The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities. This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. It aims to provide readers with an in-depth systematic review and technical analysis of the current state of aerial surveillance tasks using drones, UAVs and other airborne platforms. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed. More specifically, for each of these four tasks, we first discuss unique challenges in performing these tasks in an aerial setting compared to a ground-based setting. We then review and analyze the aerial datasets publicly available for each task, and delve deep into the approaches in the aerial literature and investigate how they presently address the aerial challenges. We conclude the paper with discussion on the missing gaps and open research questions to inform future research avenues.

Deep learning techniques for separating audio into different sound sources face several challenges. Standard architectures require training separate models for different types of audio sources. Although some universal separators employ a single model to target multiple sources, they have difficulty generalizing to unseen sources. In this paper, we propose a three-component pipeline to train a universal audio source separator from a large, but weakly-labeled dataset: AudioSet. First, we propose a transformer-based sound event detection system for processing weakly-labeled training data. Second, we devise a query-based audio separation model that leverages this data for model training. Third, we design a latent embedding processor to encode queries that specify audio targets for separation, allowing for zero-shot generalization. Our approach uses a single model for source separation of multiple sound types, and relies solely on weakly-labeled data for training. In addition, the proposed audio separator can be used in a zero-shot setting, learning to separate types of audio sources that were never seen in training. To evaluate the separation performance, we test our model on MUSDB18, while training on the disjoint AudioSet. We further verify the zero-shot performance by conducting another experiment on audio source types that are held-out from training. The model achieves comparable Source-to-Distortion Ratio (SDR) performance to current supervised models in both cases.

Using artificial intelligence inspired by the human brain, a chip developed by BrainChip Holdings, is winning commercial support from large global …

… of artificial intelligence at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT.

Alice is not herself today; in fact she is melt down. Who can blame her, the Mad Hatter (who is completely sane) failed to ground her and the white rabbits made things worse. One of the last few snarks that live in the wild. The other snarks have either been hunted by humans or domesticated. They live with the cats and the dog; not too bad I suppose. However some share a rabbit hutch with you guessed it rabbits Snarks exists, Lewis Carroll wrote about them in the The Hunting of the Snark.

Neural networks have progressed enough to comprehend large amounts of text and generate entire sequences in continuation to a short input prompt. This notion has found immense use in generating product descriptions, promotional emails and also to generate training data to create models that can detect whether a given text was generated by AI. While models like GPT-3 have achieved the benchmark in this task, we can start from scratch using LSTMs. You can use text from the free ebooks available on Project Gutenberg. After cleaning and vectorizing the data, we can train a Sequential LSTM model on several batches of sentences. Once that is done, you can give the model a test sequence from the ebook and map the output vectors with the character set. You will see that the model was able to continue your input prompt and generate (hopefully) meaningful and relevant sentences.

BigML allows you to build machine learning and deep learning models without the need for any coding.

Furnished with machine learning algorithms easily accessible to users without sophisticated technical knowledge. Yes, interactive sessions of …

Catherine Breslin, a machine learning scientist who used to work on Amazon Alexa, thinks Big Tech will race toward larger and larger language models …

USA: In a diagnostic study published in JAMA Network Open, an artificial intelligence (AI) algorithm may improve the diagnostic performance of …