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 226–250 of 2042 papers

TitleStatusHype
A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM—0
Boosting Gradient for White-Box Adversarial Attacks—0
Boosting Object Recognition in Point Clouds by Saliency Detection—0
A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth—0
Automatic Dataset Augmentation—0
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness—0
Amplitude-Based Approach to Evidence Accumulation—0
Amodal Completion and Size Constancy in Natural Scenes—0
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View—0
A Light and Smart Wearable Platform with Multimodal Foundation Model for Enhanced Spatial Reasoning in People with Blindness and Low Vision—0
Boosting with Maximum Adaptive Sampling—0
Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets—0
Audiovisual Highlight Detection in Videos—0
Augmenting Image Annotation: A Human-LMM Collaborative Framework for Efficient Object Selection and Label Generation—0
Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization—0
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning—0
Automatically Discovering Local Visual Material Attributes—0
Adaptive Object Detection with Dual Multi-Label Prediction—0
Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks—0
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture—0
Autonomous Manipulation Learning for Similar Deformable Objects via Only One Demonstration—0
AU Dataset for Visuo-Haptic Object Recognition for Robots—0
Background Invariance Testing According to Semantic Proximity—0
A Multisensory Learning Architecture for Rotation-invariant Object Recognition—0
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries—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