  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/.
