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 1051–1075 of 2042 papers

TitleStatusHype
'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems—0
PatentNet: A Large-Scale Incomplete Multiview, Multimodal, Multilabel Industrial Goods Image Database—0
PathTrack: Fast Trajectory Annotation with Path Supervision—0
PennSyn2Real: Training Object Recognition Models without Human Labeling—0
People infer recursive visual concepts from just a few examples—0
Perceptual Inductive Bias Is What You Need Before Contrastive Learning—0
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images—0
Psychophysical-Score: A Behavioral Measure for Assessing the Biological Plausibility of Visual Recognition Models—0
PERCH: Perception via Search for Multi-Object Recognition and Localization—0
Performance Assessment of Feature Detection Methods for 2-D FS Sonar Imagery—0
Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds—0
Performance Evaluation of 3D Correspondence Grouping Algorithms—0
Performance Evaluation of Learned 3D Features—0
Performance Evaluation of Raster Based Shape Vectors in Object Recognition—0
Performance of object recognition in wearable videos—0
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex—0
Periocular Recognition Using CNN Features Off-the-Shelf—0
Persistence-based Structural Recognition—0
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning—0
Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter—0
PiCoDes: Learning a Compact Code for Novel-Category Recognition—0
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence—0
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors—0
Pixel personality for dense object tracking in a 2D honeybee hive—0
Pixels to Voxels: Modeling Visual Representation in the Human Brain—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