OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering
This addresses the problem of improving multimodal AI systems for researchers and practitioners, representing an incremental advancement in self-supervised learning methods.
The paper tackled the challenge of enhancing omni-modal reasoning by proposing OmniJigsaw, a self-supervised framework using temporal reordering of audio-visual clips, which achieved substantial gains across 15 benchmarks in video, audio, and collaborative reasoning.
To extend the reinforcement learning post-training paradigm to omni-modal models for concurrently bolstering video-audio understanding and collaborative reasoning, we propose OmniJigsaw, a generic self-supervised framework built upon a temporal reordering proxy task. Centered on the chronological reconstruction of shuffled audio-visual clips, this paradigm strategically orchestrates visual and auditory signals to compel cross-modal integration through three distinct strategies: Joint Modality Integration, Sample-level Modality Selection, and Clip-level Modality Masking. Recognizing that the efficacy of such proxy tasks is fundamentally tied to puzzle quality, we design a two-stage coarse-to-fine data filtering pipeline, which facilitates the efficient adaptation of OmniJigsaw to massive unannotated omni-modal data. Our analysis reveals a ``bi-modal shortcut phenomenon'' in joint modality integration and demonstrates that fine-grained clip-level modality masking mitigates this issue while outperforming sample-level modality selection. Extensive evaluations on 15 benchmarks show substantial gains in video, audio, and collaborative reasoning, validating OmniJigsaw as a scalable paradigm for self-supervised omni-modal learning.