arXiv AI
Breaking the Uniformity Trap: Scaling Video Diffusion Model via SplitMoE
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TAME: Temporal-Aware Mixture-of-Experts for Text-Video Retrieval
TAME introduces a Temporal-Aware Mixture-of-Experts framework for Text-Video Retrieval that enhances CLIP-based models by incorporating frame-level structure and temporal relations. It adds sparse Mixture-of-Experts layers with frame-consistent routing, Frame-Temporal tokens for global cross-frame aggregation, and a Cross-Temporal Interaction and Aggregation module to refine sentence-video similarities. Experiments on multiple TVR benchmarks show consistent performance gains, such as a 4.0 R@1 improvement on MSR‑VTT over CLIP4Clip.
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VideoMM: Adaptive Macro-Micro Inference for Efficient Video MLLMs
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Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding
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MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts
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arXiv Computer Vision
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TAME: Temporal-Aware Mixture-of-Experts for Text-Video Retrieval
TAME is a CLIP‑based framework for Text‑Video Retrieval that incorporates temporal modeling through three key innovations: sparse Mixture‑of‑Experts layers with frame‑consistent routing, Frame‑Temporal tokens that aggregate cross‑frame information, and a Cross‑Temporal Interaction and Aggregation module for refining frame‑wise similarities. These components enable the model to capture both local visual patterns and long‑range temporal dependencies, leading to consistent performance gains over CLIP‑based baselines on multiple TVR benchmarks, including a 4.0 R@1 improvement on MSR‑VTT. The code is publicly available on GitHub.