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 1701–1750 of 2042 papers

TitleStatusHype
BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition—0
Bio-inspired learnable divisive normalization for ANNs—0
Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition—0
BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once—0
Block-wise Scrambled Image Recognition Using Adaptation Network—0
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations—0
Boosting Gradient for White-Box Adversarial Attacks—0
Boosting Object Recognition in Point Clouds by Saliency Detection—0
Boosting with Maximum Adaptive Sampling—0
BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition—0
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning—0
Brain Cancer Segmentation Using YOLOv5 Deep Neural Network—0
Brain Inspired Face Recognition: A Computational Framework—0
Brain-Like Object Recognition Neural Networks are more robustness to common corruptions—0
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism—0
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections—0
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition—0
Bridging the Gap Between Computational Photography and Visual Recognition—0
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation—0
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture—0
BSED: Baseline Shapley-Based Explainable Detector—0
Building a visual semantics aware object hierarchy—0
Building Machines That Learn and Think Like People—0
C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing—0
CAggNet: Crossing Aggregation Network for Medical Image Segmentation—0
Can Boosting with SVM as Week Learners Help?—0
Can domain adaptation make object recognition work for everyone?—0
Can foundation models actively gather information in interactive environments to test hypotheses?—0
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection—0
Capacity limitations of visual search in deep convolutional neural networks—0
Capturing the objects of vision with neural networks—0
Cascade Region Proposal and Global Context for Deep Object Detection—0
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks—0
Categorical Mixture Models on VGGNet activations—0
Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations—0
CausalX: Causal Explanations and Block Multilinear Factor Analysis—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
Certifiable Artificial Intelligence Through Data Fusion—0
Characterising representation dynamics in recurrent neural networks for object recognition—0
ChartKG: A Knowledge-Graph-Based Representation for Chart Images—0
ChoiceNet: CNN learning through choice of multiple feature map representations—0
Chosen methods of improving small object recognition with weak recognizable features—0
CIFAR10 to Compare Visual Recognition Performance between Deep Neural Networks and Humans—0
Classification and Geometry of General Perceptual Manifolds—0
Classifier-to-Generator Attack: Estimation of Training Data Distribution from Classifier—0
Classifying Malware Images with Convolutional Neural Network Models—0
Class incremental learning for video action classification—0
Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images—0
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation—0
Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks—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