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The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale

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TLDR
Open Images V4 as mentioned in this paper is a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection from Flickr without a predefined list of class names or tags.
Abstract
We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15x more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning. We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images. We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.

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Journal ArticleDOI

Deep Learning for Generic Object Detection: A Survey

TL;DR: A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
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Optuna: A Next-generation Hyperparameter Optimization Framework

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Proceedings ArticleDOI

Optuna: A Next-generation Hyperparameter Optimization Framework

TL;DR: Optuna as mentioned in this paper is a next-generation hyperparameter optimization software with define-by-run (DBR) API that allows users to construct the parameter search space dynamically.
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Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

TL;DR: This paper proposes a new learning method Oscar (Object-Semantics Aligned Pre-training), which uses object tags detected in images as anchor points to significantly ease the learning of alignments.
Journal ArticleDOI

GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

TL;DR: A large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k, and the first video trajectory dataset that uses the semantic hierarchy of WordNet to guide class population, which ensures a comprehensive and relatively unbiased coverage of diverse moving objects.
References
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