SOTAVerified

Object Recognition

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 )

Papers

Showing 551575 of 2042 papers

TitleStatusHype
Deep Predictive Coding Network with Local Recurrent Processing for Object RecognitionCode0
Feature Learning for Accelerometer based Gait RecognitionCode0
Detecting semantic anomaliesCode0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object ClassificationCode0
Disparity Sliding Window: Object Proposals From Disparity ImagesCode0
Distinctive Image Features from Scale-Invariant KeypointsCode0
Fit to Measure: Reasoning about Sizes for Robust Object RecognitionCode0
Discriminative Spatial-Semantic VOS Solution: 1st Place Solution for 6th LSVOSCode0
Foveation in the Era of Deep LearningCode0
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth EstimationCode0
Discriminative Unsupervised Feature Learning with Convolutional Neural NetworksCode0
GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in DarkCode0
Task-generalizable Adversarial Attack based on Perceptual MetricCode0
Domain Generalization via Model-Agnostic Learning of Semantic FeaturesCode0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Hierarchical Superpixel Segmentation via Structural Information TheoryCode0
Learning compact binary descriptors with unsupervised deep neural networksCode0
Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition0
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations0
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex0
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition0
Deep Network Guided Proof Search0
Belief Tree Search for Active Object Recognition0
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified