activity
20202026
most citedInvertible Denoising Network: A Light Solution for Real Noise Removal

11 citations · 15 across the 6 of their papers we have counts for

collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV2026

SonoSelect: Efficient Ultrasound Perception via Active Probe Exploration

Yixin Zhang, Yunzhong Hou, Longqi Li +3

Ultrasound perception typically requires multiple scan views through probe movement to reduce diagnostic ambiguity, mitigate acoustic occlusions, and improve anatomical coverage. H…

cs.CV2026

Mind the Rarities: Can Rare Skin Diseases Be Reliably Diagnosed via Diagnostic Reasoning?

Yang Liu, Jiyao Yang, Hongjin Zhao +10

Large vision-language models (LVLMs) demonstrate strong performance in dermatology; however, evaluating diagnostic reasoning for rare conditions remains largely unexplored. Existin…

cs.CV2026

Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space

Quoc-Huy Trinh, Xi Ding, Yang Liu +7

Visual spatial intelligence is critical for medical image interpretation, yet remains largely unexplored in Multimodal Large Language Models (MLLMs) for 3D imaging. This gap persis…

cs.CV2025

Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey

Seunghyuk Cho, Zhenyue Qin, Yang Liu +3

Plane geometry problem solving (PGPS) has recently gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. Des…

cs.CV2025

GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder

Seunghyuk Cho, Zhenyue Qin, Yang Liu +3

We introduce GeoDANO, a geometric vision-language model (VLM) with a domain-agnostic vision encoder, for solving plane geometry problems. Although VLMs have been employed for solvi…

cs.CV2024

Visual Prompting in LLMs for Enhancing Emotion Recognition

Qixuan Zhang, Zhifeng Wang, Dylan Zhang +5

Vision Large Language Models (VLLMs) are transforming the intersection of computer vision and natural language processing. Nonetheless, the potential of using visual prompts for em…