performance.
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- Selective Hypergraph Convolutional Networks for Skeleton-based Action Recognition48
A novel Selective Hypergraph Convolution Network is proposed, dubbed Selective-HCN, which stacks two key modules: selective-scale Hypergraphconvolution (SHC) and Selective -frame Temporal Convolution (STC), which represents the human skeleton as the graph and hypergraph to fully extract multi-scale information, and selectively fuse features at various scales.
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- Deep Multi-task Learning to Recognise Subtle Facial Expressions of Mental States47
This work addresses subtle expression recognition through convolutional neural networks (CNNs) by developing multi-task learning (MTL) methods to effectively leverage a side task: facial landmark detection and achieves very competitive performance on Oulu-Casia NIR&Vis and CK+ databases via transfer learning.
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- Classification by Retrieval44
A generic formulation that significantly expedites the training and deployment of image classification models, particularly under the scenarios of many image categories and high feature dimensions, and proposes a novel bit-flipping procedure which enjoys high efficacy and a local optimality guarantee.
- A Domain Robust Approach For Image Dataset Construction44
This work proposes a novel image dataset construction framework which can generalize well to unseen target domains and formulate image filtering as a multi-instance learning (MIL) problem with constrained positive bags.
- Kernel Low-Rank Representation for face recognition44
This work proposes a novel image classification method for robust face recognition, named Low-Rank Representation-based Classification (LRRC), based on seeking the lowest-rank representation of a set of test samples with respect to aSet of training samples, which has the natural discrimination to perform classification.
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- Pseudo Transfer with Marginalized Corrupted Attribute for Zero-shot Learning38
This work proposes a pseudo transfer mechanism, where the distribution of unseen data is synthesized using semantic embeddings, then the mismatch between the seen distribution and the synthesized unseen distribution is minimized, and successfully overcomes the Z-bias and Z-variance problems.
- Visual Coding in a Semantic Hierarchy38
An on-line semantic coding model, which simultaneously exploits the rich hierarchical semantic prior knowledge in the learned dictionary, reflects semantic sparse property of visual codes, and explores semantic relationships among concepts in the semantic hierarchy is devised.
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- PoseGTAC: Graph Transformer Encoder-Decoder with Atrous Convolution for 3D Human Pose Estimation36
This work proposes a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information in the 3D human pose estimation task.
- Neural Stereoscopic Image Style Transfer36
A dual path network for view-consistent style transfer on stereoscopic images is proposed and a novel feature aggregation strategy is proposed to effectively share information between the two paths.
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- Visual Spatial Attention Network for Relationship Detection34
A novel Vision Spatial Attention Network (VSA-Net), which employs a two-dimensional normal distribution attention scheme to effectively model small objects and design a Subject-Object-layer (SO-layer) to distinguish between the subject and object to attain more precise results.
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- A Survey Of zero shot detection: Methods and applications31
A detailed survey about zero shot detection is proposed, based on the combination of traditional detection framework and zero shot learning methods, which categorize existingzero shot detection methods into two different classes, and the representative methods under each category are introduced.
- Word-to-region attention network for visual question answering31
A novel Word-to-Region Attention Network (WRAN) is proposed, which can simultaneously locate pertinent object regions instead of a uniform grid of image regions of euqal size and identify the corresponding words of the reference question; as well as enforce consistency between image object regions and core semantics in questions.
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- Hybrid Fusion with Intra- and Cross-Modality Attention for Image-Recipe Retrieval27
This work proposes a novel framework named Hybrid Fusion with Intra- and Cross-Modality Attention (HF-ICMA), which adopts an intra-recipe fusion module to focus on the interaction between ingredients and instructions within a recipe, and further enriches the expressions of the two separate embeddings.
- Spatial and temporal scoring for egocentric video summarization27
This work utilizes egocentric salience cues, motion cues and a selection model to capture stable salience weight, discriminative weight and representative weight of a video shot respectively and combines these weights in a unified framework to predict the importance score of a shot.
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- Cross-Modal Image-Recipe Retrieval via Intra- and Inter-Modality Hybrid Fusion25
This work proposes a novel lightweight framework named Intra- and Inter-Modality Hybrid Fusion (IMHF), which abandons a separate deep vision encoder and utilizes the transformer module to unify the visual and text features.
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- Zero-shot learning via discriminative representation extraction25
A ZSL framework that maps semantic embeddings to a discriminative representation space, which is learned in two different ways: Kernelized Linear Discriminant Analysis (KLDA) and Central-loss based Network (CLN).
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- Multimodal Disentanglement Variational AutoEncoders for Zero-Shot Cross-Modal Retrieval23
A novel ZS-CMR model, which consists of two coupled disentanglement variational autoencoders (DVAEs) and a fusion-exchange VAE (FVAE), which is developed to disentangle the original representations of each modality into modality-invariant andmodality-specific features for more effective knowledge transfer.
- Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning23
A novel audio-visual representation learning method dubbed self-supervised curriculum learning (SSCL) is proposed, which regards the pervasive property of audiovisual concurrency as latent supervision and mutually distills the structure knowledge of visual to audio data.
- Joint Graph Learning and Video Segmentation via Multiple Cues and Topology Calibration23
A novel framework, joint graph learning and video segmentation (JGLVS) is proposed, which learns the similarity graph and video segmentsation simultaneously simultaneously and achieves promising performance on the benchmark dataset which outperforms the state-of-the-art by up to 11% for the BPR metric.
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- Cross-Modal Attention Preservation with Self-Contrastive Learning for Composed Query-Based Image Retrieval22
This article proposes a novel method termed Cross-Modal Attention Preservation (CMAP), which first leverage the cross-level interaction to fully account for multi-granular semantic information, which aims to supplement the high-level semantics for effective image retrieval.
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- Discriminant Cross-modal Hashing22
A novel hashing based method in a linear classification framework is proposed, in which the proposed method learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels.
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- Large-scale image retrieval with supervised sparse hashing21
This paper reduces the effective number of parameters involved in the learned projection matrix according to sparsity regularizer, which helps avoid overfitting problem and effectively lower both the storage and computation cost.
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- Multi-view feature selection and classification for Alzheimer’s Disease diagnosis20
This paper proposes a novel multi-view classification method based on l2,p -norm regularization for Alzheimer’s Disease (AD) diagnosis and experimentally demonstrated that this method enhances the performance of disease status classification, comparing to the state-of-the-art methods.
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- Similarity preserving feature generating networks for zero-shot learning18
A novel method is proposed, named similarity preserving GAN (SPGAN), which can generate unseen visual features from random noises concatenating with semantic descriptions, and can surpass all the most state-of-the-art methods.
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- Robust and real-time deep tracking via multi-scale domain adaptation18
This paper proposes to transfer the feature for image classification to the visual tracking domain via convolutional channel reductions via convolved channel reductions and illustrates the state-of-the-art accuracies in the experiment involving two well-adopted benchmarks with more than 100 test videos.
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- Transductive Visual-Semantic Embedding for Zero-shot Learning17
This work proposes a novel framework termed transductive visual-semantic embedding (TVSE) for ZSL that achieves competitive performance compared with the state-of-the-arts for zero-shot recognition and retrieval tasks.
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- Index and Retrieve Multimedia Data: Cross-Modal Hashing by Learning Subspace Relation14
A novel supervised cross-modal hashing method dubbed Subspace Relation Learning for Cross-modAL Hashing (SRLCH), which exploits relation information in semantic labels to make similar data from different modalities closer in the low-dimension Hamming subspace is proposed.
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- Kernel based latent semantic sparse hashing for large-scale retrieval from heterogeneous data sources14
KLSSH firstly employs sparse coding for obtaining primary latent features of image and matrix factorization for generating features of text concepts to learn latent semantic features in a high level abstraction space and maps the latent semantic feature to compact binary codes using kernel method.
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- Fusion by synthesizing: A multi-view deep neural network for zero-shot recognition13
A novel multi-view deep neural network, termed Fusion by Synthesis (FS), which leverages word embeddings of classes as complementary for attributes and performs zero-shot prediction by fusing the word embedDings of unseen classes and the synthesized attributes in the visual feature space.
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- Supervised hashing with adaptive discrete optimization for multimedia retrieval13
An adaptive discrete cyclic coordinate descent (ACC) method to effectively solve discrete optimization problem and achieves speed-up over compared the state-of-the-art methods, while having on-par and in some cases even better performance.
- Robust regression based face recognition with fast outlier removal12
The proposed algorithm provides a novel way to tackle the crucial occlusion problem in face recognition by computed from only the clean data, which gives a more discriminant classification rule.
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- Processing Long Queries Against Short Text11
This work proposes a novel rank-aware block-oriented inverted index scheme, and introduces a block-at-a-time search strategy based on the index scheme to support a much tighter upper bound estimation and a very early termination.
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- Combining multi-representation for multimedia event detection using co-training11
This work proposes a novel system for event detection based on combination of multi-view representations and co-training algorithm, and demonstrates the outstanding performance of the proposed approach as compared to several other state-of-the-art algorithms.
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- A System for Spatiotemporal Anomaly Localization in Surveillance Videos10
This demonstration proposes an anomaly detection algorithm called 2stream-VAE/GAN by embedding VAE and GAN in a two-stream architecture, which automatically locates anomaly based on a pre-trained model, which suits well for both streaming and local videos.
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- Cross-modal Retrieval with Label Completion10
This work formulates the subspace learning problem as a co-regularized learning framework based on multi-modal features and incomplete labels and assumes the to-be-learned common subspace can be jointly derived through two aspects: linear projection from modality-specific features and enriching mapping from the incomplete labels.
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- Zero-shot Sketch-based Image Retrieval with Adaptive Balanced Discriminability and Generalizability9
This work introduces a task-specific teacher model pre-trained with a classification objective function to emphasize the discriminability property during knowledge distillation and uses fine-grained semantic relevance to refine the ego predictions of the student model, with the aim of improving its performance as the training objective continues to converge.
- Multi-granularity Separation Network for Text-Based Person Retrieval with Bidirectional Refinement Regularization9
A novel framework termed Multi-granularity Separation Network with Bidirectional Refinement Regularization (MSN-BRR) is proposed, which outperforms current state-of-the-art methods on text-based person retrieval and alleviates the influence of intra-modal noise.
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- Temporal Denoising Mask Synthesis Network for Learning Blind Video Temporal Consistency9
A novel model termed Temporal Denoising Mask Synthesis Network (TDMS-Net) is proposed that jointly predicts the motion mask, soft optical flow and the refining mask to synthesize the temporal consistent frames to enforce temporal consistency in a video.
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- Video Representation Learning with Graph Contrastive Augmentation7
This work proposes a novel contrastive self-supervised video representation learning framework, termed Graph Contrastive Augmentation (GCA), by constructing a video temporal graph and devising a graph augmentation that is designed to enhance the correlation across frames of videos and developing a new view for exploring temporal structure in videos.
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- Multi-scale Dynamic Network for Temporal Action Detection6
The proposed MDN model incorporates multiple Multi-scale Dynamic Modules (MDMs), which can generate video-specific and segment-specific convolution kernels based on video content from different scales and adaptively capture rich semantic information for the prediction.
- Generative Reconstructive Hashing for Incomplete Video Analysis6
Generative Reconstructive Hashing is the first end-to-end framework for incomplete video analysis and outperforms the recent state-of-the-art methods by 5.44/3.22/4.82 in terms of MAPs on HMDB51/UCF101/CCV datasets, respectively.
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- Multiple hierarchical deep hashing for large scale image retrieval6
This paper proposes a Multiple Hierarchical Deep Hashing (MHDH) approach for large scale image retrieval that seeks to integrate multiple hierarchical non-linear transformations with hidden neural network layer for hashing code generation.
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- Multi-view Semi-supervised Learning for Web Image Annotation5
This work proposes a novel model, termed as multi-view semi-supervised learning (MVSSL), for robust image annotation task, which exploits both labeled images and unlabeled images to uncover the intrinsic data structural information.
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- Chatting with interactive memory for text-based person retrieval4
The proposed CIM model facilitates a more nuanced and interactive search process by allowing users to engage in multiple rounds of dialogue, providing a more comprehensive description of the person of interest, significantly improving the precision and user engagement in text-based person retrieval tasks.
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- Accelerated Sign Hunter: A Sign-based Black-box Attack via Branch-Prune Strategy and Stabilized Hierarchical Search4
A theoretical proof showing that the Accelerated Sign Hunter halves the queries without dropping the attack success rate (SR) compared with the state-of-the-art sign-based black-box attack is provided.
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- Movie Fill in the Blank with Adaptive Temporal Attention and Description Update4
This paper proposes to use a novel LSTM network called L STM with Linguistic gate (LSTMwL), which exploits adaptive temporal attention for MovieFIB, and demonstrates that this approach outperforms state-of-the-art models for movieFIB.
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- Hashing with Inductive Supervised Learning4
A new supervised hashing method to generate class-specific hash codes, which uses an inductive process based on the Inductive Manifold Hashing IMH model and leverage supervised information into hash codes generation to address these difficulties and boost the hashing quality.
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- CAA3
In this paper, a novel model termed Candidate-Aware Aggregation (CAA) is proposed, which designs the Global Awareness (GA) module to exploit long-range relations among all candidates from a global perspective, which enhances the features of action instances.
- CR-FPN: channel relation feature pyramid network for object detection3
A novel network, named Channel Relation Feature Pyramid Network (CR-FPN), is proposed that captures long-range relation of channel from different level of feature maps by similarity measure function and further to magnify the most relevant channels and suppress the irrelevant channels.
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- Domain Invariant Subspace Learning for Cross-Modal Retrieval3
Different modalities are regarded as different domains and a Domain Invariant Subspace Learning (DISL) method is proposed to associate multimodal data, which simultaneously minimize the classification error with sample-wise weighting coefficients and preserve the structure similarity within and across modalities with the graph regularization.
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- Heterogeneous Graph Embedding for Multimodal Multi-Label Emotion Recognition2
A novel Heterogeneous Graph Embedding (HGE) method is proposed for the MMER task, which exploits heterogeneous graphs to extract emotional commonality in the modal and temporal levels, and explicitly models cross-modal correlations among heterogeneous modalities.
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- PTAN: Principal Token-aware Adjacent Network for Compositional Temporal Grounding2
This work proposes a novel method called Principal Token-aware Adjacent Network (PTAN), which consists of three parts: Principal Temporal Token Recomposition combining video clip-level features obtained from the transformer backbone to capture more significant local features while retaining enough contextual information.
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- Composed Query-Based Event Retrieval in Video Corpus with Multimodal Episodic Perceptron1
This paper proposes a novel event retrieval framework termed Composed Query-Based Event Retrieval (CQBER), and proposes an initial CQBER method, termed Multimodal Episodic Perceptron (MEP), which excavates complete query semantics from both observed static visual cues and various descriptions.
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- Semantic binary coding for visual recognition via joint concept-attribute modelling1
This paper proposes a novel visual representation learning framework, which generates efficient semantic hash codes for visual samples by substantially exploring concepts, semantic attributes as well as their inter-correlations by developing an effective on-line feature coding scheme for visual objects.
- Exploiting Concept Correlation with Attributes for Semantic Binary Representation Learning1
This paper proposes a novel visual representation framework, which generates efficient semantic hash codes for visual samples by substantially exploring concepts, semantic attributes as well as their inter-correlations, and develops an effective on-line feature coding scheme for visual objects.
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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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