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

12 citations · 20 across the 7 of their papers we have counts for

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cs.CV2024

Add-SD: Rational Generation without Manual Reference

Lingfeng Yang, Xinyu Zhang, Xiang Li +7

Diffusion models have exhibited remarkable prowess in visual generalization. Building on this success, we introduce an instruction-based object addition pipeline, named Add-SD, whi…

cs.CV2024

OVLW-DETR: Open-Vocabulary Light-Weighted Detection Transformer

Yu Wang, Xiangbo Su, Qiang Chen +6

Open-vocabulary object detection focusing on detecting novel categories guided by natural language. In this report, we propose Open-Vocabulary Light-Weighted Detection Transformer…

cs.CV2024★ 3 cited

Evaluation of Text-to-Video Generation Models: A Dynamics Perspective

Mingxiang Liao, Hannan Lu, Xinyu Zhang +6

Comprehensive and constructive evaluation protocols play an important role in the development of sophisticated text-to-video (T2V) generation models. Existing evaluation protocols…

cs.CV2024★ 12 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.CV2024

Are Images Indistinguishable to Humans Also Indistinguishable to Classifiers?

Zebin You, Xinyu Zhang, Hanzhong Guo +2

The ultimate goal of generative models is to perfectly capture the data distribution. For image generation, common metrics of visual quality (e.g., FID) and the perceived truthfuln…

cs.CV2024

VRP-SAM: SAM with Visual Reference Prompt

Yanpeng Sun, Jiahui Chen, Shan Zhang +7

In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmenta…