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

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Showing 1601–1650 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
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
Pixel-wise Segmentation of Street with Neural Networks—0
Place recognition survey: An update on deep learning approaches—0
PLAICraft: Large-Scale Time-Aligned Vision-Speech-Action Dataset for Embodied AI—0
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
Pointing Novel Objects in Image Captioning—0
Polyhedral Object Recognition by Indexing—0
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines—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