3 citations · 3 across the 5 of their papers we have counts for
6 papers
Hyperbolic Prototype Routing for Rehearsal-Free Class-Incremental Learning
HongWei Zhao, Rui Liu, Yong Chen
Class-Incremental Learning (CIL) aims to continually learn new classes while preserving prior knowledge. Parameter-efficient fine-tuning with pre-trained models enables CIL with mi…
Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Function
Shuo Yin, Zhiyuan Yin, Yuqing Hou +3
Hash center-based deep hashing methods improve upon pairwise or triplet-based approaches by assigning fixed hash centers to each class as learning targets, thereby avoiding the ine…
Deep Hashing with Semantic Hash Centers for Image Retrieval
Li Chen, Rui Liu, Yuxiang Zhou +3
Deep hashing is an effective approach for large-scale image retrieval. Current methods are typically classified by their supervision types: point-wise, pair-wise, and list-wise. Re…
Large Model based Sequential Keyframe Extraction for Video Summarization
Kailong Tan, Yuxiang Zhou, Qianchen Xia +2
Keyframe extraction aims to sum up a video's semantics with the minimum number of its frames. This paper puts forward a Large Model based Sequential Keyframe Extraction for video s…
PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning
Rui Liu, Bin Yin, Ziyi Cao +3
Personalized news recommender systems help users quickly find content of their interests from the sea of information. Today, the mainstream technology for personalized news recomme…
TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence
Wang Qi, Rui Liu, Yuan Zuo +2
Creating an essay based on a few given topics is a challenging NLP task. Although several effective methods for this problem, topic-to-essay generation, have appeared recently, the…