Self-supervised learning from images with a joint-embedding predictive architecture
This paper demonstrates an approach for learning highly semantic image representations
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …
Where are we in the search for an artificial visual cortex for embodied intelligence?
We present the largest and most comprehensive empirical study of pre-trained visual
representations (PVRs) or visual 'foundation models' for Embodied AI. First, we curate …
representations (PVRs) or visual 'foundation models' for Embodied AI. First, we curate …
One-peace: Exploring one general representation model toward unlimited modalities
In this work, we explore a scalable way for building a general representation model toward
unlimited modalities. We release ONE-PEACE, a highly extensible model with 4B …
unlimited modalities. We release ONE-PEACE, a highly extensible model with 4B …
Transformers in speech processing: A survey
The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …
sparked the interest of the speech-processing community, leading to an exploration of their …
Firerisk: A remote sensing dataset for fire risk assessment with benchmarks using supervised and self-supervised learning
S Shen, S Seneviratne, X Wanyan… - … Conference on Digital …, 2023 - ieeexplore.ieee.org
In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …
CROMA: Remote sensing representations with contrastive radar-optical masked autoencoders
A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled,
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …
Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts
Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses
massive volumes of unlabeled data to train neural networks. SSL techniques have evolved …
massive volumes of unlabeled data to train neural networks. SSL techniques have evolved …
Av-data2vec: Self-supervised learning of audio-visual speech representations with contextualized target representations
Self-supervision has shown great potential for audio-visual speech recognition by vastly
reducing the amount of labeled data required to build good systems. However, existing …
reducing the amount of labeled data required to build good systems. However, existing …
emotion2vec: Self-supervised pre-training for speech emotion representation
We propose emotion2vec, a universal speech emotion representation model. emotion2vec
is pre-trained on open-source unlabeled emotion data through self-supervised online …
is pre-trained on open-source unlabeled emotion data through self-supervised online …
LeBenchmark 2.0: A standardized, replicable and enhanced framework for self-supervised representations of French speech
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many
different domains including computer vision and natural language processing. Speech …
different domains including computer vision and natural language processing. Speech …