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Works68 from public data
- Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion878
- Balanced Meta-Softmax for Long-Tailed Visual Recognition868
Balanced Softmax is presented, an elegant unbiased extension of Softmax, to accommodate the label distribution shift between training and testing, and it is demonstrated that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks.
- Efficient Attention: Attention with Linear Complexities822
A novel efficient attention mechanism equivalent to dot-product attention but with substantially less memory and computational costs is proposed, which allows more widespread and flexible integration of attention modules into a network, which leads to better accuracies.
- Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction654
STAR is presented, a Spatio-Temporal grAph tRansformer framework, which tackles trajectory prediction by only attention mechanisms, and achieves state-of-the-art performance on 5 commonly used real-world pedestrian prediction datasets.
- HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis567
A new attentionbased deep neural network, named as HydraPlus-Net (HPnet), that multi-directionally feeds the multi-level attention maps to different feature layers to enrich the final feature representations for a pedestrian image.
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- Person Re-identification with Deep Similarity-Guided Graph Neural Network324
This paper proposes a novel deep learning framework, named Similarity-Guided Graph Neural Network (SGGNN), which learns the edge weights with rich labels of gallery instance pairs directly, which provides relation fusion more precise information.
- Delving into Localization Errors for Monocular 3D Object Detection309
This work quantifies the impact introduced by each sub-task and found the ‘localization error’ is the vital factor in restricting monocular 3D detection, and investigates the underlying reasons behind localization errors.
- Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals303
A two-stage framework that incorporates complex spatio-temporal information for effectively regularizing the re-identification results is proposed and extensive experiments and analysis show the effectiveness of the proposed method and individual components.
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- Variational Relational Point Completion Network258
A variational framework, Variational Relational point Completion network (VRC-Net), with two appealing properties: Probabilistic Modeling and Relational Enhancement, which shows great generalizability and robustness on real-world point cloud scans.
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- Learning Monocular Depth by Distilling Cross-Domain Stereo Networks221
This paper proposes to use the stereo matching network as a proxy to learn depth from synthetic data and use predicted stereo disparity maps for supervising the monocular depth estimation network.
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- Pedestrian Behavior Understanding and Prediction with Deep Neural Networks168
A deep neural network (Behavior-CNN) is proposed to model pedestrian behaviors in crowded scenes, which has many applications in surveillance and multiple applications demonstrate the effectiveness of Behavior-CNN on pedestrian behavior modeling.
- GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer162
A novel group activity recognition network termed GroupFormer captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial- Temporal transformer.
- HMS-Net: Hierarchical Multi-Scale Sparsity-Invariant Network for Sparse Depth Completion160
A sparsity-invariant multi-scale encoder-decoder network (HMS-Net) for handling sparse inputs and sparse feature maps is proposed, and a model withoutRGB guidance ranks 1st among all peer-reviewed methods without using RGB information, and the model with RGB guidance ranks 2nd among all RGB-guided methods.
- GTC: Guided Training of CTC towards Efficient and Accurate Scene Text Recognition158
This work proposes the guided training of CTC (GTC), where CTC model learns a better alignment and feature representations from a more powerful attentional guidance, and achieves robust and accurate prediction for both regular and irregular scene text while maintaining a fast inference speed.
- Unsupervised 3D Shape Completion through GAN Inversion150
The proposed ShapeInversion uses a GAN pre-trained on complete shapes by searching for a latent code that gives a complete shape that best reconstructs the given partial input, and is capable of incorporating the rich prior captured in a well-trained generative model.
- Deep Group-Shuffling Random Walk for Person Re-identification150
A novel group-shuffling random walk network for fully utilizing the affinity information between gallery images in both the training and testing processes and which outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets by large margins.
- End-to-End Deep Kronecker-Product Matching for Person Re-identification147
A novel Kronecker Product Matching module to match feature maps of different persons in an end-to-end trainable deep neural network that outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets, which demonstrates the effectiveness and generalization ability of the proposed approach.
- EcoNAS: Finding Proxies for Economical Neural Architecture Search137
A reliable proxy is presented and a hierarchical proxy strategy is formulated that spends more computations on candidate networks that are potentially more accurate, while discards unpromising ones in early stage with a fast proxy to lead to an economical evolutionary-based NAS (EcoNAS), which achieves an impressive 400×search time reduction.
- Collaborative Unsupervised Visual Representation Learning from Decentralized Data135
This work proposes a novel federated unsupervised learning framework, FedU, which outperforms training with only one party by over 5% and other methods by over 14% in linear and semi-supervised evaluation on non-IID data.
- Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation112
This work proposes Multi-level Attention Encoder-Decoder Network (MAED), including a Spatial-Temporal Encoder (STE) and a Kinematic Topology Decoder (KTD) to model multi-level attentions in a unified framework.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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