Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments

We present a novel method for few-shot video classification, which performs appearance and temporal alignments. In particular, given a pair of query and support videos, we conduct appearance alignment via frame-level feature matching to achieve the appearance similarity score between the videos, while utilizing temporal order-preserving priors for obtaining the temporal similarity score between the videos. Moreover, we introduce a few-shot video classification framework that leverages the above appearance and temporal similarity scores across multiple steps, namely prototype-based training and testing as well as inductive and transductive prototype refinement. To the best of our knowledge, our work is the first to explore transductive few-shot video classification. Extensive experiments on both Kinetics and Something-Something V2 datasets show that both appearance and temporal alignments are crucial for datasets with temporal order sensitivity such as Something-Something V2. Our approach achieves similar or better results than previous methods on both datasets. Our code is available at https://github.com/VinAIResearch/fsvc-ata.Comment: Accepted to ECCV 202

Revisiting LARS for Large Batch Training Generalization of Neural Networks

LARS and LAMB have emerged as prominent techniques in Large Batch Learning (LBL), ensuring the stability of AI training. One of the primary challenges in LBL is convergence stability, where the AI agent usually gets trapped into the sharp minimizer. Addressing this challenge, a relatively recent technique, known as warm-up, has been employed. However, warm-up lacks a strong theoretical foundation, leaving the door open for further exploration of more efficacious algorithms. In light of this situation, we conduct empirical experiments to analyze the behaviors of the two most popular optimizers in the LARS family: LARS and LAMB, with and without a warm-up strategy. Our analyses give us a comprehension of the novel LARS, LAMB, and the necessity of a warm-up technique in LBL. Building upon these insights, we propose a novel algorithm called Time Varying LARS (TVLARS), which facilitates robust training in the initial phase without the need for warm-up. Experimental evaluation demonstrates that TVLARS achieves competitive results with LARS and LAMB when warm-up is utilized while surpassing their performance without the warm-up technique

Synthesis and redetermination of the crystal structure of salicyl-aldehyde <i>N</i>(4)-morpholino-thio-semi-carbazone.

The structure of the title compound (systematic name: N-{[(2-hy-droxy-phen-yl)methyl-idene]amino}-morpholine-4-carbo-thio-amide), C12H15N3O2S, was prev-iously determined (Koo et al., 1977 ▸) using multiple-film equi-inclination Weissenberg data, but has been redetermined with higher precision to explore its conformation and the hydrogen-bonding patterns and supra-molecular inter-actions. The mol-ecular structure shows intra-molecular O-H⋯N and C-H⋯S inter-actions. The configuration of the C=N bond is E. The mol-ecule is slightly twisted about the central N-N bond. The best planes through the phenyl ring and the morpholino ring make an angle of 43.44 (17)°. In the crystal, the mol-ecules are connected into chains by N-H⋯O and C-H⋯O hydrogen bonds, which combine to generate sheets lying parallel to (002). The most prominent contribution to the surface contacts are H⋯H contacts (51.6%), as concluded from a Hirshfeld surface analysis

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This work deals with the membership set estimation techniques based upon an unknown but bounded error. Only the ellipsoidal approach has been considered here. In this particular context, a realistic evaluation of the bound based upon an analysis of the kurtosis number of the error sequence has been proposed. A unified approach of the different algorithms has been given, which leads to a numerically stable implementation. Note that nearly all published algorithms do not possess this fundamental property. The convergence analysis of these estimation techniques requires the well-known persistent excitation property. A algorithmic evaluation of this persistency hypothesis of the input has been proposed and a way to built such an input in this particular context is exhibited. Lastly, the compromise between a suboptimal sequential algorithm and an optimal global but numerically unrealistic has been studied ; the different results are illustrated thanks to some simulated data but also with some real data from industrial applications.Ce travail concerne les techniques d'identification ensembliste dites à erreur inconnue mais bornée. On s'est ici exclusivement intêressé à l'approche ensembliste ellipsoïdale. Dans ce contexte précis, nos contributions portent sur une évaluation réaliste de la borne, basée sur l'analyse du kurtosis du signal d'erreur. On propose également une approche unifiée des algorithmes conduisant à leur formulation numériquement stable, à la différence de la quasi-totalité des solutions publiées. L'analyse de la convergence de ce type de techniques fait intervenir la notion habituelle d'excitation persistante pour laquelle on donne une solution algorithmique permettant de qualifier l'entrée, et comment construire des entrées " optimales ". Enfin on s'est intêressé au compromis que l'on pouvait établir entre algorithme séquentiel sous optimal et une approche globale optimale mais numériquement inaccessible. L'ensemble de ces points est illustré tant en simulation qu'avec des données réelles provenant du monde industriel

New concept to compute confidence of reported information level for logic diagnosis

International audienceThis paper proposes a model to compute con dence of reported information level (CRIL) in the domain of logic diagnosis. This level of con dence is provided by a diagnosis module allowing to quickly identify the origin of equipment failure. We studied the factors a ecting CRIL, such as measurement system reliability, production context, position of sensors in the acquisition chains, type of product, reference metrology, preventive maintenance and corrective maintenance based on historical data and reported information generated by production equipment. We have introduced a new 'CRIL' concept based on the Bayesian Network approach, Na ve Bayes model and Tree Augmented Na ve Bayes model. Our contribution includes an on-line con dence computation module for production equipment data, and an algorithm to compute CRIL. We suggest it be applied to the semiconductor manufacturing industry

Contributions à l'identification ensembliste ellipsoïdale

Ce travail concerne les techniques d'identification ensembliste dites à erreur inconnue mais bornée. On s'est ici exclusivement intêressé à l'approche ensembliste ellipsoïdale. Dans ce contexte précis, nos contributions portent sur une évaluation réaliste de la borne, basée sur l'analyse du kurtosis du signal d'erreur. On propose également une approche unifiée des algorithmes conduisant à leur formulation numériquement stable, à la différence de la quasi-totalité des solutions publiées. L'analyse de la convergence de ce type de techniques fait intervenir la notion habituelle d'excitation persistante pour laquelle on donne une solution algorithmique permettant de qualifier l'entrée, et comment construire des entrées " optimales ". Enfin on s'est intêressé au compromis que l'on pouvait établir entre algorithme séquentiel sous optimal et une approche globale optimale mais numériquement inaccessible. L'ensemble de ces points est illustré tant en simulation qu'avec des données réelles provenant du monde industriel.GRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

Vers une compréhension de l'évolution structurale des ex-voto en bois de la Source des Roches de Chamalières

International audienc

POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples

In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from outside the target classes, to improve few-shot learning. Specifically, we exploit the easily available out-of-distribution samples to drive the classifier to avoid irrelevant features by maximizing the distance from prototypes to out-of-distribution samples while minimizing that of in-distribution samples (i.e., support, query data). Our approach is simple to implement, agnostic to feature extractors, lightweight without any additional cost for pre-training, and applicable to both inductive and transductive settings. Extensive experiments on various standard benchmarks demonstrate that the proposed method consistently improves the performance of pretrained networks with different architectures.Comment: Accepted at NeurIPS 2021 (First two authors contribute equally

ARC-NUCLÉART – 50 years of radiation conservation of historical objects

Artykuł powstał z okazji 50 rocznicy utworzenia radiacyjnego laboratorium badawczego i profesjonalnej pracowni konserwacji dzieł sztuki ARC-NucleArt (Atelier de Recherche et de Conservation Nucléart). Przypomniano historię tej zasłużonej dla ratowania obiektów historycznych placówki. Jest ona pionierem w zastosowaniu technik radiacyjnych do dezynsekcji, dezynfekcji i konsolidacji. Krótko omówiono zasady wykorzystania promieniowania jonizującego do ratowania zagrożonych insektami, grzybami i bakteriami obiektów archeologicznych i dzieł sztuki. W przeglądzie literaturowym odsyłamy do publikacji podsumowujących światowe badania w zakresie radiacyjnej konserwacji różnych materiałów.The article was created on the occasion of the 50th anniversary of the creation of the radiation research laboratory and professional art conservation studio ARC-NucleArt (Atelier de Recherche et de Conservation Nucléart). The history of this institution merited for saving historical objects was recalled. She is a pioneer in the application of radiation techniques for disinfestation, disinfection and consolidation. The principles of using ionizing radiation to rescue archaeological sites and works of art endangered by insects, fungi and bacteria are briefly discussed. In the literature review, we refer to publications summarizing the global research in the field of radiation conservation of very different materials

Multiple serous membrane effusion caused by primary pericardial mesothelioma

Primary pericardial mesothelioma is an extremely rare cancer with a short survival prognosis. Clinical symptoms are often atypical, and most patients are diagnosed after surgery or at autopsy. We report a case of a 35-year-old female patient with multiple serous membrane effusion for more than 1 year. The patient underwent pericardial, pleural, and peritoneal fluid drainage many times and underwent many laboratory tests to find the cause; however, there was no definitive diagnosis. She was admitted to the hospital because of shortness of breath, cough, and sputum for 5 days. She underwent extensive pericardiectomy to resolve the dyspnea and pericardial surgery to find the cause of the multiple serous membrane effusion. After surgery, her dyspnea was relieved, and the serous effusion gradually decreased