Shape Consistent 2D Keypoint Estimation under Domain Shift

Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e.g. semantic segmentation, depth estimation). Following this trend, in this paper we present a novel deep adaptation framework for estimating keypoints under domain shift}, i.e. when the training (source) and the test (target) images significantly differ in terms of visual appearance. Our method seamlessly combines three different components: feature alignment, adversarial training and self-supervision. Specifically, our deep architecture leverages from domain-specific distribution alignment layers to perform target adaptation at the feature level. Furthermore, a novel loss is proposed which combines an adversarial term for ensuring aligned predictions in the output space and a geometric consistency term which guarantees coherent predictions between a target sample and its perturbed version. Our extensive experimental evaluation conducted on three publicly available benchmarks shows that our approach outperforms state-of-the-art domain adaptation methods in the 2D keypoint prediction task

Dominant-Set Clustering: A Review

Clustering refers to the process of extracting maximally coherent groups from a set of objects using pairwise, or high-order, similarities. Traditional approaches to this problem are based on the idea of partitioning the input data into a predetermined number of classes, thereby obtaining the clusters as a by-product of the partitioning process. A radically different perspective of the problem consists in providing a formalization of the very notion of a cluster and considering the clustering process as a sequential search of structures in the data adhering to this cluster notion. In this manuscript we review one of the pioneering approaches falling in the latter class of algorithms, which has been proposed in the early 2000s and has been found since then a number of applications in different domains. It is known as dominant set clustering and provides a notion of a cluster (a.k.a. dominant set) that has intriguing links to game-theory, graph-theory and optimization theory. From the game-theoretic perspective, clusters are regarded as equilibria of non-cooperative “clustering” games; in the graph-theoretic context, it can be shown that they generalize the notion of maximal clique to edge-weighted graphs; finally, from an optimization point of view, they can be characterized in terms of solutions to a simplex-constrained, quadratic optimization problem, as well as in terms of an exquisitely combinatorial entity. Besides introducing dominant sets from a theoretical perspective, we will also focus on the related algorithmic issues by reviewing two state-of-the-art methods that are used in the literature to find dominant sets clusters, namely the Replicator Dynamics and the Infection and Immunization Dynamics. Finally, we conclude with an overview of different extensions of the dominant set framework and of applications where dominant sets have been successfully employed

The experience of living with metastatic breast cancer

Over the last 10-15 years the medical management of metastatic breast cancer has improved survival, so women are living longer with progressive disease. Little is understood about women’s problems and needs and how they live their everyday lives. This study aimed to explore the experience of women with metastatic breast cancer by applying three phases: a cross-sectional survey exploring quality of life, experience of care and where they turned for support; exploration of the narratives of 30 women considering the social consequences of living with progressive breast cancer on identity; and finally triangulating medical and nursing documentation, a measure of physical functioning and ten women’s narratives to define the illness trajectory of metastatic breast cancer. Phase 1: Quality of life was found to be poor with women experiencing a significant symptom burden. Experience of care was poor with unmet information and support needs. There was little evidence of General Practitioner or palliative care involvement in care. Phase 2: In weathering the oscillations of progressive disease, women faced threats to their social identity. Women sought ways to maintain their social roles and social order to avoid social isolation. To do this they adopted contingent identities: stoicism or absolved responsibility. Women used these contingent identities to mediate any discontinuity between the self, the body and social order. These self-representations were used by women to maintain their social roles and social order and in doing so avoiding being discredited and socially isolated. Phase 3: Mapping women’s illness trajectories identified three typical trajectories. The illness trajectories demonstrated the complexity of living over time with progressive disease, through phases which give definition to a previously ill-defined pathway. Living with metastatic breast cancer challenges the personal resources of the individual and poses interesting questions about how healthcare professionals provide information, effective symptom control, and emotional and practical support to women. Current models of care are not meeting women’s needs and new approaches to care provision need to be considered.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Continuous-Based Approach for Partial Clique Enumeration

Proc. Graph Based representations in Pattern Recognition - GbR0

Adagraph: Unifying predictive and continuous domain adaptation through graphs

The ability to categorize is a cornerstone of visual intelligence, and a key functionality for artificial, autonomous visual machines. This problem will never be solved without algorithms able to adapt and generalize across visual domains. Within the context of domain adaptation and generalization, this paper focuses on the predictive domain adaptation scenario, namely the case where no target data are available and the system has to learn to generalize from annotated source images plus unlabeled samples with associated metadata from auxiliary domains. Our contribution is the first deep architecture that tackles predictive domain adaptation, able to leverage over the information brought by the auxiliary domains through a graph. Moreover, we present a simple yet effective strategy that allows us to take advantage of the incoming target data at test time, in a continuous domain adaptation scenario. Experiments on three benchmark databases support the value of our approac

Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation

Deep neural networks have enabled major progresses in semantic segmentation. However, even the most advanced neural architectures suffer from important limitations. First, they are vulnerable to catastrophic forgetting, i.e., they perform poorly when they are required to incrementally update their model as new classes are available. Second, they rely on large amount of pixel-level annotations to produce accurate segmentation maps. To tackle these issues, we introduce a novel incremental class learning approach for semantic segmentation taking into account a peculiar aspect of this task: since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift. Therefore, we revisit the traditional distillation paradigm by designing novel loss terms which explicitly account for the background shift. Additionally, we introduce a novel strategy to initialize classifier's parameters at each step in order to prevent biased predictions toward the background class. Finally, we demonstrate that our approach can be extended to point- and scribble-based weakly supervised segmentation, modeling the partial annotations to create priors for unlabeled pixels. We demonstrate the effectiveness of our approach with an extensive evaluation on the Pascal-VOC, ADE20K, and Cityscapes datasets, significantly outperforming state-of-the-art methods