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 18511875 of 2042 papers

TitleStatusHype
Causal importance of orientation selectivity for generalization in image recognitionCode0
Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media ContextsCode0
UAVid: A Semantic Segmentation Dataset for UAV ImageryCode0
Incremental Robot Learning of New Objects with Fixed Update TimeCode0
Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternativesCode0
Directional Regularized Tensor Modeling for Video Rain Streaks RemovalCode0
Infinite Feature SelectionCode0
Semantically Meaningful View SelectionCode0
Quaternion Capsule NetworksCode0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Semantic Edge Detection with Diverse Deep SupervisionCode0
R2-MLP: Round-Roll MLP for Multi-View 3D Object RecognitionCode0
Non-Uniform Subset Selection for Active Learning in Structured DataCode0
Feature Pyramid GridsCode0
Developmental Pretraining (DPT) for Image Classification NetworksCode0
Visual Object Recognition in Indoor Environments Using Topologically Persistent FeaturesCode0
Feature Learning for Accelerometer based Gait RecognitionCode0
OAMixer: Object-aware Mixing Layer for Vision TransformersCode0
Detecting semantic anomaliesCode0
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels MethodsCode0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Interpreting Adversarially Trained Convolutional Neural NetworksCode0
Attention Based Pruning for Shift NetworksCode0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Feature Learning by Multidimensional Scaling and its Applications in Object RecognitionCode0
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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