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 1401–1425 of 2042 papers

TitleStatusHype
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity—0
Using Web Co-occurrence Statistics for Improving Image Categorization—0
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning—0
UWN: A Large Multilingual Lexical Knowledge Base—0
UW-NET: AN INCEPTION-ATTENTION NETWORK FOR UNDERWATER IMAGE CLASSIFICATION—0
V1Net: A computational model of cortical horizontal connections—0
V^2R-Bench: Holistically Evaluating LVLM Robustness to Fundamental Visual Variations—0
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges—0
Variable-Viewpoint Representations for 3D Object Recognition—0
Variant Parallelism: Lightweight Deep Convolutional Models for Distributed Inference on IoT Devices—0
Variation of Gender Biases in Visual Recognition Models Before and After Finetuning—0
VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation—0
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention—0
VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification—0
ViEEG: Hierarchical Neural Coding with Cross-Modal Progressive Enhancement for EEG-Based Visual Decoding—0
View-Invariant Template Matching Using Homography Constraints—0
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation—0
VisBuddy -- A Smart Wearable Assistant for the Visually Challenged—0
Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways—0
Vision-Based Layout Detection from Scientific Literature using Recurrent Convolutional Neural Networks—0
Vision Transformer with Convolutions Architecture Search—0
Visual7W: Grounded Question Answering in Images—0
Visual Classifier Prediction by Distributional Semantic Embedding of Text Descriptions—0
Visual Features for Linguists: Basic image analysis techniques for multimodally-curious NLPers—0
Visual Ground Truth Construction as Faceted Classification—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