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20212024
most citedLW-DETR: A Transformer Replacement to YOLO for Real-Time Detection

12 citations · 25 across the 7 of their papers we have counts for

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cs.CV20243 cited

Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM

Huaxin Zhang, Xiaohao Xu, Xiang Wang +6

Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability. To address…

cs.CV202412 cited

LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection

Qiang Chen, Xiangbo Su, Xinyu Zhang +12

In this paper, we present a light-weight detection transformer, LW-DETR, which outperforms YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder…

cs.CV20242 cited

GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection

Huaxin Zhang, Xiang Wang, Xiaohao Xu +6

In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite signif…

cs.CV20241 cited

Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification

Jiahao Hong, Jialong Zuo, Chuchu Han +4

Recent unsupervised person re-identification (re-ID) methods achieve high performance by leveraging fine-grained local context. These methods are referred to as part-based methods.…

cs.CV20222 cited

Content-Variant Reference Image Quality Assessment via Knowledge Distillation

Guanghao Yin, Wei Wang, Zehuan Yuan +4

Generally, humans are more skilled at perceiving differences between high-quality (HQ) and low-quality (LQ) images than directly judging the quality of a single LQ image. This situ…

cs.CV2021

Weakly Supervised Person Search with Region Siamese Networks

Chuchu Han, Kai Su, Dongdong Yu +5

Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. Large-scale labeled training data is often difficult to colle…