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 1601–1650 of 2042 papers

TitleStatusHype
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation—0
Learning Detailed Face Reconstruction from a Single Image—0
Gradients of Counterfactuals—0
Crowdsourcing in Computer Vision—0
STDP-based spiking deep convolutional neural networks for object recognitionCode0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial ModelingCode0
Template Matching Advances and Applications in Image Analysis—0
Recurrent 3D Attentional Networks for End-to-End Active Object Recognition—0
Are Accuracy and Robustness Correlated?—0
Multiple Instance Learning Convolutional Neural Networks for Object Recognition—0
Tangled Splines—0
Zero Shot Hashing—0
Egocentric Height Estimation—0
ResearchDoom and CocoDoom: Learning Computer Vision with Games—0
DeepGaze II: Reading fixations from deep features trained on object recognition—0
Recognizing Open-Vocabulary Relations between Objects in Images—0
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification—0
Optimistic and Pessimistic Neural Networks for Scene and Object Recognition—0
Transfer Learning for Material Classification using Convolutional Networks—0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision—0
Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers—0
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity—0
Investigating Fluidity for Human-Robot Interaction with Real-time, Real-world Grounding Strategies—0
Ambient Sound Provides Supervision for Visual LearningCode0
Densely Connected Convolutional NetworksCode1
Towards Bayesian Deep Learning: A Framework and Some Existing Methods—0
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch RecognitionCode0
Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics—0
Adapting Deep Network Features to Capture Psychological RepresentationsCode0
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations—0
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images—0
Natural Language Descriptions of Human Activities Scenes: Corpus Generation and Analysis—0
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object RecognitionCode0
Feature Descriptors for Tracking by Detection: a Benchmark—0
Improved Deep Learning of Object Category using Pose Information—0
Learning to Recognize Objects by Retaining other Factors of Variation—0
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods—0
FusionNet: 3D Object Classification Using Multiple Data Representations—0
Distributed Coding of Multiview Sparse Sources with Joint Recovery—0
Do semantic parts emerge in Convolutional Neural Networks?—0
From Dependence to Causation—0
Deep Reconstruction-Classification Networks for Unsupervised Domain AdaptationCode0
Enlightening Deep Neural Networks with Knowledge of Confounding Factors—0
Captioning Images with Diverse ObjectsCode0
Saliency Driven Object recognition in egocentric videos with deep CNN—0
Mutual Exclusivity Loss for Semi-Supervised Deep Learning—0
Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)—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