Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks297 from public data
- 2,901
- SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks2,477
This work proves the core reason Siamese trackers still have accuracy gap comes from the lack of strict translation invariance, and proposes a new model architecture to perform depth-wise and layer-wise aggregations, which not only improves the accuracy but also reduces the model size.
- Distractor-Aware Siamese Networks for Visual Object Tracking1,501
This paper focuses on learning distractor-aware Siamese networks for accurate and long-term tracking, and extends the proposed approach for long- term tracking by introducing a simple yet effective local-to-global search region strategy.
- 882
- Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion878
- 584
- Equalization Loss for Long-Tailed Object Recognition577
This work proposes a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories, and wins the 1st place in the LVIS Challenge 2019.
- 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.
- Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks563
Differentiable Soft Quantization (DSQ) is proposed to bridge the gap between the full-precision and low-bit networks and can help pursue the accurate gradients in backward propagation, and reduce the quantization loss in forward process with an appropriate clipping range.
- Salient Color Names for Person Re-identification550
This paper proposes a novel salient color names based color descriptor (SCNCD) to describe colors that outperforms the state-of-the-art performance (without user’s feedback optimization) on two challenging datasets (VIPeR and PRID 450S).
- T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos548
A deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos is proposed, called T-CNN.
- FOTS: Fast Oriented Text Spotting with a Unified Network539
This work proposes a unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks, and introduces RoIRotate to share convolutional features between detection and Recognition.
- Applications of multi‐omics analysis in human diseases536
This review outlines the existing technical categories of multi‐omics, cautions for experimental design, and focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multiomics data integration and the corresponding tools.
- 535
- 532
- Advances in liquid metals for biomedical applications517
This tutorial review introduces the common performances of liquid metals, highlights their featured properties, as well as summarizes various state-of-the-art bio-applications involving carriers for drug delivery, molecular imaging, cancer therapy and biomedical devices.
- POI: Multiple Object Tracking with High Performance Detection and Appearance Feature480
This paper explores the high-performance detection and deep learning based appearance feature, and shows that they lead to significantly better MOT results in both online and offline setting.
- STM: SpatioTemporal and Motion Encoding for Action Recognition473
This work proposes a STM block, which contains a Channel-wise SpatioTemporal Module (CSTM) to present the spatiotemporal features and aChannel-wise Motion Module(CMM) to efficiently encode motion features, and replaces original residual blocks in the ResNet architecture with STM blcoks to form a simple yet effective STM network.
- Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift440
This paper proposes a deep cocktail network (DCTN) to battle the domain and category shifts among multiple sources and evaluates DCTN in three domain adaptation benchmarks, which clearly demonstrate the superiority of the framework.
- 386
- 366
- CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval361
This paper proposes Cross-modal Adaptive Message Passing (CAMP), which adaptively controls the information flow for message passing across modalities and properly handles negative pairs and irrelevant information with an adaptive gating scheme.
- Quality Aware Network for Set to Set Recognition339
Analysis on gradient spread of this mechanism indicates that the quality learned by the network is beneficial to set-to-set recognition and simplifies the distribution that the network needs to fit.
- Aggregate channel features for multi-view face detection321
Following the learning pipelines in Viola-Jones framework, the multi-view face detector using aggregate channel features shows competitive performance against state-of-the-art algorithms on AFW and FDDB test-sets, while runs at 42 FPS on VGA images.
- Geometry Uncertainty Projection Network for Monocular 3D Object Detection316
A GUP Net is proposed to tackle the error amplification problem at both inference and training stages and can infer more reliable object depth than existing methods and outperforms the state-of-the-art image-based monocular 3D detectors.
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 2026-10-14. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.