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 1201–1225 of 2042 papers

TitleStatusHype
Graph-based Asynchronous Event Processing for Rapid Object Recognition—0
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition—0
Graph Convolutional Networks for Classification with a Structured Label Space—0
GFCN: A New Graph Convolutional Network Based on Parallel Flows—0
Label Efficient Regularization and Propagation for Graph Node Classification—0
Graphical Gaussian Vector for Image Categorization—0
GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping—0
Grassmannian learning mutual subspace method for image set recognition—0
Grounded Language Acquisition From Object and Action Imagery—0
Guided SAM: Label-Efficient Part Segmentation—0
GuideMe: A Mobile Application based on Global Positioning System and Object Recognition Towards a Smart Tourist Guide—0
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements—0
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains—0
Hand Gestures Recognition in Videos Taken with Lensless Camera—0
Hand-Object Interaction and Precise Localization in Transitive Action Recognition—0
Hand-Priming in Object Localization for Assistive Egocentric Vision—0
Haptic in-sensor computing device made of carbon nanotube-polydimethylsiloxane nanocomposites—0
Hardening RGB-D Object Recognition Systems against Adversarial Patch Attacks—0
Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review—0
Hardware Implementation of Hyperbolic Tangent Function using Catmull-Rom Spline Interpolation—0
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition—0
Hebbian Semi-Supervised Learning in a Sample Efficiency Setting—0
HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous Systems—0
HFirst: A Temporal Approach to Object Recognition—0
Hidden Patch Attacks for Optical Flow—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