When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data

Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.Comment: 2017 IEEE Computer Vision and Pattern Recognition (CVPR) Workshop

Excitation Dropout: Encouraging Plasticity in Deep Neural Networks

We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths. In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout. In essence, we dropout with higher probability those neurons which contribute more to decision making at training time. This approach penalizes high saliency neurons that are most relevant for model prediction, i.e. those having stronger evidence. By dropping such high-saliency neurons, the network is forced to learn alternative paths in order to maintain loss minimization, resulting in a plasticity-like behavior, a characteristic of human brains too. We demonstrate better generalization ability, an increased utilization of network neurons, and a higher resilience to network compression using several metrics over four image/video recognition benchmarks

Curriculum Dropout

Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability discourages over-specific co-adaptations of feature detectors, preventing overfitting and improving network generalization. Besides, Dropout can be interpreted as an approximate model aggregation technique, where an exponential number of smaller networks are averaged in order to get a more powerful ensemble. In this paper, we show that using a fixed dropout probability during training is a suboptimal choice. We thus propose a time scheduling for the probability of retaining neurons in the network. This induces an adaptive regularization scheme that smoothly increases the difficulty of the optimization problem. This idea of "starting easy" and adaptively increasing the difficulty of the learning problem has its roots in curriculum learning and allows one to train better models. Indeed, we prove that our optimization strategy implements a very general curriculum scheme, by gradually adding noise to both the input and intermediate feature representations within the network architecture. Experiments on seven image classification datasets and different network architectures show that our method, named Curriculum Dropout, frequently yields to better generalization and, at worst, performs just as well as the standard Dropout method.Comment: Accepted at ICCV (International Conference on Computer Vision) 201

Left/Right Hand Segmentation in Egocentric Videos

Wearable cameras allow people to record their daily activities from a user-centered (First Person Vision) perspective. Due to their favorable location, wearable cameras frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications. Existent First Person Vision methods handle hand segmentation as a background-foreground problem, ignoring two important facts: i) hands are not a single "skin-like" moving element, but a pair of interacting cooperative entities, ii) close hand interactions may lead to hand-to-hand occlusions and, as a consequence, create a single hand-like segment. These facts complicate a proper understanding of hand movements and interactions. Our approach extends traditional background-foreground strategies, by including a hand-identification step (left-right) based on a Maxwell distribution of angle and position. Hand-to-hand occlusions are addressed by exploiting temporal superpixels. The experimental results show that, in addition to a reliable left/right hand-segmentation, our approach considerably improves the traditional background-foreground hand-segmentation

MLL-MLLT10 fusion in acute monoblastic leukemia: variant complex rearrangements and 11q proximal breakpoint heterogeneity

Cytogenetic studies of acute monoblastic leukemia cases presenting MLL-MLLT10 (alias MLL-AF10) fusion show a broad heterogeneity of chromosomal breakpoints. We present two new pediatric cases (French-American-British type M5) with MLL-MLLT10 fusion, which we studied with fluorescence in situ hybridization. In both we detected a paracentric inversion of the 11q region that translocated onto chromosome 10p12; one case displayed a variant complex pattern. We review the cytogenetic molecular data concerning the proximal inversion breakpoint of 11q and confirm its heterogeneit

MEIOTIC ORIGIN OF TRISOMY IN NEOPLASM: EVIDENCE IN A CASE OF ERYTROLEUKAEMIA

Trisomic cells in neoplasms may represent abnormal clones originated from a tissue-confined mosaicism, and arise therefore by a meiotic error. We report on a 16-month-old child with erythroleukaemia (AML-M6), whose marrow karyotype at onset was 48,XX,del(13)(q12q14),del(14)(q22q32),+21,+21. The parental origin of the supernumerary chromosomes 21 was investigated by comparing 10 polymorphic loci scattered along the whole chromosome on the patient's marrow and her parents' leukocytes. Three loci were informative for the presence of three alleles, two of which were of maternal origin; two further loci showed a maternal allele of higher intensity. Lymphocytes and skin fibroblasts showed a normal karyotype, and molecular analysis on leukocytes at remission, buccal smear and urinary sediment cells consistently showed only one maternal allele, whereas neonatal blood from Guthrie spot showed two maternal alleles as in the marrow. An accurate clinical re-evaluation confirmed a normal phenotype. Our results indicate that tetrasomy 21 arose from a marrow clone with trisomy 21 of meiotic origin. To the best of our knowledge, this is the first evidence that supernumerary chromosomes in neoplastic clones may in fact be present due to a meiotic error. This demonstrates that a tissue-confined constitutional mosaicism for a trisomy may indeed represent the first event in multistep carcinogenesis