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 1626–1650 of 2042 papers

Title	Date	Tasks	Status
Tangled Splines	Oct 10, 2016	Object Recognition	—Unverified
Zero Shot Hashing	Oct 9, 2016	Object RecognitionZero-Shot Learning	—Unverified
Egocentric Height Estimation	Oct 9, 2016	Object RecognitionObject Tracking	—Unverified
ResearchDoom and CocoDoom: Learning Computer Vision with Games	Oct 7, 2016	Depth EstimationMonocular Depth Estimation	—Unverified
DeepGaze II: Reading fixations from deep features trained on object recognition	Oct 5, 2016	Object RecognitionSaliency Prediction	—Unverified
Recognizing Open-Vocabulary Relations between Objects in Images	Oct 1, 2016	Language ModelingLanguage Modelling	—Unverified
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification	Sep 29, 2016	General Classificationimage-classification	—Unverified
Optimistic and Pessimistic Neural Networks for Scene and Object Recognition	Sep 26, 2016	AttributeObject Recognition	—Unverified
Transfer Learning for Material Classification using Convolutional Networks	Sep 20, 2016	ClassificationDescriptive	—Unverified
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization	Sep 14, 2016	ObjectObject Localization	CodeCode Available
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision	Sep 14, 2016	General ClassificationImage Retrieval	—Unverified
Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers	Sep 12, 2016	AttributeDecision Making	—Unverified
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity	Sep 10, 2016	Object Recognition	—Unverified
Investigating Fluidity for Human-Robot Interaction with Real-time, Real-world Grounding Strategies	Sep 1, 2016	Object RecognitionText Generation	—Unverified
Ambient Sound Provides Supervision for Visual Learning	Aug 25, 2016	Object Recognition	CodeCode Available
Towards Bayesian Deep Learning: A Framework and Some Existing Methods	Aug 24, 2016	Deep LearningObject Recognition	—Unverified
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition	Aug 11, 2016	ObjectObject Recognition	CodeCode Available
Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics	Aug 9, 2016	Object RecognitionSpecificity	—Unverified
Adapting Deep Network Features to Capture Psychological Representations	Aug 6, 2016	Object RecognitionScene Understanding	CodeCode Available
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations	Aug 4, 2016	Missing LabelsMulti-Label Learning	—Unverified
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images	Aug 1, 2016	Object DetectionObject Recognition	—Unverified
Natural Language Descriptions of Human Activities Scenes: Corpus Generation and Analysis	Aug 1, 2016	Action ClassificationObject Recognition	—Unverified
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition	Jul 29, 2016	Depiction Invariant Object RecognitionObject	CodeCode Available
Feature Descriptors for Tracking by Detection: a Benchmark	Jul 21, 2016	3D ReconstructionObject Recognition	—Unverified
Learning to Recognize Objects by Retaining other Factors of Variation	Jul 20, 2016	ObjectObject Recognition	—Unverified

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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