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 211–220 of 2042 papers

Title	Date	Tasks	Status	Hype	Score
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding	Mar 5, 2024	3D visual groundingDecision Making	CodeCode Available	1	5
Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings	Nov 22, 2020	Bayesian InferenceObject Recognition	CodeCode Available	1	5
Robust and Efficient Post-Processing for Video Object Detection (REPP)	Oct 1, 2020	Autonomous DrivingDense Object Detection	CodeCode Available	1	5
Equalization Loss for Long-Tailed Object Recognition	Mar 11, 2020	Long-tail LearningObject	CodeCode Available	1	5
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency	Jun 16, 2022	Domain AdaptationObject Recognition	CodeCode Available	1	5
EventRPG: Event Data Augmentation with Relevance Propagation Guidance	Mar 14, 2024	Action RecognitionData Augmentation	CodeCode Available	1	5
Expanding Event Modality Applications through a Robust CLIP-Based Encoder	Dec 4, 2024	Few-Shot LearningObject Recognition	CodeCode Available	1	5
Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition	Mar 23, 2022	Event-based visionObject Recognition	CodeCode Available	1	5
Explainability-Aware One Point Attack for Point Cloud Neural Networks	Oct 8, 2021	3D Object RecognitionAdversarial Robustness	CodeCode Available	1	5
Why do deep convolutional networks generalize so poorly to small image transformations?	May 30, 2018	Data AugmentationObject Recognition	CodeCode Available	1	5

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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