Top Research Papers on Computer Vision

This undergraduate textbook-reference comprehensively examines computer vision techniques, analysis, and real-world applications in which they are used.

The development and refining of deep neural networks that can now accurately identify objects in images and remember past surgical events has sparked a surge in the applications of CV to analyze intraoperative video and has allowed for the accurate identification of surgical phases (steps) and instruments across a variety of procedures.

The intersection of vision transformers and medical images is investigated, an overview of various ViT based frameworks that are being used by different researchers to decipher the obstacles in medical computer vision is proffered, and the pointers to possible solutions for future direction are deliberated.

A comprehensive review of the state of the art of computer vision techniques and their applications in manufacturing industries, including the most common methods, including feature detection, recognition, segmentation, and three-dimensional modeling.

In this work, we study a complete framework of Video Compressive Sensing (VCS), from capturing a sequence of video frames in one single compressed measurement to reconstructing the original frames. To our best knowledge, we present the first end-to-end sampling and recovery network built upon video Transformers, widely explored in vision related tasks, to capture long-range spatio-temporal relations. Our proposed Video Transformer for Snapshot Compressive Imaging recovery (ViT-SCI) is based on Spatio-temporal Convolutional Multi-Head Attention (ST-ConvMHA) which is an extended version of the f...

A comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion and highlighting promising directions for future research.

This survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving.

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unclear or difficult to solve.

During last few years the computer applications have gone dramatic transformation from simple data processing to machine learning, thanks to the availability and accessibility of huge volume of data collected through sensors and internet. The idea of machine learning demonstrates and propagates the facts that computer has the ability to improve itself with the passage of time. The western countries have shown great interest on the topic of machine learning, computer vision, and pattern recognition via organizing conferences, workshops, collective discussion, experimentation, and real life impl...

This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress.

This special issue covers a wide range of topics from the area of Computer Vision, Pattern Recognition, and Machine Learning.This breadth of scope is reflected by the papers included in this special issue, which touch topics including geometric Computer Vision, medical image processing, physical scene understanding, and interpretability of deep neural networks.This special issue consists of extended versions of the best papers originally presented at the 42nd German Conference on Pattern Recognition (DAGM GCPR 2020), held virtually between September 28th and October 1st, 2020.This special issu...

Focusing on the models of attention mechanisms commonly used in computer vision, their categorizations, principles, and outlook are summarized in this overview.

The objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress toward critical computer vision application requirements and discuss the most compelling applications in computer vision.

This survey provides a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention.

In this paper, an in-depth analysis promotes a broad vision for bringing Serverless to the Edge Computing and issues major challenges for serverless to be met before entering Edge computing.

The correlated optoelectronic characteristics of multi-terminal mixed-dimensional graphene–germanium heterostructure devices can be used for the accurate detection and robust tracking of dim targets.

This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on visual data analysis and computer vision applications.

Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and sea...

This paper proposes a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC, and shows that the proposed hybrid edge-cloud solution outperforms both the cloud-only and edge-only solutions.

Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new co...