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Object Recognition

91 papers with code · Computer Vision

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.

State-of-the-art leaderboards

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Greatest papers with code

Going Deeper with Convolutions

CVPR 2015 tensorflow/models

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). The main hallmark of this architecture is the improved utilization of the computing resources inside the network.

IMAGE CLASSIFICATION OBJECT DETECTION OBJECT RECOGNITION

Densely Connected Convolutional Networks

CVPR 2017 liuzhuang13/DenseNet

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. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion.

IMAGE CLASSIFICATION OBJECT RECOGNITION

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

6 Oct 2013jetpacapp/DeepBeliefSDK

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. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks.

DOMAIN ADAPTATION OBJECT RECOGNITION SCENE RECOGNITION TRANSFER LEARNING

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

21 Dec 2013soumith/convnet-benchmarks

We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. 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.

OBJECT DETECTION OBJECT RECOGNITION

Some Improvements on Deep Convolutional Neural Network Based Image Classification

19 Dec 2013facebook/fb.resnet.torch

We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techiques include adding more image transformations to training data, adding more transformations to generate additional predictions at test time and using complementary models applied to higher resolution images.

IMAGE CLASSIFICATION OBJECT RECOGNITION

Texture Synthesis Using Convolutional Neural Networks

NeurIPS 2015 DmitryUlyanov/texture_nets

Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition. Samples from the model are of high perceptual quality demonstrating the generative power of neural networks trained in a purely discriminative fashion.

OBJECT RECOGNITION TEXTURE SYNTHESIS

Finding Tiny Faces

CVPR 2017 peiyunh/tiny

We explore three aspects of the problem in the context of finding small faces: the role of scale invariance, image resolution, and contextual reasoning. While most recognition approaches aim to be scale-invariant, the cues for recognizing a 3px tall face are fundamentally different than those for recognizing a 300px tall face.

FACE DETECTION OBJECT RECOGNITION

Relation Networks for Object Detection

CVPR 2018 msracver/Relation-Networks-for-Object-Detection

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. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning.

OBJECT DETECTION OBJECT RECOGNITION

Sparse 3D convolutional neural networks

12 May 2015facebookresearch/SparseConvNet

We have implemented a convolutional neural network designed for processing sparse three-dimensional input data. The world we live in is three dimensional so there are a large number of potential applications including 3D object recognition and analysis of space-time objects.

3D OBJECT RECOGNITION ACTION RECOGNITION

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

25 May 2016coxlab/prednet

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. We describe a predictive neural network ("PredNet") architecture that is inspired by the concept of "predictive coding" from the neuroscience literature.

OBJECT RECOGNITION VIDEO PREDICTION