9 papers
M-STAR: Multi-Scale Spatiotemporal Autoregression for Human Mobility Modeling
Yuxiao Luo, Songming Zhang, Sijie Ruan +5
Modeling human mobility is vital for extensive applications such as transportation planning and epidemic modeling. With the rise of the Artificial Intelligence Generated Content (A…
When Top-ranked Recommendations Fail: Modeling Multi-Granular Negative Feedback for Explainable and Robust Video Recommendation
Siran Chen, Boyu Chen, Chenyun Yu +5
Existing video recommendation systems, relying mainly on ID-based embedding mapping and collaborative filtering, often fail to capture in-depth video content semantics. Moreover, m…
G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation
Boyu Chen, Siran Chen, Zhengrong Yue +7
User feedback is critical for refining recommendation systems, yet explicit feedback (e.g., likes or dislikes) remains scarce in practice. As a more feasible alternative, inferring…
VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning
Siran Chen, Boyu Chen, Chenyun Yu +6
Owing to powerful natural language processing and generative capabilities, large language model (LLM) agents have emerged as a promising solution for enhancing recommendation syste…
Super Encoding Network: Recursive Association of Multi-Modal Encoders for Video Understanding
Boyu Chen, Siran Chen, Kunchang Li +3
Video understanding has been considered as one critical step towards world modeling, which is an important long-term problem in AI research. Recently, multimodal foundation models…
LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents
Boyu Chen, Zhengrong Yue, Siran Chen +4
Existing MLLMs encounter significant challenges in modeling the temporal context within long videos. Currently, mainstream Agent-based methods use external tools to assist a single…
H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving
Siran Chen, Yuxiao Luo, Yue Ma +2
With the prevalence of Multimodal Large Language Models(MLLMs), autonomous driving has encountered new opportunities and challenges. In particular, multi-modal video understanding…
Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition
Boyu Chen, Siran Chen, Kunchang Li +3
Open-world video recognition is challenging since traditional networks are not generalized well on complex environment variations. Alternatively, foundation models with rich knowle…
M-BEV: Masked BEV Perception for Robust Autonomous Driving
Siran Chen, Yue Ma, Yu Qiao +1
3D perception is a critical problem in autonomous driving. Recently, the Bird-Eye-View (BEV) approach has attracted extensive attention, due to low-cost deployment and desirable vi…