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 1601–1625 of 2042 papers

TitleStatusHype
Out-of-distribution robustness: Limited image exposure of a four-year-old is enough to outperform ResNet-50—0
Overfitting Mechanism and Avoidance in Deep Neural Networks—0
Overhead MNIST: A Benchmark Satellite Dataset—0
Pairwise Decomposition of Image Sequences for Active Multi-View Recognition—0
Pairwise Linear Regression Classification for Image Set Retrieval—0
PAM:Point-wise Attention Module for 6D Object Pose Estimation—0
Semantic Domain Adversarial Networks for Unsupervised Domain Adaptation—0
Panoptic-aware Image-to-Image Translation—0
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds—0
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks—0
Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning—0
ParFormer: A Vision Transformer with Parallel Mixer and Sparse Channel Attention Patch Embedding—0
Parkinson's Disease Detection Using Ensemble Architecture from MR Images—0
Parkinson's Disease Detection with Ensemble Architectures based on ILSVRC Models—0
Parsing Objects at a Finer Granularity: A Survey—0
Parsing Occluded People—0
Partial Coherence for Object Recognition and Depth Sensing—0
Partitioning Large Scale Deep Belief Networks Using Dropout—0
'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
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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