32 citations · 38 across the 7 of their papers we have counts for
7 papers · 1 filter
ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models
Wei Luo, Yangfan Ou, Jin Deng +4
Large-scale Vision-Language Models (VLMs) exhibit strong zero-shot recognition, yet their real-world deployment is challenged by distribution shifts. While Test-Time Adaptation (TT…
Test-Time Model Adaptation for Quantized Neural Networks
Zeshuai Deng, Guohao Chen, Shuaicheng Niu +6
Quantizing deep models prior to deployment is a widely adopted technique to speed up inference for various real-time applications, such as autonomous driving. However, quantized mo…
Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion
Zhuokun Chen, Jinwu Hu, Zeshuai Deng +3
Multimodal LLMs (MLLMs) equip language models with visual capabilities by aligning vision encoders with language models. Existing methods to enhance the visual perception of MLLMs…
HiLo: Detailed and Robust 3D Clothed Human Reconstruction with High-and Low-Frequency Information of Parametric Models
Yifan Yang, Dong Liu, Shuhai Zhang +3
Reconstructing 3D clothed human involves creating a detailed geometry of individuals in clothing, with applications ranging from virtual try-on, movies, to games. To enable practic…
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction
Zeshuai Deng, Zhuokun Chen, Shuaicheng Niu +3
Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. Conventional SR methods typically gather th…
Towards Lightweight Super-Resolution with Dual Regression Learning
Yong Guo, Mingkui Tan, Zeshuai Deng +5
Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images…
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Yong Guo, Jian Chen, Jingdong Wang +5
Deep neural networks have exhibited promising performance in image super-resolution (SR) by learning a nonlinear mapping function from low-resolution (LR) images to high-resolution…