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From the 1 of 7 linked papers with an AI index.

activity
20242026
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7 papers

cs.CV2026

DS@GT ARC at ImageCLEFmedical 2026: Architectural Diversity for Concept Detection and Foundation-Model Scaling for Caption Prediction in Medical Image Analysis

Bowen Wang, Youwen Zhang, Ritesh Mehta

The paper details DS@GT's approaches for the ImageCLEFmedical 2026 challenge, using a late‑fusion ensemble of ConvNeXt‑V2, BiomedCLIP, and DenseNet‑169 with an Honest Threshold Tun…

cs.CV2025

PANICL: Mitigating Over-Reliance on Single Prompt in Visual In-Context Learning

Jiahao Zhang, Bowen Wang, Hong Liu +2

Visual In-Context Learning (VICL) uses input-output image pairs, referred to as in-context pairs (or examples), as prompts alongside query images to guide models in performing dive…

cs.AI2025

Taming the Untamed: Graph-Based Knowledge Retrieval and Reasoning for MLLMs to Conquer the Unknown

Bowen Wang, Zhouqiang Jiang, Yasuaki Susumu +3

The real value of knowledge lies not just in its accumulation, but in its potential to be harnessed effectively to conquer the unknown. Although recent multimodal large language mo…

cs.CL2025

DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models

Bowen Wang, Jiuyang Chang, Yiming Qian +6

Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GP…

cs.CV2025

E-InMeMo: Enhanced Prompting for Visual In-Context Learning

Jiahao Zhang, Bowen Wang, Hong Liu +3

Large-scale models trained on extensive datasets have become the standard due to their strong generalizability across diverse tasks. In-context learning (ICL), widely used in natur…

cs.CL2025

Putting People in LLMs' Shoes: Generating Better Answers via Question Rewriter

Junhao Chen, Bowen Wang, Zhouqiang Jiang +1

Large Language Models (LLMs) have demonstrated significant capabilities, particularly in the domain of question answering (QA). However, their effectiveness in QA is often undermin…