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20182026
most citedA Survey of Deep Learning-based Object Detection

1.3k citations · 1.4k across the 37 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CV2025

Edit-Your-Interest: Efficient Video Editing via Feature Most-Similar Propagation

Yi Zuo, Zitao Wang, Lingling Li +3

Text-to-image diffusion models have advanced video editing. However, existing video editing methods are limited by their high computational overhead and memory consumption. Further…

cs.CV2025

SoccerNet 2025 Challenges Results

Silvio Giancola, Anthony Cioppa, Marc Gutiérrez-Pérez +115

The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understandi…

cs.CV2025

SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop

Friedhelm Hamann, Emil Mededovic, Fabian Gülhan +21

We present an overview of the Spatio-temporal Instance Segmentation (SIS) challenge held in conjunction with the CVPR 2025 Event-based Vision Workshop. The task is to predict accur…

cs.CV2025

Domain-aware Category-level Geometry Learning Segmentation for 3D Point Clouds

Pei He, Lingling Li, Licheng Jiao +5

Domain generalization in 3D segmentation is a critical challenge in deploying models to unseen environments. Current methods mitigate the domain shift by augmenting the data distri…

cs.CV2025

AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report

Andrei Dumitriu, Florin Miron, Florin Tatui +24

This report presents an overview of the AIM 2025 RipSeg Challenge, a competition designed to advance techniques for automatic rip current segmentation in still images. Rip currents…

cs.CV2025

Logits DeConfusion with CLIP for Few-Shot Learning

Shuo Li, Fang Liu, Zehua Hao +5

With its powerful visual-language alignment capability, CLIP performs well in zero-shot and few-shot learning tasks. However, we found in experiments that CLIP's logits suffer from…

cs.NE2025

Learning Evolution via Optimization Knowledge Adaptation

Chao Wang, Lingling Li, Licheng Jiao +3

The iterative search process of evolutionary algorithms (EAs) encapsulates optimization knowledge within historical populations and fitness evaluations. Effective utilization of th…