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

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Showing 1451–1500 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
Context-Aware Zero-Shot Learning for Object Recognition—0
PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras—0
VGG Fine-tuning for Cooking State Recognition—0
Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images—0
1-HKUST: Object Detection in ILSVRC 2014—0
1 Million Captioned Dutch Newspaper Images—0
A General Ambiguity Model for Binary Edge Images with Edge Tracing and its Implementation—0
Source-Free Domain-Invariant Performance Prediction—0
MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence—0
A Light and Smart Wearable Platform with Multimodal Foundation Model for Enhanced Spatial Reasoning in People with Blindness and Low Vision—0
AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition—0
2D Image Features Detector And Descriptor Selection Expert System—0
300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning—0
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data—0
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds—0
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations—0
3D Object Recognition with Deep Belief Nets—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
A 51.3 TOPS/W, 134.4 GOPS In-memory Binary Image Filtering in 65nm CMOS—0
AAD: Adaptive Anomaly Detection through traffic surveillance videos—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View—0
A biologically plausible network for the computation of orientation dominance—0
Abnormal Object Recognition: A Comprehensive Study—0
Accurate Localization of 3D Objects from RGB-D Data Using Segmentation Hypotheses—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