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
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
Therefore, our multi-branch and multi-scale learning network(MMAL-Net) has good classification ability and robustness for images of different scales.
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
PeizeSun/SparseR-CNN
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CVPR 2021
In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
Multiple Object Recognition with Visual Attention
tianyu-tristan/Visual-Attention-Model
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We present an attention-based model for recognizing multiple objects in images.
Adapting Deep Network Features to Capture Psychological Representations
To remedy this, we develop a method for adapting deep features to align with human similarity judgments, resulting in image representations that can potentially be used to extend the scope of psychological experiments.
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification
microsoft/unilm
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We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half.
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
Ideally, continual learning should be triggered by the availability of short videos of single objects and performed on-line on on-board hardware with fine-grained updates.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
soumith/convnet-benchmarks
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This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks.
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy.
Scalable Bayesian Optimization Using Deep Neural Networks
automl/pybnn
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Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
In this paper, we propose a deep neural network architecture for object recognition based on recurrent neural networks.
