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 701–750 of 2042 papers

TitleStatusHype
Diversity in Object Proposals—0
CNN-based search model underestimates attention guidance by simple visual features—0
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition—0
A neuromorphic approach to image processing and machine vision—0
Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities—0
Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching—0
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users—0
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data—0
Evaluation of Environmental Conditions on Object Detection using Oriented Bounding Boxes for AR Applications—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
Advancing Egocentric Video Question Answering with Multimodal Large Language Models—0
300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning—0
Few-shot target-driven instance detection based on open-vocabulary object detection models—0
EventDance++: Language-guided Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
Fine-grained 3D object recognition: an approach and experiments—0
EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects—0
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN—0
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections—0
A Voxel Graph CNN for Object Classification with Event Cameras—0
Exact neural mass model for synaptic-based working memory—0
Combinatorial clustering and the beta negative binomial process—0
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment—0
Distributed Coding of Multiview Sparse Sources with Joint Recovery—0
Combined Approach for Image Segmentation—0
A Neuro-AI Interface: Learning DNNs from the Human Brain—0
Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations—0
A Dual-hierarchy Semantic Graph for Robust Object Recognition—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness—0
Exploit Bounding Box Annotations for Multi-label Object Recognition—0
Feature Space Transfer for Data Augmentation—0
Exploiting an Oracle that Reports AUC Scores in Machine Learning Contests—0
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement—0
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks—0
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
A randomized gradient-free attack on ReLU networks—0
Exploring Temporal Differences in 3D Convolutional Neural Networks—0
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks—0
Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection—0
Exponential Discriminative Metric Embedding in Deep Learning—0
Extreme Image Transformations Affect Humans and Machines Differently—0
Extreme Image Transformations Facilitate Robust Latent Object Representations—0
EZSR: Event-based Zero-Shot Recognition—0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset—0
Disentangling Properties of Contrastive Methods—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