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 476–500 of 2042 papers

TitleStatusHype
Adaptive Object Detection with Dual Multi-Label Prediction—0
Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets—0
Disentangled Deep Autoencoding Regularization for Robust Image Classification—0
Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras—0
DeepGaze II: Reading fixations from deep features trained on object recognition—0
Deep Global-Connected Net With The Generalized Multi-Piecewise ReLU Activation in Deep Learning—0
Deep Graph Reprogramming—0
DEEP HIERARCHICAL MODEL FOR HIERARCHICAL SELECTIVE CLASSIFICATION AND ZERO SHOT LEARNING—0
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning—0
Deep Learning and Continuous Representations for Natural Language Processing—0
Automatically Discovering Local Visual Material Attributes—0
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds—0
Deep-learning-based classification and retrieval of components of a process plant from segmented point clouds—0
Deep learning based infrared small object segmentation: Challenges and future directions—0
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
Deep Learning for Material recognition: most recent advances and open challenges—0
Deep Learning for the Classification of Lung Nodules—0
Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition—0
A Poodle or a Dog? Evaluating Automatic Image Annotation Using Human Descriptions at Different Levels of Granularity—0
Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks—0
Deep Learning Techniques for Geospatial Data Analysis—0
Deep Learning with Energy-efficient Binary Gradient Cameras—0
Deep Learning with Logged Bandit Feedback—0
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation—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