arXiv Computer Vision
Adaptive Tokenisation Via Temporal Redundancy Masking And Latent Inpainting
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
5d ago
Aggregating Visual Information with Optimal Transport for VideoLM Token Compression
arXiv:2608.20473v1 Announce Type: new Abstract: Video language models process videos as dense visual-token sequences with substantial representational redundancy. Compressing these sequences is there...
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 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 Computer Vision
3d ago
Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation
arXiv:2608.24293v1 Announce Type: new Abstract: Latent diffusion models have emerged as a dominant framework for high-fidelity image and video synthesis, operating in compact latent spaces with varia...
arXiv AI
Aug 5
Adaptive Two-Stage Visual Token Pruning for Efficient Inference in Video-Language Models
arXiv:2608. 03112v1 Announce Type: cross Abstract: Vision-language models excel at image and video understanding but suffer from high inference latency due to the need to process thousands of tokens per image, limiting their deployment on resource-constrained edge devices and in real-time surveillance applications.
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.