---
title: "Computer Vision"
slug: "computer-vision"
discipline: "Computer Science / AI"
description: "Visual AI and image understanding. Object detection, segmentation, 3D vision, video understanding, visual transformers, and multimodal vision-language models."
icon: "👁️"
url: "https://science-database.com/technology/computer-vision"
api: "https://science-database.com/api/v1/technology/computer-vision"
llms_txt: "https://science-database.com/technology/computer-vision/llms.txt"
articles_indexed: 15
last_updated: "2026-09-08T05:41:33.538Z"
search_terms:
  - "computer vision transformer detection"
  - "image segmentation deep learning"
  - "vision language model multimodal"
source: "science-database.com"
license: "metadata CC0, abstracts belong to respective publishers"
---

# Computer Vision

Visual AI and image understanding. Object detection, segmentation, 3D vision, video understanding, visual transformers, and multimodal vision-language models.

**Discipline:** Computer Science / AI  
**Indexed Papers:** 15  
**Last Updated:** 2026-09-08

## Top Publications

Ranked by citation impact across Semantic Scholar, OpenAlex & arXiv.

### Gradient-based learning applied to document recognition

- **Authors:** Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner
- **Journal:** Proceedings of the IEEE
- **Published:** 1998-01-01
- **DOI:** [10.1109/5.726791](https://doi.org/10.1109/5.726791)
- **Citations:** 59,536
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://hal.science/hal-03926082/document)
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W2112796928/llms.txt)

> Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper rev...

### Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

- **Authors:** Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo
- **Journal:** 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- **Published:** 2021-10-01
- **DOI:** [10.1109/iccv48922.2021.00986](https://doi.org/10.1109/iccv48922.2021.00986)
- **Citations:** 32,421
- **Source:** OpenAlex
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W3138516171/llms.txt)

> This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differ...

### A ConvNet for the 2020s

- **Authors:** Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie
- **Journal:** 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- **Published:** 2022-06-01
- **DOI:** [10.1109/cvpr52688.2022.01167](https://doi.org/10.1109/cvpr52688.2022.01167)
- **Citations:** 7,886
- **Source:** OpenAlex
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W4312443924/llms.txt)

> The “Roaring 20s” of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) th...

### Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

- **Authors:** Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lü, Ping Luo, Ling Shao
- **Journal:** 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- **Published:** 2021-10-01
- **DOI:** [10.1109/iccv48922.2021.00061](https://doi.org/10.1109/iccv48922.2021.00061)
- **Citations:** 4,898
- **Source:** OpenAlex
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W3131500599/llms.txt)

> Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network use-fid for many dense prediction tasks. Unlike the recently-proposed Vision Transformer (ViT) that was designed for image classification specifically, we introduce the Pyramid Vision Transformer (PVT), which overcomes the difficulties of...

### SegFormer: Simple and Efficient Design for Semantic Segmentation with\n Transformers

- **Authors:** Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Álvarez, Ping Luo
- **Journal:** arXiv (Cornell University)
- **Published:** 2021-05-31
- **DOI:** [10.48550/arxiv.2105.15203](https://doi.org/10.48550/arxiv.2105.15203)
- **Citations:** 3,363
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://arxiv.org/pdf/2105.15203)
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W3211490618/llms.txt)

> We present SegFormer, a simple, efficient yet powerful semantic segmentation\nframework which unifies Transformers with lightweight multilayer perception\n(MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a\nnovel hierarchically structured Transformer encoder which outputs multiscale\nfeatures. It does not need positional encoding, thereby avoiding the\ninterpolation of ...

### Are Transformers Effective for Time Series Forecasting?

- **Authors:** Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu
- **Journal:** Proceedings of the AAAI Conference on Artificial Intelligence
- **Published:** 2023-06-26
- **DOI:** [10.1609/aaai.v37i9.26317](https://doi.org/10.1609/aaai.v37i9.26317)
- **Citations:** 3,044
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://ojs.aaai.org/index.php/AAAI/article/download/26317/26089)
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W4382203079/llms.txt)

> Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time...

### A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS

- **Authors:** Juan Terven, Diana‐Margarita Córdova‐Esparza, Julio-Alejandro Romero-González
- **Journal:** Machine Learning and Knowledge Extraction
- **Published:** 2023-11-20
- **DOI:** [10.3390/make5040083](https://doi.org/10.3390/make5040083)
- **Citations:** 2,821
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://doi.org/10.3390/make5040083)
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W4388823657/llms.txt)

> YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. We start by describing the standard metrics and postprocessing; then, we discuss...

### Attention mechanisms in computer vision: A survey

- **Authors:** Meng-Hao Guo, Tian-Xing Xu, Jiangjiang Liu, Zheng-Ning Liu, Peng-Tao Jiang, Tai‐Jiang Mu, Song–Hai Zhang, Ralph R. Martin, Ming‐Ming Cheng, Shi‐Min Hu
- **Journal:** Computational Visual Media
- **Published:** 2022-03-15
- **DOI:** [10.1007/s41095-022-0271-y](https://doi.org/10.1007/s41095-022-0271-y)
- **Citations:** 2,494
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://link.springer.com/content/pdf/10.1007/s41095-022-0271-y.pdf)
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W3212386989/llms.txt)

> Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great succes...

### PVT v2: Improved baselines with pyramid vision transformer

- **Authors:** Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lü, Ping Luo, Ling Shao
- **Journal:** Computational Visual Media
- **Published:** 2022-03-16
- **DOI:** [10.1007/s41095-022-0274-8](https://doi.org/10.1007/s41095-022-0274-8)
- **Citations:** 2,366
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://link.springer.com/content/pdf/10.1007/s41095-022-0274-8.pdf)
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W3175515048/llms.txt)

> Transformers have recently lead to encouraging progress in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by adding three designs: (i) a linear complexity attention layer, (ii) an overlapping patch embedding, and (iii) a convolutional feed-forward network. With these modifications, PVT v2 reduces the computational complexity of...

### Learning RoI Transformer for Oriented Object Detection in Aerial Images

- **Authors:** Jian Ding, Nan Xue, Yang Long, Gui-Song Xia, Qikai Lu
- **Published:** 2019-06-01
- **DOI:** [10.1109/cvpr.2019.00296](https://doi.org/10.1109/cvpr.2019.00296)
- **Citations:** 1,598
- **Source:** OpenAlex
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W2964979676/llms.txt)

> Object detection in aerial images is an active yet challenging task in computer vision because of the bird’s-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (Ro...

### UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery

- **Authors:** Libo Wang, Rui Li, Ce Zhang, Shenghui Fang, Chenxi Duan, Xiaoliang Meng, Peter M. Atkinson
- **Journal:** ISPRS Journal of Photogrammetry and Remote Sensing
- **Published:** 2022-06-24
- **DOI:** [10.1016/j.isprsjprs.2022.06.008](https://doi.org/10.1016/j.isprsjprs.2022.06.008)
- **Citations:** 1,293
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://arxiv.org/pdf/2109.08937)
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W4283450732/llms.txt)

### Transformers in medical imaging: A survey

- **Authors:** Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu
- **Journal:** Medical Image Analysis
- **Published:** 2023-04-05
- **DOI:** [10.1016/j.media.2023.102802](https://doi.org/10.1016/j.media.2023.102802)
- **Citations:** 1,243
- **Source:** OpenAlex
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W4362603432/llms.txt)

### BiFormer: Vision Transformer with Bi-Level Routing Attention

- **Authors:** Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, Rynson W. H. Lau
- **Published:** 2023-06-01
- **DOI:** [10.1109/cvpr52729.2023.00995](https://doi.org/10.1109/cvpr52729.2023.00995)
- **Citations:** 1,126
- **Source:** OpenAlex
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W4386075524/llms.txt)

> As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pairwise token interaction across all spatial locations is computed. A series of works attempt to alleviate this problem by introducing handcrafted and content-agnostic sparsity into ...

### Transformers in Time Series: A Survey

- **Authors:** Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun
- **Published:** 2023-08-01
- **DOI:** [10.24963/ijcai.2023/759](https://doi.org/10.24963/ijcai.2023/759)
- **Citations:** 1,097
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://www.ijcai.org/proceedings/2023/0759.pdf)
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W4385763767/llms.txt)

> Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applicati...

### MaxViT: Multi-axis Vision Transformer

- **Authors:** Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan C. Bovik, Yinxiao Li
- **Journal:** Lecture notes in computer science
- **Published:** 2022-01-01
- **DOI:** [10.1007/978-3-031-20053-3_27](https://doi.org/10.1007/978-3-031-20053-3_27)
- **Citations:** 878
- **Source:** OpenAlex
- **llms.txt:** [View](https://science-database.com/technology/computer-vision/paper/oa-W4312847199/llms.txt)

---

*Generated by [science-database.com](https://science-database.com) — The Knowledge Interface*  
*Full data available via [JSON API](https://science-database.com/api/v1/technology/computer-vision)*