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 1301–1325 of 2042 papers

TitleStatusHype
Incorporating Textual Evidence in Visual Storytelling—0
Incremental Learning for Robot Perception through HRI—0
Inducing Functions through Reinforcement Learning without Task Specification—0
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19—0
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach—0
Information Mandala: Statistical Distance Matrix with Clustering—0
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
Insights From A Large-Scale Database of Material Depictions In Paintings—0
In Silico Modelling of Neurodegeneration Using Deep Convolutional Neural Networks—0
Integrating Knowledge and Reasoning in Image Understanding—0
Integration of 3D Object Recognition and Planning for Robotic Manipulation: A Preliminary Report—0
Interactive Open-Ended Learning for 3D Object Recognition—0
Interactive Segmentation on RGBD Images via Cue Selection—0
Interpretable Graph Capsule Networks for Object Recognition—0
Interpretable multimodal fusion networks reveal mechanisms of brain cognition—0
Interpreting Convolutional Neural Networks Through Compression—0
Interpreting the Residual Stream of ResNet18—0
Introducing Memory and Association Mechanism into a Biologically Inspired Visual Model—0
Introducing VaDA: Novel Image Segmentation Model for Maritime Object Segmentation Using New Dataset—0
Invariant feature extraction from event based stimuli—0
Invariant recognition drives neural representations of action sequences—0
Investigating Fluidity for Human-Robot Interaction with Real-time, Real-world Grounding Strategies—0
Investigating the Importance of Shape Features, Color Constancy, Color Spaces and Similarity Measures in Open-Ended 3D Object Recognition—0
Investigation of event-based memory surfaces for high-speed tracking, unsupervised feature extraction and object recognition—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