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 701–725 of 2042 papers

TitleStatusHype
Reducing Overconfidence Predictions for Autonomous Driving Perception—0
BViT: Broad Attention based Vision TransformerCode0
Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection—0
Positive-Unlabeled Domain Adaptation—0
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest ClassificationCode0
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers—0
Radar-based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units—0
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning—0
A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method—0
Dynamic Rectification Knowledge DistillationCode0
Winograd Convolution for Deep Neural Networks: Efficient Point Selection—0
A Machine Learning Framework for Distributed Functional Compression over Wireless Channels in IoT—0
Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations—0
Task Independent Capsule-Based Agents for Deep Q-Learning—0
Geometric and Textural Augmentation for Domain Gap ReductionCode0
AU Dataset for Visuo-Haptic Object Recognition for Robots—0
Contrastive Object Detection Using Knowledge Graph Embeddings—0
Generating Photo-realistic Images from LiDAR Point Clouds with Generative Adversarial Networks—0
Object Recognition as Classification via Visual Properties—0
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision—0
Co-training Transformer with Videos and Images Improves Action Recognition—0
Controlled-rearing studies of newborn chicks and deep neural networks—0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition—0
Sparse Depth Completion with Semantic Mesh Deformation Optimization—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