activity
20152026
most citedWhen Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges

7 citations · 11 across the 18 of their papers we have counts for

collaborators

22 papers

cs.CV2026

0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation

Xiaoqiang Lu, Licheng Jiao, Lingling Li +5

Recent advances in vision foundation models (VFMs) have shown remarkable capabilities across diverse unimodal visual tasks. However, adapting VFMs to referring image segmentation (…

cs.CV2026

DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding

Xiaoqiang Lu, Licheng Jiao, Long Sun +5

Multi-task visual grounding requires models to jointly understand linguistic semantics and perform accurate visual localization and segmentation. Despite the success of multimodal…

cs.CV2026

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…

cs.CV2026

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…

physics.ins-det2026

Embedded underwater front-end electronics for the 3-inch photomultipliers in the JUNO experiment

Cédric Cerna, Miao He, Xiaoshan Jiang +591

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kton liquid scintillator-based, low-radioactivity, multi-purpose neutrino detector located 693 meters (1800 m.w.e.) und…

cs.NE2026

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…

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

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…

cs.NE2024

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…

cs.CV2024★ 3 cited

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…

cs.CV2024

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…

cs.CV2024

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…

cs.NE2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.NE2024★ 7 cited

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…

cs.CV2023

Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification

Yuwei Guo, Wenhao Zhang, Licheng Jiao +3

Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities. Existing models mainly focus on compensat…

cs.CV2015★ 1 cited

Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification

Fang Liu, Junfei Shi, Licheng Jiao +5

For polarimetric SAR (PolSAR) image classification, it is a challenge to classify the aggregated terrain types, such as the urban area, into semantic homogenous regions due to shar…