32,247 research outputs found
A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition
The goal of building a benchmark (suite of datasets) is to provide a unified
protocol for fair evaluation and thus facilitate the evolution of a specific
area. Nonetheless, we point out that existing protocols of action recognition
could yield partial evaluations due to several limitations. To comprehensively
probe the effectiveness of spatiotemporal representation learning, we introduce
BEAR, a new BEnchmark on video Action Recognition. BEAR is a collection of 18
video datasets grouped into 5 categories (anomaly, gesture, daily, sports, and
instructional), which covers a diverse set of real-world applications. With
BEAR, we thoroughly evaluate 6 common spatiotemporal models pre-trained by both
supervised and self-supervised learning. We also report transfer performance
via standard finetuning, few-shot finetuning, and unsupervised domain
adaptation. Our observation suggests that current state-of-the-art cannot
solidly guarantee high performance on datasets close to real-world
applications, and we hope BEAR can serve as a fair and challenging evaluation
benchmark to gain insights on building next-generation spatiotemporal learners.
Our dataset, code, and models are released at:
https://github.com/AndongDeng/BEARComment: ICCV 202
RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning
Automating radiology report generation can significantly alleviate
radiologists' workloads. Previous research has primarily focused on realizing
highly concise observations while neglecting the precise attributes that
determine the severity of diseases (e.g., small pleural effusion). Since
incorrect attributes will lead to imprecise radiology reports, strengthening
the generation process with precise attribute modeling becomes necessary.
Additionally, the temporal information contained in the historical records,
which is crucial in evaluating a patient's current condition (e.g., heart size
is unchanged), has also been largely disregarded. To address these issues, we
propose RECAP, which generates precise and accurate radiology reports via
dynamic disease progression reasoning. Specifically, RECAP first predicts the
observations and progressions (i.e., spatiotemporal information) given two
consecutive radiographs. It then combines the historical records,
spatiotemporal information, and radiographs for report generation, where a
disease progression graph and dynamic progression reasoning mechanism are
devised to accurately select the attributes of each observation and
progression. Extensive experiments on two publicly available datasets
demonstrate the effectiveness of our model.Comment: Accepted by Findings of EMNLP 202
The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns
The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models
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