Efficient tracking of team sport players with few game-specific annotations

One of the requirements for team sports analysis is to track and recognize players. Many tracking and reidentification methods have been proposed in the context of video surveillance. They show very convincing results when tested on public datasets such as the MOT challenge. However, the performance of these methods are not as satisfactory when applied to player tracking. Indeed, in addition to moving very quickly and often being occluded, the players wear the same jersey, which makes the task of reidentification very complex. Some recent tracking methods have been developed more specifically for the team sport context. Due to the lack of public data, these methods use private datasets that make impossible a comparison with them. In this paper, we propose a new generic method to track team sport players during a full game thanks to few human annotations collected via a semi-interactive system. Non-ambiguous tracklets and their appearance features are automatically generated with a detection and a reidentification network both pre-trained on public datasets. Then an incremental learning mechanism trains a Transformer to classify identities using few game-specific human annotations. Finally, tracklets are linked by an association algorithm. We demonstrate the efficiency of our approach on a challenging rugby sevens dataset. To overcome the lack of public sports tracking dataset, we publicly release this dataset at https://kalisteo.cea.fr/index.php/free-resources/. We also show that our method is able to track rugby sevens players during a full match, if they are observable at a minimal resolution, with the annotation of only 6 few seconds length tracklets per player.Comment: Accepted to 2022 8th International Workshop on Computer Vision in Sports (CVsports 2022

Classifying All Interacting Pairs in a Single Shot

In this paper, we introduce a novel human interaction detection approach, based on CALIPSO (Classifying ALl Interacting Pairs in a Single shOt), a classifier of human-object interactions. This new single-shot interaction classifier estimates interactions simultaneously for all human-object pairs, regardless of their number and class. State-of-the-art approaches adopt a multi-shot strategy based on a pairwise estimate of interactions for a set of human-object candidate pairs, which leads to a complexity depending, at least, on the number of interactions or, at most, on the number of candidate pairs. In contrast, the proposed method estimates the interactions on the whole image. Indeed, it simultaneously estimates all interactions between all human subjects and object targets by performing a single forward pass throughout the image. Consequently, it leads to a constant complexity and computation time independent of the number of subjects, objects or interactions in the image. In detail, interaction classification is achieved on a dense grid of anchors thanks to a joint multi-task network that learns three complementary tasks simultaneously: (i) prediction of the types of interaction, (ii) estimation of the presence of a target and (iii) learning of an embedding which maps interacting subject and target to a same representation, by using a metric learning strategy. In addition, we introduce an object-centric passive-voice verb estimation which significantly improves results. Evaluations on the two well-known Human-Object Interaction image datasets, V-COCO and HICO-DET, demonstrate the competitiveness of the proposed method (2nd place) compared to the state-of-the-art while having constant computation time regardless of the number of objects and interactions in the image.Comment: WACV 2020 (to appear

Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised Learning

Contrastive representation learning has proven to be an effective self-supervised learning method for images and videos. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives that should be contrasted with other instances, called negatives, that are considered as noise. However, several instances in a dataset are drawn from the same distribution and share underlying semantic information. A good data representation should contain relations between the instances, or semantic similarity and dissimilarity, that contrastive learning harms by considering all negatives as noise. To circumvent this issue, we propose a novel formulation of contrastive learning using semantic similarity between instances called Similarity Contrastive Estimation (SCE). Our training objective is a soft contrastive one that brings the positives closer and estimates a continuous distribution to push or pull negative instances based on their learned similarities. We validate empirically our approach on both image and video representation learning. We show that SCE performs competitively with the state of the art on the ImageNet linear evaluation protocol for fewer pretraining epochs and that it generalizes to several downstream image tasks. We also show that SCE reaches state-of-the-art results for pretraining video representation and that the learned representation can generalize to video downstream tasks.Comment: Extended version of our WACV 2023 paper to video self-supervised learnin

COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using Transformers

We present COMEDIAN, a novel pipeline to initialize spatio-temporal transformers for action spotting, which involves self-supervised learning and knowledge distillation. Action spotting is a timestamp-level temporal action detection task. Our pipeline consists of three steps, with two initialization stages. First, we perform self-supervised initialization of a spatial transformer using short videos as input. Additionally, we initialize a temporal transformer that enhances the spatial transformer's outputs with global context through knowledge distillation from a pre-computed feature bank aligned with each short video segment. In the final step, we fine-tune the transformers to the action spotting task. The experiments, conducted on the SoccerNet-v2 dataset, demonstrate state-of-the-art performance and validate the effectiveness of COMEDIAN's pretraining paradigm. Our results highlight several advantages of our pretraining pipeline, including improved performance and faster convergence compared to non-pretrained models.Comment: Source code is available here: https://github.com/juliendenize/eztorc

What is known about neuroplacentology in fetal growth restriction and in preterm infants: A narrative review of literature

The placenta plays a fundamental role during pregnancy for fetal growth and development. A suboptimal placental function may result in severe consequences during the infant's first years of life. In recent years, a new field known as neuroplacentology has emerged and it focuses on the role of the placenta in fetal and neonatal brain development. Because of the limited data, our aim was to provide a narrative review of the most recent knowledge about the relation between placental lesions and fetal and newborn neurological development. Papers published online from 2000 until February 2022 were taken into consideration and particular attention was given to articles in which placental lesions were related to neonatal morbidity and short-term and long-term neurological outcome. Most research regarding the role of placental lesions in neurodevelopment has been conducted on fetal growth restriction and preterm infants. Principal neurological outcomes investigated were periventricular leukomalacia, intraventricular hemorrhages, neonatal encephalopathy and autism spectrum disorder. No consequences in motor development were found. All the considered studies agree about the crucial role played by placenta in fetal and neonatal neurological development and outcome. However, the causal mechanisms remain largely unknown. Knowledge on the pathophysiological mechanisms and on placenta-related risks for neurological problems may provide clues for early interventions aiming to improve neurological outcomes, especially among pediatricians and child psychiatrists

The effect of soy dietary supplement and low dose of hormone therapy on main cardiovascular health biomarkers: a randomized controlled trial

To assess the effects of a soy dietary supplement on the main biomarkers of cardiovascular health in postmenopausal women compared with the effects of low-dose hormone therapy (HT) and placebo. Double-blind, randomized and controlled intention-to-treat trial. Sixty healthy postmenopausal women, aged 40-60 years, 4.1 years mean time since menopause were recruited and randomly assigned to 3 groups: a soy dietary supplement group (isoflavone 90mg), a low-dose HT group (estradiol 1 mg plus noretisterone 0.5 mg) and a placebo group. Lipid profile, glucose level, body mass index, blood pressure and abdominal/hip ratio were evaluated in all the participants at baseline and after 16 weeks. Statistical analyses were performed using the χ2 test, Fisher's exact test, Kruskal-Wallis non-parametric test, analysis of variance (ANOVA), paired Student's t-test and Wilcoxon test. After a 16-week intervention period, total cholesterol decreased 11.3% and LDL-cholesterol decreased 18.6% in the HT group, but both did not change in the soy dietary supplement and placebo groups. Values for triglycerides, HDL-cholesterol, glucose level, body mass index, blood pressure and abdominal/hip ratio did not change over time in any of the three groups. The use of dietary soy supplement did not show any significant favorable effect on cardiovascular health biomarkers compared with HT. The trial is registered at the Brazilian Clinical Trials Registry (Registro Brasileiro de Ensaios Clínicos - ReBEC), number RBR-76mm75.To assess the effects of a soy dietary supplement on the main biomarkers of cardiovascular health in postmenopausalwomen compared with the effects of low-dose hormone therapy (HT) and placebo. Double-blind, randomizedand controlled intention-to-treat36251258FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO03/04464-

II.5 Where to find the CoRoT data?

This book is dedicated to all the people interested in the CoRoT mission and the beautiful data that were delivered during its six year duration. Either amateurs, professional, young or senior researchers, they will find treasures not only at the time of this publication but also in the future twenty or thirty years. It presents the data in their final version, explains how they have been obtained, how to handle them, describes the tools necessary to understand them, and where to find them. It also highlights the most striking first results obtained up to now. CoRoT has opened several unexpected directions of research and certainly new ones still to be discovered

New vaginal moisturizer is well-accepted and reduces symptoms related to the genitourinary syndrome of menopause in Brazilian women: O novo hidratante vaginal é bem aceito e reduz os sintomas relacionados à síndrome geniturinária da menopausa nas mulheres brasileiras

Objectives: To evaluate the effectiveness and acceptability of a new vaginal moisturizer composed of a combination of humectants, moisturizers, bioadhesives, and viscosity polymers (sodium hyaluronate 0.1%, polycarbophil 1.5%, carbomer 1.5%, and glycerin 1%) for relief of vulvovaginal symptoms associated with genitourinary syndrome of menopause in Brazilian postmenopausal women. Methods: An open clinical trial was performed with 33 postmenopausal women between 47 and 68 years of age with genitourinary syndrome of menopause symptoms such as vaginal dryness accompanied by pain or discomfort during intercourse. Clinical efficacy in relieving symptoms of genitourinary syndrome of menopause and acceptability were assessed using a Likert scale questionnaire. Efficacy and safety were assessed through gynecological examination, which included visual inspection of the epithelium and vaginal contents, and determination of vaginal pH. Wilcoxon's non-parametric test and paired Student's t-test were used for statistical analyses.Results: After 3 weeks of treatment, most participants reported improvement in GSM symptoms like vaginal dryness (p<0.001), discomfort during or after sexual intercourse (p<0.001), pain during or after sexual intercourse (p<0.001), vaginal odor (p=0.047) and itching (p=0.032) and good acceptability of the moisturizer. There was no alteration in the vaginal health index; however, a reduction in vaginal inflammation (p=0.046) was observed. No clinically significant adverse events were reported by participants. Conclusions: Treatment with non-hormonal vaginal moisturizer for 3 consecutive weeks in postmenopausal women with symptoms of genitourinary syndrome of menopause promoted improvements in vaginal hydration, showing a clinically adequate profile of efficacy and safety, in addition to a satisfactory degree of acceptability