155 research outputs found
Are object detection assessment criteria ready for maritime computer vision?
Maritime vessels equipped with visible and infrared cameras can complement
other conventional sensors for object detection. However, application of
computer vision techniques in maritime domain received attention only recently.
The maritime environment offers its own unique requirements and challenges.
Assessment of the quality of detections is a fundamental need in computer
vision. However, the conventional assessment metrics suitable for usual object
detection are deficient in the maritime setting. Thus, a large body of related
work in computer vision appears inapplicable to the maritime setting at the
first sight. We discuss the problem of defining assessment metrics suitable for
maritime computer vision. We consider new bottom edge proximity metrics as
assessment metrics for maritime computer vision. These metrics indicate that
existing computer vision approaches are indeed promising for maritime computer
vision and can play a foundational role in the emerging field of maritime
computer vision
Client Selection in Federated Learning under Imperfections in Environment
Federated learning promises an elegant solution for learning global models across distributed and privacy-protected datasets. However, challenges related to skewed data distribution, limited computational and communication resources, data poisoning, and free riding clients affect the performance of federated learning. Selection of the best clients for each round of learning is critical in alleviating these problems. We propose a novel sampling method named the irrelevance sampling technique. Our method is founded on defining a novel irrelevance score that incorporates the client characteristics in a single floating value, which can elegantly classify the client into three numerical sign defined pools for easy sampling. It is a computationally inexpensive, intuitive and privacy preserving sampling technique that selects a subset of clients based on quality and quantity of data on edge devices. It achieves 50–80% faster convergence even in highly skewed data distribution in the presence of free riders based on lack of data and severe class imbalance under both Independent and Identically Distributed (IID) and Non-IID conditions. It shows good performance on practical application datasets
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems
Nearest neighbor (NN) sampling provides more semantic variations than
pre-defined transformations for self-supervised learning (SSL) based image
recognition problems. However, its performance is restricted by the quality of
the support set, which holds positive samples for the contrastive loss. In this
work, we show that the quality of the support set plays a crucial role in any
nearest neighbor based method for SSL. We then provide a refined baseline
(pNNCLR) to the nearest neighbor based SSL approach (NNCLR). To this end, we
introduce pseudo nearest neighbors (pNN) to control the quality of the support
set, wherein, rather than sampling the nearest neighbors, we sample in the
vicinity of hard nearest neighbors by varying the magnitude of the resultant
vector and employing a stochastic sampling strategy to improve the performance.
Additionally, to stabilize the effects of uncertainty in NN-based learning, we
employ a smooth-weight-update approach for training the proposed network.
Evaluation of the proposed method on multiple public image recognition and
medical image recognition datasets shows that it performs up to 8 percent
better than the baseline nearest neighbor method, and is comparable to other
previously proposed SSL methods.Comment: 15 pages, 5 figure
Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective
Coreset selection is among the most effective ways to reduce the training
time of CNNs, however, only limited is known on how the resultant models will
behave under variations of the coreset size, and choice of datasets and models.
Moreover, given the recent paradigm shift towards transformer-based models, it
is still an open question how coreset selection would impact their performance.
There are several similar intriguing questions that need to be answered for a
wide acceptance of coreset selection methods, and this paper attempts to answer
some of these. We present a systematic benchmarking setup and perform a
rigorous comparison of different coreset selection methods on CNNs and
transformers. Our investigation reveals that under certain circumstances,
random selection of subsets is more robust and stable when compared with the
SOTA selection methods. We demonstrate that the conventional concept of uniform
subset sampling across the various classes of the data is not the appropriate
choice. Rather samples should be adaptively chosen based on the complexity of
the data distribution for each class. Transformers are generally pretrained on
large datasets, and we show that for certain target datasets, it helps to keep
their performance stable at even very small coreset sizes. We further show that
when no pretraining is done or when the pretrained transformer models are used
with non-natural images (e.g. medical data), CNNs tend to generalize better
than transformers at even very small coreset sizes. Lastly, we demonstrate that
in the absence of the right pretraining, CNNs are better at learning the
semantic coherence between spatially distant objects within an image, and these
tend to outperform transformers at almost all choices of the coreset size
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