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Found 23 papers, 7 papers with code
Map-Relative Pose Regression for Visual Re-Localization
We present a new approach to pose regression, map-relative pose regression (marepo), that satisfies the data hunger of the pose regression network in a scene-agnostic fashion.
HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation
In addition, we take advantage of the uncertainty for pose refinement to enhance the performance of APR.
A Two-stage Personalized Virtual Try-on Framework with Shape Control and Texture Guidance
The Diffusion model has a strong ability to generate wild images.
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer
For example, consider cross-domain fraud detection, where there are two types of transactions: credit and non-credit.
Source Identification: A Self-Supervision Task for Dense Prediction
The paradigm of self-supervision focuses on representation learning from raw data without the need of labor-consuming annotations, which is the main bottleneck of current data-driven methods.
Pay More Attention to Relation Exploration for Knowledge Base Question Answering
In this study, we propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation and introduce additional supervision.
Neural Refinement for Absolute Pose Regression with Feature Synthesis
To enhance the robustness of our model, we introduce a feature fusion module and a progressive training strategy.
Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation
In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
DFNet: Enhance Absolute Pose Regression with Direct Feature Matching
We introduce a camera relocalization pipeline that combines absolute pose regression (APR) and direct feature matching.
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
Thus, in this paper, we further propose a transfer framework to tackle the cross-domain fraud detection problem, which aims to transfer knowledge from existing domains (source domains) with enough and mature data to improve the performance in the new domain (target domain).
Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance.
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.
Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
We present a relocalization pipeline, which combines an absolute pose regression (APR) network with a novel view synthesis based direct matching module, offering superior accuracy while maintaining low inference time.
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.
Region-of-interest guided Supervoxel Inpainting for Self-supervision
Self-supervised learning has proven to be invaluable in making best use of all of the available data in biomedical image segmentation.
A Deep Learning-Based System for PharmaCoNER
The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical {\&} drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2).
A New Local Transformation Module for Few-shot Segmentation
The result by the matrix transformation can be regarded as an attention map with high-level semantic cues, based on which a transformation module can be built simply. The proposed transformation module is a general module that can be used to replace the transformation module in the existing few-shot segmentation frameworks.
Ranked #79 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
Class Activation Map generation by Multiple Level Class Grouping and Orthogonal Constraint
Moreover, a new orthogonal module and a two-branch based CAM generation method are proposed to generate class regions that are orthogonal and complementary.
HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019.
Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation
The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes.
DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors
Working with multi-scale features, the designed dual scale residual unit makes dual scale detectors no longer run independently.
An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images
Fully-connected Conditional Random Field (CRF) is often used as post-processing to refine voxel classification results by encouraging spatial coherence.
