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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 826–850 of 2042 papers

TitleStatusHype
FusionNet: 3D Object Classification Using Multiple Data Representations—0
Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking—0
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study—0
EZSR: Event-based Zero-Shot Recognition—0
GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs—0
Extreme Image Transformations Facilitate Robust Latent Object Representations—0
A Real-time Junk Food Recognition System based on Machine Learning—0
Extreme Image Transformations Affect Humans and Machines Differently—0
Generalization Boosted Adapter for Open-Vocabulary Segmentation—0
Generalized Adaptive Dictionary Learning via Domain Shift Minimization—0
Generalized K-fan Multimodal Deep Model with Shared Representations—0
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition—0
Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval—0
Exponential Discriminative Metric Embedding in Deep Learning—0
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks—0
Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks—0
Generating Image Descriptions with Gold Standard Visual Inputs: Motivation, Evaluation and Baselines—0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models—0
Are Accuracy and Robustness Correlated?—0
Exploring Temporal Differences in 3D Convolutional Neural Networks—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing—0
Exploration of object recognition from 3D point cloud—0
A randomized gradient-free attack on ReLU networks—0
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All datasetsshape biasCIFAR10-DVSN-Caltech 101ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 GestureMECCANON-CARS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7—Unverified
2Stable Diffusionshape bias92.7—Unverified
3Partishape bias91.7—Unverified
4ViT-22B-384shape bias86.4—Unverified
5ViT-22B-560shape bias83.8—Unverified
6CLIP (ViT-B)shape bias79.9—Unverified
7ViT-22B-224shape bias78—Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5—Unverified
9ResNet-50 (with strong augmentations)shape bias62.2—Unverified
10SWSL (ResNeXt-101)shape bias49.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55—Unverified
2SSNNAccuracy (% )78.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62—Unverified
2SSNNAccuracy (% )79.25—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75—Unverified
2yunTop 5 Accuracy14.75—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2DYTop 5 Accuracy0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2AJ2021Top 5 Accuracy27.68—Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96—Unverified