Gaze Embeddings for Zero-Shot Image Classification

Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts. We instead propose a method that relies on human gaze as auxiliary information, exploiting that even non-expert users have a natural ability to judge class membership. We present a data collection paradigm that involves a discrimination task to increase the information content obtained from gaze data. Our method extracts discriminative descriptors from the data and learns a compatibility function between image and gaze using three novel gaze embeddings: Gaze Histograms (GH), Gaze Features with Grid (GFG) and Gaze Features with Sequence (GFS). We introduce two new gaze-annotated datasets for fine-grained image classification and show that human gaze data is indeed class discriminative, provides a competitive alternative to expert-annotated attributes, and outperforms other baselines for zero-shot image classification

Person recognition based on deep gait: a survey.

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future

Human Attributes from 3D Pose Tracking

Abstract. We show that, from the output of a simple 3D human pose tracker one can infer physical attributes (e.g., gender and weight) and aspects of mental state (e.g., happiness or sadness). This task is useful for man-machine communication, and it provides a natural benchmark for evaluating the performance of 3D pose tracking methods (vs. conventional Euclidean joint error metrics). Based on an extensive corpus of motion capture data, with physical and perceptual ground truth, we analyze the inference of subtle biologically-inspired attributes from cyclic gait data. It is shown that inference is also possible with partial observations of the body, and with motions as short as a single gait cycle. Learning models from small amounts of noisy video pose data is, however, prone to over-fitting. To mitigate this we formulate learning in terms of domain adaptation, for which mocap data is uses to regularize models for inference from video-based data.