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 21–30 of 2042 papers

Title	Date	Tasks	Status	Hype
The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition	Oct 11, 2022	image-classificationImage Classification	CodeCode Available	2
Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud	Jul 25, 2022	Object RecognitionSegmentation	CodeCode Available	2
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild	Jul 21, 2022	3D Object Detection3D Object Detection From Monocular Images	CodeCode Available	2
HAKE: A Knowledge Engine Foundation for Human Activity Understanding	Feb 14, 2022	Action RecognitionHuman-Object Interaction Detection	CodeCode Available	2
A Simple Episodic Linear Probe Improves Visual Recognition in the Wild	Jan 1, 2022	Fine-Grained Image ClassificationImage Classification	CodeCode Available	2
Learning Transferable Visual Models From Natural Language Supervision	Feb 26, 2021	Action RecognitionBenchmarking	CodeCode Available	2
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals	Nov 25, 2020	2D Object DetectionObject	CodeCode Available	2
A Simple Framework for Contrastive Learning of Visual Representations	Feb 13, 2020	Contrastive LearningImage Classification	CodeCode Available	2
SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation	Jul 25, 2019	3D Object RecognitionObject Recognition	CodeCode Available	2
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond	Apr 25, 2019	Instance SegmentationObject Detection	CodeCode Available	2

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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