Harnessing Weak Pair Uncertainty for Text-based Person Search

• URL: http://arxiv.org/abs/2604.08877v1
• Date: Fri, 10 Apr 2026 02:36:57 GMT
• Title: Harnessing Weak Pair Uncertainty for Text-based Person Search
• Abstract summary: We study the text-based person search, which is to retrieve the person of interest via natural language description.<n>To take full use of weak positives, we introduce an uncertainty-aware method to explicitly estimate image-text pair uncertainty.
• Score: 18.155384388834175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the text-based person search, which is to retrieve the person of interest via natural language description. Prevailing methods usually focus on the strict one-to-one correspondence pair matching between the visual and textual modality, such as contrastive learning. However, such a paradigm unintentionally disregards the weak positive image-text pairs, which are of the same person but the text descriptions are annotated from different views (cameras). To take full use of weak positives, we introduce an uncertainty-aware method to explicitly estimate image-text pair uncertainty, and incorporate the uncertainty into the optimization procedure in a smooth manner. Specifically, our method contains two modules: uncertainty estimation and uncertainty regularization. (1) Uncertainty estimation is to obtain the relative confidence on the given positive pairs; (2) Based on the predicted uncertainty, we propose the uncertainty regularization to adaptively adjust loss weight. Additionally, we introduce a group-wise image-text matching loss to further facilitate the representation space among the weak pairs. Compared with existing methods, the proposed method explicitly prevents the model from pushing away potentially weak positive candidates. Extensive experiments on three widely-used datasets, .e.g, CUHK-PEDES, RSTPReid and ICFG-PEDES, verify the mAP improvement of our method against existing competitive methods +3.06%, +3.55% and +6.94%, respectively.

Related papers

• Rethinking Uncertainty in Segmentation: From Estimation to Decision [0.0]
  In medical image segmentation, uncertainty estimates are often reported but rarely used to guide decisions.<n>We formulate segmentation as a two-stage pipeline, estimation followed by decision, and show that optimizing uncertainty alone fails to capture most of the achievable safety gains.<n>Our results show that the best method and policy combination removes up to 80 percent of segmentation errors at only 25 percent pixel deferral.
  arXiv  Detail & Related papers  (2026-04-14T19:52:05Z)
• Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning [49.28548464288051]
  Composed Image Retrieval (CIR) enables image search by combining a reference image with modification text.<n>In intrinsic noise in CIR triplets incurs intrinsic uncertainty and threatens the model's robustness.<n>This paper introduces a Heterogeneous Uncertainty-Guided (HUG) paradigm to overcome these limitations.
  arXiv  Detail & Related papers  (2026-01-16T16:05:49Z)
• Uncertainty-Aware Prototype Semantic Decoupling for Text-Based Person Search in Full Images [16.071286104558393]
  Text-based pedestrian search (TBPS) in full images aims to locate a target pedestrian in untrimmed images using natural language descriptions.<n>We propose UPD-TBPS, a novel framework comprising three modules: Multi-granularity Uncertainty Estimation (MUE), Prototype-based Uncertainty Decoupling (PUD), and Cross-modal Re-identification (ReID)
  arXiv  Detail & Related papers  (2025-05-06T14:25:30Z)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization [73.04187954213471]
  We introduce a unified learning approach to simultaneously modeling the coarse- and fine-grained retrieval. The proposed method has achieved +4.03%, +3.38%, and +2.40% Recall@50 accuracy over a strong baseline.
  arXiv  Detail & Related papers  (2022-11-14T14:25:40Z)
• Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval [51.83967175585896]
  UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction.
  arXiv  Detail & Related papers  (2022-10-24T17:53:20Z)
• ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation [125.52743832477404]
  Adversarial Examples Detection (AED) is a crucial defense technique against adversarial attacks. We propose a new technique, textbfADDMU, which combines two types of uncertainty estimation for both regular and FB adversarial example detection. Our new method outperforms previous methods by 3.6 and 6.0 emphAUC points under each scenario.
  arXiv  Detail & Related papers  (2022-10-22T09:11:12Z)
• Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations [2.543865489517869]
  This work motivates textual counterfactuals by laying the ground for a novel evaluation scheme inspired by the faithfulness of explanations. Experiments on sentiment analysis data show that the connectedness of counterfactuals to their original counterparts is not obvious in both models.
  arXiv  Detail & Related papers  (2022-10-17T09:50:02Z)
• Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval [10.743633102172236]
  Uncertainty quantification in image retrieval is crucial for downstream decisions. We present a new method that views image embeddings as features rather than deterministic features. We derive a variational approximation of the posterior, called the Bayesian triplet loss, that produces state-of-the-art uncertainty estimates.
  arXiv  Detail & Related papers  (2020-11-25T11:47:33Z)
• Uncertainty-Aware Few-Shot Image Classification [118.72423376789062]
  Few-shot image classification learns to recognize new categories from limited labelled data. We propose Uncertainty-Aware Few-Shot framework for image classification.
  arXiv  Detail & Related papers  (2020-10-09T12:26:27Z)

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.