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 1451–1475 of 2042 papers

TitleStatusHype
What's in my Room? Object Recognition on Indoor Panoramic Images—0
What takes the brain so long: Object recognition at the level of minimal images develops for up to seconds of presentation time—0
What you need to know about the state-of-the-art computational models of object-vision: A tour through the models—0
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation—0
Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks—0
Why The Brain Separates Face Recognition From Object Recognition—0
Winograd Convolution for Deep Neural Networks: Efficient Point Selection—0
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model—0
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation—0
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud—0
You Only Speak Once to See—0
Your head is there to move you around: Goal-driven models of the primate dorsal pathway—0
Zero-Aliasing Correlation Filters for Object Recognition—0
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery—0
Zero-shot counting with a dual-stream neural network model—0
Zero Shot Hashing—0
Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature—0
Zero-shot Learning with Deep Neural Networks for Object Recognition—0
Zero-shot object prediction using semantic scene knowledge—0
Zero-Shot Object Recognition by Semantic Manifold Distance—0
Zero-Shot Object Recognition System based on Topic Model—0
Zero-shot recognition with unreliable attributes—0
Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?—0
Zoomer: Adaptive Image Focus Optimization for Black-box MLLM—0
ZSpeedL -- Evaluating the Performance of Zero-Shot Learning Methods using Low-Power Devices—0
Show:102550
← PrevPage 59 of 82Next →
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