Object Agnostic 3D Lifting in Space and Time

We present a spatio-temporal perspective on category-agnostic 3D lifting of 2D keypoints over a temporal sequence. Our approach differs from existing state-of-the-art methods that are either: (i) object agnostic, but can only operate on individual frames, or (ii) can model space-time dependencies, but are only designed to work with a single object category. Our approach is grounded in two core principles. First, when there is a lack of data about an object, general information from similar objects can be leveraged for better performance. Second, while temporal information is important, the most critical information is in immediate temporal proximity. These two principles allow us to outperform current state-of-the-art methods on per-frame and per-sequence metrics for a variety of objects. Lastly, we release a new synthetic dataset containing 3D skeletons and motion sequences of a diverse set animals. Dataset and code will be made publicly available.

Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype using Behaviour

Zebrafish are a common model organism used to identify new disease therapeutics. High-throughput drug screens can be performed on larval zebrafish in multi-well plates by observing changes in behaviour following a treatment. Analysis of this behaviour can be difficult, however, due to the high dimensionality of the data obtained. Statistical analysis of individual statistics (such as the distance travelled) is generally not powerful enough to detect meaningful differences between treatment groups. Here, we propose a method for classifying zebrafish models of Parkinson's disease by genotype at 5 days old. Using a set of 2D behavioural features, we train a multi-layer perceptron neural network. We further show that the use of integrated gradients can give insight into the impact of each behaviour feature on genotype classifications by the model. In this way, we provide a novel pipeline for classifying zebrafish larvae, beginning with feature preparation and ending with an impact analysis of said features.