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

Showing 19261950 of 2042 papers

TitleStatusHype
EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving ObjectCode0
Canonical Saliency Maps: Decoding Deep Face ModelsCode0
SilVar: Speech Driven Multimodal Model for Reasoning Visual Question Answering and Object LocalizationCode0
Can Large Language Models Grasp Event Signals? Exploring Pure Zero-Shot Event-based RecognitionCode0
Transformers: State-of-the-Art Natural Language ProcessingCode0
MetaCOG: A Hierarchical Probabilistic Model for Learning Meta-Cognitive Visual RepresentationsCode0
Unsupervised Learning from Video with Deep Neural EmbeddingsCode0
Occlusion Coherence: Detecting and Localizing Occluded FacesCode0
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled HolesCode0
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial ModelingCode0
Learning a smooth kernel regularizer for convolutional neural networksCode0
Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental LearningCode0
Ambient Sound Provides Supervision for Visual LearningCode0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked ObjectsCode0
Single camera pose estimation using Bayesian filtering and Kinect motion priorsCode0
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningCode0
BViT: Broad Attention based Vision TransformerCode0
Deep supervised learning for hyperspectral data classification through convolutional neural networksCode0
Learning compact binary descriptors with unsupervised deep neural networksCode0
Ensemble learning in CNN augmented with fully connected subnetworksCode0
OLÉ: Orthogonal Low-Rank Embedding - A Plug and Play Geometric Loss for Deep LearningCode0
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep LearningCode0
Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object RecognitionCode0
DeepSat - A Learning framework for Satellite ImageryCode0
Triangle-Net: Towards Robustness in Point Cloud LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified