1 citations · 2 across the 16 of their papers we have counts for
16 papers
Does Seeing More Mean Knowing More? Mono-Anchored Advantage Normalization for Multi-Source Visual Reasoning
Fanhu Zeng, Zhicong Luo, Zefan Wang +3
Visual reasoning through reinforcement learning with verifiable rewards (RLVR) has achieved remarkable progress. However, when dealing with multi-source inputs, existing approaches…
Unveiling Fine-Grained Visual Traces: Evaluating Multimodal Interleaved Reasoning Chains in Multimodal STEM Tasks
Jing Jin, Hao Liu, Yan Bai +9
Multimodal large language models (MLLMs) have shown promising reasoning abilities, yet evaluating their performance in specialized domains remains challenging. STEM reasoning is a…
CL-VISTA: Benchmarking Continual Learning in Video Large Language Models
Haiyang Guo, Yichen Shi, Fei Zhu +6
Video Large Language Models (Video-LLMs) require continual learning to adapt to non-stationary real-world data. However, existing benchmarks fall short of evaluating modern foundat…
Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients
Ziwei Xiang, Fanhu Zeng, Hongjian Fang +6
Large Vision Language Models (LVLMs) have achieved remarkable success in a range of downstream tasks that require multimodal interaction, but their capabilities come with substanti…
HyTRec: A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendation
Lei Xin, Yuhao Zheng, Ke Cheng +3
Modeling long sequences of user behaviors has emerged as a critical frontier in generative recommendation. However, existing solutions face a dilemma: linear attention mechanisms a…
Imagination Helps Visual Reasoning, But Not Yet in Latent Space
You Li, Chi Chen, Yanghao Li +4
Latent visual reasoning aims to mimic human's imagination process by meditating through hidden states of Multimodal Large Language Models. While recognized as a promising paradigm…
VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?
Hongbo Zhao, Meng Wang, Fei Zhu +5
The computational and memory overheads associated with expanding the context window of LLMs severely limit their scalability. A noteworthy solution is vision-text compression (VTC)…
MCITlib: Multimodal Continual Instruction Tuning Library and Benchmark
Haiyang Guo, Fei Zhu, Hongbo Zhao +5
Continual learning enables AI systems to acquire new knowledge while retaining previously learned information. While traditional unimodal methods have made progress, the rise of Mu…
Continual Learning for Generative AI: From LLMs to MLLMs and Beyond
Haiyang Guo, Fanhu Zeng, Fei Zhu +9
The rapid advancement of generative models has empowered modern AI systems to comprehend and produce highly sophisticated content, even achieving human-level performance in specifi…
Towards Efficient and General-Purpose Few-Shot Misclassification Detection for Vision-Language Models
Fanhu Zeng, Zhen Cheng, Fei Zhu +1
Reliable prediction by classifiers is crucial for their deployment in high security and dynamically changing situations. However, modern neural networks often exhibit overconfidenc…
HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model
Haiyang Guo, Fanhu Zeng, Ziwei Xiang +4
Instruction tuning is widely used to improve a pre-trained Multimodal Large Language Model (MLLM) by training it on curated task-specific datasets, enabling better comprehension of…
Federated Continual Instruction Tuning
Haiyang Guo, Fanhu Zeng, Fei Zhu +5
A vast amount of instruction tuning data is crucial for the impressive performance of Large Multimodal Models (LMMs), but the associated computational costs and data collection dem…
RobustMerge: Parameter-Efficient Model Merging for MLLMs with Direction Robustness
Fanhu Zeng, Haiyang Guo, Fei Zhu +2
Fine-tuning pre-trained models with custom data leads to numerous expert models on specific tasks. Merging models into one universal model to empower multi-task ability refraining…
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning
Haiyang Guo, Fei Zhu, Fanhu Zeng +2
Continual learning aims to equip models with the ability to retain previously learned knowledge like a human. Recent work incorporating Parameter-Efficient Fine-Tuning has revitali…
ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt
Fanhu Zeng, Fei Zhu, Haiyang Guo +2
Large Multimodal Models (LMMs) exhibit remarkable multi-tasking ability by learning mixed instruction datasets. However, novel tasks would be encountered sequentially in dynamic wo…
Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection
Fanhu Zeng, Zhen Cheng, Fei Zhu +2
Out-of-Distribution (OOD) detection, aiming to distinguish outliers from known categories, has gained prominence in practical scenarios. Recently, the advent of vision-language mod…