29 citations · 116 across the 52 of their papers we have counts for
9 papers · 1 filter
SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments
Yundaichuan Zhan, Minghe Gao, Zhongqi Yue +7
Recent works have explored integrating Vision-Language Models (VLMs) with classical planners that rely on symbolic representations of planning problems to generate long-horizon pla…
Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration
Weile Chen, Bingchen Miao, Qifan Yu +6
Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents. However, existing web agents often rely on handcrafted execution pipelines…
Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs
Zhaoyu Fan, Kaihang Pan, Mingze Zhou +7
Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information. However, existing benchmarks primarily emphasize cogniti…
Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework
Jiang Liu, Bolin Li, Haoyuan Li +13
Efficient multimodal large language models (EMLLMs), in contrast to multimodal large language models (MLLMs), reduce model size and computational costs and are often deployed on re…
AlignLLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation
Hongzhe Huang, Jiang Liu, Zhewen Yu +8
Recent advances in Multi-modal Large Language Models (MLLMs), such as LLaVA-series models, are driven by massive machine-generated instruction-following data tuning. Such automatic…
WorldGPT: Empowering LLM as Multimodal World Model
Zhiqi Ge, Hongzhe Huang, Mingze Zhou +4
World models are progressively being employed across diverse fields, extending from basic environment simulation to complex scenario construction. However, existing models are main…
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Wenqiao Zhang, Tianwei Lin, Jiang Liu +10
Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks. The prevailing MLLM paradigm,…
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer
Wenqiao Zhang, Zheqi Lv, Hao Zhou +5
Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate.This setting neglec…
BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval
Wenqiao Zhang, Jiannan Guo, Mengze Li +5
Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text, which potentially…