Cheng et al., 2022 - Google Patents
MIAM: Motion information aggregation module for action recognitionCheng et al., 2022
View PDF- Document ID
- 2988318537570182782
- Author
- Cheng Q
- Ren Z
- Liu Z
- Cheng J
- Zhang Q
- Liu J
- Publication year
- Publication venue
- Electronics Letters
External Links
Snippet
In the field of action recognition based on RGB videos, it is infeasible to train deep networks on dozens or hundreds of frames because of limits on computational complexity and memory. Previous works commonly adopted a sparse sampling strategy, which …
- 238000004220 aggregation 0 title abstract description 4
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
- G06F15/163—Interprocessor communication
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Koohzadi et al. | Survey on deep learning methods in human action recognition | |
Li et al. | Multi‐scale attention encoder for street‐to‐aerial image geo‐localization | |
Yu et al. | Fully convolutional networks for action recognition | |
Xia et al. | Temporal saliency query network for efficient video recognition | |
Belal et al. | Knowledge distillation methods for efficient unsupervised adaptation across multiple domains | |
Xia et al. | Nsnet: Non-saliency suppression sampler for efficient video recognition | |
Wang et al. | Quality-aware dual-modal saliency detection via deep reinforcement learning | |
Nguyen et al. | A robust and efficient method for skeleton‐based human action recognition and its application for cross‐dataset evaluation | |
Wu et al. | Active learning with label correlation exploration for multi‐label image classification | |
Wu et al. | A sample‐proxy dual triplet loss function for object re‐identification | |
Qu et al. | A method of single‐shot target detection with multi‐scale feature fusion and feature enhancement | |
Wang et al. | Self‐supervised image clustering from multiple incomplete views via constrastive complementary generation | |
Liu et al. | Multilevel receptive field expansion network for small object detection | |
Li et al. | A tri‐attention enhanced graph convolutional network for skeleton‐based action recognition | |
Gao et al. | Efficient 6D object pose estimation based on attentive multi‐scale contextual information | |
Huang et al. | Bidirectional mutual guidance transformer for salient object detection in optical remote sensing images | |
Zuo et al. | Improving multispectral pedestrian detection with scale‐aware permutation attention and adjacent feature aggregation | |
Yang et al. | Ghost shuffle lightweight pose network with effective feature representation and learning for human pose estimation | |
Shvai et al. | Multiple auxiliary classifiers GAN for controllable image generation: Application to license plate recognition | |
Zhong et al. | Multimodal cooperative self‐attention network for action recognition | |
Cheng et al. | MIAM: Motion information aggregation module for action recognition | |
Oh et al. | Pre-training local and non-local geographical influences with contrastive learning | |
Wang et al. | Multi‐level feature fusion network for crowd counting | |
Huang et al. | Multi‐scale feature combination for person re‐identification | |
Li et al. | Human interaction recognition fusing multiple features of depth sequences |