1 citations · 1 across the 10 of their papers we have counts for
12 papers
G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification
Zehua Hao, Fang Liu, Qinliang Wang +3
Zero-shot classification needs efficient label retrieval and fine-grained visual reasoning, yet discriminative and generative vision-language models fail in complementary ways.When…
SoccerNet 2026 Challenges Results
Anthony Cioppa, Silvio Giancola, Håkan Ardö +102
The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video underst…
Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models
Ruiyang Li, Fang Liu, Licheng Jiao +8
Medical image segmentation supports clinical workflows by precisely delineating anatomical structures and lesions. However, medical image datasets medical image datasets suffer fro…
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…
ContextRefine-CLIP for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2025
Jing He, Yiqing Wang, Lingling Li +2
This report presents ContextRefine-CLIP (CR-CLIP), an efficient model for visual-textual multi-instance retrieval tasks. The approach is based on the dual-encoder AVION, on which w…
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
Xin Li, Kun Yuan, Bingchen Li +110
This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Qualit…
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…
MASSeg : 2nd Technical Report for 4th PVUW MOSE Track
Xuqiang Cao, Linnan Zhao, Jiaxuan Zhao +3
Complex video object segmentation continues to face significant challenges in small object recognition, occlusion handling, and dynamic scene modeling. This report presents our sol…
Multiplane Prior Guided Few-Shot Aerial Scene Rendering
Zihan Gao, Licheng Jiao, Lingling Li +4
Neural Radiance Fields (NeRF) have been successfully applied in various aerial scenes, yet they face challenges with sparse views due to limited supervision. The acquisition of den…
Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds
Yanbiao Ma, Licheng Jiao, Fang Liu +5
Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is p…
Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification
Yanbiao Ma, Licheng Jiao, Fang Liu +3
Real-world data are long-tailed, the lack of tail samples leads to a significant limitation in the generalization ability of the model. Although numerous approaches of class re-bal…
Data-Centric Long-Tailed Image Recognition
Yanbiao Ma, Licheng Jiao, Fang Liu +3
In the context of the long-tail scenario, models exhibit a strong demand for high-quality data. Data-centric approaches aim to enhance both the quantity and quality of data to impr…