WorldSense provides the first benchmark requiring synergistic audio-video-text understanding on 1,662 real-world videos and 3,172 QA pairs, where the best current multimodal LLM reaches only 65.1% accuracy.
X-instructblip: A framework for aligning x-modal instruction- aware representations to llms and emergent cross-modal reasoning
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The paper introduces the MICL scenario for MLLMs with modality and task shifts and proposes MoInCL using pseudo-target generation and instruction-based distillation, reporting gains over continual learning baselines on six tasks.
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.
VideoLLaMA3 uses a vision-centric training paradigm and token-reduction design to reach competitive results on image and video benchmarks.
VideoLLaMA 2 improves video LLMs via a new STC connector for spatial-temporal dynamics and joint audio training, reaching competitive results on video QA and captioning benchmarks.
citing papers explorer
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Modality-Inconsistent Continual Learning of Multimodal Large Language Models
The paper introduces the MICL scenario for MLLMs with modality and task shifts and proposes MoInCL using pseudo-target generation and instruction-based distillation, reporting gains over continual learning baselines on six tasks.
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VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
VideoLLaMA 2 improves video LLMs via a new STC connector for spatial-temporal dynamics and joint audio training, reaching competitive results on video QA and captioning benchmarks.