7 citations · 10 across the 7 of their papers we have counts for
8 papers
What You Think is What You See: Driving Exploration in VLM Agents via Visual-Linguistic Curiosity
Haoxi Li, Qinglin Hou, Jianfei Ma +6
To navigate partially observable visual environments, recent VLM agents increasingly internalize world modeling capabilities into their policies via explicit CoT reasoning, enablin…
TTVS: Boosting Self-Exploring Reinforcement Learning via Test-time Variational Synthesis
Sikai Bai, Haoxi Li, Jie Zhang +2
Despite significant advances in Large Reasoning Models (LRMs) driven by reinforcement learning with verifiable rewards (RLVR), this paradigm is fundamentally limited in specialized…
DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning
Sikai Bai, Haoxi Li, Jie Zhang +2
Despite the significant breakthrough of Mixture-of-Experts (MoE), the increasing scale of these MoE models presents huge memory and storage challenges. Existing MoE pruning methods…
CoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs
Haoxi Li, Sikai Bai, Jie Zhang +1
Large reasoning models (LRMs) have demonstrated impressive capabilities in domains like mathematics and program synthesis. Despite their strong performance, LRMs often exhibit over…
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
Sikai Bai, Jie Zhang, Shuaicheng Li +5
Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target doma…
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Sikai Bai, Shuaicheng Li, Weiming Zhuang +7
Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fracti…
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning
Xiaocheng Lu, Ziming Liu, Song Guo +4
Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts composed of known knowledge without training samples. Standard CZSL either identifies visual primitives or…
Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification
Qi Wang, Sikai Bai, Junyu Gao +2
Person re-identification (re-ID) has gained more and more attention due to its widespread applications in intelligent video surveillance. Unfortunately, the mainstream deep learnin…