Object Recognition
484 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
ImageNet Large Scale Visual Recognition Challenge
yaoyao-liu/meta-transfer-learning
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The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.
Efficient Attention: Attention with Linear Complexities
cmsflash/efficient-attention
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Dot-product attention has wide applications in computer vision and natural language processing.
Improving neural networks by preventing co-adaptation of feature detectors
When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data.
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
xvjiarui/GCNet
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In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation.
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.
DeepID3: Face Recognition with Very Deep Neural Networks
Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.
Chessboard and chess piece recognition with the support of neural networks
maciejczyzewski/neural-chessboard
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However, its solution is crucial for many experienced players who wish to compete against AI bots, but also prefer to make decisions based on the analysis of a physical chessboard.
Scalable Object Detection using Deep Neural Networks
sgrvinod/a-pytorch-tutorial-to-object-detection
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CVPR 2014
Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012).
Relation Networks for Object Detection
msracver/Relation-Networks-for-Object-Detection
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CVPR 2018
Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era.
