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"Question-Aware Global-Local Video Understanding Network for Audio-Visu" by Zailong Chen, Lei Wang et al.

As a newly emerging task, audio-visual question answering (AVQA) has attracted research attention. Compared with traditional single-modality (e.g., audio or visual) QA tasks, it poses new challenges due to the higher complexity of feature extraction and fusion brought by the multimodal inputs. First, AVQA requires more comprehensive understanding of the scene which involves both audio and visual information; Second, in the presence of more information, feature extraction has to be better connected with a given question; Third, features from different modalities need to be sufficiently correlated and fused. To address this situation, this work proposes a novel framework for multimodal question answering task. It characterises an audiovisual scene at both global and local levels, and within each level, the features from different modalities are well fused. Furthermore, the given question is utilised to guide not only the feature extraction at the local level but also the final fusion of ....

Audio Visual Question Answering , Data Mining , Deep Learning , Feature Extraction , Multimodal Learning , Uestion Answering Information Retrieval , Task Analysis , Video Understanding ,

Clearing the Confusion About AQP, CBTA, & EBT

This paper reviews the history of these traditions, the primary reason why the US has, to date, preferred to maintain the proficiency tradition embodied in AQP, and more detailed guidance regarding the use of competencies within the AQP framework. ....

United States , Al Iskandariyah , Palo Alto , New York , San Francisco , San Diego , Taylor Ellis , Jacke Bowsher , John Wiley , Curtis Jentsch , David Mcclelland , Douglasr Farrow , Brock Bocher , Weitzel Lehrer , Pearson Prentice Hall , Van Melle , Robert Gagne , Hodge Kearns , International Civil Aviation Organization , Journal Of Aviation Aerospace Education Research , Transportation Division , Qualification Initiative , T Company Human Resources Laboratory , Us Air , Evaluation Of Instructional Systems Development , Association For Talent Development ,

"LibFewShot: A Comprehensive Library for Few-Shot Learning" by Wenbin Li, Ziyi Wang et al.

Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or “tricks”, such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few-shot learning methods in a unified framework with the same single codebase in PyTorch. Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different train ....

Benchmark Testing , Deep Learning , Hair Comparison , Few Shot Learning , Image Classification , Task Analysis , Unified Framework ,

"PrivacyEAFL: Privacy-Enhanced Aggregation for Federated Learning in Mo" by Mingwu Zhang, Shijin Chen et al.

Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In this paper, we present a Privacy-Enhanced Aggregation for Federated Learning in MCS, namely PrivacyEAFL, to implement the training of federated learning under mobile crowdsensing system in terms of privacy protection of all participants. Firstly, considering that the crowdsensing server might share information with some participants to obtain and leak some local models, we design a collusion-resistant data aggregation approach by combining homomorphic cryptosystem and hashed Diffie-Hellman key ....

Privacy Enhanced Aggregation , Federated Learning , Car Evaluation , Computational Modeling , Data Aggregation , Data Models , Data Privacy , Federated Learning , Homomorphic Encryption , Obile Crowdsensing , Task Analysis ,

The US Navy is Looking for Your AI Solution

The U.S. Navy is proposing a challenge for AI Large Language solutions to streamline its Instructional Systems Design Analysis, used for training for military readiness and human performance. Registration for the challenge closes 30 October 2023. ....

United States , Us Navy , Office Of Naval Research , Naval Air Warfare Center Training Systems Division , Team Orlando , National Security Innovation Network , Central Florida Tech Grove , Naval Research , Artificial Intelligence , Instructional Systems Design , Ready Relevant Learning , Language Models , Task Analysis , Learning Analysis , Media Selection , Ms Ampt Magazine , Isd Analysis ,