Search Results for author:
Found 9 papers, 3 papers with code
Blended Grammar Network for Human Parsing
Although human parsing has made great progress, it still faces a challenge, i. e., how to extract the whole foreground from similar or cluttered scenes effectively.
SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses.
Masked Language Model Based Textual Adversarial Example Detection
To explore how to use the masked language model in adversarial detection, we propose a novel textual adversarial example detection method, namely Masked Language Model-based Detection (MLMD), which can produce clearly distinguishable signals between normal examples and adversarial examples by exploring the changes in manifolds induced by the masked language model.
High-Fidelity Clothed Avatar Reconstruction from a Single Image
This paper presents a framework for efficient 3D clothed avatar reconstruction.
Graphics Capsule: Learning Hierarchical 3D Face Representations from 2D Images
The function of constructing the hierarchy of objects is important to the visual process of the human brain.
Deep Learning for Human Parsing: A Survey
Human parsing is a key topic in image processing with many applications, such as surveillance analysis, human-robot interaction, person search, and clothing category classification, among many others.
HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing Capsule Network
Capsule networks are designed to present the objects by a set of parts and their relationships, which provide an insight into the procedure of visual perception.
Part-Aware Context Network for Human Parsing
By fusing the outputs of the relational aggregation module, the relational dispersion module and the backbone network, our PCNet generates adaptive contextual features for various sizes of human parts, improving the parsing accuracy.
30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine
Heretofore, global burned area (BA) products are only available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution.
