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 476–500 of 2042 papers

TitleStatusHype
Learning by Asking Questions for Knowledge-based Novel Object Recognition—0
The Equalization Losses: Gradient-Driven Training for Long-tailed Object RecognitionCode2
GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping—0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN ModelsCode0
Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition—0
Multipod Convolutional Network—0
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022Code1
RECALL: Rehearsal-free Continual Learning for Object ClassificationCode0
Reconstruction-guided attention improves the robustness and shape processing of neural networksCode0
Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking—0
OBBStacking: An Ensemble Method for Remote Sensing Object DetectionCode1
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in VideoCode1
Li-ion battery degradation modes diagnosis via Convolutional Neural NetworksCode1
Transformer-Based Microbubble Localization—0
TANDEM3D: Active Tactile Exploration for 3D Object Recognition—0
Continual Learning for Class- and Domain-Incremental Semantic Segmentation—0
Visual Recognition with Deep Nearest CentroidsCode1
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes—0
Continual Learning for Pose-Agnostic Object Recognition in 3D Point Clouds—0
Measuring Human Perception to Improve Open Set Recognition—0
Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method—0
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer—0
Chosen methods of improving small object recognition with weak recognizable features—0
A Simulation Method for MMW Radar Sensing in Traffic Intersection Based on BART Algorithm—0
How good are deep models in understanding the generated 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