arXiv Machine Learning By Hui Wei, Licai Sun, Guoying Zhao

Human-JEPA: A Human-Centric Vision Model that Perceives and Anticipates

Read the original on arXiv Machine Learning →

Human‑JEPA is a human‑centric vision model trained on video that uses anchored forecasting to prevent silent collapse of dense perception. By replacing block masks with a pure past‑to‑future split, it avoids action‑tax and re‑identification collapse, enabling strong performance on pose and person re‑identification with far fewer parameters. The released predictor head uniquely preserves anticipation capability, allowing a single model to handle both perception and anticipation of humans.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Aug 24

WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving

arXiv:2608.20974v1 Announce Type: cross Abstract: Video Joint Embedding Predictive Architecture (V-JEPA) learns powerful spatiotemporal representations from video through self-supervised latent featu...

By Xinlin Wang, Yujiao Xiang, Yuheng Zhou, Jingqi Wang, Minqing Huang, Jiajie Huang, Dongxu Wei, Tingguang Zhou, Xiyang Wang, Gong Chen, Zhi Xu, Feiyang Tan, Hangning Zhou, Mu Yang
arXiv AI
Jul 13

Video Generation Models are General-Purpose Vision Learners

arXiv:2607. 09024v1 Announce Type: cross Abstract: Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models.

By Letian Wang, Chuhan Zhang, Rishabh Kabra, Jasper Uijlings, Steven Waslander, Andrew Zisserman, Joao Carreira, Kaiming He, Misha Andriluka, Eduard Gabriel Bazavan, Andrei Zanfir, Cristian Sminchisescu
arXiv AI
Aug 28

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.

By Lukas Kuhn, Lucas Maes, Giuseppe Serra, Quentin Le Lidec, Yann LeCun, Randall Balestriero, Florian Buettner
arXiv Computer Vision
Sep 11

MC-DeTra: Motion-Consistent Joint Object Detection and Socially-Aware Trajectory Forecasting in Bird's-Eye-View Images

MC-DeTra is a reimplementation of the DeTra model that jointly performs object detection and socially-aware trajectory forecasting in bird's-eye-view images. It introduces motion-consistency mechanisms that add supervision from each actor’s past motion, surrounding traffic occupancy, and a consistency constraint aligning predicted heading with motion direction. The added losses are train‑only and inference‑safe, improving dynamic trajectory forecasting on the Waymo Open Dataset while maintaining or enhancing detection accuracy.

By Vladislav Diuzhev, Dmitry Yudin