Detecting and Recognizing Humans, Objects, and their Interactions
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Scene understanding is a high-level vision task which involves not just localizing and recognizing objects and people but also inferring their layouts and interactions with each other. However, current systems for even atomic tasks like object detection suffer from several shortcomings. Most object detectors can only detect a limited number of object categories; face recognition systems are prone to make mistakes for faces in extreme poses or illuminations; and automated systems for detecting interactions between humans and objects perform poorly. We hypothesize that scene understanding can be improved by using additional semantic data from outside sources and intelligently and efficiently using the available data. Given the fact that it is nearly impossible to collect labeled training data for thousands of object categories, we introduce the problem of zero-shot object detection (ZSD). Here, “zero-shot” means recognizing/detecting without using any visual data during training. We first present an approach for ZSD using semantic information encoded in word-vectors which are trained on a large text corpus. We discuss some challenges associated with ZSD. The most important of these challenges is the definition of a “background” class in this setting. It is easy to define a “background” class in fully-supervised settings. However, it’s not clear what constitutes a “background” ZSD. We present principled approaches for dealing with this challenge and evaluate our approaches on challenging sets of object classes, not restricting ourselves to similar and/or fine-grained categories as in prior works on zero-shot classification. Next, we tackle the problem of detecting human-object interactions (HOIs). Here, again, it is impossible to collect labeled data for each type of possible interaction. We show that solutions for HOI detection can greatly benefit from semantic information. We present two approaches for solving this problem. In the first approach, we exploit functional similarities between objects to share knowledge between models for different classes. The main idea is that humans look similar while interacting with functionally similar objects. We show that, using this idea, even a simple model can achieve state-of-the-art results for HOI detection both in the supervised and zero-shot settings. Our second model uses semantic information in the form of spatial layout of a person and an object to detect their interactions. This model contains a layout module which primes the visual module to make the final prediction. An automated scene understanding system should, further, be able to answer natural language questions posed by humans about a scene. We introduce the problem of Image-Set Visual Question Answering (ISVQA) as a generalization of existing tasks of Visual Question Answering (VQA) for still images, and video VQA. We describe two large-scale datasets collected for this problem: one for indoor scenes and one for outdoor scenes. We provide a comprehensive analysis of the two datasets. We also adapt VQA models to design baselines for this task and demonstrate the difficulty of the problem. Finally, we present new datasets for training face recognition systems. Using these datasets, we show that careful consideration of some critical questions before training can lead to significant improvements in face verification performance. We use some lessons from these experiments to train a face recognition system which can identify and verify faces accurately. We show that our model, trained with the recently introduced Crystal Loss, can achieve state-of-the-art performance for many challenging face recognition benchmarks like IJB-A, IJB-B, and IJB-C. We evaluate our system on the Disguised Faces in the Wild (DFW) dataset and show convincing first results.