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 751–800 of 2042 papers

TitleStatusHype
VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation—0
Projection: A Mechanism for Human-like Reasoning in Artificial Intelligence—0
DNN Quantization with Attention—0
Contrastive Reasoning in Neural Networks—0
F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object LearningCode1
Neural Networks for Semantic Gaze Analysis in XR Settings—0
Hebbian Semi-Supervised Learning in a Sample Efficiency Setting—0
Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation—0
PatchNet -- Short-range Template Matching for Efficient Video ProcessingCode1
Structure-From-Motion and RGBD Depth Fusion—0
A Study of Face Obfuscation in ImageNetCode1
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing ImageryCode1
A Discriminative Vectorial Framework for Multi-modal Feature Representation—0
Contemplating real-world object classificationCode1
Offboard 3D Object Detection from Point Cloud Sequences—0
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
Learning Transferable Visual Models From Natural Language SupervisionCode2
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
Learning Visual Models using a Knowledge Graph as a Trainer—0
Grid Cell Path Integration For Movement-Based Visual Object RecognitionCode0
Comparison of semi-supervised deep learning algorithms for audio classificationCode1
An ecologically motivated image dataset for deep learning yields better models of human vision—0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—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
Sill-Net: Feature Augmentation with Separated Illumination RepresentationCode1
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
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape BiasCode1
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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