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20242026
most citedAIM 2024 Challenge on Video Saliency Prediction: Methods and Results

8 citations · 20 across the 39 of their papers we have counts for

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12 papers · 1 filter

cs.CV2026

P-SAM: SAM with Perceptual Parallel Prompt for Few-Shot Strip Steel Surface Defect Segmentation

Qian Xu, Hang Xiong, Anpeng Wang +3

Few-shot semantic segmentation (FSS) of strip steel surface defects (SD) has posed significant challenges distinct from natural scenes. Unlike natural images, SD task exhib…

cs.CV2026

SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection

Junsong Zhang, Zhijie Shen, Shuai Zheng +4

360° salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP) introduces severe spatial dist…

cs.CV2026

ICME 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing

Wei Sun, Weixia Zhang, Linhan Cao +30

This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for Hi…

cs.CL2026

Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models

Zongji Yu, Wenshui Luo, Yiliu Sun +5

Post-training via Reinforcement Learning (RL) has enabled Large Reasoning Models (LRMs) to achieve strong performance in individual domain. However, real-world applications increas…

cs.CV2026

M-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection

Jiyuan Liu, Jia Lin, Xiaofei Zhou +3

The Segment Anything Model 2 (SAM2) has emerged as a foundation model for universal segmentation. Owing to its generalizable visual representations, SAM2 has been successfully appl…

cs.RO2026

DecoVLN: Decoupling Observation, Reasoning, and Correction for Vision-and-Language Navigation

Zihao Xin, Wentong Li, Yixuan Jiang +4

Vision-and-Language Navigation (VLN) requires agents to follow long-horizon instructions and navigate complex 3D environments. However, existing approaches face two major challenge…

cs.CV2026

G2HFNet: GeoGran-Aware Hierarchical Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Images

Bin Wan, Runmin Cong, Xiaofei Zhou +3

Remote sensing images captured from aerial perspectives often exhibit significant scale variations and complex backgrounds, posing challenges for salient object detection (SOD). Ex…

cs.CV2026

RSONet: Region-guided Selective Optimization Network for RGB-T Salient Object Detection

Bin Wan, Runmin Cong, Xiaofei Zhou +3

This paper focuses on the inconsistency in salient regions between RGB and thermal images. To address this issue, we propose the Region-guided Selective Optimization Network for RG…

cs.CV2026

Mastering Negation: Boosting Grounding Models via Grouped Opposition-Based Learning

Zesheng Yang, Xi Jiang, Bingzhang Hu +4

Current vision-language detection and grounding models predominantly focus on prompts with positive semantics and often struggle to accurately interpret and ground complex expressi…

cs.CV2026

RDNet: Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network in Optical Remote Sensing Images

Bin Wan, Runmin Cong, Xiaofei Zhou +3

Salient object detection (SOD) in remote sensing images faces significant challenges due to large variations in object sizes, the computational cost of self-attention mechanisms, a…

cs.AI2026

TCAP: Tri-Component Attention Profiling for Unsupervised Backdoor Detection in MLLM Fine-Tuning

Mingzu Liu, Hao Fang, Runmin Cong

Fine-Tuning-as-a-Service (FTaaS) facilitates the customization of Multimodal Large Language Models (MLLMs) but introduces critical backdoor risks via poisoned data. Existing defens…

cs.CV2026

Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion

Shuoyan Wei, Feng Li, Chen Zhou +3

Diffusion models have demonstrated exceptional success in video super-resolution (VSR), exhibiting powerful capabilities for generating fine-grained details. However, their potenti…