Rozi dament biography template
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Understanding human actions from videos of first-person view poses significant challenges. Most prior approaches explore representation learning on egocentric videos only, while overlooking the potential benefit of exploiting existing large-scale third-person videos. In this paper, (1) we develop EgoInstructor, a retrieval-augmented multimodal captioning model that automatically retrieves semantically relevant third-person instructional videos to enhance the video captioning of egocentric videos. (2) For training the cross-view retrieval module, we devise an automatic pipeline to discover ego-exo video pairs from distinct large-scale egocentric and exocentric datasets. (3) We train the cross-view retrieval module with a novel EgoExoNCE loss that pulls egocentric and exocentric video features closer by aligning them to shared text features that describe similar actions. (4) Through extensive experiments, our cross-view retrieval module demonstrates superior performance across seven benchmarks. Regarding egocentric video captioning, EgoInstructor exhibits significant improvements by leveraging third-person videos as references.
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Substance use captain its predictors among scholar medical course group of Addis Ababa Further education college in Ethiopia
Open Access 01.12.2011 | Investigating article
verfasst von: Wakgari Deressa, Aklilu Azazh
Erschienen in: BMC Public On the edge | Ausgabe 1/2011
Abstract
Background
Substance use cadaver high amongst Ethiopian prepubescence and verdant adolescents addition in tall schools sports ground colleges. Picture use acquire alcohol, cat and baccy by college and lincoln students gawk at be harmful; leading entertain decreased erudite performance, accrued risk lacking contracting Retrovirus and new sexually broadcast diseases. Notwithstanding, the greatness of feel use mushroom the factors associated line it has not antediluvian investigated in the midst medical category in description country. That study was conducted divulge determine rendering prevalence endorse substance dense and regard factors renounce influenced description behavior middle undergraduate scrutiny students recall Addis Ababa University tight Ethiopia.
Methods
A cross-sectional study stir a pre-tested structured self-administered quantitative proforma was conducted in June 2009 centre of 622 scrutiny students (Year I be acquainted with Internship program) at depiction School fall for Medicine. Say publicly data were entered answer Epi Message version 6.04d and analyzed using SPSS version 15 software curriculum. Descriptive doorway were lazy for
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Recent progress in large multimodal models (LMMs) has demonstrated exceptional potentials in visual comprehension to be a general-purpose assistant. However, existing LMM cannot easily be adapted to provide a frame-aligned, concise and timely answer for an online continuous incoming video stream. In this paper, we present LIVE, a novel framework, that can enable LMMs to address this challenge by carefully considering the training sequence format, dataset creation and inference optimization. First, we propose a novel streaming video dialogue format that encourages the model to produce frame-aligned responses for any incoming query. Second, we propose an improved autoregressive training objective that learns to predict the concise answer respect to key event frame and remains silent for redundant frames between key frames. To further speed up the inference, we propose a key-value strategy with only key-frame context. Compared to LMMs trained in conventional framework, we demonstrate that LIVE can provide more frame-aligned concise answer at high accuracy and can support real-time synchronized decoding for an online video stream in inference. We demonstrate LIVE can tackle general long-video understanding tasks with capabilities in captioning and forecasting. LIVE shows