4 citations · 9 across the 12 of their papers we have counts for
13 papers
Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation
Jiaqi Tang, Yu Wang, Yang Liu +7
Self-supervised pretraining has expanded the pool of 3D medical foundation models, but published evaluations use different datasets, endpoints and fine-tuning protocols. Models als…
Explainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization
Zhu Xu, Jiaqi Tang, Pokai Chen +2
Explainable deepfake detection extends binary classification by requiring models to not only predict authenticity but also provide interpretable justifications. This expanded scope…
Topology-Driven Transferability Estimation for 3D Medical Vision Foundation Models
Jiaqi Tang, Shaoyang Zhang, Fandong Zhang +3
The growing number of medical vision foundation models highlights the need for effective model selection. However, mainstream selection methods rely on exhaustive fine-tuning, whic…
Topology-Driven Transferability Estimation of Medical Foundation Models for Segmentation
Jiaqi Tang, Shaoyang Zhang, Xiaoqi Wang +3
The advent of large-scale self-supervised learning (SSL) has produced a vast zoo of medical foundation models. However, selecting optimal medical foundation models for specific seg…
Shaping Initial State Prevents Modality Competition in Multi-modal Fusion: A Two-stage Scheduling Framework via Fast Partial Information Decomposition
Jiaqi Tang, Yinsong Xu, Yang Liu +1
Multi-modal fusion often suffers from modality competition during joint training, where one modality dominates the learning process, leaving others under-optimized. Overlooking the…
Investigating Domain Gaps for Indoor 3D Object Detection
Zijing Zhao, Zhu Xu, Qingchao Chen +2
As a fundamental task for indoor scene understanding, 3D object detection has been extensively studied, and the accuracy on indoor point cloud data has been substantially improved.…
TRKT: Weakly Supervised Dynamic Scene Graph Generation with Temporal-enhanced Relation-aware Knowledge Transferring
Zhu Xu, Ting Lei, Zhimin Li +4
Dynamic Scene Graph Generation (DSGG) aims to create a scene graph for each video frame by detecting objects and predicting their relationships. Weakly Supervised DSGG (WS-DSGG) re…
scDD: Latent Codes Based scRNA-seq Dataset Distillation with Foundation Model Knowledge
Zhen Yu, Jianan Han, Yang Liu +1
Single-cell RNA sequencing (scRNA-seq) technology has profiled hundreds of millions of human cells across organs, diseases, development and perturbations to date. However, the high…
ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual Grounding
Minghang Zheng, Jiahua Zhang, Qingchao Chen +2
Visual grounding aims to localize the object referred to in an image based on a natural language query. Although progress has been made recently, accurately localizing target objec…
Training-free Video Temporal Grounding using Large-scale Pre-trained Models
Minghang Zheng, Xinhao Cai, Qingchao Chen +2
Video temporal grounding aims to identify video segments within untrimmed videos that are most relevant to a given natural language query. Existing video temporal localization mode…
OED: Towards One-stage End-to-End Dynamic Scene Graph Generation
Guan Wang, Zhimin Li, Qingchao Chen +1
Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos. Conventional approaches often employ multi-stage pip…
Progressive trajectory matching for medical dataset distillation
Zhen Yu, Yang Liu, Qingchao Chen
It is essential but challenging to share medical image datasets due to privacy issues, which prohibit building foundation models and knowledge transfer. In this paper, we propose a…
Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo
Jiangbo Pei, Ruizhe Li, Aidong Men +3
Conventional Multi-Source Free Domain Adaptation (MSFDA) assumes that each source domain provides a single source model, and all source models adopt a uniform architecture. This pa…