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 1001–1050 of 2042 papers

TitleStatusHype
On Binary Classification with Single-Layer Convolutional Neural Networks—0
On-board classification of underwater images using hybrid classical-quantum CNN based method—0
One-Shot Concept Learning by Simulating Evolutionary Instinct Development—0
ColorSense: A Study on Color Vision in Machine Visual Recognition—0
On Invariance in Hierarchical Models—0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
On Learning Density Aware Embeddings—0
Online Continual Learning for Robust Indoor Object Recognition—0
Online Vision- and Action-Based Object Classification Using Both Symbolic and Subsymbolic Knowledge Representations—0
On Numerosity of Deep Neural Networks—0
On Pre-Trained Image Features and Synthetic Images for Deep Learning—0
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities—0
On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations—0
On the Performance of Convolutional Neural Networks under High and Low Frequency Information—0
On the Relationship Between Visual Attributes and Convolutional Networks—0
On the Robustness of Sentiment Analysis for Stock Price Forecasting—0
On the surprising similarities between supervised and self-supervised models—0
OOWL500: Overcoming Dataset Collection Bias in the Wild—0
Open-Set Object Recognition Using Mechanical Properties During Interaction—0
Efficient architecture for deep neural networks with heterogeneous sensitivity—0
Optimistic and Pessimistic Neural Networks for Scene and Object Recognition—0
Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systems—0
Optimized CNNs for Rapid 3D Point Cloud Object Recognition—0
Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies—0
ORCEA: Object Recognition by Continuous Evidence Assimilation—0
Orientation-boosted Voxel Nets for 3D Object Recognition—0
Oriented Feature Alignment for Fine-grained Object Recognition in High-Resolution Satellite Imagery—0
Origami: A 803 GOp/s/W Convolutional Network Accelerator—0
OSMa-Bench: Evaluating Open Semantic Mapping Under Varying Lighting Conditions—0
OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation—0
Out-of-Distribution Detection for Automotive Perception—0
Out-of-distribution detection in 3D applications: a review—0
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
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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