1.3k citations · 1.4k across the 32 of their papers we have counts for
33 papers
Task-free Adaptive Meta Black-box Optimization
Chao Wang, Licheng Jiao, Lingling Li +4
Handcrafted optimizers become prohibitively inefficient for complex black-box optimization (BBO) tasks. MetaBBO addresses this challenge by meta-learning to automatically configure…
PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild
Henghui Ding, Chang Liu, Nikhila Ravi +33
This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025. It summarizes the challen…
STSeg-Complex Video Object Segmentation: The 1st Solution for 4th PVUW MOSE Challenge
Kehuan Song, Xinglin Xie, Kexin Zhang +3
Segmentation of video objects in complex scenarios is highly challenging, and the MOSE dataset has significantly contributed to the development of this field. This technical report…
RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings
Shuai Chen, Yong Zu, Zhixi Feng +2
The growing scarcity of spectrum resources and rapid proliferation of wireless devices make efficient radio network management critical. While deep learning-enhanced Cognitive Radi…
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…
Knowledge-aware Evolutionary Graph Neural Architecture Search
Chao Wang, Jiaxuan Zhao, Lingling Li +4
Graph neural architecture search (GNAS) can customize high-performance graph neural network architectures for specific graph tasks or datasets. However, existing GNAS methods begin…
Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis
Qiang Wu, Gechang Yao, Zhixi Feng +1
Time series analysis finds wide applications in fields such as weather forecasting, anomaly detection, and behavior recognition. Previous methods attempted to model temporal variat…
SoccerNet 2024 Challenges Results
Anthony Cioppa, Silvio Giancola, Vladimir Somers +81
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple t…
Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery
Fan Zhang, Lingling Li, Licheng Jiao +4
Satellite imagery, due to its long-range imaging, brings with it a variety of scale-preferred tasks, such as the detection of tiny/small objects, making the precise localization an…
Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification
Xiaoyang Hao, Zhixi Feng, Tongqing Peng +1
Automatic modulation classification (AMC) is an effective way to deal with physical layer threats of the internet of things (IoT). However, there is often label mislabeling in prac…
Technique Report of CVPR 2024 PBDL Challenges
Ying Fu, Yu Li, Shaodi You +96
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to info…
Fast and Efficient: Mask Neural Fields for 3D Scene Segmentation
Zihan Gao, Lingling Li, Licheng Jiao +5
Understanding 3D scenes is a crucial challenge in computer vision research with applications spanning multiple domains. Recent advancements in distilling 2D vision-language foundat…
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Henghui Ding, Chang Liu, Yunchao Wei +34
Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentat…
3rd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex Video Object Segmentation
Xinyu Liu, Jing Zhang, Kexin Zhang +3
Video Object Segmentation (VOS) is a vital task in computer vision, focusing on distinguishing foreground objects from the background across video frames. Our work draws inspiratio…
Automatic Graph Topology-Aware Transformer
Chao Wang, Jiaxuan Zhao, Lingling Li +3
Existing efforts are dedicated to designing many topologies and graph-aware strategies for the graph Transformer, which greatly improve the model's representation capabilities. How…
Edit-Your-Motion: Space-Time Diffusion Decoupling Learning for Video Motion Editing
Yi Zuo, Lingling Li, Licheng Jiao +5
Existing diffusion-based methods have achieved impressive results in human motion editing. However, these methods often exhibit significant ghosting and body distortion in unseen i…
Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification
Yanbiao Ma, Licheng Jiao, Fang Liu +3
In semi-supervised learning, methods that rely on confidence learning to generate pseudo-labels have been widely proposed. However, increasing research finds that when faced with n…
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…
Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover Classification
Jianfeng Cai, Yue Ma, Zhixi Feng +1
Polarimetric synthetic aperture radar (PolSAR) image interpretation is widely used in various fields. Recently, deep learning has made significant progress in PolSAR image classifi…
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…
When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges
Chao Wang, Jiaxuan Zhao, Licheng Jiao +3
Pre-trained large language models (LLMs) exhibit powerful capabilities for generating natural text. Evolutionary algorithms (EAs) can discover diverse solutions to complex real-wor…
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…
Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning
Yanbiao Ma, Licheng Jiao, Fang Liu +3
In scenarios with long-tailed distributions, the model's ability to identify tail classes is limited due to the under-representation of tail samples. Class rebalancing, information…
Changes-Aware Transformer: Learning Generalized Changes Representation
Dan Wang, Licheng Jiao, Jie Chen +2
Difference features obtained by comparing the images of two periods play an indispensable role in the change detection (CD) task. However, a pair of bi-temporal images can exhibit…