6 papers
REA: From Correct LLM Judgments to Safe Decision Delivery
Jingxiang Meng, Aofan Liu, Yaoshuai Ma +2
Generative safety guards must classify requests correctly and return outputs that applications can parse. Reasoning guards can improve classification accuracy while producing outpu…
CURE: Local Uncertainty Repair for Block-Parallel Speculative Decoding
Aofan Liu, Jingxiang Meng, Fangxin Liu +1
Speculative decoding mitigates the latency of sequential generation in autoregressive Large Language Models (LLMs) by interleaving draft generation with target verification. Howeve…
TaPR: Test-Aware Policy Refinement for Feedback-Conditioned Code Generation
Aofan Liu, Jingxiang Meng, Fangxin Liu +1
Multi-turn code agents rely on execution feedback to repair incorrect programs, yet standard reinforcement learning paradigms optimize and evaluate policy performance primarily usi…
Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification
Zhigang Chang, Zhao Yang, Yongbiao Chen +2
ideo-based person re-identification (Re-ID) aims to match person images in video sequences captured by disjoint surveillance cameras. Traditional video-based person Re-ID methods f…
DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification
Yongbiao Chen, Sheng Zhang, Fangxin Liu +3
In this paper, we make the very first attempt to investigate the integration of deep hash learning with vehicle re-identification. We propose a deep hash-based vehicle re-identific…
TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval
Yongbiao Chen, Sheng Zhang, Fangxin Liu +3
Deep hamming hashing has gained growing popularity in approximate nearest neighbour search for large-scale image retrieval. Until now, the deep hashing for the image retrieval comm…