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Claim this profileWorks260 from public data
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- Reasoning with Heterogeneous Graph Alignment for Video Question Answering225
This work proposes a deep heterogeneous graph alignment network over the video shots and question words and explores the network architecture from four steps: representation, fusion, alignment, and reasoning.
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- Vector-Decomposed Disentanglement for Domain-Invariant Object Detection147
This paper tries to disentangle domain-invariant representations from domain-specific representations, and proposes a novel disentangled method based on vector decomposition.
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- Curls & Whey: Boosting Black-Box Adversarial Attacks127
The Curls & Whey attack is proposed, which extends the attack to the targeted misclassification, effectively reducing the difficulty of targeted attacks under black-box condition and shows promising transferability against ensemble models as well as adversarially trained models.
- Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation123
A Bidirectional Adversarial Training (BiAT) network is devised to perform diverse adversarial trainings jointly and evaluates the effectiveness of BiAT on three benchmark datasets and experimental results demonstrate the proposed method achieves the state-of-the-art.
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- Universal-Prototype Enhancing for Few-Shot Object Detection120
A new framework of few-shot object detection with universal prototypes (F SODup) that owns the merit of feature generalization towards novel objects is developed and a new prototype is proposed that is learned from all object categories.
- Multi-modal Circulant Fusion for Video-to-Language and Backward118
A new approach of multi-modal fusion, namely Multi- modal Circulant Fusion (MCF), which defines two types of interaction operations between vectors and matrices and obtains the state-of-the-art for video captioning.
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- Multi-label boosting for image annotation by structural grouping sparsity95
The proposed Multi-label Boosting by the selection of heterogeneous features with structural Grouping Sparsity (MtBGS) has better multi-label image annotation performance and leads to a quite interpretable model for image understanding.
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- Understanding the effective receptive field in semantic image segmentation76
A Dense Global Context Module is designed, which makes the effective receptive field coverage larger and density higher and reduces a large number of parameters while the performance has been substantially improved in segmentation model.
- Catching the Temporal Regions-of-Interest for Video Captioning75
A novel approach which will automatically focus on regions-of-interest and catch their temporal structures and obtains the state-of theart results on popular evaluation metrics like BLEU-4, CIDEr, and METEOR.
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- Weakly-Supervised Video Anomaly Detection With Snippet Anomalous Attention68
This paper proposes an Anomalous Attention mechanism for weakly-supervised anomaly detection that takes into account snippet-level encoded features without the supervision of pseudo labels, and obtains a more precise anomaly localization.
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- Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation61
This paper introduces a novel approach, Behavior-Contextualized Item Preference Modeling (BCIPM), which discerns and learns users' specific item preferences within each behavior and considers only those preferences relevant to the target behavior for final recommendations, significantly reducing noise from auxiliary behaviors.
- Explore Multi-Step Reasoning in Video Question Answering58
This paper designs a system to automatically generate a large-scale dataset, namely SVQA (Synthetic Video Question Answering), and develops a new VideoQA model that contains a new attention module, which contains spatial attention mechanism to address crucial and multiple logical sub-tasks embedded in questions, as well as a refined GRU called ta-GRU (temporal-attention GRU).
- Adaptive iterative attack towards explainable adversarial robustness55
Ada-FGSM (Adaptive FGSM), a new iterative attack that adaptively allocates step size of noises according to gradient information at each step is proposed, proving that if the upper bound of noise added to the original image is fixed, the attack effect can be improved if the step size is positively correlated with the gradient obtained at each steps by querying the target model.
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- Detecting adversarial examples via prediction difference for deep neural networks52
It is found that the adversarial examples have lager prediction difference for various DNN models due to their various complicated decision boundaries, which can be used to identify the adversary examples by converging decision boundaries to a prediction difference threshold.
- Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents51
A Layered Memory Network (LMN) that represents frame-level and clip-level movie content by the Static Word Memory module and the Dynamic Subtitle Memory module is put forward, which achieves the state-of-the-art performance on the online evaluation task of 'Video+Subtitles'.
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- Tucker decomposition-based tensor learning for human action recognition46
A robust human action recognition algorithm by tensor representation and Tucker decomposition, where the still image containing human action is represented by a tensor descriptor (Histograms of Oriented Gradients).
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- Semi-supervised feature selection via hierarchical regression for web image classification43
This paper proposes a semi-supervised feature selection algorithm that is based on a hierarchical regression model that utilizes a statistical approach to exploit both labeled and unlabeled data, which preserves the manifold structure of each feature type.
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- Summarization-based Video Caption via Deep Neural Networks42
A novel framework of video caption via deep neural networks that owns the merit of describing out-of-sample videos by transferring knowledge from pre-captioned images in language generation metrics as well as SVO accuracy.
- Saliency Prototype for RGB-D and RGB-T Salient Object Detection41
A new bi-modal salient detection architecture Saliency Prototype Network (SPNet), which can be used for both RGB-D and RGB-T SOD, and applies the saliency prototypes obtained by the primary modality to perform semantic enhancement of the auxiliary modality.
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- Convolutional Neural Network Style Transfer Towards Chinese Paintings39
A novel algorithm of Chinese painting style transfer (CPST) towards transferring with unique ink and wash characteristics, which automatically generates Chinese paintings with machine learning technology is proposed.
- DCT–CNN-based classification method for the Gongbi and Xieyi techniques of Chinese ink-wash paintings39
A novel framework by combining a discrete cosine transformation (DCT) and convolutional neural networks (CNNs) is presented that achieves competitive classification performance compared to existing benchmark methods.
- Cluster structure preserving unsupervised feature selection for multi-view tasks38
This work considers the unsupervised feature selection problem for multi-view tasks, and develops a new algorithm, called Cluster Structure Preserving Unsupervised Feature Selection (CSP-UFS), which incorporates discriminative analysis, spectral clustering and correlation information between multiple views into a unified framework.
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- Inferring painting style with multi-task dictionary learning28
This paper presents a novel dictionary learning approach to automatically uncover the artistic style from paintings, by automatically decoupling style-specific and artist-specific patterns, and shows that this approach significantly outperforms state-of-the-art methods.
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- Image aesthetics enhancement using composition-based saliency detection7
A novel saliency detection method is presented, which utilizes the knowledge of photographic composition as priors to improve theSaliency detection results, and an online parameter selection method is proposed when utilizing GrabCut to achieve the saliency segmentation result.
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- Visual saliency detection based on photographic composition7
A novel saliency detection method is presented, which utilizes the knowledge of photographic composition as priors to improve theSaliency detection results and an online parameter selection method is proposed when utilizing GrabCut to achieve the saliency segmentation result.
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- Feature selection with spatial path coding for multimedia analysis6
A model of spatial path coding (SPC) that uses a supervised technique to select sparse features that shows better performance in terms of classification and recognition accuracy as compared with state-of-the-art algorithms is proposed.
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- Object coding on the semantic graph for scene classification3
The experimental results show that the Object Coding on semantic graph can improve the classification accuracy and is proposed to address the scene classification problem.
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- Multi-Speaker Video Dialog with Frame-Level Temporal Localization2
A novel task of Multi-Speaker Video Dialog with frame-level Temporal Localization (MSVD-TL) to make video dialog systems more applicable and focuses on the characteristic of the video dialog generation process and exploit the relation among the video fragment, the chat history, and the following response to refine their representations.
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- Research on Hanger Force and Main Arch Stability of Long-Span Concrete-Filled Steel Tube Arch Bridge1
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- Experimental Study on Atomization Characteristics of an Effervescent Atomizer Under Ambient Pressure–
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- Camouflaged Object Segmentation Based on Dense Multi-Scale Transformer–
A camouflaged object segmentation method called dense multi-scale Transformer, which consists of two main modules: two-branch separable dense multi-scale feature extraction module and fast attention-induced cross-level interaction fusion module, to make deep self-attentive transform networks reach their full potential in the task of camouflaged object segmentation.
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- Choose the Largest Contributor: A Fusion Coefficient Learning Network for Semantic Segmentation–
A fusion coefficient learning method that can guide us to select effective layers to fuse and is verified by massive experiments and proved to be effective on the PASCAL VOC2012, PASCal Context data set.
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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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