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We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and handobject interaction. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set. Unfortunately, since the space of hand poses is highly dimensional, it is inherently not feasible to cover the whole space densely, despite recent efforts in collecting large-scale training datasets. This sampling problem is even more severe when hands are interacting with objects and/or inputs are RGB rather than depth images, as RGB images also vary with lighting conditions and colors. To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set. More exactly, HANDS'19 is designed (a) to evaluate the influence of both depth and color modalities on 3D hand pose estimation, under the presence or absence of objects; (b) to assess the generalisation abilities w.r.t. four main axes: shapes, articulations, viewpoints, and objects; (c) to explore the use of a synthetic hand models to fill the gaps of current datasets. Through the challenge, the overall accuracy has dramatically improved over the baseline, especially on extrapolation tasks, from 27mm to 13mm mean joint error. Our analyses highlight the impacts of: Data pre-processing, ensemble approaches, the use of a parametric 3D hand model (MANO), and different HPE methods/backbones.

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The aim of this paper is to investigate the benefit of information from a speaker change detection system based on Convolutional Neural Network (CNN) when applied to the process of accumulation of statistics for an i-vector generation. The investigation is carried out on the problem of diarization. In our system, the output of the CNN is a probability value of a speaker change in a conversation for a given time segment. According to this probability, we cut the conversation into short segments that are then represented by the i-vector (to describe a speaker in it). We propose a technique to utilize the information from the CNN for the weighting of the acoustic data in a segment to refine the statistics accumulation process. This technique enables us to represent the speaker better in the final i-vector. The experiments on the English part of the CallHome corpus show that our proposed refinement of the statistics accumulation is beneficial with the relative improvement of Diarization Error Rate almost by 16 % when compared to the speaker diarization system without statistics refinement.

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