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With the advent of Deep Learning, people have begun to use it with computer vision approaches to identify plant diseases on a large scale targeting multiple crops and diseases. However, this requires a large amount of plant disease data, which is often not readily available, and the cost of acquiring disease images is high. Thus, developing a generalized model for recognizing unseen classes is very important and remains a major challenge to date. Existing methods solve the problem with general supervised recognition tasks based on the seen composition of the crop and the disease.

Capturing subtle visual differences between subordinate categories is crucial for improving the performance of Finegrained Visual Classification (FGVC). Recent works proposed deep learning models based on Vision Transformer (ViT) to take advantage of its self-attention mechanism to locate important regions of the objects and extract global information. However, their large number of layers with self-attention mechanism requires intensive computational cost and makes them impractical to be deployed on resource-restricted hardware including internet of things (IoT) devices.

In recent years, automotive radar has become an integral part of the advanced safety sensor stack. Although radar gives a significant advantage over a camera or Lidar, it suffers from poor angular resolution, unwanted noises and significant object smearing across the angular bins, making radar-based object detection challenging. We propose a novel radar-based object detection utilizing a deep learning-based super-resolution (DLSR) model. Due to the unavailability of low-high resolution radar data pair, we first simulate the data to train a DLSR model.

This paper proposes a multichannel method for discriminative region localization in Camouflaged Object Detection (COD) tasks. In one channel, processing the phase and amplitude of 2-D Fourier spectra generate a modified form of the original image, used later for a pixel-wise optimal local entropy analysis. The other channel implements a class activation map (CAM) and Global Average Pooling (GAP) for object localization. We combine the channels linearly to form the final localized version of the COD images.

This paper proposes a multichannel method for discriminative region localization in Camouflaged Object Detection (COD) tasks. In one channel, processing the phase and amplitude of 2-D Fourier spectra generate a modified form of the original image, used later for a pixel-wise optimal local entropy analysis. The other channel implements a class activation map (CAM) and Global Average Pooling (GAP) for object localization. We combine the channels linearly to form the final localized version of the COD images.

This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge transfer is achieved by joint optimization of self-supervised learning pseudo task and supervised learning task, improving supervised learning task performance. Recent progress in self-supervised learning uses a large volume of data, which becomes a constraint for its applications on small-scale datasets.

Breast ultrasound is useful for the diagnosis of breast tumors which can be benign or malignant. However, accurate segmentation of breast tumors and the classification of breast ultrasound into benign, malignant, or normal (no tumor) categories is challenging because of different reasons including poor contrast of the tumor region and absence of clear margins. We propose a Multibranch UNet architecture that uses multitask learning for the automated segmentation of breast tumors and classification of breast ultrasound images.

Clustering via representation learning is one of the most promising approaches for self-supervised learning of deep neural networks. It aims at obtaining artificial supervisory signals from unlabeled data. In this paper, we propose an online clustering method called CLOT (\underline{C}ontrastive \underline{L}earning-Driven and \underline{O}ptimal \underline{T}ransport-Based Clustering) that is based on robust and multiple losses training settings. More specifically, CLOT learns representations by contrasting both the features at the latent space and the cluster assignments.