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 12011250 of 2042 papers

TitleStatusHype
Causal importance of orientation selectivity for generalization in image recognitionCode0
Contextual Recurrent Convolutional Model for Robust Visual Learning0
Learning what and where to attend with humans in the loop0
Learning to Find Common Objects Across Few Image CollectionsCode0
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers0
Pointing Novel Objects in Image Captioning0
Improved visible to IR image transformation using synthetic data augmentation with cycle-consistent adversarial networks0
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and BeyondCode2
Context-Aware Zero-Shot Learning for Object Recognition0
PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras0
Facial Expression Recognition Research Based on Deep LearningCode0
ChoiceNet: CNN learning through choice of multiple feature map representations0
Context-Aware Zero-Shot RecognitionCode0
3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning ModelsCode0
People infer recursive visual concepts from just a few examples0
End-to-End Learning of Representations for Asynchronous Event-Based DataCode0
Collaboration Analysis Using Deep Learning0
Texture image analysis and texture classification methods - A review0
Improved training of binary networks for human pose estimation and image recognition0
An Application-Specific VLIW Processor with Vector Instruction Set for CNN Acceleration0
On zero-shot recognition of generic objectsCode0
On Learning Density Aware Embeddings0
Target-Aware Deep Tracking0
Learning Good Representation via Continuous Attention0
Local Aggregation for Unsupervised Learning of Visual EmbeddingsCode0
Wasserstein Dependency Measure for Representation Learning0
Counting the learnable functions of structured data0
Looking Fast and Slow: Memory-Guided Mobile Video Object DetectionCode0
The functional role of cue-driven feature-based feedback in object recognition0
Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM0
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design0
Direct Object Recognition Without Line-of-Sight Using Optical Coherence0
Robust Shape Regularity Criteria for Superpixel Evaluation0
Spatiotemporal Feature Learning for Event-Based Vision0
Domain Generalization by Solving Jigsaw PuzzlesCode0
Visual recognition in the wild by sampling deep similarity functions0
An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis0
Image Privacy Prediction Using Deep Neural NetworksCode0
Unsupervised Domain Adaptation using Feature-Whitening and Consensus LossCode0
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing0
Understanding and Visualizing Deep Visual Saliency ModelsCode0
Learning a smooth kernel regularizer for convolutional neural networksCode0
TKD: Temporal Knowledge Distillation for Active Perception0
Crowding in humans is unlike that in convolutional neural networks0
Disentangled Deep Autoencoding Regularization for Robust Image Classification0
GFCN: A New Graph Convolutional Network Based on Parallel Flows0
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 Modalities0
NeurAll: Towards a Unified Visual Perception Model for Automated Driving0
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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