With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lag far behind. Current action recognition databases contain on the order of ten different action categories collected under fairly controlled conditions. State-of-the-art performance on these datasets is now near ceiling and thus there is a need for the design and creation of new benchmarks. To address this issue we collected the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube. We use this database to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions such as camera motion, viewpoint, video quality and occlusion.

Neurobehavioural analysis of mouse phenotypes requires the monitoring of mouse behaviour over long periods of time. In this study, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviours. We provide software and an extensive manually annotated video database used for training and testing the system. Our system performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home-cage behaviours of two standard inbred and two non-standard mouse strains. From these data, we were able to predict in a blind test the strain identity of individual animals with high accuracy. Our video-based software will complement existing sensor-based automated approaches and enable an adaptable, comprehensive, high-throughput, fi ne-grained, automated analysis of mouse behaviour. A utomated quantitative analysis of mouse behaviour will have a signifi cant role in comprehensive phenotypic analyses -both on the small scale of detailed characterization of individual gene mutants and on the large scale of assigning gene function across the entire mouse genome 1 . One key benefi t of automating behavioural analysis arises from inherent limitations of human assessment, namely, cost, time and reproducibility. Although automation in and of itself is not a panacea for neurobehavioural experiments 2 , it allows for addressing an entirely new set of questions about mouse behaviour and to conduct experiments on time scales that are orders of magnitude larger than those traditionally assayed. For example, reported tests of grooming behaviour span time scales of minutes 3,4 , whereas an automated analysis will allow for analysis of this behaviour over hours or even days and weeks.Indeed, the signifi cance of alterations in home-cage behaviour has recently gained attention as an eff ective means of detecting perturbations in neural circuit function -both in the context of disease detection and more generally to measure food consumption and activity parameters 5 -10 . Previous automated systems (see refs 8, 9, 11, 12 and Supplementary Note ) rely mostly on the use of simple detectors such as infrared beams to monitor behaviour. Th ese sensor-based approaches tend to be limited in the complexity of the behaviour that they can measure, even in the case of costly commercial systems using transponder technologies 13 . Although such systems can be used eff ectively to monitor locomotor activity and perform operant conditioning, they cannot be used to study homecage behaviours such as grooming, hanging, jumping and smaller movements (termed ' micromovements ' below). Visual analysis is a potentially powerful complement to these sensor-based approaches for the recognition of such fi ne animal behaviours.Advances in computer vision and machine learning over the last decade have yielded robust computer vision systems for the recognition of objects 14,15 and human actions (see Moeslund et ...

This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of wholescene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image. arXiv:2005.03412v1 [eess.IV] 7 May 2020

Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks-are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44.7% and a Relative RMSE drop of 47.0% on the ICVL natural hyperspectral image dataset.