20 citations · 30 across the 12 of their papers we have counts for
12 papers
MUSE: A Unified Agentic Harness for MLLMs
Jianglin Lu, Hailing Wang, Xu Ma +4
Despite rapid progress, multimodal large language models (MLLMs) still fail on tasks that humans solve effortlessly, such as navigating a grid maze from a screenshot or selecting t…
Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning
Qihua Dong, Ruozhen He, Junwen Chen +4
Advanced chart question answering requires both precise perception of small visual elements and multi-step reasoning across several subplots. While existing MLLMs are strong at und…
Visual Reasoning through Tool-supervised Reinforcement Learning
Qihua Dong, Gozde Sahin, Pei Wang +4
In this paper, we investigate the problem of how to effectively master tool-use to solve complex visual reasoning tasks for Multimodal Large Language Models. To achieve that, we pr…
Beyond Referring Expressions: Scenario Comprehension Visual Grounding
Ruozhen He, Nisarg A. Shah, Qihua Dong +3
Existing visual grounding benchmarks primarily evaluate alignment between image regions and literal referring expressions, where models can often succeed by matching a prominent na…
Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks
Qihua Dong, Kuo Yang, Lin Ju +6
Referring Expression Comprehension (REC) links language to region level visual perception. Standard benchmarks (RefCOCO, RefCOCO+, RefCOCOg) have progressed rapidly with multimodal…
Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning
Xu Ma, Yitian Zhang, Qihua Dong +1
High-quality and open datasets remain a major bottleneck for text-to-image (T2I) fine-tuning. Despite rapid progress in model architectures and training pipelines, most publicly av…
CoT Referring: Improving Referring Expression Tasks with Grounded Reasoning
Qihua Dong, Luis Figueroa, Handong Zhao +5
Referring Expression Comprehension and Segmentation are critical tasks for assessing the integration of language understanding and image comprehension, serving as benchmarks for Mu…
Cautious Next Token Prediction
Yizhou Wang, Lingzhi Zhang, Yue Bai +7
Next token prediction paradigm has been prevailing for autoregressive models in the era of LLMs. The current default sampling choice for popular LLMs is temperature scaling togethe…
Boosting Large Language Models with Mask Fine-Tuning
Mingyuan Zhang, Yue Bai, Huan Wang +4
The large language model (LLM) is typically integrated into the mainstream optimization protocol. However, it remains underexplored whether maintaining the model integrity is \text…
Preserving Tumor Volumes for Unsupervised Medical Image Registration
Qihua Dong, Hao Du, Ying Song +2
Medical image registration is a critical task that estimates the spatial correspondence between pairs of images. However, current traditional and deep-learning-based methods rely o…
Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity
Hao Du, Qihua Dong, Yan Xu +1
Medical image segmentation is almost the most important pre-processing procedure in computer-aided diagnosis but is also a very challenging task due to the complex shapes of segmen…
Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
Ruozhen He, Qihua Dong, Jiaying Lin +1
Existing camouflaged object detection (COD) methods rely heavily on large-scale datasets with pixel-wise annotations. However, due to the ambiguous boundary, annotating camouflage…