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 701–750 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
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision—0
Panoptic-aware Image-to-Image Translation—0
An online passive-aggressive algorithm for difference-of-squares classification—0
Your head is there to move you around: Goal-driven models of the primate dorsal pathway—0
Material Classification Using Active Temperature Controllable Robotic Gripper—0
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Inducing Functions through Reinforcement Learning without Task Specification—0
Object Recognition by a Minimally Pre-Trained System in the Process of Environment Exploration—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
The Neural Correlates of Image Texture in the Human Vision Using Magnetoencephalography—0
ShapeY: Measuring Shape Recognition Capacity Using Nearest Neighbor Matching—0
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
Unsupervised Spiking Instance Segmentation on Event Data using STDP—0
Grassmannian learning mutual subspace method for image set recognition—0
Development of collective behavior in newborn artificial agents—0
Certifiable Artificial Intelligence Through Data Fusion—0
Resampling and super-resolution of hexagonally sampled images using deep learning—0
Investigating Negation in Pre-trained Vision-and-language ModelsCode0
Latent Cognizance: What Machine Really Learns—0
HR-RCNN: Hierarchical Relational Reasoning for Object Detection—0
MVT: Multi-view Vision Transformer for 3D Object RecognitionCode0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
Knowledge-driven Active LearningCode0
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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