The paper presents "Learn and Search," a method that integrates deep learning principles and contrastive learning to address object search challenges. By leveraging these techniques, the methodology promises transformative applications in image recognition, recommendation systems, content tagging, and content-based search and retrieval. The study focuses on unsupervised learning methodologies to alleviate human annotation labor's time-intensive nature.
The research explores various augmentations like color jitter, Gaussian blur, random cropping, zooming, aspect ratio distortion, downscaling, upscaling, minor rotations, JPEG compression, and HSV color jitter. These augmentations are meticulously designed to enhance the robustness of experiments. The models developed in the study aim to refine the learning process through controlled approaches like optimized color jitter parameters or introducing projection heads for feature representation refinement.
Extensive experiments evaluate the models' performance based on layer-wise Similarity Grid Accuracy (SGA) across different models. Model 4 consistently outperforms others due to the inclusion of a Projection Head. The study also delves into classification capabilities with Top-1, Top-5, and Top-10 accuracy metrics across all models.
The results showcase how each model excels in recognizing similarities within images concerning cropped sections at different layers of the Feature Pyramid Network (FPN). Additionally, visual representations illustrate the behavior of representations from positive pairs versus different image pairs. The loss dynamics during training provide insights into model performance nuances.
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by Chandan Kuma... um arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07231.pdfTiefere Fragen