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Works84 from public data
- Scale Match for Tiny Person Detection402
A simple yet effective Scale Match approach to align the object scales between the two datasets for favorable tiny-object representation and the significant performance gain of this proposed approach over state-of-the-art detectors is shown.
- Effective Fusion Factor in FPN for Tiny Object Detection343
This paper argues that the top-down connections between adjacent layers in FPN bring two-side influences for tiny object detection, not only positive, and proposes a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN totiny object detection.
- TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization274
This paper introduces the token semantic coupled attention map (TS-CAM) to take full advantage of the self-attention mechanism in visual transformer for long-range dependency extraction and achieves state-of-the-art performance.
- Min-Entropy Latent Model for Weakly Supervised Object Detection232
A min-entropy latent model (MELM) is proposed for weakly supervised object detection, unified with feature learning and optimized with a recurrent learning algorithm, which progressively transfers the weak supervision to object locations.
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- Visual abnormal behavior detection based on trajectory sparse reconstruction analysis100
A novel abnormal behavior detection approach by introducing trajectory sparse reconstruction analysis (SRA), solved by L1-norm minimization, leading to that a few of dictionary samples are used when reconstructing a behavior trajectory, which guarantees that the proposed approach is valid even when the dictionary set is very small.
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- Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)83
A new approach for visual object tracking based on Sample-Based Adaptive Sparse Representation (AdaSR), which ensures that the tracked object is adaptively and compactly expressed with predefined samples, which is better than those of several representative tracking methods.
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- Multi-Attention Network for Compressed Video Referring Object Segmentation69
A multi-attention network which consists of dual-path dual-att attention module and a query-based cross-modal Transformer module designed to extract effective representation from compressed data in three modalities, i.e., I-frame, Motion Vector and Residual is proposed.
- Rethinking Sampling Strategies for Unsupervised Person Re-Identification67
It is suggested that deteriorated over-fitting is an important factor causing poor performance, and enhancing statistical stability can rectify this problem, and a simple yet effective approach is proposed, termed group sampling, which gathers samples from the same class into groups.
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- Object Localization under Single Coarse Point Supervision40
A coarse point refinement (CPR) approach, which to the best knowledge is the first attempt to alleviate semantic variance from the perspective of algorithm, which ensures training high-performance object localizer under coarse point supervision.
- Combined feature evaluation for adaptive visual object tracking38
A combined feature evaluation approach in filter frameworks for adaptive object tracking by combining color histogram and gradient orientation histogram, which gives a representation of both color and contour.
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- Semantic-aware SAM for Point-Prompted Instance Segmentation34
A Semantic-Aware Instance Segmentation Network (SAPNet) that integrates Multiple Instance Learning with matching capability and SAM with point prompts is devised that strategically selects the most representative mask proposals generated by SAM to supervise segmentation, with a specific focus on object category information.
- SM+: Refined Scale Match for Tiny Person Detection34
Experiments show that SM+ noticeably improves the performance on TinyPerson, and outperforms the state-of-the-art detectors with a significant margin, and a new probabilistic structure inpainting method is proposed for the background processing.
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- The 1st Tiny Object Detection Challenge: Methods and Results33
A brief summary of the 1st Tiny Object Detection (TOD) Challenge is provided including brief introductions to the top three methods.
- End-to-End Weakly Supervised Object Detection with Sparse Proposal Evolution25
Experiments on VOC and COCO object detection datasets show that SPE outperforms the state-of-the-art end-to-end methods by 7.0% mAP and 8.1% AP50, and is an order of magnitude faster than the two-stage methods, setting the first solid baseline for end- to-end WSOD with sparse proposals.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. 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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