arXiv AI
PRISM: Predictive Recomposition via Semantic Latent Decomposition for View-invariant Video Representation Learning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 7
InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem
arXiv:2512. 05672v2 Announce Type: replace-cross Abstract: Recent approaches in controllable novel view video generation often rely on fine-tuning pre-trained Video Diffusion Models (VDMs).
arXiv Computer Vision
Oct 1
Image Classifiers are Efficient Self-Supervised Video Representation Learners
arXiv:2609.40347v1 Announce Type: new Abstract: We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of r...
arXiv AI
Jun 16
Momentum-Guided Semantic Forecasting (MoFore) for Self-Supervised Video Representation Learning
arXiv:2606. 14765v1 Announce Type: cross Abstract: Self-supervised video representation learning has recently advanced through contrastive learning, masked reconstruction, and predictive representation learning.
arXiv AI
Jun 12
HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers
arXiv:2606. 13289v1 Announce Type: cross Abstract: Holistic visual tokenizers are fundamental to unified multimodal models (UMMs) as they map diverse visual inputs into a unified representation space.
arXiv AI
Jul 15
The TIME Machine: On The Power of Motion for Efficient Perception
arXiv:2605. 23045v2 Announce Type: replace-cross Abstract: Video representation learning has seen tremendous progress in recent years.
arXiv AI
Jul 7
Learning to Visually Connect Actions and their Effects
arXiv:2401. 10805v4 Announce Type: replace-cross Abstract: We introduce the novel concept of visually Connecting Actions and Their Effects (CATE) in video understanding.