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 1551–1600 of 2042 papers

TitleStatusHype
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis—0
Inception Recurrent Convolutional Neural Network for Object RecognitionCode0
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition—0
Residual Attention Network for Image ClassificationCode0
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition—0
Invariant recognition drives neural representations of action sequences—0
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections—0
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality—0
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image ClassificationCode0
Deep Affordance-grounded Sensorimotor Object Recognition—0
Generate To Adapt: Aligning Domains using Generative Adversarial NetworksCode0
Pragmatic descriptions of perceptual stimuli—0
Learning with Privileged Information for Multi-Label Classification—0
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer—0
Is Second-order Information Helpful for Large-scale Visual Recognition?Code0
Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields—0
Multilevel Context Representation for Improving Object Recognition—0
Weakly-supervised DCNN for RGB-D Object Recognition in Real-World Applications Which Lack Large-scale Annotated Training DataCode0
Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep LearningCode0
Using Human Brain Activity to Guide Machine Learning—0
End-to-end Binary Representation Learning via Direct Binary Embedding—0
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets—0
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop ObjectsCode0
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks—0
Object classification in images of Neoclassical furniture using Deep Learning—0
PathTrack: Fast Trajectory Annotation with Path Supervision—0
SegICP: Integrated Deep Semantic Segmentation and Pose EstimationCode0
Genetic CNNCode0
Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features—0
Central Moment Discrepancy (CMD) for Domain-Invariant Representation LearningCode0
MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information—0
Synthesising Dynamic Textures using Convolutional Neural Networks—0
Scene Recognition by Combining Local and Global Image DescriptorsCode0
A large comparison of feature-based approaches for buried target classification in forward-looking ground-penetrating radar—0
ImageNet MPEG-7 Visual Descriptors - Technical Report—0
Deep Network Guided Proof Search—0
Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features—0
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras—0
Synthetic to Real Adaptation with Generative Correlation Alignment Networks—0
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition—0
Incremental Learning for Robot Perception through HRI—0
Bandwidth limited object recognition in high resolution imagery—0
What are the visual features underlying human versus machine vision?—0
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching—0
Few-Shot Object Recognition from Machine-Labeled Web Images—0
Deep Learning and Its Applications to Machine Health Monitoring: A SurveyCode0
Beyond Holistic Object Recognition: Enriching Image Understanding with Part States—0
Finding Tiny FacesCode0
Statistics of Visual Responses to Object Stimuli from Primate AIT Neurons to DNN Neurons—0
AGA: Attribute Guided AugmentationCode0
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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