29 citations · 47 across the 18 of their papers we have counts for
19 papers
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
We describe the DS@GT submissions to the ImageCLEFmedical Caption 2026 challenge, which continues a long-running benchmark on the ROCOv2 dataset with two tracks: Concept Detection…
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…
A Unified Evaluation Framework for Multi-Annotator Tendency Learning
Liyun Zhang, Fengkai Liu, Xuanmeng Sha +3
Recent works have emerged in multi-annotator learning that shift focus from Consensus-oriented Learning (CoL), which aggregates multiple annotations into a single ground-truth pred…
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…
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…
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training
Zhouqiang Jiang, Bowen Wang, Junhao Chen +1
Recent approaches for visually-rich document understanding (VrDU) uses manually annotated semantic groups, where a semantic group encompasses all semantically relevant but not obvi…
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…
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…
Explainable Image Recognition via Enhanced Slot-attention Based Classifier
Bowen Wang, Liangzhi Li, Jiahao Zhang +2
The imperative to comprehend the behaviors of deep learning models is of utmost importance. In this realm, Explainable Artificial Intelligence (XAI) has emerged as a promising aven…
Instruct Me More! Random Prompting for Visual In-Context Learning
Jiahao Zhang, Bowen Wang, Liangzhi Li +2
Large-scale models trained on extensive datasets, have emerged as the preferred approach due to their high generalizability across various tasks. In-context learning (ICL), a popul…
Improving Facade Parsing with Vision Transformers and Line Integration
Bowen Wang, Jiaxing Zhang, Ran Zhang +3
Facade parsing stands as a pivotal computer vision task with far-reaching applications in areas like architecture, urban planning, and energy efficiency. Despite the recent success…
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning
Feiyu Zhang, Liangzhi Li, Junhao Chen +3
With the increasing size of pre-trained language models (PLMs), fine-tuning all the parameters in the model is not efficient, especially when there are a large number of downstream…
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
Tong Xiang, Liangzhi Li, Wangyue Li +4
The recent advances in natural language processing (NLP), have led to a new trend of applying large language models (LLMs) to real-world scenarios. While the latest LLMs are astoni…
Learning Bottleneck Concepts in Image Classification
Bowen Wang, Liangzhi Li, Yuta Nakashima +1
Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel…
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
Vivek Singh Bawa, Gurkirt Singh, Francis KapingA +16
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging. The challenges come from the peculiar struct…
Match Them Up: Visually Explainable Few-shot Image Classification
Bowen Wang, Liangzhi Li, Manisha Verma +3
Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to nove…
Automated Grading System of Retinal Arterio-venous Crossing Patterns: A Deep Learning Approach Replicating Ophthalmologist's Diagnostic Process of Arteriolosclerosis
Liangzhi Li, Manisha Verma, Bowen Wang +3
The status of retinal arteriovenous crossing is of great significance for clinical evaluation of arteriolosclerosis and systemic hypertension. As an ophthalmology diagnostic criter…
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation
Bowen Wang, Liangzhi Li, Yuta Nakashima +3
Semantic video segmentation is a key challenge for various applications. This paper presents a new model named Noisy-LSTM, which is trainable in an end-to-end manner, with convolut…
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
Liangzhi Li, Bowen Wang, Manisha Verma +3
Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not…