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"VTnet+Handcrafted based approach for food cuisines classification" by Rahul Nijhawan, Garima Sinha et al.

In this paper, we propose a novel hybrid transformer architecture for food cuisine detection and classification. The work carried out within this paper develops a combination of Vision Transformer ensemble architecture with hand-crafted features, thereby making a hybrid Vision Transformer food recognition system. Recently, Vision transformers have been introduced as an alternative means of classification to convolutional neural networks. It performs pattern detection and classification without convolutions and interprets an image as a sequence of patches. The combination of Vision Transformer and hand-crafted features like GIST, HoG (Histogram of Oriented Gradients), and LBP (Local Binary Pattern) were employed on the dataset. The dataset was specifically created (for this work) from the public logging system. It consisted of 13 food categories with 400 images of Indian food items like Ghevar, Idli, Dosa, and much more. It helped to capture a variety of images from every domain and cul ....

Vision Transformer , Oriented Gradients , Local Binary Pattern , Feature Extraction , Ood Pattern Detection , Good Recognition , And Crafted Features , Multi Class Classification ,

"AdaptorNAS: A New Perturbation-based Neural Architecture Search for Hy" by Sui Paul Ang, Son Lam Phung et al.

Hyperspectral image segmentation is an emerging area with numerous applications, including agriculture, forestry, environment monitoring, and remote sensing. This paper proposes a new neural architecture search algorithm, named AdaptorNAS, for hyperspectral image segmentation. AdaptorNAS aims to design the optimum decoder for any given encoder. In our approach, the search space of AdaptorNAS is a large deep neural network (DNN), and the optimal decoder is derived by pruning the large DNN via a perturbation-based pruning strategy. Verified on three popular encoders, i.e., ResNet-34, MobileNet-V2, and EfficientNet-B2, AdaptorNAS can design high-speed decoders that are significantly better than six common hand-crafted decoders. Additionally, with the EfficientNet-B2 encoder, AdaptorNAS (mIoU of 92.47% and mDice of 95.15%) outperforms the state-of-the-art NAS algorithms and hand-crafted network architectures on the hyperspectral image segmentation task. We also introduce a new hyperspectra ....

Biosecurity Scanning , Computer Architecture , Deep Learning , Feature Extraction , Yperspectral Image Segmentation , Hyperspectral Imaging , Image Segmentation , Neural Architecture Search , Erturbation Based Search , Semantic Segmentation ,

"Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait " by Renfei Sun, Kun Hu et al.

Freezing of Gait (FoG) is a common symptom of Parkinson’s disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, despite a patient’s intention to move. Clinical assessment of FoG events from manual observations by experts is both time-consuming and highly subjective. Therefore, machine learning-based FoG identification methods would be desirable. In this article, we address this task as a fine-grained human action recognition problem based on vision inputs. A novel deep learning architecture, namely, higher order polynomial transformer (HP-Transformer), is proposed to incorporate pose and appearance feature sequences to formulate fine-grained FoG patterns. In particular, a higher order self-attention mechanism is proposed based on higher order polynomials. To this end, linear, bilinear, and trilinear transformers are formulated in pursuit of discriminative fine-grained representations. These representations are treated as multiple streams and furthe ....

Deep Learning , Feature Extraction , Igher Order Attention , Olynomial Transformation , Self Attention , Patiotemporal Phenomena , Task Analysis ,

"Privacy-preserving Offloading in Edge Intelligence Systems with Induct" by Jude Tchaye-Kondi, Yanlong Zhai et al.

We address privacy and latency issues in edge-cloud computing environments where the neural network training is centralized. This paper considers the scenario where the edge devices are the only data sources for the deep learning model to be trained on the central server. Improper access to the massive amounts of data generated by edge devices could lead to privacy concerns. As a result, existing solutions for preserving privacy and reducing network latency in the edge environment rely on auxiliary datasets with no privacy risks or pre-trained models to build the client side feature extractor. However, finding auxiliary datasets or pre-trained models is not always guaranteed and may be challenging. To bridge this gap and eliminate the reliance on auxiliary datasets or pre-trained models of existing solutions, this paper presents DeepGuess, a privacy-preserving and latency-aware deep-learning framework. DeepGuess introduces a new learning mechanism enabled by the AutoEncoder architectur ....

Cloud Computing , Cloud Computing , Data Models , Deep Learning , Differential Privacy , Edge Intelligence , Feature Extraction , Nternet Of Things , Ocal Differential Privacy ,

"Reducing Background Induced Domain Shift for Adaptive Person Re-Identi" by Jianjun Lei, Tianyi Qin et al.

Cross-domain person re-identification (Re-ID) is a challenging and important task in monitoring safety and procedure compliance of industrial work places. In this paper, a novel method is proposed to reduce background induced domain shift for adaptive person Re-ID. Specifically, a foreground-background joint clustering module is proposed to extract discriminative foreground and background features and an attention-based feature disentanglement module is designed to reduce the interference of background with the extraction of discriminative foreground features. Experimental results on three widely used person Re-ID benchmarking datasets (Market-1501, DukeMTMC-reID, and MSMT17) have demonstrated that the proposed method achieves promising performance compared with the state-of-the-art methods. ....

Adaptation Models , Domain Adaptation , Eature Disentanglement , Feature Extraction , Intelligent Surveillance , Person Re Identification , Task Analysis ,