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20232026
most citedYOLOv10: Real-Time End-to-End Object Detection

1.1k citations · 1.1k across the 38 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

What to Preserve in Recursive Computation: A Local Predictive Sufficiency Principle

Peilin Wang, Feng Shiyang, Hongfu Gao +4

Recursive computation repeatedly compresses or reuses intermediate states, creating a simple tension: information that must remain useful across longer recursive paths is also expo…

cs.CV2026

RA-CFGCache: From Branch-Level Criteria to Guided-Risk Control under Classifier-Free Guidance

Yiming Liu, Ben Wan, Tongxuan Liu +5

Diffusion models enable high-quality visual generation, but iterative denoising remains computationally expensive, especially under classifier-free guidance (CFG), which requires b…

cs.CV2026

Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models

Yiwen Liang, Hui Chen, Yizhe Xiong +7

Test-time adaptation (TTA) has been widely explored in single-label recognition, effectively mitigating distribution shifts, especially when combined with vision-language models. H…

cs.CV2026

Attention-Spectrum Regularization for Replay-Free Continual Multimodal LLMs

Chuangxin Zhao, Canran Xiao, Siyuan Ma +5

Multimodal large language models (MLLMs) are increasingly required to adapt to non-stationary streams of visual domains, question types, and user instructions, yet continual fine-t…

cs.CV2026

FastOCR: Dynamic Visual Fixation via KV Cache Pruning for Efficient Document Parsing

Zihan Tang, Leqi Shen, Hui Chen +7

Vision-Language Models (VLMs) have shown strong promise on Optical Character Recognition (OCR), yet the sheer number of visual tokens required to encode dense documents incurs proh…

cs.CV2026

CREM: Compression-Driven Representation Enhancement for Multimodal Retrieval and Comprehension

Lihao Liu, Yan Wang, Biao Yang +10

Multimodal Large Language Models (MLLMs) have shown remarkable success in comprehension tasks such as visual description and visual question answering. However, their direct applic…