345 citations · 719 across the 23 of their papers we have counts for
6 papers · 1 filter
Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
The Contrastive Language-Image Pre-Training (CLIP) model excels in few-shot learning by aligning visual and textual representations. Our study shows that template-sample similarity…
Start Small, Think Big: Curriculum-based Relative Policy Optimization for Visual Grounding
Qingyang Yan, Guangyao Chen, Yixiong Zou
Chain-of-Thought (CoT) prompting has recently shown significant promise across various NLP and computer vision tasks by explicitly generating intermediate reasoning steps. However,…
When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network
Dong Xiao, Guangyao Chen, Peixi Peng +4
Anomaly detection is essential for the safety and reliability of autonomous driving systems. Current methods often focus on detection accuracy but neglect response time, which is c…
Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
Jintao Tong, Ran Ma, Yixiong Zou +3
Cross-domain few-shot segmentation (CD-FSS) is proposed to pre-train the model on a source-domain dataset with sufficient samples, and then transfer the model to target-domain data…
Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation
Jintao Tong, Yixiong Zou, Guangyao Chen +2
Cross-Domain Few-Shot Segmentation (CD-FSS) aims to transfer knowledge from a source-domain dataset to unseen target-domain datasets with limited annotations. Current methods typic…
From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning
Yuhui Zeng, Haoxiang Wu, Wenjie Nie +6
Current object detectors excel at entity localization and classification, yet exhibit inherent limitations in event recognition capabilities. This deficiency arises from their arch…