activity
20212023
most citedBoth Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding

8 citations · 26 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 6 cited

Unifying Image Processing as Visual Prompting Question Answering

Yihao Liu, Xiangyu Chen, Xianzheng Ma +4

Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally…

cs.CV2023★ 6 cited

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

Ziyu Guo, Renrui Zhang, Xiangyang Zhu +8

We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video. Guided by ImageBind, we construct a joint embedding space betwee…

cs.CV2023

Networks are Slacking Off: Understanding Generalization Problem in Image Deraining

Jinjin Gu, Xianzheng Ma, Xiangtao Kong +2

Deep deraining networks consistently encounter substantial generalization issues when deployed in real-world applications, although they are successful in laboratory benchmarks. A…

cs.CV2022★ 3 cited

Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation

Yulu Gan, Yan Bai, Yihang Lou +4

Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Existing methods mainly foc…

cs.CV2022★ 3 cited

CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention

Ziyu Guo, Renrui Zhang, Longtian Qiu +4

Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with great transferability, which achieves promising accuracy for zero-shot classifica…

cs.CV2021★ 8 cited

Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding

Xianzheng Ma, Zhixiang Wang, Yacheng Zhan +4

Although considerable progress has been made in semantic scene understanding under clear weather, it is still a tough problem under adverse weather conditions, such as dense fog, d…