Learning to Predict Visual Attributes in the Wild

Abstract

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-thewild 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 multihop 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/.

A Simple Framework for Scene Graph Reasoning with Semantic Understanding of Complex Sentence Structure

“…The Visual Genome [35] is a large amount of data built into a graph structure by manually labeling the relationships and characteristics between objects included in static images in natural language. Therefore, graph convolution networks [27,36] specialized in graph data processing are used as a representative encoder architecture. Despite significant progress, the premise of needing a separate encoder for scene graph understanding is an issue that needs to be improved.…”