Segmentation Assisted Incremental Test Time Adaptation in an Open World

• URL: http://arxiv.org/abs/2508.20029v1
• Date: Wed, 27 Aug 2025 16:33:32 GMT
• Title: Segmentation Assisted Incremental Test Time Adaptation in an Open World
• Authors: Manogna Sreenivas, Soma Biswas,
• Abstract summary: In dynamic environments, unfamiliar objects and distribution shifts are often encountered.<n>This work addresses Incremental Test Time Adaptation of Vision Language Models, tackling scenarios where unseen classes and unseen domains continuously appear during testing.<n>We propose a segmentation assisted active labeling module, termed SegAssist, which is training free and repurposes the segmentation capabilities of VLMs to refine active sample selection.
• Score: 11.054383308831001
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In dynamic environments, unfamiliar objects and distribution shifts are often encountered, which challenge the generalization abilities of the deployed trained models. This work addresses Incremental Test Time Adaptation of Vision Language Models, tackling scenarios where unseen classes and unseen domains continuously appear during testing. Unlike traditional Test Time Adaptation approaches, where the test stream comes only from a predefined set of classes, our framework allows models to adapt simultaneously to both covariate and label shifts, actively incorporating new classes as they emerge. Towards this goal, we establish a new benchmark for ITTA, integrating single image TTA methods for VLMs with active labeling techniques that query an oracle for samples potentially representing unseen classes during test time. We propose a segmentation assisted active labeling module, termed SegAssist, which is training free and repurposes the segmentation capabilities of VLMs to refine active sample selection, prioritizing samples likely to belong to unseen classes. Extensive experiments on several benchmark datasets demonstrate the potential of SegAssist to enhance the performance of VLMs in real world scenarios, where continuous adaptation to emerging data is essential. Project-page:https://manogna-s.github.io/segassist/

Related papers

• Adapting Vision-Language Models Without Labels: A Comprehensive Survey [74.17944178027015]
  Vision-Language Models (VLMs) have demonstrated remarkable generalization capabilities across a wide range of tasks.<n>Recent research has increasingly focused on unsupervised adaptation methods that do not rely on labeled data.<n>We propose a taxonomy based on the availability and nature of unlabeled visual data, categorizing existing approaches into four key paradigms.
  arXiv  Detail & Related papers  (2025-08-07T16:27:37Z)
• TAPS : Frustratingly Simple Test Time Active Learning for VLMs [0.0]
  Test-Time Optimization enables models to adapt to new data during inference by updating parameters on-the-fly.<n>We propose a novel Test-Time Active Learning framework that adaptively queries uncertain samples and updates prompts dynamically.<n>Our framework provides a practical and effective solution for real-world deployment in safety-critical applications such as autonomous systems and medical diagnostics.
  arXiv  Detail & Related papers  (2025-07-26T18:04:49Z)
• Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation [18.33878596057853]
  Test-time adaptation has attracted wide interest in the context of vision-language models for image classification.<n>We propose a novel TTA method tailored to adapting for segmentation during test time.<n>Our approach could be used as plug-and-play for any segmentation network, does not require additional training data or labels, and remains effective even with a single test sample.
  arXiv  Detail & Related papers  (2025-05-28T00:24:47Z)
• Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation [20.49085411104439]
  Incremental Few-Shot Semantic (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach.
  arXiv  Detail & Related papers  (2024-10-16T23:42:27Z)
• Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence [60.37934652213881]
  Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation. We present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
  arXiv  Detail & Related papers  (2024-07-26T17:51:58Z)
• Efficient Open Set Single Image Test Time Adaptation of Vision Language Models [15.621092104244003]
  Adapting models to dynamic, real-world environments is a critical challenge in deep learning.<n>We propose ROSITA, a novel framework that leverages dynamically updated feature banks to identify reliable test samples.<n>Our approach effectively adapts models to domain shifts for known classes while rejecting unfamiliar samples.
  arXiv  Detail & Related papers  (2024-06-01T16:21:42Z)
• Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks [59.12108527904171]
  A model should recognize new classes and maintain discriminability over old classes. The task of recognizing few-shot new classes without forgetting old classes is called few-shot class-incremental learning (FSCIL) We propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT)
  arXiv  Detail & Related papers  (2022-03-31T13:46:41Z)
• Novel Class Discovery in Semantic Segmentation [104.30729847367104]
  We introduce a new setting of Novel Class Discovery in Semantic (NCDSS) It aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes. In NCDSS, we need to distinguish the objects and background, and to handle the existence of multiple classes within an image. We propose the Entropy-based Uncertainty Modeling and Self-training (EUMS) framework to overcome noisy pseudo-labels.
  arXiv  Detail & Related papers  (2021-12-03T13:31:59Z)
• Unsupervised Domain Adaptation for Spatio-Temporal Action Localization [69.12982544509427]
  S-temporal action localization is an important problem in computer vision. We propose an end-to-end unsupervised domain adaptation algorithm. We show that significant performance gain can be achieved when spatial and temporal features are adapted separately or jointly.
  arXiv  Detail & Related papers  (2020-10-19T04:25:10Z)
• UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation [52.487469544343305]
  Methods for object detection and segmentation rely on large scale instance-level annotations for training. We propose an intuitive and unified semi-supervised model that is applicable to a range of supervision.
  arXiv  Detail & Related papers  (2020-06-12T22:45:47Z)

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.