65 research outputs found
Improving Trajectory Prediction in Dynamic Multi-Agent Environment by Dropping Waypoints
The inherently diverse and uncertain nature of trajectories presents a
formidable challenge in accurately modeling them. Motion prediction systems
must effectively learn spatial and temporal information from the past to
forecast the future trajectories of the agent. Many existing methods learn
temporal motion via separate components within stacked models to capture
temporal features. Furthermore, prediction methods often operate under the
assumption that observed trajectory waypoint sequences are complete,
disregarding scenarios where missing values may occur, which can influence
their performance. Moreover, these models may be biased toward particular
waypoint sequences when making predictions. We propose a novel approach called
Temporal Waypoint Dropping (TWD) that explicitly incorporates temporal
dependencies during the training of a trajectory prediction model. By
stochastically dropping waypoints from past observed trajectories, the model is
forced to learn the underlying temporal representation from the remaining
waypoints, resulting in an improved model. Incorporating stochastic temporal
waypoint dropping into the model learning process significantly enhances its
performance in scenarios with missing values. Experimental results demonstrate
our approach's substantial improvement in trajectory prediction capabilities.
Our approach can complement existing trajectory prediction methods to improve
their prediction accuracy. We evaluate our proposed approach on three datasets:
NBA Sports VU, ETH-UCY, and TrajNet++.Comment: Under Revie
Accuracy Booster: Performance Boosting using Feature Map Re-calibration
Convolution Neural Networks (CNN) have been extremely successful in solving
intensive computer vision tasks. The convolutional filters used in CNNs have
played a major role in this success, by extracting useful features from the
inputs. Recently researchers have tried to boost the performance of CNNs by
re-calibrating the feature maps produced by these filters, e.g.,
Squeeze-and-Excitation Networks (SENets). These approaches have achieved better
performance by Exciting up the important channels or feature maps while
diminishing the rest. However, in the process, architectural complexity has
increased. We propose an architectural block that introduces much lower
complexity than the existing methods of CNN performance boosting while
performing significantly better than them. We carry out experiments on the
CIFAR, ImageNet and MS-COCO datasets, and show that the proposed block can
challenge the state-of-the-art results. Our method boosts the ResNet-50
architecture to perform comparably to the ResNet-152 architecture, which is a
three times deeper network, on classification. We also show experimentally that
our method is not limited to classification but also generalizes well to other
tasks such as object detection.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
202
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Minimizing Supervision in Multi-label Categorization
Multiple categories of objects are present in most images. Treating this as a
multi-class classification is not justified. We treat this as a multi-label
classification problem. In this paper, we further aim to minimize the
supervision required for providing supervision in multi-label classification.
Specifically, we investigate an effective class of approaches that associate a
weak localization with each category either in terms of the bounding box or
segmentation mask. Doing so improves the accuracy of multi-label
categorization. The approach we adopt is one of active learning, i.e.,
incrementally selecting a set of samples that need supervision based on the
current model, obtaining supervision for these samples, retraining the model
with the additional set of supervised samples and proceeding again to select
the next set of samples. A crucial concern is the choice of the set of samples.
In doing so, we provide a novel insight, and no specific measure succeeds in
obtaining a consistently improved selection criterion. We, therefore, provide a
selection criterion that consistently improves the overall baseline criterion
by choosing the top k set of samples for a varied set of criteria. Using this
criterion, we are able to show that we can retain more than 98% of the fully
supervised performance with just 20% of samples (and more than 96% using 10%)
of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach
consistently outperforms all other baseline metrics for all benchmark datasets
and model combinations.Comment: Accepted in CVPR-W 202
Small and Dim Target Detection in IR Imagery: A Review
While there has been significant progress in object detection using
conventional image processing and machine learning algorithms, exploring small
and dim target detection in the IR domain is a relatively new area of study.
The majority of small and dim target detection methods are derived from
conventional object detection algorithms, albeit with some alterations. The
task of detecting small and dim targets in IR imagery is complex. This is
because these targets often need distinct features, the background is cluttered
with unclear details, and the IR signatures of the scene can change over time
due to fluctuations in thermodynamics. The primary objective of this review is
to highlight the progress made in this field. This is the first review in the
field of small and dim target detection in infrared imagery, encompassing
various methodologies ranging from conventional image processing to
cutting-edge deep learning-based approaches. The authors have also introduced a
taxonomy of such approaches. There are two main types of approaches:
methodologies using several frames for detection, and single-frame-based
detection techniques. Single frame-based detection techniques encompass a
diverse range of methods, spanning from traditional image processing-based
approaches to more advanced deep learning methodologies. Our findings indicate
that deep learning approaches perform better than traditional image
processing-based approaches. In addition, a comprehensive compilation of
various available datasets has also been provided. Furthermore, this review
identifies the gaps and limitations in existing techniques, paving the way for
future research and development in this area.Comment: Under Revie
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
CPWC:Contextual Point Wise Convolution for Object Recognition
Convolutional layers are a major driving force behind the successes of deep
learning. Pointwise convolution (PWC) is a 1x1 convolutional filter that is
primarily used for parameter reduction. However, the PWC ignores the spatial
information around the points it is processing. This design is by choice, in
order to reduce the overall parameters and computations. However, we
hypothesize that this shortcoming of PWC has a significant impact on the
network performance. We propose an alternative design for pointwise
convolution, which uses spatial information from the input efficiently. Our
design significantly improves the performance of the networks without
substantially increasing the number of parameters and computations. We
experimentally show that our design results in significant improvement in the
performance of the network for classification as well as detection.Comment: Accepted in ICASSP 202
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