STRNet improves goal-conditioned visual navigation by replacing simplistic encoders and pooling with a spatio-temporal fusion module that performs spatial graph reasoning and hybrid temporal modeling.
Learning spatiotemporal features with 3d convolutional networks
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B-MoE framework achieves state-of-the-art performance on micro-action recognition by using region-specific experts and cross-attention routing.
KeyTailor improves video virtual try-on realism by using instruction-guided keyframes to enhance garment details and background integrity in DiT models without major architectural changes.
citing papers explorer
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STRNet: Visual Navigation with Spatio-Temporal Representation through Dynamic Graph Aggregation
STRNet improves goal-conditioned visual navigation by replacing simplistic encoders and pooling with a spatio-temporal fusion module that performs spatial graph reasoning and hybrid temporal modeling.
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B-MoE: A Body-Part-Aware Mixture-of-Experts "All Parts Matter" Approach to Micro-Action Recognition
B-MoE framework achieves state-of-the-art performance on micro-action recognition by using region-specific experts and cross-attention routing.
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The devil is in the details: Enhancing Video Virtual Try-On via Keyframe-Driven Details Injection
KeyTailor improves video virtual try-on realism by using instruction-guided keyframes to enhance garment details and background integrity in DiT models without major architectural changes.