output
20062026
most citedThe Medical Segmentation Decathlon

1.3k citations

Showing 2025 · cs.CVShow all

11 papers · 2 filters

cs.CV2025

CalibCLIP: Contextual Calibration of Dominant Semantics for Text-Driven Image Retrieval

Bin Kang, Bin Chen, Junjie Wang +3

Existing Visual Language Models (VLMs) suffer structural limitations where a few low contribution tokens may excessively capture global semantics, dominating the information aggreg…

cs.CV2025

VISA: Group-wise Visual Token Selection and Aggregation via Graph Summarization for Efficient MLLMs Inference

Pengfei Jiang, Hanjun Li, Linglan Zhao +4

In this study, we introduce a novel method called group-wise \textbf{VI}sual token \textbf{S}election and \textbf{A}ggregation (VISA) to address the issue of inefficient inference…

cs.CV2025

UniSVG: A Unified Dataset for Vector Graphic Understanding and Generation with Multimodal Large Language Models

Jinke Li, Jiarui Yu, Chenxing Wei +5

Unlike bitmap images, scalable vector graphics (SVG) maintain quality when scaled, frequently employed in computer vision and artistic design in the representation of SVG code. In…

cs.CV2025

Regularizing Subspace Redundancy of Low-Rank Adaptation

Yue Zhu, Haiwen Diao, Shang Gao +8

Low-Rank Adaptation (LoRA) and its variants have delivered strong capability in Parameter-Efficient Transfer Learning (PETL) by minimizing trainable parameters and benefiting from…

cs.CV2025

TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency

Minye Shao, Xingyu Miao, Haoran Duan +7

3D medical image generation is essential for data augmentation and patient privacy, calling for reliable and efficient models suited for clinical practice. However, current methods…

cs.CV2025

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

Zhaoyang Wei, Bowen Jiang, Xumeng Han +6

While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes unde…