Computer Vision
 
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Image Classification

3802 papers with code • 142 benchmarks • 238 datasets

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

microsoft/Swin-Transformer • • ICCV 2021

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.

71
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Pyramid Scene Parsing Network

hszhao/PSPNet • • CVPR 2017

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.

67
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Searching for MobileNetV3

tensorflow/models • • ICCV 2019

We achieve new state of the art results for mobile classification, detection and segmentation.

61
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Explaining and Harnessing Adversarial Examples

tensorflow/cleverhans • • 20 Dec 2014

Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence.

59
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

DeepScale/SqueezeNet • • 24 Feb 2016

(2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car.

58
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Aggregated Residual Transformations for Deep Neural Networks

facebookresearch/ResNeXt • • CVPR 2017

Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set.

58
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Towards Deep Learning Models Resistant to Adversarial Attacks

MadryLab/mnist_challenge • • ICLR 2018

Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.

57
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DARTS: Differentiable Architecture Search

quark0/darts • • ICLR 2019

This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner.

57
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Learning Transferable Visual Models From Natural Language Supervision

openai/CLIP • • 26 Feb 2021

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.

57
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Identity Mappings in Deep Residual Networks

KaimingHe/resnet-1k-layers • • 16 Mar 2016

Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors.

55
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