162 research outputs found
Efficient Self-supervised Vision Transformers for Representation Learning
This paper investigates two techniques for developing efficient
self-supervised vision transformers (EsViT) for visual representation learning.
First, we show through a comprehensive empirical study that multi-stage
architectures with sparse self-attentions can significantly reduce modeling
complexity but with a cost of losing the ability to capture fine-grained
correspondences between image regions. Second, we propose a new pre-training
task of region matching which allows the model to capture fine-grained region
dependencies and as a result significantly improves the quality of the learned
vision representations. Our results show that combining the two techniques,
EsViT achieves 81.3% top-1 on the ImageNet linear probe evaluation,
outperforming prior arts with around an order magnitude of higher throughput.
When transferring to downstream linear classification tasks, EsViT outperforms
its supervised counterpart on 17 out of 18 datasets. The code and models will
be publicly available.Comment: 24 pages, 12 figures, file size 13.6M
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Automatic age progression and estimation from faces
Recently, automatic age progression has gained popularity due to its numerous applications. Among these is the frequent search for missing people, in the UK alone up to 300,000 people are reported missing every year. Although many algorithms have been proposed, most of the methods are affected by image noise, illumination variations, and facial expressions. Furthermore, most of the algorithms use a pattern caricaturing approach which infers ages by manipulating the target image and a template face formed by averaging faces at the intended age. To this end, this thesis investigates the problem with a view to tackling the most prominent issues associated with the existing algorithms. Initially using active appearance models (AAM), facial features are extracted and mapped to people’s ages, afterward a formula is derived which allows the convenient generation of age progressed images irrespective of whether the intended age exists in the training database or not. In order to handle image noise as well as varying facial expressions, a nonlinear appearance model called kernel appearance model (KAM) is derived. To illustrate the real application of automatic age progression, both AAM and KAM based algorithms are then used to synthesise faces of two popular long missing British and Irish kids; Ben Needham and Mary Boyle. However, both statistical techniques exhibit image rendering artefacts such as low-resolution output and the generation of inconsistent skin tone. To circumvent this problem, a hybrid texture enhancement pipeline is developed. To further ensure that the progressed images preserve people’s identities while at the same time attaining the intended age, rigorous human and machine based tests are conducted; part of this tests resulted to the development of a robust age estimation algorithm. Eventually, the results of the rigorous assessment reveal that the hybrid technique is able to handle all existing problems of age progression with minimal error.National Information Technology Development Agency of Nigeria (NITDA
Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability
Video segmentation encompasses a wide range of categories of problem
formulation, e.g., object, scene, actor-action and multimodal video
segmentation, for delineating task-specific scene components with pixel-level
masks. Recently, approaches in this research area shifted from concentrating on
ConvNet-based to transformer-based models. In addition, various
interpretability approaches have appeared for transformer models and video
temporal dynamics, motivated by the growing interest in basic scientific
understanding, model diagnostics and societal implications of real-world
deployment. Previous surveys mainly focused on ConvNet models on a subset of
video segmentation tasks or transformers for classification tasks. Moreover,
component-wise discussion of transformer-based video segmentation models has
not yet received due focus. In addition, previous reviews of interpretability
methods focused on transformers for classification, while analysis of video
temporal dynamics modelling capabilities of video models received less
attention. In this survey, we address the above with a thorough discussion of
various categories of video segmentation, a component-wise discussion of the
state-of-the-art transformer-based models, and a review of related
interpretability methods. We first present an introduction to the different
video segmentation task categories, their objectives, specific challenges and
benchmark datasets. Next, we provide a component-wise review of recent
transformer-based models and document the state of the art on different video
segmentation tasks. Subsequently, we discuss post-hoc and ante-hoc
interpretability methods for transformer models and interpretability methods
for understanding the role of the temporal dimension in video models. Finally,
we conclude our discussion with future research directions
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