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Found 10 papers, 4 papers with code
An inexact Bregman proximal point method and its acceleration version for unbalanced optimal transport
The algorithm (1) converges to the true solution of UOT, (2) has theoretical guarantees and robust regularization parameter selection, (3) mitigates numerical stability issues, and (4) can achieve comparable computational complexity to the Scaling algorithm in specific practice.
Event-Based Visual Odometry on Non-Holonomic Ground Vehicles
Despite the promise of superior performance under challenging conditions, event-based motion estimation remains a hard problem owing to the difficulty of extracting and tracking stable features from event streams.
Image Segmentation with Adaptive Spatial Priors from Joint Registration
In medical imaging for instance, intensity inhomogeneity and noise are common.
Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction
As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the model continuously.
Zero-Shot Instance Segmentation
We follow this motivation and propose a new task set named zero-shot instance segmentation (ZSI).
Background Learnable Cascade for Zero-Shot Object Detection
The major contributions for BLC are as follows: (i) we propose a multi-stage cascade structure named Cascade Semantic R-CNN to progressively refine the alignment between visual and semantic of ZSD; (ii) we develop the semantic information flow structure and directly add it between each stage in Cascade Semantic RCNN to further improve the semantic feature learning; (iii) we propose the background learnable region proposal network (BLRPN) to learn an appropriate word vector for background class and use this learned vector in Cascade Semantic R CNN, this design makes \Background Learnable" and reduces the confusion between background and unseen classes.
Ranked #6 on
Zero-Shot Object Detection
on PASCAL VOC'07
Volume Preserving Image Segmentation with Entropic Regularization Optimal Transport and Its Applications in Deep Learning
Image segmentation with a volume constraint is an important prior for many real applications.
EasiCS: the objective and fine-grained classification method of cervical spondylosis dysfunction
The precise diagnosis is of great significance in developing precise treatment plans to restore neck function and reduce the burden posed by the cervical spondylosis (CS).
EasiCSDeep: A deep learning model for Cervical Spondylosis Identification using surface electromyography signal
In this paper, we present an intelligent method based on the deep learning to identify CS, using the surface electromyography (sEMG) signal.
A Geometric View of Optimal Transportation and Generative Model
In this work, we show the intrinsic relations between optimal transportation and convex geometry, especially the variational approach to solve Alexandrov problem: constructing a convex polytope with prescribed face normals and volumes.
