DADO: A Depth-Attention framework for Object Discovery

Unsupervised object discovery, the task of identifying and localizing objects in images without human-annotated labels, remains a significant challenge and a growing focus in computer vision. In this work, we introduce a novel model, DADO (Depth-Attention self-supervised technique for Discovering unseen Objects), which combines an attention mechanism and a depth model to identify potential objects in images. To address challenges such as noisy attention maps or complex scenes with varying depth planes, DADO employs dynamic weighting to adaptively emphasize attention or depth features based on the global characteristics of each image. We evaluated DADO on standard benchmarks, where it outperforms state-of-the-art methods in object discovery accuracy and robustness without the need for fine-tuning.

Inteligencia artificial para la multi-clasificación de fauna en fotografías automáticas utilizadas en investigación científica

The management of natural environments, whether for conservation or production, requires a deep understanding of wildlife. The number, location, and behavior of wild animals are among the main subjects of study in ecology and wildlife research. The use of camera traps offers the opportunity to quickly collect large quantities of photographs that capture wildlife in its natural habitat, avoiding factors that could alter their behavior. In Tierra del Fuego, Argentina, research is being conducted on forest use by different herbivores (guanacos, cows, sheep) to optimize management and protect these natural ecosystems. Although camera traps allow for the collection of millions of images, interpreting such photographs presents a scalability challenge for manual processing. As a result, much of the valuable knowledge stored in these vast data repositories remains untapped. Neural Networks and Deep Learning are areas of study within Artificial Intelligence. Over the past decade, these two disciplines have made significant contributions to image recognition on a global scale. Ecological and wildlife conservation studies can be combined with these new technologies to extract important information from the photographs obtained by camera traps, contributing to the understanding of various natural processes and improving the management of the involved wild areas. Our project aims to develop neural network models to classify animal species in photographs taken with camera traps, addressing large-scale challenges in scientific research.