most citedReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation

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

collaborators

9 papers

cs.AI2026

Concise Geometric Description as a Bridge: Unleashing the Potential of LLM for Plane Geometry Problem Solving

Jingyun Wang, Dian Li, Xiaohan Wang +3

Plane Geometry Problem Solving (PGPS) is a multimodal reasoning task that aims to solve a plane geometric problem based on a geometric diagram and problem textual descriptions. Alt…

cs.LG2026

Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation

Jingyun Wang, Cilin Yan, Guoliang Kang

Recent works modify CLIP to perform open-vocabulary semantic segmentation in a training-free manner (TF-OVSS). In vanilla CLIP, patch-wise image representations mainly encode homog…

cs.CV20264 cited

ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation

Jingyun Wang, Guoliang Kang

Recent works utilize CLIP to perform the challenging unsupervised semantic segmentation task where only images without annotations are available. However, we observe that when adop…

cs.CV2026

Motion-Guided Semantic Alignment with Negative Prompts for Zero-Shot Video Action Recognition

Yiming Wang, Frederick W. B. Li, Jingyun Wang

Zero-shot action recognition is challenging due to the semantic gap between seen and unseen classes. We present a novel framework that enhances CLIP with disentangled embeddings an…

cs.CV2025

CrossVid: A Comprehensive Benchmark for Evaluating Cross-Video Reasoning in Multimodal Large Language Models

Jingyao Li, Jingyun Wang, Molin Tan +6

Cross-Video Reasoning (CVR) presents a significant challenge in video understanding, which requires simultaneous understanding of multiple videos to aggregate and compare informati…

cs.CV2025

LTCA: Long-range Temporal Context Attention for Referring Video Object Segmentation

Cilin Yan, Jingyun Wang, Guoliang Kang

Referring Video Segmentation (RVOS) aims to segment objects in videos given linguistic expressions. The key to solving RVOS is to extract long-range temporal context information fr…