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Register a new userWhat Matters for Ad-hoc Video Search? A Large-scale Evaluation on TRECVID
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Aozhu Chen
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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For quantifying progress in Ad-hoc Video Search (AVS), the annual TRECVID AVS task is an important international evaluation. Solutions submitted by the task participants vary in terms of their choices of cross-modal matching models, visual features and training data. As such, what one may conclude from the evaluation is at a high level that is insufficient to reveal the influence of the individual components. In order to bridge the gap between the current solution-level comparison and the desired component-wise comparison, we propose in this paper a large-scale and systematic evaluation on TRECVID. By selected combinations of state-of-the-art matching models, visual features and (pre-)training data, we construct a set of 25 different solutions and evaluate them on the TRECVID AVS tasks 2016--2020. The presented evaluation helps answer the key question of what matters for AVS. The resultant observations and learned lessons are also instructive for developing novel AVS solutions.
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In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom20]. As a step towards filling that gap, we implement >50 such ``choices in a unified on-policy RL framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for on-policy training of RL agents.
The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last nineteen 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 2019 represented a continuation of four tasks from TRECVID 2018. In total, 27 teams from various research organizations worldwide completed one or more of the following four tasks: 1. Ad-hoc Video Search (AVS) 2. Instance Search (INS) 3. Activities in Extended Video (ActEV) 4. Video to Text Description (VTT) This paper is an introduction to the evaluation framework, tasks, data, and measures used in the workshop.
This paper aims to solve the problem of large-scale video retrieval by a query image. Firstly, we define the problem of top-$k$ image to video query. Then, we combine the merits of convolutional neural networks(CNN for short) and Bag of Visual Word(BoVW for short) module to design a model for video frames information extraction and representation. In order to meet the requirements of large-scale video retrieval, we proposed a visual weighted inverted index(VWII for short) and related algorithm to improve the efficiency and accuracy of retrieval process. Comprehensive experiments show that our proposed technique achieves substantial improvements (up to an order of magnitude speed up) over the state-of-the-art techniques with similar accuracy.
With the proliferation of mobile computing devices, the demand for continuous network connectivity regardless of physical location has spurred interest in the use of mobile ad hoc networks. Since Transmission Control Protocol (TCP) is the standard network protocol for communication in the internet, any wireless network with Internet service need to be compatible with TCP. TCP is tuned to perform well in traditional wired networks, where packet losses occur mostly because of congestion. However, TCP connections in Ad-hoc mobile networks are plagued by problems such as high bit error rates, frequent route changes, multipath routing and temporary network partitions. The throughput of TCP over such connection is not satisfactory, because TCP misinterprets the packet loss or delay as congestion and invokes congestion control and avoidance algorithm. In this research, the performance of TCP in Adhoc mobile network with high Bit Error rate (BER) and mobility is studied and investigated. Simulation model is implemented and experiments are performed using the Network Simulatior 2 (NS2).
195 -
M. Rajendiran
, S. K. Srivatsa Department of Computer Science andn Engineering
, Sathyabama University
2011
Adhoc networks are characterized by connectivity through a collection of wireless nodes and fast changing network topology. Wireless nodes are free to move independent of each other which makes routing much difficult. This calls for the need of an efficient dynamic routing protocol. Mesh-based multicast routing technique establishes communications between mobile nodes of wireless adhoc networks in a faster and efficient way. In this article the performance of prominent on-demand routing protocols for mobile adhoc networks such as ODMRP (On Demand Multicast Routing Protocol), AODV (Adhoc on Demand Distance Vector) and FSR (Fisheye State Routing protocol) was studied. The parameters viz., average throughput, packet delivery ration and end-to-end delay were evaluated. From the simulation results and analysis, a suitable routing protocol can be chosen for a specified network. The results show that the ODMRP protocol performance is remarkably superior as compared with AODV and FSR routing protocols. Keywords: MANET, Multicast Routing, ODMRP, AODV, FSR.
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