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- Benchmarking Single-Image Dehazing and Beyond2,349
This work presents a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single-Image DEhazing (RESIDE), which highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes.
- Generalizing to Unseen Domains: A Survey on Domain Generalization1,906
This paper provides a formal definition of domain generalization and discusses several related fields, and categorizes recent algorithms into three classes: data manipulation, representation learning, and learning strategy, and presents several popular algorithms in detail for each category.
- An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data1,071
This work proposes an end-to-end spatial and temporal attention model for human action recognition from skeleton data on top of the Recurrent Neural Networks with Long Short-Term Memory (LSTM), which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames.
- Image-adaptive watermarking using visual models993
This work proposes perceptually based watermarking schemes in two frameworks: the block-based discrete cosine transform and multiresolution wavelet framework and discusses the merits of each one, which are shown to provide very good results both in terms of image transparency and robustness.
- Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks930
This work takes the skeleton as the input at each time slot and introduces a novel regularization scheme to learn the co-occurrence features of skeleton joints, and proposes a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data675
A novel view adaptation scheme to automatically regulate observation viewpoints during the occurrence of an action by design a view adaptive recurrent neural network with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.
- A Twofold Siamese Network for Real-Time Object Tracking672
The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks and proposes a channel attention mechanism for the semantic branch.
- Relation-Aware Global Attention for Person Re-Identification651
This work proposes an effective Relation-Aware Global Attention (RGA) module which captures the global structural information for better attention learning and proposes to stack the relations, i.e., its pairwise correlations/affinities with all the feature positions together to learn the attention with a shallow convolutional model.
- Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition645
A simple yet effective semantics-guided neural network (SGN) for skeleton-based action recognition that achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.
- View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition522
A novel view adaptation scheme, which automatically determines the virtual observation viewpoints over the course of an action in a learning based data driven manner, and a two-stream scheme (referred to as VA-fusion) that fuses the scores of the two networks to provide the final prediction, obtaining enhanced performance.
- 496
- Style Normalization and Restitution for Generalizable Person Re-Identification425
The aim of this paper is to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains, and to enforce a dual causal loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features.
- 422
- PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network412
This paper proposes a phase-and-harmonics-aware deep neural network (DNN), named PHASEN, which has the ability to handle detailed phase patterns and to utilize harmonic patterns, and outperforms previous methods by a large margin on four metrics.
- Efficient frequency domain selective scrambling of digital video391
A joint encryption and compression framework in which video data are scrambled efficiently in the frequency domain by employing selective bit scrambling, block shuffling and block rotation of the transform coefficients and motion vectors is presented.
- Inpaint Anything: Segment Anything Meets Image Inpainting334
The core idea behind IA is to combine the strengths of different models in order to build a very powerful and user-friendly pipeline for solving inpainting-related problems.
- 308
- Densely Semantically Aligned Person Re-Identification302
This work is the first to make use of fine grained semantics for addressing misalignment problems for re-ID and construct a set of densely semantically aligned part images (DSAP-images), where the same spatial positions have the same semantics across different person images.
- SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking263
The greatest challenge facing visual object tracking is the simultaneous requirements on robustness and discrimination power, and a SiamFC-based tracker, named SPM-Tracker, is proposed, to address the two requirements in two separate matching stages.
- 257
- Cross View Fusion for 3D Human Pose Estimation255
This work introduces a cross-view fusion scheme into CNN to jointly estimate 2D poses for multiple views and presents a recursive Pictorial Structure Model to recover the 3D pose from the multi-view 2D pose.
- 248
- VoxelPose: Towards Multi-camera 3D Human Pose Estimation in Wild Environment233
An end-to-end solution which directly operates in the $3$D space, therefore avoids making incorrect decisions in the 2D space and outperforms the state-of-the-arts on the public datasets.
- Online Human Action Detection Using Joint Classification-Regression Recurrent Neural Networks226
This paper proposes a multi-task end-to-end Joint Classification-Regression Recurrent Neural Network to better explore the action type and temporal localization information and demonstrates very promising performance of this scheme.
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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