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 351–400 of 2042 papers

TitleStatusHype
DAS: A Deformable Attention to Capture Salient Information in CNNs—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
A Framework for Multi-View Classification of Features—0
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations—0
Combinatorial clustering and the beta negative binomial process—0
A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method—0
Combinational neural network using Gabor filters for the classification of handwritten digits—0
Coloring Objects: Adjective-Noun Visual Semantic Compositionality—0
A priori compression of convolutional neural networks for wave simulators—0
A Fog Robotic System for Dynamic Visual Servoing—0
A General Ambiguity Model for Binary Edge Images with Edge Tracing and its Implementation—0
Combined Approach for Image Segmentation—0
Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
Approximation of dilation-based spatial relations to add structural constraints in neural networks—0
Collaborative Descriptors: Convolutional Maps for Preprocessing—0
Co-training Transformer with Videos and Images Improves Action Recognition—0
A randomized gradient-free attack on ReLU networks—0
Are Accuracy and Robustness Correlated?—0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
Collaboration Analysis Using Deep Learning—0
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study—0
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification—0
Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation—0
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable—0
Complex-valued Iris Recognition Network—0
Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception?—0
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion—0
Compositional Embeddings for Multi-Label One-Shot Learning—0
Compositional Hierarchical Tensor Factorization: Representing Hierarchical Intrinsic and Extrinsic Causal Factors—0
CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
Compression of Deep Neural Networks on the Fly—0
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward—0
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis—0
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data—0
Connecting metrics for shape-texture knowledge in computer vision—0
Consistency of Silhouettes and Their Duals—0
Instance Scale Normalization for image understanding—0
Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models—0
Construction of Latent Descriptor Space and Inference Model of Hand-Object Interactions—0
Application of Faster R-CNN model on Human Running Pattern Recognition—0
CONTEMPLATING REAL-WORLDOBJECT RECOGNITION—0
Content Placement in Networks of Similarity Caches—0
Context Augmentation for Convolutional Neural Networks—0
Context-Dependent Diffusion Network for Visual Relationship Detection—0
Affordance Labeling and Exploration: A Manifold-Based Approach—0
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection—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