6 citations · 8 across the 24 of their papers we have counts for
8 papers · 1 filter
Dual-LoRA and Quality-Enhanced Pseudo Replay for Multimodal Continual Food Learning
Xinlan Wu, Bin Zhu, Feng Han +2
Food analysis has become increasingly critical for health-related tasks such as personalized nutrition and chronic disease prevention. However, existing large multimodal models (LM…
Efficient Test-Time Retrieval Augmented Generation
Hailong Yin, Bin Zhu, Jingjing Chen +1
Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG)…
Actor-Critic for Continuous Action Chunks: A Reinforcement Learning Framework for Long-Horizon Robotic Manipulation with Sparse Reward
Jiarui Yang, Bin Zhu, Jingjing Chen +1
Existing reinforcement learning (RL) methods struggle with long-horizon robotic manipulation tasks, particularly those involving sparse rewards. While action chunking is a promisin…
Benchmarking Gaslighting Negation Attacks Against Reasoning Models
Bin Zhu, Hailong Yin, Jingjing Chen +1
Recent advances in reasoning-centric models promise improved robustness through mechanisms such as chain-of-thought prompting and test-time scaling. However, their ability to withs…
Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion
Huiyan Qi, Bin Zhu, Chong-Wah Ngo +2
Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingre…
Efficient Prompt Tuning for Hierarchical Ingredient Recognition
Yinxuan Gui, Bin Zhu, Jingjing Chen +1
Fine-grained ingredient recognition presents a significant challenge due to the diverse appearances of ingredients, resulting from different cutting and cooking methods. While exis…
Don't Deceive Me: Mitigating Gaslighting through Attention Reallocation in LMMs
Pengkun Jiao, Bin Zhu, Jingjing Chen +2
Large Multimodal Models (LMMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their vulnerability to user gaslighting-the deliberate use of mislea…
Benchmarking Gaslighting Negation Attacks Against Multimodal Large Language Models
Bin Zhu, Yinxuan Gui, Huiyan Qi +3
Multimodal Large Language Models (MLLMs) have exhibited remarkable advancements in integrating different modalities, excelling in complex understanding and generation tasks. Despit…