1 citations · 1 across the 9 of their papers we have counts for
9 papers
Human-Centric Intelligence in the Era of Foundation Models: A Survey
Yang Chen, Tianqi Wang, Xiaorui Jiang +13
Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling. Yet it has not fully integrated w…
Continual Video-MLLM Adaptation over Evolving Domains
Rui Cheng, Meixing Shi, Yuxiang Cai +3
Video multimodal large language models have shown strong capability in video understanding, yet their adaptation to sequentially evolving domains remains underexplored. In real-wor…
Spectral Evolution-Guided Token Pruning in Multimodal Large Language Models
Bin Chen, Yuxiang Cai, Yadan Luo +3
Reducing visual token redundancy is critical for accelerating Multimodal Large Language Models (MLLMs) without degrading cross-modal reasoning performance. Existing token pruning m…
Learning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detection
Wanqi Wang, Jingcai Guo, Yuxiang Cai +1
Cross-Domain Few-Shot Object Detection (CD-FSOD) aims to detect novel classes in unseen target domains given only a few labeled examples. While open-vocabulary detectors built on v…
SRSUPM: Sequential Recommender System Based on User Psychological Motivation
Yicheng Di, Yuan Liu, Zhi Chen +1
Sequential recommender infers users' evolving psychological motivations from historical interactions to recommend the next preferred items. Most existing methods compress recent be…
Fine-Grained Zero-Shot Learning with Attribute-Centric Representations
Zhi Chen, Jingcai Guo, Taotao Cai +1
Recognizing unseen fine-grained categories demands a model that can distinguish subtle visual differences. This is typically achieved by transferring visual-attribute relationships…
On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning
Tianqi Wang, Jingcai Guo, Depeng Li +1
Exemplar-free class incremental learning (EF-CIL) is a nontrivial task that requires continuously enriching model capability with new classes while maintaining previously learned k…
On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey
Jingcai Guo, Zhijie Rao, Zhi Chen +3
Zero-shot image recognition (ZSIR) aims to recognize and reason in unseen domains by learning generalized knowledge from limited data in the seen domain. The gist of ZSIR is constr…
Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects
Jingcai Guo, Zhijie Rao, Zhi Chen +2
Recent zero-shot learning (ZSL) approaches have integrated fine-grained analysis, i.e., fine-grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned visu…