2 citations · 3 across the 14 of their papers we have counts for
14 papers
Model Merging for Medical LVLMs: A Benchmark and a Winner-Take-All Approach
Lichao Mou, Shilan Zhang, Chunlei Li +7
Large vision-language models (LVLMs) can be adapted to specialized medical imaging tasks via parameter-efficient fine-tuning approaches such as low-rank adaptation (LoRA), leading…
ProPL: Universal Semi-Supervised Ultrasound Image Segmentation via Prompt-Guided Pseudo-Labeling
Yaxiong Chen, Qicong Wang, Chunlei Li +5
Existing approaches for the problem of ultrasound image segmentation, whether supervised or semi-supervised, are typically specialized for specific anatomical structures or tasks,…
AnyArtisticGlyph: Multilingual Controllable Artistic Glyph Generation
Xiongbo Lu, Yaxiong Chen, Shengwu Xiong
Artistic Glyph Image Generation (AGIG) differs from current creativity-focused generation models by offering finely controllable deterministic generation. It transfers the style of…
Location-Oriented Sound Event Localization and Detection with Spatial Mapping and Regression Localization
Xueping Zhang, Yaxiong Chen, Ruilin Yao +2
Sound Event Localization and Detection (SELD) combines the Sound Event Detection (SED) with the corresponding Direction Of Arrival (DOA). Recently, adopted event oriented multi-tra…
CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal Intervention
Yaxiong Chen, Minghong Wei, Zixuan Zheng +5
Referring medical image segmentation targets delineating lesions indicated by textual descriptions. Aligning visual and textual cues is challenging due to their distinct data prope…
UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report Generation
Yaxiong Chen, Chuang Du, Chunlei Li +5
Automated radiology report generation aims to expedite the tedious and error-prone reporting process for radiologists. While recent works have made progress, learning to align medi…
One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks
Yaxiong Chen, Junjian Hu, Chunlei Li +6
Video object segmentation is crucial for the efficient analysis of complex medical video data, yet it faces significant challenges in data availability and annotation. We introduce…
Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation
Yaxiong Chen, Yujie Wang, Zixuan Zheng +5
Medical ultrasound imaging is ubiquitous, but manual analysis struggles to keep pace. Automated segmentation can help but requires large labeled datasets, which are scarce. Semi-su…
RealisID: Scale-Robust and Fine-Controllable Identity Customization via Local and Global Complementation
Zhaoyang Sun, Fei Du, Weihua Chen +4
Recently, the success of text-to-image synthesis has greatly advanced the development of identity customization techniques, whose main goal is to produce realistic identity-specifi…
SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models
Zhaoyang Sun, Shengwu Xiong, Yaxiong Chen +4
This paper studies the challenging task of makeup transfer, which aims to apply diverse makeup styles precisely and naturally to a given facial image. Due to the absence of paired…
Adaptive Learning via a Negative Selection Strategy for Few-Shot Bioacoustic Event Detection
Yaxiong Chen, Xueping Zhang, Yunfei Zi +1
Although the Prototypical Network (ProtoNet) has demonstrated effectiveness in few-shot biological event detection, two persistent issues remain. Firstly, there is difficulty in co…
Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth
Zhaoyang Sun, Shengwu Xiong, Yaxiong Chen +1
The absence of real targets to guide the model training is one of the main problems with the makeup transfer task. Most existing methods tackle this problem by synthesizing pseudo…
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning
Yi Rong, Xiongbo Lu, Zhaoyang Sun +2
Self-supervised learning (SSL) techniques have recently been integrated into the few-shot learning (FSL) framework and have shown promising results in improving the few-shot image…
SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal
Zhaoyang Sun, Yaxiong Chen, Shengwu Xiong
Makeup transfer is not only to extract the makeup style of the reference image, but also to render the makeup style to the semantic corresponding position of the target image. Howe…