Data augmentation is widely used to enhance generalization in visual classification tasks. However, traditional methods struggle when source and target domains differ, as in domain adaptation, due to their inability to address domain gaps. This paper introduces GenMix, a generalizable prompt-guided generative data augmentation approach that enhances both in-domain and cross-domain image classification. Our technique leverages image editing to generate augmented images based on custom conditional prompts, designed specifically for each problem type. By blending portions of the input image with its edited generative counterpart and incorporating fractal patterns, our approach mitigates unrealistic images and label ambiguity, improving the performance and adversarial robustness of the resulting models. Efficacy of our method is established with extensive experiments on eight public datasets for general and fine-grained classification, in both in-domain and cross-domain settings. Additionally, we demonstrate performance improvements for self-supervised learning, learning with data scarcity, and adversarial robustness. As compared to the existing state-of-the-art methods, our technique achieves stronger performance across the board.

Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the gen-eralization of deep neural networks. In these techniques, two or more randomly selected natural images are mixed together to generate an augmented image. Such methods may not only omit important portions of the input images but also introduce label ambiguities by mixing images across labels resulting in misleading supervisory signals. To address these limitations, we propose Diffusemix, a novel data augmentation technique that leverages a diffusion model to reshape training images, supervised by our bespoke conditional prompts. First, concatenation of a partial natural image and its generated counterpart is ob-tained which helps in avoiding the generation of unrealistic images or label ambiguities. Then, to enhance resilience against adversarial attacks and improves safety measures, a randomly selected structural pattern from a set of frac-tal images is blended into the concatenated image to form the final augmented image for training. Our empirical results on seven different datasets reveal that Diffusemix achieves superior performance compared to existing state-of-the-art methods on tasks including general classification, fine- grained classification, fine-tuning, data scarcity, and adversarial robustness. Augmented datasets and codes are available here: https://diffusemix.github.io/

Person Search (PS) has become a major field because of its need in community and in the field of research among researchers. This task aims to find a probe person from whole scene which shows great significance in video surveillance field to track lost people, re-identification, and verification of person. In last few years, deep learning has played unremarkable role for the solution of re-identification problem. Deep learning shows incredible performance in person (re-ID) and search. Researchers experience more flexibility in proposing new methods and solve challenging issues such as low resolution, pose variation, background clutter, occlusion, viewpoints, and low illumination. Specially, convolutional neural network (CNN) achieves breakthrough performance and extracts useful patterns and characteristics. Development of new framework takes substantial efforts; hard work and computation cost are required to acquire excellent results. This survey paper includes brief discussion about feature representation learning and deep metric learning with novel loss functions. We thoroughly review datasets with performance analysis on existing datasets. Finally, we are reviewing current solutions for further consideration.