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20182026
most citedVideoSSL: Semi-Supervised Learning for Video Classification

6 citations · 19 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning

Abrar Majeedi, Zhiyuan Ruan, Ziyi Zhao +3

Multimodal large language models (MLLMs) have achieved impressive performance on visual perception and reasoning tasks with RGB imagery, yet they remain fragile under common degrad…

cs.CV2025

Restore-R1: Efficient Image Restoration Agents via Reinforcement Learning with Multimodal LLM Perceptual Feedback

Jianglin Lu, Yuanwei Wu, Ziyi Zhao +4

Complex image restoration aims to recover high-quality images from inputs affected by multiple degradations such as blur, noise, rain, and compression artifacts. Recent restoration…

cs.CV2024

SynCDR : Training Cross Domain Retrieval Models with Synthetic Data

Samarth Mishra, Carlos D. Castillo, Hongcheng Wang +2

In cross-domain retrieval, a model is required to identify images from the same semantic category across two visual domains. For instance, given a sketch of an object, a model need…

cs.CV2023

Lightweight Delivery Detection on Doorbell Cameras

Pirazh Khorramshahi, Zhe Wu, Tianchen Wang +2

Despite recent advances in video-based action recognition and robust spatio-temporal modeling, most of the proposed approaches rely on the abundance of computational resources to a…

cs.CV20206 cited

VideoSSL: Semi-Supervised Learning for Video Classification

Longlong Jing, Toufiq Parag, Zhe Wu +2

We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised…

cs.CV2018

Layout-induced Video Representation for Recognizing Agent-in-Place Actions

Ruichi Yu, Hongcheng Wang, Ang Li +3

We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home surveillance. We…