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 1176–1200 of 2042 papers

TitleStatusHype
Salt Detection Using Segmentation of Seismic Image—0
Sample Balancing for Improving Generalization under Distribution Shifts—0
SCaLE: Supervised and Cascaded Laplacian Eigenmaps for Visual Object Recognition Based on Nearest Neighbors—0
Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs—0
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization—0
ScatterNet Hybrid Deep Learning (SHDL) Network For Object Classification—0
Scenarios: A New Representation for Complex Scene Understanding—0
Scene Labeling with Contextual Hierarchical Models—0
Scene Parsing by Integrating Function, Geometry and Appearance Models—0
Scientific Preparation for CSST: Classification of Galaxy and Nebula/Star Cluster Based on Deep Learning—0
SdcNet: A Computation-Efficient CNN for Object Recognition—0
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition—0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation—0
Seeing What's Not There: Spurious Correlation in Multimodal LLMs—0
See No Evil, Say No Evil: Description Generation from Densely Labeled Images—0
"See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models—0
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing—0
Selective Segmentation Networks Using Top-Down Attention—0
Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)—0
Selective Visual Representations Improve Convergence and Generalization for Embodied AI—0
Self-Adaptable Templates for Feature Coding—0
Self-informed neural network structure learning—0
Self-Supervised Learning Across Domains—0
Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects—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