Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph

TL;DR

This paper introduces VSGraph-LC, a method that uses visual-semantic graphs and metadata to automatically correct noisy web labels in image classification, improving accuracy without human annotation.

Contribution

It proposes a novel automatic label correction method leveraging metadata and graph neural networks to handle noisy web labels effectively.

Findings

Improves classification accuracy on Webvision-1000 and NUS-81-Web datasets.

Demonstrates robustness and effectiveness of VSGraph-LC in noisy label scenarios.

Shows advantage in open-set validation tasks.

Abstract

Webly supervised learning becomes attractive recently for its efficiency in data expansion without expensive human labeling. However, adopting search queries or hashtags as web labels of images for training brings massive noise that degrades the performance of DNNs. Especially, due to the semantic confusion of query words, the images retrieved by one query may contain tremendous images belonging to other concepts. For example, searching `tiger cat' on Flickr will return a dominating number of tiger images rather than the cat images. These realistic noisy samples usually have clear visual semantic clusters in the visual space that mislead DNNs from learning accurate semantic labels. To correct real-world noisy labels, expensive human annotations seem indispensable. Fortunately, we find that metadata can provide extra knowledge to discover clean web labels in a labor-free fashion, making it feasible to automatically provide correct semantic guidance among the massive label-noisy web data. In this paper, we propose an automatic label corrector VSGraph-LC based on the visual-semantic graph. VSGraph-LC starts from anchor selection referring to the semantic similarity between metadata and correct label concepts, and then propagates correct labels from anchors on a visual graph using graph neural network (GNN). Experiments on realistic webly supervised learning datasets Webvision-1000 and NUS-81-Web show the effectiveness and robustness of VSGraph-LC. Moreover, VSGraph-LC reveals its advantage on the open-set validation set.