Object Recognition

377 papers with code • 4 benchmarks • 35 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 )

Libraries

Use these libraries to find Object Recognition models and implementations

Most implemented papers

Densely Connected Convolutional Networks

liuzhuang13/DenseNet 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

worksheets/0xbcd424d2 CVPR 2015

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 21 Dec 2014

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 1 May 2014

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.

Finding Tiny Faces

peiyunh/tiny CVPR 2017

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 25 May 2016

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

leongatys/DeepTextures NeurIPS 2015

Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition.

Residual Attention Network for Image Classification

tengshaofeng/ResidualAttentionNetwork-pytorch 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.

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

yhenon/keras-spp 18 Jun 2014

This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale.

Efficient Attention: Attention with Linear Complexities

cmsflash/efficient-attention 4 Dec 2018

Dot-product attention has wide applications in computer vision and natural language processing.