1,349 research outputs found
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Deep Architectures and Ensembles for Semantic Video Classification
This work addresses the problem of accurate semantic labelling of short
videos. To this end, a multitude of different deep nets, ranging from
traditional recurrent neural networks (LSTM, GRU), temporal agnostic networks
(FV,VLAD,BoW), fully connected neural networks mid-stage AV fusion and others.
Additionally, we also propose a residual architecture-based DNN for video
classification, with state-of-the art classification performance at
significantly reduced complexity. Furthermore, we propose four new approaches
to diversity-driven multi-net ensembling, one based on fast correlation measure
and three incorporating a DNN-based combiner. We show that significant
performance gains can be achieved by ensembling diverse nets and we investigate
factors contributing to high diversity. Based on the extensive YouTube8M
dataset, we provide an in-depth evaluation and analysis of their behaviour. We
show that the performance of the ensemble is state-of-the-art achieving the
highest accuracy on the YouTube-8M Kaggle test data. The performance of the
ensemble of classifiers was also evaluated on the HMDB51 and UCF101 datasets,
and show that the resulting method achieves comparable accuracy with
state-of-the-art methods using similar input features
A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition
Visual Place Recognition (VPR) approaches have typically attempted to match
places by identifying visual cues, image regions or landmarks that have high
``utility'' in identifying a specific place. But this concept of utility is not
singular - rather it can take a range of forms. In this paper, we present a
novel approach to deduce two key types of utility for VPR: the utility of
visual cues `specific' to an environment, and to a particular place. We employ
contrastive learning principles to estimate both the environment- and
place-specific utility of Vector of Locally Aggregated Descriptors (VLAD)
clusters in an unsupervised manner, which is then used to guide local feature
matching through keypoint selection. By combining these two utility measures,
our approach achieves state-of-the-art performance on three challenging
benchmark datasets, while simultaneously reducing the required storage and
compute time. We provide further analysis demonstrating that unsupervised
cluster selection results in semantically meaningful results, that finer
grained categorization often has higher utility for VPR than high level
semantic categorization (e.g. building, road), and characterise how these two
utility measures vary across different places and environments. Source code is
made publicly available at https://github.com/Nik-V9/HEAPUtil.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L) and IROS 202
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