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 1401–1450 of 2042 papers

TitleStatusHype
Do deep nets really need weight decay and dropout?Code0
Scenarios: A New Representation for Complex Scene Understanding—0
Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental LearningCode0
Deep Predictive Coding Network for Object Recognition—0
Learning Inductive Biases with Simple Neural NetworksCode0
Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System—0
Recent Advances in Neural Program SynthesisCode0
Object Sorting Using a Global Texture-Shape 3D Feature Descriptor—0
Artwork Recognition for Panorama Images Based on Optimized ASIFT and Cubic Projection—0
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional NetworksCode0
DeepSIC: Deep Semantic Image Compression—0
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
Image Captioning using Deep Neural ArchitecturesCode0
Faster gaze prediction with dense networks and Fisher pruningCode0
Feature Space Transfer for Data Augmentation—0
LSD-Net: Look, Step and Detect for Joint Navigation and Multi-View Recognition with Deep Reinforcement Learning—0
Deep Boosting of Diverse Experts—0
Deep Learning with Logged Bandit Feedback—0
Classifier-to-Generator Attack: Estimation of Training Data Distribution from Classifier—0
A Resilient Image Matching Method with an Affine Invariant Feature Detector and Descriptor—0
Improved Inception-Residual Convolutional Neural Network for Object Recognition—0
Handwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural NetworksCode0
Deformable Classifiers—0
Pointwise Convolutional Neural NetworksCode0
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review—0
Quantifying Translation-Invariance in Convolutional Neural Networks—0
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery—0
Transformational Sparse Coding—0
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep LearningCode0
Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks—0
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks—0
Learning to Segment Moving Objects—0
Label Efficient Learning of Transferable Representations across Domains and Tasks—0
Relation Networks for Object DetectionCode1
Memory Aware Synapses: Learning what (not) to forgetCode0
Context Augmentation for Convolutional Neural Networks—0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning—0
ADVISE: Symbolism and External Knowledge for Decoding Advertisements—0
Robust Unsupervised Domain Adaptation for Neural Networks via Moment AlignmentCode0
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled HolesCode0
Latent Constrained Correlation Filter—0
Analysis of Dropout in Online Learning—0
Interpreting Convolutional Neural Networks Through Compression—0
Few-Shot Adversarial Domain Adaptation—0
Procedural Text Generation from an Execution Video—0
Cascade Region Proposal and Global Context for Deep Object Detection—0
On Pre-Trained Image Features and Synthetic Images for Deep Learning—0
Object Recognition by Using Multi-level Feature Point Extraction—0
Classification and Geometry of General Perceptual Manifolds—0
Show:102550
← PrevPage 29 of 41Next →
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