5 papers
CPath: Class-Conditional Pathway Decoupling for Vision-Language Incremental Object Detection
Lecheng Xu, Feifei Shao, Ouyangzi Ye +5
Incremental Object Detection (IOD) aims to enable detectors to continuously learn novel categories while preserving previously acquired knowledge. However, existing methods suffer…
Direct Product Flow Matching: Decoupling Radial and Angular Dynamics for Few-Shot Adaptation
Hongxu Chen, Yanghao Wang, Bowei Zhu +6
Recent flow matching (FM) methods improve the few-shot adaptation of vision-language models, by modeling cross-modal alignment as a continuous multi-step flow. In this paper, we ar…
Path-Decoupled Hyperbolic Flow Matching for Few-Shot Adaptation
Lin Li, Ziqi Jiang, Gefan Ye +5
Recent advances in cross-modal few-shot adaptation treat visual-semantic alignment as a continuous feature transport problem via Flow Matching (FM). However, we argue that Euclidea…
em: Learning Hierarchical Hyperbolic Embeddings for Compositional Zero-Shot Learning
Lin Li, Jiahui Li, Jiaming Lei +3
Compositional zero-shot learning (CZSL) aims to recognize unseen state-object compositions by generalizing from a training set of their primitives (state and object). Current metho…
RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution
Jiahui Li, Lin Li, Tai-wei Chang +4
Reinforcement learning from human feedback (RLHF) offers a promising approach to aligning large language models (LLMs) with human preferences. Typically, a reward model is trained…