91 citations · 172 across the 10 of their papers we have counts for
10 papers
Pay More Attention To Text In High-Resolution MLLMs
Zhongkuan Mao, Wenzhuo Zhao, Xianjie Liu +7
Failures of high-resolution MLLMs are commonly attributed to a visual problem, motivating zooming, cropping, and related visual interventions to recover fine-grained evidence or su…
Is It Time for the Renaissance of Salient Object Detection in the Era of MLLMs?
Wenzhuo Zhao, Xiuzhi Li, Zhongkuan Mao +6
The zero-shot capabilities of multimodal large language models (MLLMs) are pushing salient object detection (SOD) beyond task-specific supervision. To disentangle MLLMs beyond conv…
Thinking Once Is Enough: Intermediate-Layer Evidence Routing for High-Resolution VQA
Zhongkuan Mao, Xianjie Liu, Tianyu Meng +9
High-resolution visual question answering (HR-VQA) is often treated as a problem of insufficient evidence acquisition, where failing multimodal large language models must inspect i…
Camouflage-aware Image-Text Retrieval via Expert Collaboration
Yao Jiang, Zhongkuan Mao, Xuan Wu +2
Camouflaged scene understanding (CSU) has attracted significant attention due to its broad practical implications. However, in this field, robust image-text cross-modal alignment r…
Patch is Enough: Naturalistic Adversarial Patch against Vision-Language Pre-training Models
Dehong Kong, Siyuan Liang, Xiaopeng Zhu +2
Visual language pre-training (VLP) models have demonstrated significant success across various domains, yet they remain vulnerable to adversarial attacks. Addressing these adversar…
Effectiveness Assessment of Recent Large Vision-Language Models
Yao Jiang, Xinyu Yan, Ge-Peng Ji +5
The advent of large vision-language models (LVLMs) represents a remarkable advance in the quest for artificial general intelligence. However, the model's effectiveness in both spec…
Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation
Xianjie Liu, Keren Fu, Yao Jiang +1
The Segment Anything Model (SAM) represents a significant breakthrough into foundation models for computer vision, providing a large-scale image segmentation model. However, despit…
Guided Focal Stack Refinement Network for Light Field Salient Object Detection
Bo Yuan, Yao Jiang, Keren Fu +1
Light field salient object detection (SOD) is an emerging research direction attributed to the richness of light field data. However, most existing methods lack effective handling…
BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection
Wenbo Zhang, Yao Jiang, Keren Fu +1
Depth information has been proved beneficial in RGB-D salient object detection (SOD). However, depth maps obtained often suffer from low quality and inaccuracy. Most existing RGB-D…
Light Field Salient Object Detection: A Review and Benchmark
Keren Fu, Yao Jiang, Ge-Peng Ji +3
Salient object detection (SOD) is a long-standing research topic in computer vision and has drawn an increasing amount of research interest in the past decade. This paper provides…