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20222026
most citedMAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition

4 citations · 9 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Dynamic Resolution Routing for Efficient Egocentric Grounding

Huixin Sun, Wangbo Zhao, Fanyue Wei +3

Egocentric visual grounding requires high-resolution inputs to localize small objects. However, scaling Multimodal Large Language Models to this domain is constrained by the excess…

cs.CV2026

Noise-Robust Tiny Object Localization with Flows

Huixin Sun, Linlin Yang, Ronyu Chen +4

Despite significant advances in generic object detection, a persistent performance gap remains for tiny objects compared to normal-scale objects. We demonstrate that tiny objects a…

cs.CV2024★ 1 cited

P4Q: Learning to Prompt for Quantization in Visual-language Models

Huixin Sun, Runqi Wang, Yanjing Li +4

Large-scale pre-trained Vision-Language Models (VLMs) have gained prominence in various visual and multimodal tasks, yet the deployment of VLMs on downstream application platforms…

cs.CV2023★ 3 cited

Q-YOLO: Efficient Inference for Real-time Object Detection

Mingze Wang, Huixin Sun, Jun Shi +3

Real-time object detection plays a vital role in various computer vision applications. However, deploying real-time object detectors on resource-constrained platforms poses challen…

cs.CV2023★ 1 cited

Uncertainty-Aware Gradient Stabilization for Small Object Detection

Huixin Sun, Yanjing Li, Linlin Yang +2

Despite advances in generic object detection, there remains a performance gap in detecting small objects compared to normal-scale objects. We reveal that conventional object locali…

cs.CV2022★ 4 cited

MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition

Yunhao Wang, Huixin Sun, Xiaodi Wang +6

Vision Transformer and its variants have demonstrated great potential in various computer vision tasks. But conventional vision transformers often focus on global dependency at a c…