2,548 research outputs found
The TREC2001 video track: information retrieval on digital video information
The development of techniques to support content-based access to archives of digital video information has recently started to receive much attention from the research community. During 2001, the annual TREC activity, which has been benchmarking the performance of information retrieval techniques on a range of media for 10 years, included a ”track“ or activity which allowed investigation into approaches to support searching through a video library. This paper is not intended to provide a comprehensive picture of the different approaches taken by the TREC2001 video track participants but instead we give an overview of the TREC video search task and a thumbnail sketch of the approaches taken by different groups. The reason for writing this paper is to highlight the message from the TREC video track that there are now a variety of approaches available for searching and browsing through digital video archives, that these approaches do work, are scalable to larger archives and can yield useful retrieval performance for users. This has important implications in making digital libraries of video information attainable
Computer Center Bulletin / July 5, 1990
This publication is published as required and is written by members of the staff, W. R. Church Computer Cente
Res2Net: A New Multi-scale Backbone Architecture
Representing features at multiple scales is of great importance for numerous
vision tasks. Recent advances in backbone convolutional neural networks (CNNs)
continually demonstrate stronger multi-scale representation ability, leading to
consistent performance gains on a wide range of applications. However, most
existing methods represent the multi-scale features in a layer-wise manner. In
this paper, we propose a novel building block for CNNs, namely Res2Net, by
constructing hierarchical residual-like connections within one single residual
block. The Res2Net represents multi-scale features at a granular level and
increases the range of receptive fields for each network layer. The proposed
Res2Net block can be plugged into the state-of-the-art backbone CNN models,
e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these
models and demonstrate consistent performance gains over baseline models on
widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies
and experimental results on representative computer vision tasks, i.e., object
detection, class activation mapping, and salient object detection, further
verify the superiority of the Res2Net over the state-of-the-art baseline
methods. The source code and trained models are available on
https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure
Quantum autoencoders via quantum adders with genetic algorithms
The quantum autoencoder is a recent paradigm in the field of quantum machine
learning, which may enable an enhanced use of resources in quantum
technologies. To this end, quantum neural networks with less nodes in the inner
than in the outer layers were considered. Here, we propose a useful connection
between approximate quantum adders and quantum autoencoders. Specifically, this
link allows us to employ optimized approximate quantum adders, obtained with
genetic algorithms, for the implementation of quantum autoencoders for a
variety of initial states. Furthermore, we can also directly optimize the
quantum autoencoders via genetic algorithms. Our approach opens a different
path for the design of quantum autoencoders in controllable quantum platforms
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