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 1101–1150 of 2042 papers

TitleStatusHype
Incorporating Textual Evidence in Visual Storytelling—0
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks—0
Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons—0
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data—0
Putting visual object recognition in contextCode0
Sensory Optimization: Neural Networks as a Model for Understanding and Creating Art—0
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep LearningCode0
Compositional Hierarchical Tensor Factorization: Representing Hierarchical Intrinsic and Extrinsic Causal Factors—0
Boosting Object Recognition in Point Clouds by Saliency Detection—0
Generalizing to unseen domains via distribution matchingCode0
A Review of methods for Textureless Object Recognition—0
Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images—0
Domain Generalization via Model-Agnostic Learning of Semantic FeaturesCode0
A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images—0
Hierarchical Prototype Learning for Zero-Shot Recognition—0
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries—0
A new GAN-based anomaly detection (GBAD) approach for multi-threat object classification on large-scale x-ray security images—0
A low-power end-to-end hybrid neuromorphic framework for surveillance applications—0
Convolutional Prototype Learning for Zero-Shot Recognition—0
CPWC: Contextual Point Wise Convolution for Object Recognition—0
KerCNNs: biologically inspired lateral connections for classification of corrupted images—0
Encoder-Decoder based CNN and Fully Connected CRFs for Remote Sensed Image Segmentation—0
What's in my Room? Object Recognition on Indoor Panoramic Images—0
Contour Sparse Representation with SDD Features for Object Recognition—0
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference—0
Machine Learning and Big Scientific Data—0
Rotation-invariant shipwreck recognition with forward-looking sonar—0
Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternativesCode0
Continual Learning in Neural NetworksCode0
Learning Relationships for Multi-View 3D Object Recognition—0
Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence—0
Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition—0
Enhancing Object Detection in Adverse Conditions using Thermal Imaging—0
PROTOTYPE-ASSISTED ADVERSARIAL LEARNING FOR UNSUPERVISED DOMAIN ADAPTATION—0
V1Net: A computational model of cortical horizontal connections—0
Salient Explanation for Fine-grained Classification—0
Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust PerformanceCode0
Towards Interpreting Recurrent Neural Networks through Probabilistic AbstractionCode0
Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition—0
Task-Aware Monocular Depth Estimation for 3D Object DetectionCode0
Meta-neural-network for Realtime and Passive Deep-learning-based Object Recognition—0
A Dual-hierarchy Semantic Graph for Robust Object Recognition—0
Performance Evaluation of Learned 3D Features—0
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNsCode0
FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural NetworksCode0
Recurrent Connectivity Aids Recognition of Partly Occluded Objects—0
The Natural Tendency of Feed Forward Neural Networks to Favor Invariant Units—0
Foveated Downsampling Techniques—0
Dual-attention Focused Module for Weakly Supervised Object Localization—0
Significance of feedforward architectural differences between the ventral visual stream and DenseNet—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