Object Recognition
486 papers with code • 7 benchmarks • 42 datasets
Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.
( Image credit: Tensorflow Object Detection API )
Benchmarks
These leaderboards are used to track progress in Object Recognition
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Libraries
Use these libraries to find Object Recognition models and implementationsDatasets
CIFAR-10
ImageNet
CORe50
N-Caltech 101
DVS128 Gesture
SUN Attribute
OCID
CIFAR10-DVS
Most implemented papers
Densely Connected Convolutional Networks
liuzhuang13/DenseNet
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CVPR 2017
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
Going Deeper with Convolutions
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).
Striving for Simplicity: The All Convolutional Net
pytorch/captum
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Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers.
Microsoft COCO: Common Objects in Context
PaddlePaddle/PaddleDetection
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We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.
Residual Attention Network for Image Classification
tengshaofeng/ResidualAttentionNetwork-pytorch
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CVPR 2017
In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion.
Finding Tiny Faces
We explore three aspects of the problem in the context of finding small faces: the role of scale invariance, image resolution, and contextual reasoning.
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
coxlab/prednet
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Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world.
Texture Synthesis Using Convolutional Neural Networks
Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition.
Describing Textures in the Wild
deeplearning-wisc/knn-ood
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CVPR 2014
Patterns and textures are defining characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly.
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
yhenon/keras-spp
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This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale.
