arXiv Computer Vision
evMLP: An Efficient Event-Driven MLP Architecture for Vision
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UniMem: Unifying Multimodal Memory and Control for Vision-Language-Action Models
arXiv:2608.22869v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have leveraged internet-scale pretraining and task-focused finetuning to achieve strong performance on long...
arXiv Computer Vision
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UniMem: Unifying Visual and Textual Memory in VLAs for Non-Markovian Tasks
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arXiv AI
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LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics
LeVJEPA is a video encoder that eliminates the need for architectural asymmetries, exponential-moving-average target encoders, stop-gradients, and capacity-limited predictors used in prior self‑supervised methods. It trains a single encoder with an invariance loss over global and local views, regularized by SIGReg to prevent collapse, and achieves strong performance with far less pretraining compute. The approach also allows block‑causal attention, making temporal ordering a property of the encoder itself, and matches or surpasses state‑of‑the‑art baselines on both appearance‑centric and motion‑centric benchmarks.
arXiv AI
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Fusion: A Framework for Unified Sequential Token AdaptatIon in VisiOn TraNsformers
arXiv:2607. 02612v1 Announce Type: cross Abstract: Vision Transformers achieve strong image classification accuracy but process all image regions with nearly the same computation, even when many regions are redundant or uninformative.
arXiv Machine Learning
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CodecSight: Leveraging Video Codec Signals for Efficient Streaming VLM Inference
arXiv:2604.06036v4 Announce Type: replace-cross Abstract: Continuous inference over concurrent video streams imposes substantial compute and memory demands on vision-language model (VLM) serving. Str...
arXiv AI
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StreamingVLM: Real-Time Understanding for Infinite Video Streams
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