Tang et al., 2023 - Google Patents
Learning spatial-frequency transformer for visual object trackingTang et al., 2023
View PDF- Document ID
- 633544829314473240
- Author
- Tang C
- Wang X
- Bai Y
- Wu Z
- Zhang J
- Huang Y
- Publication year
- Publication venue
- IEEE Transactions on Circuits and Systems for Video Technology
External Links
Snippet
Recently, some researchers have begun to adopt the Transformer to combine or replace the widely used ResNet as their new backbone network. As the Transformer captures the long- range relations between pixels well using the self-attention scheme, which complements the …
Classifications
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- 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
- 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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- 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
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- 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
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tang et al. | Learning spatial-frequency transformer for visual object tracking | |
Wang et al. | Learning attentions: residual attentional siamese network for high performance online visual tracking | |
Wang et al. | Inferring salient objects from human fixations | |
Zhang et al. | Synthetic data generation for end-to-end thermal infrared tracking | |
Yuan et al. | Robust thermal infrared tracking via an adaptively multi-feature fusion model | |
Zhang et al. | A background-aware correlation filter with adaptive saliency-aware regularization for visual tracking | |
Zhang et al. | Learning background-aware and spatial-temporal regularized correlation filters for visual tracking | |
Li et al. | Visual object tracking via multi-stream deep similarity learning networks | |
Li et al. | Dynamic feature-memory transformer network for RGBT tracking | |
Sun et al. | Transformer-based moving target tracking method for Unmanned Aerial Vehicle | |
Lu et al. | Siamese graph attention networks for robust visual object tracking | |
Gu et al. | RTSformer: A Robust Toroidal Transformer With Spatiotemporal Features for Visual Tracking | |
Wang et al. | SiamADT: Siamese attention and deformable features fusion network for visual object tracking | |
Li et al. | MULS-Net: A Multilevel Supervised Network for Ship Tracking From Low-Resolution Remote-Sensing Image Sequences | |
Ma et al. | Robust visual tracking via adaptive feature channel selection | |
Liu et al. | Siamdmu: Siamese dual mask update network for visual object tracking | |
Zhou et al. | Regression-selective feature-adaptive tracker for visual object tracking | |
Tian et al. | Towards class-agnostic tracking using feature decorrelation in point clouds | |
Pan et al. | SiamCA: Siamese visual tracking with customized anchor and target-aware interaction | |
Gong et al. | ASAFormer: Visual tracking with convolutional vision transformer and asymmetric selective attention | |
Gong et al. | Visual tracking with pyramidal feature fusion and transformer based model predictor | |
Wang et al. | Basketball technique action recognition using 3D convolutional neural networks | |
Cao et al. | Weighted optical flow prediction and attention model for object tracking | |
Liang et al. | Paf-tracker: a novel pre-frame auxiliary and fusion visual tracker | |
An et al. | Self-supervised facial expression recognition with fine-grained feature selection |