ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain

Transformer design is the de facto standard for natural language processing tasks. The success of the transformer design in natural language processing has lately piqued the interest of researchers in the domain of computer vision. When compared to Convolutional Neural Networks (CNNs), Vision Transformers (ViTs) are becoming more popular and dominant solutions for many vision problems. Transformer-based models outperform other types of networks, such as convolutional and recurrent neural networks, in a range of visual benchmarks. We evaluate various vision transformer models in this work by dividing them into distinct jobs and examining their benefits and drawbacks. ViTs can overcome several possible difficulties with convolutional neural networks (CNNs). The goal of this survey is to show the first use of ViTs in CV. In the first phase, we categorize various CV applications where ViTs are appropriate. Image classification, object identification, image segmentation, video transformer, image denoising, and NAS are all CV applications. Our next step will be to analyze the state-of-the-art in each area and identify the models that are currently available. In addition, we outline numerous open research difficulties as well as prospective research possibilities.

DANet: Enhancing Small Object Detection through an Efficient Deformable Attention Network

Efficient and accurate detection of small objects in manufacturing settings, such as defects and cracks, is crucial for ensuring product quality and safety. To address this issue, we proposed a comprehensive strategy by synergizing Faster R-CNN with cutting-edge methods. By combining Faster R-CNN with Feature Pyramid Network, we enable the model to efficiently handle multi-scale features intrinsic to manufacturing environments. Additionally, Deformable Net is used that contorts and conforms to the geometric variations of defects, bringing precision in detecting even the minuscule and complex features. Then, we incorporated an attention mechanism called Convolutional Block Attention Module in each block of our base ResNet50 network to selectively emphasize informative features and suppress less useful ones. After that we incorporated RoI Align, replacing RoI Pooling for finer region-of-interest alignment and finally the integration of Focal Loss effectively handles class imbalance, crucial for rare defect occurrences. The rigorous evaluation of our model on both the NEU-DET and Pascal VOC datasets underscores its robust performance and generalization capabilities. On the NEU-DET dataset, our model exhibited a profound understanding of steel defects, achieving state-of-the-art accuracy in identifying various defects. Simultaneously, when evaluated on the Pascal VOC dataset, our model showcases its ability to detect objects across a wide spectrum of categories within complex and small scenes.

EnHMM: On the Use of Ensemble HMMs and Stack Traces to Predict the Reassignment of Bug Report Fields

Bug reports (BR) contain vital information that can help triaging teams prioritize and assign bugs to developers who will provide the fixes. However, studies have shown that BR fields often contain incorrect information that need to be reassigned, which delays the bug fixing process. There exist approaches for predicting whether a BR field should be reassigned or not. These studies use mainly BR descriptions and traditional machine learning algorithms (SVM, KNN, etc.). As such, they do not fully benefit from the sequential order of information in BR data, such as function call sequences in BR stack traces, which may be valuable for improving the prediction accuracy. In this paper, we propose a novel approach, called EnHMM, for predicting the reassignment of BR fields using ensemble Hidden Markov Models (HMMs), trained on stack traces. EnHMM leverages the natural ability of HMMs to represent sequential data to model the temporal order of function calls in BR stack traces. When applied to Eclipse and Gnome BR repositories, EnHMM achieves an average precision, recall, and F-measure of 54%, 76%, and 60% on Eclipse dataset and 41%, 69%, and 51% on Gnome dataset. We also found that EnHMM improves over the best single HMM by 36% for Eclipse and 76% for Gnome. Finally, when comparing EnHMM to Im.ML.KNN, a recent approach in the field, we found that the average F-measure score of EnHMM improves the average F-measure of Im.ML.KNN by 6.80% and improves the average recall of Im.ML.KNN by 36.09%. However, the average precision of EnHMM is lower than that of Im.ML.KNN (53.93% as opposed to 56.71%).