8 citations · 31 across the 26 of their papers we have counts for
27 papers
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 (…
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…
SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation
Linnan Zhao, Xu Liu, Lingling Li +3
Reasoning segmentation converts an implicit linguistic conclusion into a precise mask, requiring both semantic identification and spatial grounding. Existing MLLM-segmenter interfa…
ExACT: Exemplar-Driven Calibrated Refinement for Training-Free Visual Grounding in Remote Sensing Images
Zixiao Zhang, Lingling Li, Pei He +2
Remote sensing visual grounding (RSVG) aims to locate specific objects in high-resolution RS imagery using free-form natural language descriptions. While recent advances in multimo…
CASTLE2026 Team WDL Technical Report
Zhengyang Li, Zhenglin Du, Yi Wen +3
The CASTLE Challenge @ EgoVis 2026 evaluates long-form egocentric video question answering over 600+ hours of multi-perspective recordings. Each four-choice question requires evide…
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…
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…
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…
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…
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…
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…
AIM 2024 Sparse Neural Rendering Challenge: Methods and Results
Michal Nazarczuk, Sibi Catley-Chandar, Thomas Tanay +27
This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript…
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…
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…
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…
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…
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…
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…
Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
Yanbiao Ma, Licheng Jiao, Fang Liu +6
To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are w…
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search
Chao Wang, Licheng Jiao, Jiaxuan Zhao +4
The construction of machine learning models involves many bi-level multi-objective optimization problems (BL-MOPs), where upper level (UL) candidate solutions must be evaluated via…
Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy
Zhicheng Guo, Jiaxuan Zhao, Licheng Jiao +1
We propose a balanced coarsening scheme for multilevel hypergraph partitioning. In addition, an initial partitioning algorithm is designed to improve the quality of k-way hypergrap…