It not only suffers from heavy computational cost but also difficult optimization. As both sparse and dense queries are imperfect, then \emph{what are expected queries in end-to-end object detection}? This paper
Addressing the need for a robust, consistently performing approach that can effectively address the above challenges, this paper presents a new Soft Set-based end-to-end system for text detection, recognition and prediction in occluded natural scene images. This is the first approach to integrate ...
Lately, the self-attention mechanism has marked a new milestone in the field of automatic speech recognition (ASR). Nevertheless, its performance is susceptible to environmental intrusions as the system predicts the next output symbol depending on the full input sequence and the previous predictions. ...
For example, in [34], a deep deconvolutional network followed by a conditional random field (CRF) were used to fine-tune the output segmentation. Similarly, [15] builds upon this idea and uses a deeper network with residual layers and shortcut connections to learn an identity mapping. [26...
Afterward, the specific segments are then assessed for their texture, size, and color to measure any change, such as the presence of a pest or disease. Unsupervised feature learning, with fully convolutional networks (FCN) followed by conditional random fields, makes it possible to segment images...
Inspired by variational approaches, we achieve multiple reconstructions by sampling from the conditional latent distribution38. We see that this stochasticity in output can be helpful if the initial guess is incorrect; in principle, we can resample to obtain a more reasonable prediction that matches ...
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, tran
This is evidenced by its more robust understanding of the environment, effectively distinguishing between front vehicles and structures such as buildings. The discrepancy in performance between Town02 and Town01 may stem from its inability to isolate the front vehicle from the urban background This ...
We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model arch
The main endpoint accepts an image file and an optional text input for conditional image captioning. We utilized the pre-trained BLIP (Bootstrapping Language-Image Pre-training) model from Hugging Face Transformers for image captioning. BLIP is a powerful model that has been trained on a large ...