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

Showing 19011925 of 2042 papers

TitleStatusHype
SESS: Saliency Enhancing with Scaling and SlidingCode0
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker IdentificationCode0
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural NetworksCode0
A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation ProcessCode0
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from VideosCode0
Knowledge-driven Active LearningCode0
Cartesian K-MeansCode0
Object recognition in atmospheric turbulence scenesCode0
Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory AnnotationsCode0
Machine learning with neural networksCode0
Reconstruction-guided attention improves the robustness and shape processing of neural networksCode0
Facial Expression Recognition Research Based on Deep LearningCode0
Optimizing Spatio-Temporal Information Processing in Spiking Neural Networks via Unconstrained Leaky Integrate-and-Fire Neurons and Hybrid CodingCode0
Recurrent Attention Models with Object-centric Capsule Representation for Multi-object RecognitionCode0
SHTOcc: Effective 3D Occupancy Prediction with Sparse Head and Tail VoxelsCode0
Captioning Images with Diverse ObjectsCode0
Recurrent Convolutional Fusion for RGB-D Object RecognitionCode0
Exploring Novel Object Recognition and Spontaneous Location Recognition Machine Learning Analysis Techniques in Alzheimer's MiceCode0
Experiments with mmWave Automotive Radar Test-bedCode0
Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance EstimationCode0
Teacher-Student Consistency For Multi-Source Domain AdaptationCode0
Object Recognition with and without ObjectsCode0
A Fast Method For Computing Principal Curvatures From Range ImagesCode0
Deliberative Explanations: visualizing network insecuritiesCode0
Recurrent Soft Attention Model for Common Object RecognitionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified