Learning to Predict Visual Attributes in the Wild
This work addresses the challenge of attribute prediction in unconstrained environments, which is incremental as it builds on existing methods but introduces new dataset and techniques.
The paper tackles the problem of predicting visual attributes for objects in diverse real-world scenes by introducing a large-scale dataset with over 927K annotations and proposing techniques like multi-hop attention and contrastive learning, resulting in a 3.7 mAP and 5.7 F1 improvement over state-of-the-art methods.
Visual attributes constitute a large portion of information contained in a scene. Objects can be described using a wide variety of attributes which portray their visual appearance (color, texture), geometry (shape, size, posture), and other intrinsic properties (state, action). Existing work is mostly limited to study of attribute prediction in specific domains. In this paper, we introduce a large-scale in-the-wild visual attribute prediction dataset consisting of over 927K attribute annotations for over 260K object instances. Formally, object attribute prediction is a multi-label classification problem where all attributes that apply to an object must be predicted. Our dataset poses significant challenges to existing methods due to large number of attributes, label sparsity, data imbalance, and object occlusion. To this end, we propose several techniques that systematically tackle these challenges, including a base model that utilizes both low- and high-level CNN features with multi-hop attention, reweighting and resampling techniques, a novel negative label expansion scheme, and a novel supervised attribute-aware contrastive learning algorithm. Using these techniques, we achieve near 3.7 mAP and 5.7 overall F1 points improvement over the current state of the art. Further details about the VAW dataset can be found at http://vawdataset.com/.