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 1251–1300 of 2042 papers

TitleStatusHype
On Learning Density Aware Embeddings—0
Target-Aware Deep Tracking—0
Local Aggregation for Unsupervised Learning of Visual EmbeddingsCode0
Learning Good Representation via Continuous Attention—0
Wasserstein Dependency Measure for Representation Learning—0
Counting the learnable functions of structured data—0
The functional role of cue-driven feature-based feedback in object recognition—0
Looking Fast and Slow: Memory-Guided Mobile Video Object DetectionCode0
Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM—0
Direct Object Recognition Without Line-of-Sight Using Optical Coherence—0
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design—0
Robust Shape Regularity Criteria for Superpixel Evaluation—0
Domain Generalization by Solving Jigsaw PuzzlesCode0
Spatiotemporal Feature Learning for Event-Based Vision—0
Visual recognition in the wild by sampling deep similarity functions—0
An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis—0
Image Privacy Prediction Using Deep Neural NetworksCode0
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing—0
Unsupervised Domain Adaptation using Feature-Whitening and Consensus LossCode0
Understanding and Visualizing Deep Visual Saliency ModelsCode0
Learning a smooth kernel regularizer for convolutional neural networksCode0
TKD: Temporal Knowledge Distillation for Active Perception—0
Crowding in humans is unlike that in convolutional neural networks—0
Disentangled Deep Autoencoding Regularization for Robust Image Classification—0
GFCN: A New Graph Convolutional Network Based on Parallel Flows—0
Directional Regularized Tensor Modeling for Video Rain Streaks RemovalCode0
Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance EstimationCode0
Learning to see across Domains and Modalities—0
NeurAll: Towards a Unified Visual Perception Model for Automated Driving—0
OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended DomainsCode0
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images—0
Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications—0
Lift-the-flap: what, where and when for context reasoning—0
Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need—0
Bridging the Gap Between Computational Photography and Visual Recognition—0
State-Regularized Recurrent Neural Networks—0
Imaging-free object recognition enabled by optical coherence—0
Overfitting Mechanism and Avoidance in Deep Neural Networks—0
Multisource Region Attention Network for Fine-Grained Object Recognition in Remote Sensing Imagery—0
Machine learning with neural networksCode0
Characterizing and evaluating adversarial examples for Offline Handwritten Signature VerificationCode0
Volumetric Convolution: Automatic Representation Learning in Unit Ball—0
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision—0
Pixel personality for dense object tracking in a 2D honeybee hive—0
DCI: Discriminative and Contrast Invertible Descriptor—0
Fine-tuning Convolutional Neural Networks for fine art classification—0
Artistic Object Recognition by Unsupervised Style Adaptation—0
Deep Metric Transfer for Label Propagation with Limited Annotated DataCode0
Robust Graph Learning from Noisy DataCode0
Pre-Trained Convolutional Neural Network Features for Facial Expression Recognition—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