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

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Showing 601–650 of 2042 papers

TitleStatusHype
Generating Photo-realistic Images from LiDAR Point Clouds with Generative Adversarial Networks—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
Rethinking the Two-Stage Framework for Grounded Situation RecognitionCode1
Sparse Depth Completion with Semantic Mesh Deformation Optimization—0
Implicit Feature Refinement for Instance SegmentationCode1
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision—0
Panoptic-aware Image-to-Image Translation—0
N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event CamerasCode1
PartImageNet: A Large, High-Quality Dataset of PartsCode1
The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by NormalizationCode1
Your head is there to move you around: Goal-driven models of the primate dorsal pathway—0
Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual CortexCode1
An online passive-aggressive algorithm for difference-of-squares classification—0
Material Classification Using Active Temperature Controllable Robotic Gripper—0
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds—0
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNsCode1
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge DistillationCode1
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
Resampling and super-resolution of hexagonally sampled images using deep learning—0
Certifiable Artificial Intelligence Through Data Fusion—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
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
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
Oriented Feature Alignment for Fine-grained Object Recognition in High-Resolution Satellite Imagery—0
In Silico Modelling of Neurodegeneration Using Deep Convolutional Neural Networks—0
Out-of-distribution robustness: Limited image exposure of a four-year-old is enough to outperform ResNet-50—0
Bio-inspired learnable divisive normalization for ANNs—0
Recurrent Attention Models with Object-centric Capsule Representation for Multi-object RecognitionCode0
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model—0
ZSpeedL -- Evaluating the Performance of Zero-Shot Learning Methods using Low-Power Devices—0
Explainability-Aware One Point Attack for Point Cloud Neural NetworksCode1
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition—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