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- Nenghai YuSuggested from co-authorship
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Works31 from public data
- Attention-Aware Compositional Network for Person Re-identification502
This work introduces a novel framework called Attention-Aware Compositional Network (AACN) for person ReID, which consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC).
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- Learning Spatial Regularization with Image-Level Supervisions for Multi-label Image Classification393
Analysis of the learned SRN model demonstrates that it can effectively capture both semantic and spatial relations of labels for improving classification performance, and significantly outperforms state-of-the-arts and has strong generalization capability.
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- Feature Erasing and Diffusion Network for Occluded Person Re-Identification207
A novel Feature Erasing and Diffusion Network (FED) to simultaneously handle challenges from NPO and NTP is proposed and the superiority of FED over state-of-the-art methods is demonstrated.
- VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results133
A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
- Progressive Correspondence Pruning by Consensus Learning112
This work introduces a "pruning" block that lets us identify reliable candidates among the initial matches according to consensus scores estimated using local-to-global dynamic graphs and achieves progressive pruning by stacking multiple pruning blocks sequentially.
- Instruct-ReID: A Multi-Purpose Person Re-Identification Task with Instructions84
A new instruct-ReID task that requires the model to retrieve images according to the given image or language instructions, and a large-scale OmniReID benchmark and an adaptive triplet loss as a baseline method to facilitate research in this new setting.
- Structured Domain Adaptation With Online Relation Regularization for Unsupervised Person Re-ID63
This work argues that domain translation has great potential on exploiting valuable source-domain data but the existing methods did not provide proper regularization on the translation process and proposes an end-to-end structured domain adaptation framework with an online relation-consistency regularization term.
- MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing56
This work proposes MixMix based on two simple yet effective techniques, which outperforms existing methods on the mainstream compression tasks, including quantization, knowledge distillation and pruning, and proves the effectiveness of MixMix from both theoretical and empirical perspectives.
- Crowd Tracking with Dynamic Evolution of Group Structures54
This paper proposes to discover distinctive and stable mid-level patches and track them jointly with dynamic evolution of group structures through the integration of low-level keypoint tracking, mid- level patch tracking, and high-level group evolution.
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- Hulk: A Universal Knowledge Translator for Human-Centric Tasks33
Hulk is presented, the first multimodal human-centric generalist model, capable of addressing 2D vision, 3D vision, skeleton-based, and vision-language tasks without task-specific finetuning, and achieving state-of-the-art performance on 12 benchmarks.
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- Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report28
The first Mobile AI challenge is introduced, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
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- Re-Aligning Language to Visual Objects with an Agentic Workflow6
This work proposes an agentic workflow controlled by an LLM to re-align language to visual objects via adaptively adjusting image and text prompts and reveals a potential to preserve data quality along with scaling up data quantity, which further improves LOD performance from a data-alignment perspective.
- Learning in School: Multi-teacher Knowledge Inversion for Data-Free Quantization.4
This work proposes Learning in School~(LIS) algorithm, capable to generate the images suitable for all models by inverting the knowledge in multiple teachers and introduces a decentralized training strategy by sampling teachers from hierarchical courses to simultaneously maintain the diversity of generated images.
- Multi-level visual tracking with hierarchical tree structural constraint4
A robust tracker based on multi-level target representation and hierarchical tree structural constraint that outperforms the state-of-the-art trackers significantly is proposed.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. 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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