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 851–900 of 2042 papers

TitleStatusHype
Deep Clustering by Semantic Contrastive Learning—0
Cooking Object's State Identification Without Using Pretrained Model—0
Domain Generalization: A Survey—0
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness—0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
The Labeled Multiple Canonical Correlation Analysis for Information Fusion—0
Achieving Competitive Play Through Bottom-Up Approach in Semantic Segmentation—0
CausalX: Causal Explanations and Block Multilinear Factor Analysis—0
Comparative evaluation of CNN architectures for Image Caption GenerationCode0
Approximation of dilation-based spatial relations to add structural constraints in neural networks—0
The Effects of Image Distribution and Task on Adversarial Robustness—0
Efficient Online ML API Selection for Multi-Label Classification Tasks—0
Grid Cell Path Integration For Movement-Based Visual Object RecognitionCode0
Learning Visual Models using a Knowledge Graph as a Trainer—0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
An ecologically motivated image dataset for deep learning yields better models of human vision—0
A Too-Good-to-be-True Prior to Reduce Shortcut Reliance—0
Audiovisual Highlight Detection in Videos—0
What does LIME really see in images?Code0
Enhancing efficiency of object recognition in different categorization levels by reinforcement learning in modular spiking neural networks—0
Content Placement in Networks of Similarity Caches—0
Large Scale Long-tailed Product Recognition System at Alibaba—0
Overhead MNIST: A Benchmark Satellite Dataset—0
Zero-shot Learning with Deep Neural Networks for Object Recognition—0
The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs—0
ISP Distillation—0
A Spike Learning System for Event-driven Object Recognition—0
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels MethodsCode0
A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application—0
Machine learning with limited data—0
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations—0
Energy-based Dropout in Restricted Boltzmann Machines: Why not go random—0
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices—0
Look Twice: A Generalist Computational Model Predicts Return Fixations across Tasks and SpeciesCode0
On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations—0
Unity of Opposites: SelfNorm and CrossNorm for Model Robustness—0
CONTEMPLATING REAL-WORLDOBJECT RECOGNITION—0
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation—0
EMPIRICAL UPPER BOUND IN OBJECT DETECTION—0
Learning Semantic Similarities for Prototypical Classifiers—0
Modeling Human Development: Effects of Blurred Vision on Category Learning in CNNs—0
Sample Balancing for Improving Generalization under Distribution Shifts—0
On the Robustness of Sentiment Analysis for Stock Price Forecasting—0
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods.—0
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral StreamCode0
Visual Probing and Correction of Object Recognition Models with Interactive user feedbackCode0
Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications—0
Flexible deep transfer learning by separate feature embeddings and manifold alignment—0
Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception—0
Projected Distribution Loss for Image EnhancementCode0
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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