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Jingwen Li

7 accepted papers

2026

SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation

CVPR 2026

Medical image segmentation is vital for clinical diagnosis and quantitative analysis, yet remains challenging due to the heterogeneity of imaging modalities and the high cost of pixel-level annotations. Although general interactive segmentation models like SAM have achieved remarkable progress, thei

Cited by 0SourceScholar
2025

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline

CVPR 2025poster

Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark datas…

2025

MM-LogVec: System Log Anomaly Detection Method Based on Multimodal Representation Learning

ICASSP 2025accepted

Advanced persistent threats (APTs) pose significant risks to national infrastructure and corporate security. System logs record interactions between system entities, which are widely used for APT detection. However, the complex syntax and intricate relationships in system logs pose significant chall…

Cited by 0SourceScholar
2025

SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation

NeurIPS 2025spotlight

While spatial reasoning has made progress in object localization relationships, it often overlooks object orientation—a key factor in 6-DoF fine-grained manipulation. Traditional pose representations rely on pre-defined frames or templates, limiting generalization and semantic grounding. In this pap…

Cited by 0SourceScholar
2024

Prompt3D: Random Prompt Assisted Weakly-Supervised 3D Object Detection

CVPR 2024poster

The prohibitive cost of annotations for fully supervised 3D indoor object detection limits its practicality. In this work we propose Random Prompt Assisted Weakly-supervised 3D Object Detection termed as Prompt3D a weakly-supervised approach that leverages position-level labels to overcome this chal…

2022

Efficient Neural Neighborhood Search for Pickup and Delivery Problems

IJCAI 2022poster

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized d…

2021

Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer

NeurIPS 2021poster

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dua…