Browse SoTA > Computer Vision > Object Recognition

Object Recognition

195 papers with code ยท 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.

( Image credit: Tensorflow Object Detection API )

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

Latest papers without code

Quaternion Capsule Networks

8 Jul 2020

Capsules are grouping of neurons that allow to represent sophisticated information of a visual entity such as pose and features.

OBJECT RECOGNITION

Single Shot MC Dropout Approximation

7 Jul 2020

We demonstrate that our single shot MC dropout approximation resembles the point estimate and the uncertainty estimate of the predictive distribution that is achieved with an MC approach, while being fast enough for real-time deployments of BDNNs.

AUTONOMOUS DRIVING OBJECT RECOGNITION

Are Labels Necessary for Classifier Accuracy Evaluation?

6 Jul 2020

Specifically, given a labeled training set and a model, we aim to estimate the model accuracy on unlabeled test datasets.

OBJECT RECOGNITION

Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks

2 Jul 2020

In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.

IMAGE RECONSTRUCTION OBJECT RECOGNITION SPEECH SEPARATION

Parkinson's Disease Detection Using Ensemble Architecture from MR Images

1 Jul 2020

We find that detection accuracy increases drastically when we focus on the Gray Matter (GM) and White Matter (WM) regions from the MR images instead of using whole MR images.

DECISION MAKING OBJECT RECOGNITION

Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency

30 Jun 2020

Taken together, error consistency analysis suggests that the strategies used by human and machine vision are still very different--but we envision our general-purpose error consistency analysis to serve as a fruitful tool for quantifying future progress.

DECISION MAKING OBJECT RECOGNITION

Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion

28 Jun 2020

The structure of the compositional model enables CompositionalNets to decompose images into objects and context, as well as to further decompose object representations in terms of individual parts and the objects' pose.

IMAGE CLASSIFICATION OBJECT DETECTION OBJECT RECOGNITION

Fast Training of Deep Networks with One-Class CNNs

28 Jun 2020

For face recognition, a 1000 frame RGB video, featuring many faces together, has been used for benchmarking of the proposed approach.

FACE RECOGNITION OBJECT RECOGNITION

CognitiveCNN: Mimicking Human Cognitive Models to resolve Texture-Shape Bias

25 Jun 2020

Recent works demonstrate the texture bias in Convolutional Neural Networks (CNNs), conflicting with early works claiming that networks identify objects using shape.

OBJECT RECOGNITION

Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation

23 Jun 2020

To achieve this aim, a previous study has proven an upper bound of the target-domain risk, and the open set difference, as an important term in the upper bound, is used to measure the risk on unknown target data.

DOMAIN ADAPTATION FACE RECOGNITION OBJECT RECOGNITION