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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 401–410 of 2042 papers

Title	Date	Tasks	Status	Score
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization	Sep 14, 2016	ObjectObject Localization	CodeCode Available	5
ContextMix: A context-aware data augmentation method for industrial visual inspection systems	Jan 18, 2024	Data AugmentationObject Recognition	CodeCode Available	5
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition	Aug 11, 2016	ObjectObject Recognition	CodeCode Available	5
Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object Recognition	May 24, 2024	Object Recognition	CodeCode Available	5
Look Twice: A Generalist Computational Model Predicts Return Fixations across Tasks and Species	Jan 5, 2021	Object Recognition	CodeCode Available	5
Lost in Context: The Influence of Context on Feature Attribution Methods for Object Recognition	Nov 5, 2024	ObjectObject Recognition	CodeCode Available	5
Efficient Event Stream Super-Resolution with Recursive Multi-Branch Fusion	Jun 28, 2024	Object RecognitionSuper-Resolution	CodeCode Available	5
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators	Aug 30, 2019	image-classificationImage Classification	CodeCode Available	5
Continual Learning in Neural Networks	Oct 7, 2019	Continual LearningObject Recognition	CodeCode Available	5
BViT: Broad Attention based Vision Transformer	Feb 13, 2022	image-classificationImage Classification	CodeCode Available	5

Show:10 25 50

← PrevPage 41 of 205Next →

All datasets shape bias CIFAR10-DVS N-Caltech 101 ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 Gesture MECCANO N-CARS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Imagen	shape bias	98.7	—	Unverified
2	Stable Diffusion	shape bias	92.7	—	Unverified
3	Parti	shape bias	91.7	—	Unverified
4	ViT-22B-384	shape bias	86.4	—	Unverified
5	ViT-22B-560	shape bias	83.8	—	Unverified
6	CLIP (ViT-B)	shape bias	79.9	—	Unverified
7	ViT-22B-224	shape bias	78	—	Unverified
8	ResNet-50 (L2 eps 5.0 adv trained)	shape bias	69.5	—	Unverified
9	ResNet-50 (with strong augmentations)	shape bias	62.2	—	Unverified
10	SWSL (ResNeXt-101)	shape bias	49.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.55	—	Unverified
2	SSNN	Accuracy (% )	78.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.62	—	Unverified
2	SSNN	Accuracy (% )	79.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	18.75	—	Unverified
2	yun	Top 5 Accuracy	14.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	DY	Top 5 Accuracy	0.08	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	AJ2021	Top 5 Accuracy	27.68	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SSNN	Accuracy (% )	94.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Faster-RCNN	mAP	30.39	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	96	—	Unverified