19 papers · 1 filter
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…
Attend to Anything: Foundation Model for Unified Human Attention Modeling
Wenzhuo Zhao, Ronghao Xian, Keren Fu +1
Existing human attention (saliency) modeling methods persist as highly fragmented across modalities, scenes, and task formulations. Consequently, even with increasing model capacit…
NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Ivan Kosmynin +40
This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automatic saliency map prediction me…
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…
Samba+: General and Accurate Salient Object Detection via A More Unified Mamba-based Framework
Wenzhuo Zhao, Keren Fu, Jiahao He +3
Existing salient object detection (SOD) models are generally constrained by the limited receptive fields of convolutional neural networks (CNNs) and quadratic computational complex…