HumanVBench provides a 16-task benchmark for human-centric video understanding in MLLMs, created through automated annotation and distractor synthesis pipelines, and shows top models lag human performance on emotion perception and cross-modal alignment.
Chatbridge: Bridging modalities with large language model as a language catalyst
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ContextGuard prunes 55% of tokens in Qwen2.5-Omni 7B while matching full performance on five of six audio-visual benchmarks by preserving audio-irrecoverable visual context.
RaTA-Tool retrieves suitable external tools for multimodal queries by matching generated task descriptions against tool metadata, supported by a new Hugging Face-derived dataset and DPO optimization.
MME is a manually annotated benchmark evaluating MLLMs on perception and cognition across 14 subtasks to avoid data leakage and support fair model comparisons.
AV-Master reports state-of-the-art accuracy on four audio-visual question answering benchmarks by combining sequential question-guided focus sampling with modality-preference activation.
This survey organizes the architectures, training strategies, data, evaluation methods, extensions, and challenges of Multimodal Large Language Models.
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.
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A Survey on Multimodal Large Language Models
This survey organizes the architectures, training strategies, data, evaluation methods, extensions, and challenges of Multimodal Large Language Models.