Audio-Contrastive Preference Optimization (ACPO) mitigates audio hallucination in AVLMs via output-contrastive and input-contrastive objectives that enforce faithful audio grounding.
Valor: Vision-audio-language omni-perception pretraining model and dataset
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HPP decouples perception from reasoning in long-video VLMs by having an LLM run iterative programmatic probes on hierarchically segmented video, reporting gains on LongVideoBench, EgoSchema, VideoMME, and MLVU.
Contrastive Fusion (ConFu) adds a fused-modality contrastive term to jointly align individual modalities and their combinations, enabling capture of higher-order dependencies like XOR relations while preserving pairwise alignments.
CalMRL mitigates anchor shift in multimodal representation learning by calibrating incomplete alignments through representation-level imputation of missing modalities using priors and a bi-step optimization with closed-form shared latent posteriors.
LanguageBind aligns video, infrared, depth, and audio to a frozen language encoder via contrastive learning on the new VIDAL-10M dataset, extending video-language pretraining to N modalities.
InternVid supplies 7M videos and LLM captions to train ViCLIP, which reaches leading zero-shot action recognition and competitive retrieval performance.
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.
InternVideo3 introduces Multimodal Contextual Reasoning and M^2LA attention to enable closed-loop evidence accumulation in long-video understanding and agentic tool use, reporting strong benchmark results.
citing papers explorer
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Don't Let the Video Speak: Audio-Contrastive Preference Optimization for Audio-Visual Language Models
Audio-Contrastive Preference Optimization (ACPO) mitigates audio hallucination in AVLMs via output-contrastive and input-contrastive objectives that enforce faithful audio grounding.
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HPP: Hierarchical Programmatic Probing for Long Video Understanding by Decoupling Perception and Reasoning
HPP decouples perception from reasoning in long-video VLMs by having an LLM run iterative programmatic probes on hierarchically segmented video, reporting gains on LongVideoBench, EgoSchema, VideoMME, and MLVU.
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The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment
Contrastive Fusion (ConFu) adds a fused-modality contrastive term to jointly align individual modalities and their combinations, enabling capture of higher-order dependencies like XOR relations while preserving pairwise alignments.
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Calibrated Multimodal Representation Learning with Missing Modalities
CalMRL mitigates anchor shift in multimodal representation learning by calibrating incomplete alignments through representation-level imputation of missing modalities using priors and a bi-step optimization with closed-form shared latent posteriors.
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LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
LanguageBind aligns video, infrared, depth, and audio to a frozen language encoder via contrastive learning on the new VIDAL-10M dataset, extending video-language pretraining to N modalities.
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InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
InternVid supplies 7M videos and LLM captions to train ViCLIP, which reaches leading zero-shot action recognition and competitive retrieval performance.
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AV-Master: Dual-Path Comprehensive Perception Makes Better Audio-Visual Question Answering
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.
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InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning
InternVideo3 introduces Multimodal Contextual Reasoning and M^2LA attention to enable closed-loop evidence accumulation in long-video understanding and agentic tool use, reporting strong benchmark results.