arXiv Computer Vision
ViTAL-X: Video-Text Alignment with Cross-Modal Temporal Edits
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Sep 1
ViTAL-X: Video-Text Alignment with Cross-Modal Temporal Edits
Video-text models adapted from image-text architectures (e.g., CLIP) frequently exhibit temporal blindness, the inability to perceive fundamental cues like order, direction, and motion dynamics. Stand...
arXiv AI
Jun 30
UniMotion: A Unified Framework for Motion-Text-Vision Understanding and Generation
arXiv:2603. 22282v2 Announce Type: replace-cross Abstract: We present UniMotion, to our knowledge the first unified framework for simultaneous understanding and generation of human motion, natural language, and RGB images within a single architecture.
arXiv AI
Jul 23
OSVE: One Step Video Editing with One Step Diffusion Models
arXiv:2607. 19895v1 Announce Type: cross Abstract: Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion.
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
4d ago
Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics
Kairos is a new video dataset designed for fine-grained video-language modeling, featuring long-duration videos from ten minutes to half an hour. Each video is annotated with time-resolved labels that capture ongoing actions, entity appearances, attributes, interactions, and evolving contextual cues throughout the timeline. The dataset supports fine-grained evaluation, long-range modeling, reasoning, instruction data construction, representation learning, and video generation.
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.