Towards Automatic Construction of Diverse, High-quality Image Dataset
This work addresses the need for efficient, high-quality image dataset construction for computer vision researchers, though it is incremental as it builds on existing weakly supervised methods.
The authors tackled the labor-intensive problem of manual image dataset annotation by proposing a novel framework that uses multiple textual queries to automatically collect diverse and accurate images from the web, resulting in significant performance gains on tasks like image classification and object detection.
The availability of labeled image datasets has been shown critical for high-level image understanding, which continuously drives the progress of feature designing and models developing. However, constructing labeled image datasets is laborious and monotonous. To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries. We aim at collecting diverse and accurate images for given queries from the Web. Specifically, we formulate noisy textual queries removing and noisy images filtering as a multi-view and multi-instance learning problem separately. Our proposed approach not only improves the accuracy but also enhances the diversity of the selected images. To verify the effectiveness of our proposed approach, we construct an image dataset with 100 categories. The experiments show significant performance gains by using the generated data of our approach on several tasks, such as image classification, cross-dataset generalization, and object detection. The proposed method also consistently outperforms existing weakly supervised and web-supervised approaches.