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 801–850 of 2042 papers

TitleStatusHype
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition—0
Flexible deep transfer learning by separate feature embeddings and manifold alignment—0
Flexible ViG: Learning the Self-Saliency for Flexible Object Recognition—0
AI-based Density Recognition—0
Active Perception using Light Curtains for Autonomous Driving—0
ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection—0
A Dual-hierarchy Semantic Graph for Robust Object Recognition—0
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains—0
Formula-Supervised Visual-Geometric Pre-training—0
Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition—0
Foveated Downsampling Techniques—0
Continual Hyperbolic Learning of Instances and Classes—0
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models—0
Improved training of binary networks for human pose estimation and image recognition—0
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement—0
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism—0
From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes—0
From Virtual to Real: A Framework for Verbal Interaction with Robots—0
From Visual Attributes to Adjectives through Decompositional Distributional Semantics—0
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis—0
Efficient Online ML API Selection for Multi-Label Classification Tasks—0
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements—0
Disentangling Properties of Contrastive Methods—0
Disentangled Deep Autoencoding Regularization for Robust Image Classification—0
Fully-Connected CRFs with Non-Parametric Pairwise Potential—0
FusionNet: 3D Object Classification Using Multiple Data Representations—0
Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking—0
Contrastive Reasoning in Neural Networks—0
Hand Gestures Recognition in Videos Taken with Lensless Camera—0
GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs—0
Context-Aware Zero-Shot Learning for Object Recognition—0
Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured—0
Grounded Language Acquisition From Object and Action Imagery—0
Generalization Boosted Adapter for Open-Vocabulary Segmentation—0
Generalized Adaptive Dictionary Learning via Domain Shift Minimization—0
Generalized K-fan Multimodal Deep Model with Shared Representations—0
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition—0
Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval—0
Convolutional Networks with Dense Connectivity—0
A ``Shape Aware'' Model for semi-supervised Learning of Objects and its Context—0
Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks—0
Generating Image Descriptions with Gold Standard Visual Inputs: Motivation, Evaluation and Baselines—0
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition—0
Discriminatively Trained Sparse Code Gradients for Contour Detection—0
Brain-Like Object Recognition Neural Networks are more robustness to common corruptions—0
Generic decoding of seen and imagined objects using hierarchical visual features—0
Guided SAM: Label-Efficient Part Segmentation—0
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing—0
Convolutional Prototype Learning for Zero-Shot Recognition—0
Discriminative Ferns Ensemble for Hand Pose 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