4 research outputs found
Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR Videos
The success of deep learning based models for computer vision applications
requires large scale human annotated data which are often expensive to
generate. Self-supervised learning, a subset of unsupervised learning, handles
this problem by learning meaningful features from unlabeled image or video
data. In this paper, we propose a self-supervised learning approach to learn
transferable features from MR video clips by enforcing the model to learn
anatomical features. The pretext task models are designed to predict the
correct ordering of the jumbled image patches that the MR video frames are
divided into. To the best of our knowledge, none of the supervised learning
models performing injury classification task from MR video provide any
explanation for the decisions made by the models and hence makes our work the
first of its kind on MR video data. Experiments on the pretext task show that
this proposed approach enables the model to learn spatial context invariant
features which help for reliable and explainable performance in downstream
tasks like classification of Anterior Cruciate Ligament tear injury from knee
MRI. The efficiency of the novel Convolutional Neural Network proposed in this
paper is reflected in the experimental results obtained in the downstream task
DySTreSS: Dynamically Scaled Temperature in Self-Supervised Contrastive Learning
In contemporary self-supervised contrastive algorithms like SimCLR, MoCo,
etc., the task of balancing attraction between two semantically similar samples
and repulsion between two samples from different classes is primarily affected
by the presence of hard negative samples. While the InfoNCE loss has been shown
to impose penalties based on hardness, the temperature hyper-parameter is the
key to regulating the penalties and the trade-off between uniformity and
tolerance. In this work, we focus our attention to improve the performance of
InfoNCE loss in SSL by studying the effect of temperature hyper-parameter
values. We propose a cosine similarity-dependent temperature scaling function
to effectively optimize the distribution of the samples in the feature space.
We further analyze the uniformity and tolerance metrics to investigate the
optimal regions in the cosine similarity space for better optimization.
Additionally, we offer a comprehensive examination of the behavior of local and
global structures in the feature space throughout the pre-training phase, as
the temperature varies. Experimental evidence shows that the proposed framework
outperforms or is at par with the contrastive loss-based SSL algorithms. We
believe our work (DySTreSS) on temperature scaling in SSL provides a foundation
for future research in contrastive learning
SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation
Document layout analysis is a known problem to the documents research
community and has been vastly explored yielding a multitude of solutions
ranging from text mining, and recognition to graph-based representation, visual
feature extraction, etc. However, most of the existing works have ignored the
crucial fact regarding the scarcity of labeled data. With growing internet
connectivity to personal life, an enormous amount of documents had been
available in the public domain and thus making data annotation a tedious task.
We address this challenge using self-supervision and unlike, the few existing
self-supervised document segmentation approaches which use text mining and
textual labels, we use a complete vision-based approach in pre-training without
any ground-truth label or its derivative. Instead, we generate pseudo-layouts
from the document images to pre-train an image encoder to learn the document
object representation and localization in a self-supervised framework before
fine-tuning it with an object detection model. We show that our pipeline sets a
new benchmark in this context and performs at par with the existing methods and
the supervised counterparts, if not outperforms. The code is made publicly
available at: https://github.com/MaitySubhajit/SelfDocSegComment: Accepted at The 17th International Conference on Document Analysis
and Recognition (ICDAR 2023
SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification
Writer independent offline signature verification is one of the most
challenging tasks in pattern recognition as there is often a scarcity of
training data. To handle such data scarcity problem, in this paper, we propose
a novel self-supervised learning (SSL) framework for writer independent offline
signature verification. To our knowledge, this is the first attempt to utilize
self-supervised setting for the signature verification task. The objective of
self-supervised representation learning from the signature images is achieved
by minimizing the cross-covariance between two random variables belonging to
different feature directions and ensuring a positive cross-covariance between
the random variables denoting the same feature direction. This ensures that the
features are decorrelated linearly and the redundant information is discarded.
Through experimental results on different data sets, we obtained encouraging
results.Comment: Accepted at IEEE ICIP 202