TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers

Leveraging the characteristics of convolutional layers, neural networks are extremely effective for pattern recognition tasks. However in some cases, their decisions are based on unintended information leading to high performance on standard benchmarks but also to a lack of generalization to challenging testing conditions and unintuitive failures. Recent work has termed this "shortcut learning" and addressed its presence in multiple domains. In text recognition, we reveal another such shortcut, whereby recognizers overly depend on local image statistics. Motivated by this, we suggest an approach to regulate the reliance on local statistics that improves text recognition performance. Our method, termed TextAdaIN, creates local distortions in the feature map which prevent the network from overfitting to local statistics. It does so by viewing each feature map as a sequence of elements and deliberately mismatching fine-grained feature statistics between elements in a mini-batch. Despite TextAdaIN's simplicity, extensive experiments show its effectiveness compared to other, more complicated methods. TextAdaIN achieves state-of-the-art results on standard handwritten text recognition benchmarks. It generalizes to multiple architectures and to the domain of scene text recognition. Furthermore, we demonstrate that integrating TextAdaIN improves robustness towards more challenging testing conditions. The official Pytorch implementation can be found at https://github.com/amazon-research/textadain-robust-recognition.Comment: 12 pages, 8 figures, Accepted to ECCV 202

CLIPTER: Looking at the Bigger Picture in Scene Text Recognition

Reading text in real-world scenarios often requires understanding the context surrounding it, especially when dealing with poor-quality text. However, current scene text recognizers are unaware of the bigger picture as they operate on cropped text images. In this study, we harness the representative capabilities of modern vision-language models, such as CLIP, to provide scene-level information to the crop-based recognizer. We achieve this by fusing a rich representation of the entire image, obtained from the vision-language model, with the recognizer word-level features via a gated cross-attention mechanism. This component gradually shifts to the context-enhanced representation, allowing for stable fine-tuning of a pretrained recognizer. We demonstrate the effectiveness of our model-agnostic framework, CLIPTER (CLIP TExt Recognition), on leading text recognition architectures and achieve state-of-the-art results across multiple benchmarks. Furthermore, our analysis highlights improved robustness to out-of-vocabulary words and enhanced generalization in low-data regimes.Comment: Accepted for publication by ICCV 202

Progesterone Increases Bifidobacterium Relative Abundance during Late Pregnancy

Summary: Gestation is accompanied by alterations in the microbial repertoire; however, the mechanisms driving these changes are unknown. Here, we demonstrate a dramatic shift in the gut microbial composition of women and mice during late pregnancy, including an increase in the relative abundance of Bifidobacterium. Using in-vivo-transplanted pellets, we found that progesterone, the principal gestation hormone, affects the microbial community. The effect of progesterone on the richness of several bacteria species, including Bifidobacterium, was also demonstrated in vitro, indicating a direct effect. Altogether, our results delineate a model in which progesterone promotes Bifidobacterium growth during late pregnancy. : Nuriel-Ohayon et al. demonstrate a dramatic shift in the gut microbial composition of women and mice during late pregnancy, including an increase in the relative abundance of Bifidobacterium. Using in vitro and in vivo experiments, they show that supplementation of progesterone affects the microbial communities, including increasing the relative abundance of Bifidobacterium. Keywords: progesterone, Bifidobacterium, pregnancy, gut microbiota, 16S rRNA, microbiom

Ségrégation et justice spatiale

Alors que depuis quelques années, une réflexion s’est développée sur le concept de justice spatiale, cet ouvrage a pour ambition de contribuer au renouveau des analyses portant plus spécifiquement sur les liens entre ségrégation urbaine et justice, en favorisant les échanges scientifiques entre chercheurs issus d’horizons disciplinaires, géographiques et culturels variés. Il vise également à apporter des éléments nouveaux permettant d’éclairer les politiques urbaines qui, dans de nombreux pays, annoncent haut et fort vouloir remédier à la ségrégation parce qu’elle serait injuste par définition. Issus de l’atelier « Justice spatiale et ségrégation » du colloque international « Justice et injustices spatiales » qui s’est tenu à l’université Paris Ouest Nanterre La Défense en avril 2008, les textes rassemblés dans ce volume explorent ainsi plusieurs grandes questions : toute division socio-spatiale de l’espace - urbain en particulier - est-elle injuste ? Quels sont les processus qui produisent de la ségrégation et en quoi sont-ils injustes ? Les situations de ségrégation produisent-elles des effets injustes (les effets de lieu par exemple) ? Symétriquement, l’objectif de la mixité socio-spatiale, souvent implicitement donné comme l’idéal de la ville juste, ne mérite-t-il pas d’être questionné ? Enfin, la prise en compte de la mobilité n’impose-t-elle pas de repenser les relations entre justice et ségrégation

Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis

ObjectiveGestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities. DesignWe comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts. ResultsWe found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy. ConclusionGDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention.Peer reviewe