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Found 33 papers, 18 papers with code
ChimpVLM: Ethogram-Enhanced Chimpanzee Behaviour Recognition
We show that chimpanzee behaviour understanding from camera traps can be enhanced by providing visual architectures with access to an embedding of text descriptions that detail species behaviours.
RBF-PINN: Non-Fourier Positional Embedding in Physics-Informed Neural Networks
While many recent Physics-Informed Neural Networks (PINNs) variants have had considerable success in solving Partial Differential Equations, the empirical benefits of feature mapping drawn from the broader Neural Representations research have been largely overlooked.
Training dynamics in Physics-Informed Neural Networks with feature mapping
Physics-Informed Neural Networks (PINNs) have emerged as an iconic machine learning approach for solving Partial Differential Equations (PDEs).
PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition
We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment.
Diving with Penguins: Detecting Penguins and their Prey in Animal-borne Underwater Videos via Deep Learning
In this paper, we publish an animal-borne underwater video dataset of penguins and introduce a ready-to-deploy deep learning system capable of robustly detecting penguins (mAP50@98. 0%) and also instances of fish (mAP50@73. 3%).
Deep Visual-Genetic Biometrics for Taxonomic Classification of Rare Species
We demonstrate for the first time that such alignment can be achieved via deep embedding models and that the approach is directly applicable to boosting long-tailed recognition (LTR) particularly for rare species.
Use Your Head: Improving Long-Tail Video Recognition
We demonstrate that, unlike naturally-collected video datasets and existing long-tail image benchmarks, current video benchmarks fall short on multiple long-tailed properties.
Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation
This paper presents a deep learning framework for medical video segmentation.
Triple-stream Deep Metric Learning of Great Ape Behavioural Actions
We propose the first metric learning system for the recognition of great ape behavioural actions.
Video-TransUNet: Temporally Blended Vision Transformer for CT VFSS Instance Segmentation
Our findings suggest that the proposed model can indeed enhance the TransUNet architecture via exploiting temporal information and improving segmentation performance by a significant margin.
Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things
We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) which takes advantage of learning using privileged information (LUPI).
Towards Individual Grevy's Zebra Identification via Deep 3D Fitting and Metric Learning
This paper combines deep learning techniques for species detection, 3D model fitting, and metric learning in one pipeline to perform individual animal identification from photographs by exploiting unique coat patterns.
Dynamic Curriculum Learning for Great Ape Detection in the Wild
We propose a novel end-to-end curriculum learning approach for sparsely labelled animal datasets leveraging large volumes of unlabelled data to improve supervised species detectors.
Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification
In particular, for the task of automatic identification of individual Holstein-Friesians in real-world farm CCTV, we show that self-supervision, metric learning, cluster analysis, and active learning can complement each other to significantly reduce the annotation requirements usually needed to train cattle identification frameworks.
Visual Microfossil Identification via Deep Metric Learning
We show that metric learning outperforms all published CNN-based state-of-the-art benchmarks in this domain.
Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance
In particular, we show that the integration of a holistic attention network based super-resolution approach and a custom-built altitude data exploitation network into standard recognition pipelines can considerably increase the detection efficacy in real-world settings.
Seeing biodiversity: perspectives in machine learning for wildlife conservation
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices.
Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset
Motivated by the labelling burden involved in constructing visual cattle identification systems, we propose exploiting the temporal coat pattern appearance across videos as a self-supervision signal for animal identity learning.
Temporal-Relational CrossTransformers for Few-Shot Action Recognition
We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set.
A Dataset and Application for Facial Recognition of Individual Gorillas in Zoo Environments
We put forward a video dataset with 5k+ facial bounding box annotations across a troop of 7 western lowland gorillas at Bristol Zoo Gardens.
Visual Recognition of Great Ape Behaviours in the Wild
We propose a first great ape-specific visual behaviour recognition system utilising deep learning that is capable of detecting nine core ape behaviours.
Back to the Future: Cycle Encoding Prediction for Self-supervised Contrastive Video Representation Learning
In this paper we show that learning video feature spaces in which temporal cycles are maximally predictable benefits action classification.
Meta-Learning with Context-Agnostic Initialisations
This produces an initialisation for fine-tuning to target which is both context-agnostic and task-generalised.
Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning
Holstein-Friesian cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems.
Sit-to-Stand Analysis in the Wild using Silhouettes for Longitudinal Health Monitoring
We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes.
Great Ape Detection in Challenging Jungle Camera Trap Footage via Attention-Based Spatial and Temporal Feature Blending
We propose the first multi-frame video object detection framework trained to detect great apes.
Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian cattle in freely moving herds.
DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph
This paper describes DeepKey, an end-to-end deep neural architecture capable of taking a digital RGB image of an 'everyday' scene containing a pin tumbler key (e. g. lying on a table or carpet) and fully automatically inferring a printable 3D key model.
CaloriNet: From silhouettes to calorie estimation in private environments
We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes.
Semantically Selective Augmentation for Deep Compact Person Re-Identification
We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks.
Automated Visual Fin Identification of Individual Great White Sharks
To the best of our knowledge this line of work establishes the first fully automated contour-based visual ID system in the field of animal biometrics.
Calorie Counter: RGB-Depth Visual Estimation of Energy Expenditure at Home
We present a new framework for vision-based estimation of calorific expenditure from RGB-D data - the first that is validated on physical gas exchange measurements and applied to daily living scenarios.
Multiple Human Tracking in RGB-D Data: A Survey
Finally, we present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets.
