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 301–350 of 2042 papers

TitleStatusHype
Human Pose Estimation for Real-World Crowded ScenariosCode0
AGA: Attribute Guided AugmentationCode0
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial NoisesCode0
A comparison between humans and AI at recognizing objects in unusual posesCode0
Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language ModelsCode0
A Framework of Transfer Learning in Object Detection for Embedded SystemsCode0
Hierarchical Superpixel Segmentation via Structural Information TheoryCode0
Handwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural NetworksCode0
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual RecognitionCode0
How much human-like visual experience do current self-supervised learning algorithms need in order to achieve human-level object recognition?Code0
Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agentsCode0
A Probabilistic Theory of Deep LearningCode0
Grid Cell Path Integration For Movement-Based Visual Object RecognitionCode0
A Computational Acquisition Model for Multimodal Word CategorizationCode0
Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes ProsthesisCode0
Grounded Human-Object Interaction Hotspots from VideoCode0
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop ObjectsCode0
Geometric and Textural Augmentation for Domain Gap ReductionCode0
Generate To Adapt: Aligning Domains using Generative Adversarial NetworksCode0
Context-Aware Zero-Shot RecognitionCode0
Genetic CNNCode0
Global Second-order Pooling Convolutional NetworksCode0
Tuned Compositional Feature Replays for Efficient Stream LearningCode0
CNN-based Methods for Object Recognition with High-Resolution Tactile SensorsCode0
GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in DarkCode0
FPNN: Field Probing Neural Networks for 3D DataCode0
A Fast Method For Computing Principal Curvatures From Range ImagesCode0
Foveated Instance SegmentationCode0
Generalisation in humans and deep neural networksCode0
Fit to Measure: Reasoning about Sizes for Robust Object RecognitionCode0
An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation DatasetCode0
Food Image Recognition by Using Convolutional Neural Networks (CNNs)Code0
Feature Pyramid GridsCode0
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural networkCode0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Feature Learning by Multidimensional Scaling and its Applications in Object RecognitionCode0
Fast Feature Fool: A data independent approach to universal adversarial perturbationsCode0
Feature Learning for Accelerometer based Gait RecognitionCode0
Finding Tiny FacesCode0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN ModelsCode0
FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural NetworksCode0
Exploring Novel Object Recognition and Spontaneous Location Recognition Machine Learning Analysis Techniques in Alzheimer's MiceCode0
Facial Expression Recognition Research Based on Deep LearningCode0
Foveation in the Era of Deep LearningCode0
Chessboard and chess piece recognition with the support of neural networksCode0
THOR2: Topological Analysis for 3D Shape and Color-Based Human-Inspired Object Recognition in Unseen EnvironmentsCode0
Characterizing and evaluating adversarial examples for Offline Handwritten Signature VerificationCode0
Experiments with mmWave Automotive Radar Test-bedCode0
Generalizing to unseen domains via distribution matchingCode0
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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