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 1351–1400 of 2042 papers

TitleStatusHype
Multi-View Harmonized Bilinear Network for 3D Object Recognition—0
A Classification approach towards Unsupervised Learning of Visual RepresentationsCode0
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash—0
Why do deep convolutional networks generalize so poorly to small image transformations?Code1
Multi-level 3D CNN for Learning Multi-scale Spatial FeaturesCode0
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-OrganizationCode0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Deep Watershed Detector for Music Object Recognition—0
Learning Illuminant Estimation from Object Recognition—0
Unsupervised Domain Adaptation using Regularized Hyper-graph Matching—0
Learning what and where to attendCode1
Deformable Part Networks—0
Object Localization with a Weakly Supervised CapsNet—0
Wavelet Convolutional Neural NetworksCode1
Deep Predictive Coding Network with Local Recurrent Processing for Object RecognitionCode0
Disparity Sliding Window: Object Proposals From Disparity ImagesCode0
Identifying Object States in Cooking-Related Images—0
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation—0
Energy Efficient Hadamard Neural Networks—0
Dense and Diverse Capsule Networks: Making the Capsules Learn BetterCode0
SqueezeJet: High-level Synthesis Accelerator Design for Deep Convolutional Neural Networks—0
Object and Text-guided Semantics for CNN-based Activity Recognition—0
SdcNet: A Computation-Efficient CNN for Object Recognition—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
Semi-supervised Training Data Generation for Multilingual Question Answering—0
Incorporating Semantic Attention in Video Description Generation—0
Dynamic Few-Shot Visual Learning without ForgettingCode1
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving ObjectsCode0
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism—0
Semantic Edge Detection with Diverse Deep SupervisionCode0
Performance Evaluation of 3D Correspondence Grouping Algorithms—0
Hierarchical Novelty Detection for Visual Object Recognition—0
Learning Beyond Human Expertise with Generative Models for Dental Restorations—0
What deep learning can tell us about higher cognitive functions like mindreading?—0
Deep Learning Object Detection Methods for Ecological Camera Trap Data—0
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny ObjectsCode1
Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision—0
Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach—0
Low-Shot Learning for the Semantic Segmentation of Remote Sensing ImageryCode0
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment—0
Discrete Potts Model for Generating Superpixels on Noisy Images—0
C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing—0
Triplet-Center Loss for Multi-View 3D Object RetrievalCode0
Exponential Discriminative Metric Embedding in Deep Learning—0
Categorical Mixture Models on VGGNet activations—0
A Non-Technical Survey on Deep Convolutional Neural Network Architectures—0
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image ClassificationCode0
Co-occurrence matrix analysis-based semi-supervised training for object detection—0
Show:102550
← PrevPage 28 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