Referring Image Segmentation

Follow this topic Ask about this topic

Referring image segmentation studies how to identify and delineate the image region described by language, including expressions that distinguish one object from similar instances. Common approaches fuse vision and language features in segmentation networks, first ground a phrase to a box or region and then refine a mask, or adapt and prompt foundation models such as vision-language models and Segment Anything. Systems are commonly evaluated on RefCOCO, RefCOCO+, and RefCOCOg, with work also extending to video, remote sensing, and medical images. A central challenge is combining robust semantic and relational understanding with precise boundaries, while generalizing to unfamiliar domains and keeping inference and annotation costs manageable.

Latest papers

45 papers4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Channel Attention-Guided Cross-Modal Knowledge Distillation for Referring Image Segmentation
Apr 18, 2026Chen YangCross-Modal Learning Cross-Modal Knowledge Distillation
School of Information Science and Technology, TaiShan University, Taian, China
EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detection
Apr 13, 2026Ye Wang, Kai Huang, Sumin Shen +1Unsupervised Camouflaged Object Detection Camouflaged Object Detection
West China Hospital, Sichuan University · Independent Researcher · Department of Data Science and Artificial Intelligence, Auckland University of Technology
Toward an Artificial General Teacher: Procedural Geometry Data Generation and Visual Grounding with Vision-Language Models
Apr 3, 2026Hai Nguyen-Truong, Alper Balbay, Tunga BayrakReferring Image Segmentation Visual Reasoning
Freya
Moondream Segmentation: From Words to Masks
Apr 3, 2026Ethan ReidReferring Image Segmentation Image Segmentation
M87 Labs, San Francisco, CA, USA.
TALENT: Target-aware Efficient Tuning for Referring Image Segmentation
Apr 1, 2026Shuo Jin, Siyue Yu, Bingfeng Zhang +3Referring Image Segmentation Parameter-Efficient Fine-Tuning
XJTLU · University of Liverpool · China University of Petroleum (East China) +2
Progressive Prompt-Guided Cross-Modal Reasoning for Referring Image Segmentation
Mar 30, 2026Jiachen Li, Hongyun Wang, Jinyu Xu +5Referring Image Segmentation Promptable Image Segmentation
School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China · School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China · University of Electronic Science and Technology of China, Chengdu, China +1
GenMask: Adapting DiT for Segmentation via Direct Mask Generation
Mar 25, 2026Yuhuan Yang, Xianwei Zhuang, Yuxuan Cai +6Image Segmentation Vision Foundation Model Adaptation
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University · Alibaba Group · School of Artificial Intelligence, Shanghai Jiao Tong University +1
Towards Motion-aware Referring Image Segmentation
Mar 18, 2026Chaeyun Kim, Seunghoon Yi, Yejin Kim +2Referring Image Segmentation Multimodal Contrastive Learning
Seoul National University · AIM Intelligence
Spatio-Semantic Expert Routing Architecture with Mixture-of-Experts for Referring Image Segmentation
Mar 13, 2026Alaa Dalaq, Muzammil BehzadReferring Image Segmentation Boundary-Aware Segmentation
King Fahd University of Petroleum and Mineral, Dhahran, 31261, Saudi Arabia · SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Dhahran, 31261, Saudi Arabia
GroundedSurg: A Multi-Procedure Benchmark for Language-Conditioned Surgical Tool Segmentation
Mar 1, 2026Tajamul Ashraf, Abrar Ul Riyaz, Wasif Tak +4Medical Image Grounding Referring Image Segmentation
King Abdullah University of Science and Technology (KAUST), Saudi Arabia · Gaash Research Lab, National Institute of Technology Srinagar, India · Thapar Institute of Engineering and Technology, India +1
AMLRIS: Alignment-aware Masked Learning for Referring Image Segmentation
Feb 26, 2026Tongfei Chen, Shuo Yang, Yuguang Yang +7Referring Image Segmentation Vision-Language Alignment
School of Artificial Intelligence, Beihang University, China · School of Electronic Information Engineering, Beihang University, China · AI Research, Qihoo 360, China +3
Enabling Training-Free Text-Based Remote Sensing Segmentation
Feb 19, 2026Jose Sosa, Danila Rukhovich, Anis Kacem +1Text-Guided Image Segmentation Remote Sensing Image Segmentation
SnT, University of Luxembourg
VIPA: Visual Informative Part Attention for Referring Image Segmentation
Feb 16, 2026Yubin Cho, Hyunwoo Yu, Kyeongbo Kong +3Referring Image Segmentation Cross-Modal Attention
School of Artificial Intelligence, Sogang University · AI Lab of CTO division, LG Electronics · School of Electronic Engineering, Sogang University +2
RefAM: Attention Magnets for Zero-Shot Referral Segmentation
Sep 26, 2025Anna Kukleva, Enis Simsar, Alessio Tonioni +4Attention Mechanisms Image Segmentation
Max Planck Institute for Informatics, SIC · ETH Zürich · Google +1
Intra- and Cross-Scale Feature Interaction Network for Referring Remote Sensing Image Segmentation
Jun 4, 2025Zhigang Yang, Huiguang Yao, Linmao Tian +3Referring Image Segmentation Remote Sensing Image Segmentation
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, P.R. China · School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, P.R. China · department of Software, Northwest Institute of Nuclear Technology, Xi’an 710072, P.R. China

Apr 18, 2026J/K move · Enter open · S save

Channel Attention-Guided Cross-Modal Knowledge Distillation for Referring Image Segmentation

Chen Yang

School of Information Science and Technology, TaiShan University, Taian, China

Cross-Modal Learning Cross-Modal Knowledge Distillation Image Segmentation Vision-Language Models Channel Attention Referring Image Segmentation

Open paper AskPDF arXiv

Referring image segmentation (RIS) requires accurate segmentation of target regions in images according to language descriptions, which is a cross-modal task integrating vision and language. Existing RIS methods typically employ large-scale vision and language encoding models to improve performance, but their enormous parameter size severely restricts deployment in scenarios with limited computing resources. To solve this problem, this paper proposes a channel attention-guided cross-modal knowledge distillation method, which transfers the high-order fine-grained correlations between vision and language learned by the teacher network, as well as the correlations between semantic components represented by each channel, to the student network. Compared with the traditional pixel-wise relational distillation, this method not only enables the student to learn the knowledge of the teacher, but also retains part of its independent learning ability, alleviating the transfer of learning bias. Experimental results on two public datasets show that the proposed distillation method does not introduce additional parameters during inference and can achieve significant performance improvement for the student model.