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 251–275 of 2042 papers

TitleStatusHype
Basic Level Categorization Facilitates Visual Object Recognition—0
An Adaptive Descriptor Design for Object Recognition in the Wild—0
Belief Tree Search for Active Object Recognition—0
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex—0
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries—0
A Machine Learning Framework for Distributed Functional Compression over Wireless Channels in IoT—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
Attribute-Based Classification for Zero-Shot Visual Object Categorization—0
A low-power end-to-end hybrid neuromorphic framework for surveillance applications—0
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design—0
Attention Mechanisms for Object Recognition with Event-Based Cameras—0
All-Weather Object Recognition Using Radar and Infrared Sensing—0
Adaptive Hierarchical Decomposition of Large Deep Networks—0
Can domain adaptation make object recognition work for everyone?—0
Fractional order graph neural network—0
All About Knowledge Graphs for Actions—0
MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence—0
A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration—0
A Too-Good-to-be-True Prior to Reduce Shortcut Reliance—0
Aligning Text, Images, and 3D Structure Token-by-Token—0
CAggNet: Crossing Aggregation Network for Medical Image Segmentation—0
A Theoretical Analysis of Deep Neural Networks for Texture Classification—0
A temporal neural network model for object recognition using a biologically plausible decision making layer—0
`Lighter' Can Still Be Dark: Modeling Comparative Color Descriptions—0
A Systematic Study on Object Recognition Using Millimeter-wave Radar—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