Zero-Shot Object Detection

Publication Date: 9/8/2018

Event: European Conference on Computer Vision – ECCV 2018, Munich, Germany

Reference: pp 397-141, 2018

Authors: Ankan Bansal, University of Maryland; Karan Sikka, SRI International; Gaurav Sharma, NEC Laboratories America, Inc.; Rama Chellappa, University of Maryland; Ajay Divakaran, SRI International

Abstract: We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes which are not observed during training. We work with a challenging set of object classes, not restricting ourselves to similar and/or fine-grained categories as in prior works on zero-shot classification. We present a principled approach by first adapting visual-semantic embeddings for ZSD. We then discuss the problems associated with selecting a background class and motivate two background-aware approaches for learning robust detectors. One of these models uses a fixed background class and the other is based on iterative latent assignments. We also outline the challenge associated with using a limited number of training classes and propose a solution based on dense sampling of the semantic label space using auxiliary data with a large number of categories. We propose novel splits of two standard detection datasets – MSCOCO and VisualGenome, and present extensive empirical results in both the traditional and generalized zero-shot settings to highlight the benefits of the proposed methods. We provide useful insights into the algorithm and conclude by posing some open questions to encourage further research.

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