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 1926–1950 of 2042 papers

TitleStatusHype
Domain-invariant Face Recognition using Learned Low-rank Transformation—0
Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection—0
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer—0
Domain-robust VQA with diverse datasets and methods but no target labels—0
Do semantic parts emerge in Convolutional Neural Networks?—0
Do We Need More Training Data?—0
DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments—0
Dreaming with ARC—0
DTM: Deformable Template Matching—0
Dual-attention Focused Module for Weakly Supervised Object Localization—0
Dual coordinate solvers for large-scale structural SVMs—0
Dual Local-Global Contextual Pathways for Recognition in Aerial Imagery—0
Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval—0
DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset—0
DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving—0
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation—0
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment—0
Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation—0
Dynamic reshaping of functional brain networks during visual object recognition—0
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields—0
Easy Batch Normalization—0
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference—0
ECOR: Explainable CLIP for Object Recognition—0
Edge Aware Learning for 3D Point Cloud—0
Edge Detection Based Shape Identification—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