SaFiRe: Saccade-Fixation Reiteration with Mamba for Referring Image Segmentation

• URL: http://arxiv.org/abs/2510.10160v1
• Date: Sat, 11 Oct 2025 10:50:58 GMT
• Title: SaFiRe: Saccade-Fixation Reiteration with Mamba for Referring Image Segmentation
• Authors: Zhenjie Mao, Yuhuan Yang, Chaofan Ma, Dongsheng Jiang, Jiangchao Yao, Ya Zhang, Yanfeng Wang,
• Abstract summary: Referring Image (RIS) aims to segment the target object in an image given a natural language expression.<n>Recent methods predominantly focus on simple expressions like "red car" or "left girl"
• Score: 58.80001825332851
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Image Segmentation (RIS) aims to segment the target object in an image given a natural language expression. While recent methods leverage pre-trained vision backbones and more training corpus to achieve impressive results, they predominantly focus on simple expressions--short, clear noun phrases like "red car" or "left girl". This simplification often reduces RIS to a key word/concept matching problem, limiting the model's ability to handle referential ambiguity in expressions. In this work, we identify two challenging real-world scenarios: object-distracting expressions, which involve multiple entities with contextual cues, and category-implicit expressions, where the object class is not explicitly stated. To address the challenges, we propose a novel framework, SaFiRe, which mimics the human two-phase cognitive process--first forming a global understanding, then refining it through detail-oriented inspection. This is naturally supported by Mamba's scan-then-update property, which aligns with our phased design and enables efficient multi-cycle refinement with linear complexity. We further introduce aRefCOCO, a new benchmark designed to evaluate RIS models under ambiguous referring expressions. Extensive experiments on both standard and proposed datasets demonstrate the superiority of SaFiRe over state-of-the-art baselines.

Related papers

• Understanding What Is Not Said:Referring Remote Sensing Image Segmentation with Scarce Expressions [45.04317112354794]
  Referring Remote Sensing Image aims to segment instances in remote sensing images according to referring expressions.<n>This paper introduces a new learning paradigm, Weakly Referring Expression Learning (WREL) for RRSIS.<n>We show that mixed-referring training yields a provable upper bound on the performance gap relative to training with fully annotated referring expressions.
  arXiv  Detail & Related papers  (2025-10-26T17:18:48Z)
• ConText: Driving In-context Learning for Text Removal and Segmentation [59.6299939669307]
  This paper presents the first study on adapting the visual in-context learning paradigm to optical character recognition tasks.<n>We propose a task-chaining compositor in the form of image-removal-segmentation.<n>We also introduce context-aware aggregation, integrating the chained prompt pattern into the latent query representation.
  arXiv  Detail & Related papers  (2025-06-04T10:06:32Z)
• IteRPrimE: Zero-shot Referring Image Segmentation with Iterative Grad-CAM Refinement and Primary Word Emphasis [46.502962768034166]
  Zero-shot Referring Image identifies the instance mask that best aligns with a referring expression without training and fine-tuning.<n>Previous CLIP-based models exhibit a notable reduction in their capacity to discern relative spatial relationships of objects.<n>IteRPrimE outperforms previous state-of-the-art zero-shot methods, particularly excelling in out-of-domain scenarios.
  arXiv  Detail & Related papers  (2025-03-02T15:19:37Z)
• Towards Generalizable Referring Image Segmentation via Target Prompt and Visual Coherence [48.659338080020746]
  Referring image segmentation (RIS) aims to segment objects in an image conditioning on free-from text descriptions. We present a novel RIS approach, which substantially improves the generalization ability by addressing the two dilemmas mentioned above. Specially, to deal with unconstrained texts, we propose to boost a given expression with an explicit and crucial prompt, which complements the expression in a unified context.
  arXiv  Detail & Related papers  (2023-12-01T09:31:24Z)
• Referring Expression Comprehension Using Language Adaptive Inference [15.09309604460633]
  This paper explores the adaptation between expressions and REC models for dynamic inference. We propose a framework named Language Adaptive Subnets (LADS), which can extract language-adaptives from the REC model conditioned on the referring expressions. Experiments on RefCOCO, RefCO+, RefCOCOg, and Referit show that the proposed method achieves faster inference speed and higher accuracy against state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-06-06T07:58:59Z)
• Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation [102.25240608024063]
  Referring image segments an image from a language expression. We develop an algorithm that shifts from being localization-centric to segmentation-language. Compared to its counterparts, our method is more versatile yet effective.
  arXiv  Detail & Related papers  (2023-03-11T08:42:40Z)
• Exploring Multi-Modal Representations for Ambiguity Detection & Coreference Resolution in the SIMMC 2.0 Challenge [60.616313552585645]
  We present models for effective Ambiguity Detection and Coreference Resolution in Conversational AI. Specifically, we use TOD-BERT and LXMERT based models, compare them to a number of baselines and provide ablation experiments. Our results show that (1) language models are able to exploit correlations in the data to detect ambiguity; and (2) unimodal coreference resolution models can avoid the need for a vision component.
  arXiv  Detail & Related papers  (2022-02-25T12:10:02Z)
• Two-stage Visual Cues Enhancement Network for Referring Image Segmentation [89.49412325699537]
  Referring Image (RIS) aims at segmenting the target object from an image referred by one given natural language expression. In this paper, we tackle this problem by devising a Two-stage Visual cues enhancement Network (TV-Net) Through the two-stage enhancement, our proposed TV-Net enjoys better performances in learning fine-grained matching behaviors between the natural language expression and image.
  arXiv  Detail & Related papers  (2021-10-09T02:53:39Z)
• Referring Expression Comprehension: A Survey of Methods and Datasets [20.42495629501261]
  Referring expression comprehension (REC) aims to localize a target object in an image described by a referring expression phrased in natural language. We first examine the state of the art by comparing modern approaches to the problem. We discuss modular architectures and graph-based models that interface with structured graph representation.
  arXiv  Detail & Related papers  (2020-07-19T01:45:02Z)
• Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions [87.33156149634392]
  We critically examine RefCOg, a standard benchmark for visual referring expression recognition. We show that 83.7% of test instances do not require reasoning on linguistic structure. We propose two methods, one based on contrastive learning and the other based on multi-task learning, to increase the robustness of ViLBERT.
  arXiv  Detail & Related papers  (2020-05-04T17:09:15Z)
• Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension [39.40351938417889]
  Referring expression comprehension (REF) aims at identifying a particular object in a scene by a natural language expression. Some popular referring expression datasets fail to provide an ideal test bed for evaluating the reasoning ability of the models. We propose a new dataset for visual reasoning in context of referring expression comprehension with two main features.
  arXiv  Detail & Related papers  (2020-03-01T04:59:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.