Although the means of catching remote sensing images are becoming more effective and abundant, samples that can be collected in some specific environments quite scarce. When there limited labeled samples, methods for analyzing scene classification perform drastically worse. Methods classify few-shot image scenes often based on meta-learning algorithms handling sparse data. However, this researc...

We propose a self-supervised learning approach for videos that learns representations of both the RGB frames and accompanying audio without human supervision. In contrast to images capture static scene appearance, also contain sound temporal dynamics. To leverage aural dimension inherent videos, our method extends self-supervision audio-visual setting integrates it with multi-modal contrastive ...

The task of Text-to-Image synthesis is a difficult challenge, especially when dealing with low-data regimes, where the number training samples limited. In order to address this Self-Supervision Generative Adversarial Networks (SS-TiGAN) has been proposed. method employs bi-level architecture, which allows for use self-supervision increase by generating rotation variants. This, in turn, maximize...

Recent work has shown that self-supervised pre-training leads to improvements over supervised learning on challenging visual recognition tasks. CLIP, an exciting new approach with language supervision, demonstrates promising performance a wide variety of benchmarks. In this work, we explore whether can aid in the use supervision for representation Vision Transformers. We introduce SLIP, multi-t...

Open information extraction (IE) is a weakly supervised IE paradigm that aims to extract relation-independent information from large-scale natural language documents without significant annotation efforts. A key challenge for Open IE is to achieve self-supervision, in which the training examples are automatically obtained. Although the feasibility of Open IE systems has been demonstrated for En...

There is now expert consensus that directly observing the work of trainee therapists vs. relying upon selfreport of sessions, is critical to providing the accurate feedback required to attain a range of competencies. In spite of this expert consensus however, and the broadly positive attitudes towards video review among supervisees, video feedback methods remain under-utilized in clinical super...

Objective The individually delivered Supervising for Home Safety (SHS) program improves caregivers' injury-related beliefs and supervision practices. The current randomized controlled trial used a group delivery in a community setting and assessed program impact, feasibility, and acceptance. Methods Caregivers of 2-5-year-olds were randomized to receive either the SHS or an attention-matched ...

Learning general-purpose representations from multisensor data produced by the omnipresent sensing systems (or IoT in general) has numerous applications diverse use cases. Existing purely supervised end-to-end deep learning techniques depend on availability of a massive amount well-curated data, acquiring which is notoriously difficult but required to achieve sufficient level generalization tas...