5 research outputs found
whu-nercms at trecvid2021:instance search task
We will make a brief introduction of the experimental methods and results of
the WHU-NERCMS in the TRECVID2021 in the paper. This year we participate in the
automatic and interactive tasks of Instance Search (INS). For the automatic
task, the retrieval target is divided into two parts, person retrieval, and
action retrieval. We adopt a two-stage method including face detection and face
recognition for person retrieval and two kinds of action detection methods
consisting of three frame-based human-object interaction detection methods and
two video-based general action detection methods for action retrieval. After
that, the person retrieval results and action retrieval results are fused to
initialize the result ranking lists. In addition, we make attempts to use
complementary methods to further improve search performance. For interactive
tasks, we test two different interaction strategies on the fusion results. We
submit 4 runs for automatic and interactive tasks respectively. The
introduction of each run is shown in Table 1. The official evaluations show
that the proposed strategies rank 1st in both automatic and interactive tracks.Comment: 9 pages, 4 figure
An overview on the evaluated video retrieval tasks at TRECVID 2022
The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis
and retrieval evaluation with the goal of promoting progress in research and
development of content-based exploitation and retrieval of information from
digital video via open, tasks-based evaluation supported by metrology. Over the
last twenty-one years this effort has yielded a better understanding of how
systems can effectively accomplish such processing and how one can reliably
benchmark their performance. TRECVID has been funded by NIST (National
Institute of Standards and Technology) and other US government agencies. In
addition, many organizations and individuals worldwide contribute significant
time and effort. TRECVID 2022 planned for the following six tasks: Ad-hoc video
search, Video to text captioning, Disaster scene description and indexing,
Activity in extended videos, deep video understanding, and movie summarization.
In total, 35 teams from various research organizations worldwide signed up to
join the evaluation campaign this year. This paper introduces the tasks,
datasets used, evaluation frameworks and metrics, as well as a high-level
results overview.Comment: arXiv admin note: substantial text overlap with arXiv:2104.13473,
arXiv:2009.0998
TRECVID 2018: Benchmarking Video Activity Detection, Video Captioning and Matching, Video Storytelling Linking and Video Search
International audienc
Intelligent Data Analytics using Deep Learning for Data Science
Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization