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 1726–1750 of 2042 papers

TitleStatusHype
Can Boosting with SVM as Week Learners Help?—0
Can domain adaptation make object recognition work for everyone?—0
Can foundation models actively gather information in interactive environments to test hypotheses?—0
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection—0
Capacity limitations of visual search in deep convolutional neural networks—0
Capturing the objects of vision with neural networks—0
Cascade Region Proposal and Global Context for Deep Object Detection—0
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks—0
Categorical Mixture Models on VGGNet activations—0
Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations—0
CausalX: Causal Explanations and Block Multilinear Factor Analysis—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
Certifiable Artificial Intelligence Through Data Fusion—0
Characterising representation dynamics in recurrent neural networks for object recognition—0
ChartKG: A Knowledge-Graph-Based Representation for Chart Images—0
ChoiceNet: CNN learning through choice of multiple feature map representations—0
Chosen methods of improving small object recognition with weak recognizable features—0
CIFAR10 to Compare Visual Recognition Performance between Deep Neural Networks and Humans—0
Classification and Geometry of General Perceptual Manifolds—0
Classifier-to-Generator Attack: Estimation of Training Data Distribution from Classifier—0
Classifying Malware Images with Convolutional Neural Network Models—0
Class incremental learning for video action classification—0
Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images—0
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation—0
Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural 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