BECLR: Batch Enhanced Contrastive Unsupervised Few-Shot Learning

There exists a fundamental gap between human and artificial intelligence. Deep learning models are exceedingly data hungry for learning even the simplest of tasks, whereas humans can easily adapt to new tasks with just a handful of samples. Unsupervised few-shot learning (U-FSL) aspires to bridge this gap, without relying on costly annotations....

Self-calibrated plant counting in early crop stand scenarios using deep clustering: Jonacount: make every plant count

In recent years, the agricultural sector has seen significant techno- logical improvements under the flag of precision agriculture, assisting farmers in the manageability that coincides with large-scale farming. Moreover, precision agriculture aims to enable plant-specific farming on the macro scale that is demanded by the current global...

Improving Cross-View Matching with Self-Supervised Learning

We explored the possibility of improving cross-view matching performance with self-supervised learning techniques and perform interpretations in terms of the embedding space of image features. The effect of pre-training by contrastive learning is verified quantitatively by experiments, and also exhibited by visualization of the feature space.

EMGSense: A Low-Effort Self-Supervised Domain Adaptation Framework for EMG Sensing

This paper presents EMGSense, a low-effort self-supervised domain adaptation framework for sensing applications based on Electromyography (EMG). EMGSense addresses one of the fundamental challenges in EMG cross-user sensing—the significant performance degradation caused by time-varying biological heterogeneity—in a low-effort (data-efficient and...

Unsupervised Facial Expression Recognition using Periocular Images

Facial expression recognition on head-mounted devices (HMDs) is an intriguing research field because of its potential in various applications, such as interactive virtual reality video meetings. Existing work focuses on building a supervised learning pipeline that utilizes a vast amount of labeled periocular images taken by the built-in cameras....

Self-Supervised Few Shot Learning: Prototypical Contrastive Learning with Graphs

A primary trait of humans is the ability to learn rich representations and relationships between entities from just a handful of examples without much guidance. Unsupervised few-shot learning is an undertaking aimed at reducing this fundamental gap between smart human adaptability and machines. We present a contrastive learning scheme for...

Self-supervised Audio-reactive Music Video Synthesis: Measuring and optimizing audiovisual correlation

Synthesizing audio-reactive videos to accompany music is challenging multi-domain task that requires both a visual synthesis skill-set and an understanding of musical information extraction. In recent years a new flexible class of visual synthesis methods has gained popularity: generative adversarial networks. These deep neural networks can be...

Analyzing and comparing different self-supervised learning speech pre-trained models in the view of phonetics

In this thesis, we analyzed and compared speech representations extracted from different frozen self-supervised learning (SSL) speech pre-trained models on their ability to capture articulatory feature (AF) information and their subsequent prediction of phone recognition performance in within-language and cross-language scenarios. Specifically,...

Recurrent Affine Transform Encoder for Image Representation

This paper proposes a Recurrent Affine Transform Encoder (RATE) that can be used for image representation learning. We propose a learning architecture that enables a CNN encoder to learn the affine transform parameter of images. The proposed learning architecture decomposes an affine transform matrix into two transform matrices and learns them...

Push-the-Boundary: Boundary-Aware Feature Propagation for Semantic Segmentation of 3D Point Clouds

Feedforward fully convolutional neural networks currently dominate in semantic segmentation of 3D point clouds. Despite their great success, they suffer from the loss of local information at low-level layers, posing significant challenges to accurate scene segmentation and precise object boundary delineation. Prior works either address this...

Self-Supervised Class-Cognizant Few-Shot Classification: 2022 IEEE International Conference on Image Processing (ICIP)

Unsupervised learning is argued to be the dark matter of human intelligence. To build in this direction, this paper focuses on unsupervised learning from an abundance of unlabeled data followed by few-shot fine-tuning on a downstream classification task. To this aim, we extend a recent study on adopting contrastive learning for self...

Self-­Supervised Learning for Visual Obstacle Avoidance

With a growing number of drones, the risk of collision with other air traffic or fixed obstacles increases. New safety measures are required to keep the operation of Unmanned Aerial Vehicles (UAVs) safe. One of these measures is the use of a Collision Avoidance System (CAS), a system that helps the drone autonomously detect and avoid obstacles....

Autonomous landing of Micro Air Vehicles through bio-inspired monocular vision