arXiv Computer Vision
Aggregating Visual Information with Optimal Transport for VideoLM Token Compression
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
4d ago
Adaptive Tokenisation Via Temporal Redundancy Masking And Latent Inpainting
arXiv:2606.06158v2 Announce Type: replace Abstract: Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous...
arXiv AI
Jul 16
Fre-Res: Frequency-Residual Video Token Compression for Efficient Video MLLMs
arXiv:2605. 16366v2 Announce Type: replace-cross Abstract: Video MLLMs face a persistent tension between spatial fidelity and temporal coverage: preserving fine-grained visual details requires many spatial tokens, while capturing short-lived events requires dense temporal sampling.
arXiv AI
Jul 2
Stateful Token Reduction for Long-Video Hybrid VLMs
arXiv:2603. 00198v2 Announce Type: replace-cross Abstract: Token reduction accelerates long-video vision--language models (VLMs), but existing methods target Transformers, where reduction is treated as token pruning.
Hugging Face Trending Papers
Aug 3
CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models
In video understanding, vision-language models (VLMs) must ingest massive numbers of visual tokens, causing the computational and memory cost of the prefill stage to rise sharply. Such visual sequences are highly redundant along the spatio-temporal dimension, yet a high compression ratio is often accompanied by the loss of critical details.
arXiv Computation and Language
1d ago
Visual Information-Guided Parallel Decoding for Diffusion Multimodal Large Language Models
arXiv:2608.26580v1 Announce Type: cross Abstract: Diffusion multimodal large language models (dMLLMs) have recently emerged as a new decoding paradigm for multimodal generation. Starting from a fully...
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.