Deep Image Retrieval is not Robust to Label Noise
Large-scale datasets are essential for the success of deep learning in image retrieval. However, manual assessment errors and semi-supervised annotation techniques can lead to label noise even in popular datasets. As previous works primarily studied annotation quality in image classification tasks, it is still unclear how label noise affects deep learning approaches to image retrieval. In this work, we show that image retrieval methods are less robust to label noise than image classification ones. Furthermore, we, for the first time, investigate different types of label noise specific to image retrieval tasks and study their effect on model performance.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationDeep Learningimage-classificationImage ClassificationImage RetrievalRetrievalSimilar Papers 제목 키워드 기반
Learning with Label Noise for Image Retrieval by Selecting Interactions
Learning with noisy labels is an active research area for image classification. However, the effect of noisy labels on image retrieval has been less studied. In this work, we propose a noise-resistant method for image re…
image-classificationImage ClassificationImage RetrievalLearning with noisy labels+1Prototypical Mixing and Retrieval-Based Refinement for Label Noise-Resistant Image Retrieval
Label noise is pervasive in real-world applications, which influences the optimization of neural network models. This paper investigates a realistic but understudied problem of image retrieval under label noise, whic…
Image RetrievalMemorizationRetrievalImage-Text Retrieval with Binary and Continuous Label Supervision
Most image-text retrieval work adopts binary labels indicating whether a pair of image and text matches or not. Such a binary indicator covers only a limited subset of image-text semantic relations, which is insufficient…
Image CaptioningImage-text RetrievalRetrievalText Retrieval+2AMNS: Attention-Weighted Selective Mask and Noise Label Suppression for Text-to-Image Person Retrieval
Text-to-image person retrieval aims to retrieve images of person given textual descriptions, and most methods implicitly assume that the training image-text pairs are correctly aligned, but in practice, under-correlated …
Person RetrievalRetrievalLearning to Retrieve with Weakened Labels: Robust Training under Label Noise
Neural Encoders are frequently used in the NLP domain to perform dense retrieval tasks, for instance, to generate the candidate documents for a given query in question-answering tasks. However, sparse annotation and labe…