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Found 14 papers, 7 papers with code
Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation
Moreover, to facilitate disentangled representation learning, we introduce four well-designed constraints: an auxiliary style classifier, an auxiliary inverse classifier, a content contrastive loss, and a pair of latent cycle losses, which can effectively contribute to the construction of the identity-related style space and semantic-related content space.
Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition
Specifically, we unify a pre-trained acoustic model (wav2vec 2. 0) and a language model (BERT) into an end-to-end trainable framework.
Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer
Prior highly-tuned image parsing models are usually studied in a certain domain with a specific set of semantic labels and can hardly be adapted into other scenarios (e. g., sharing discrepant label granularity) without extensive re-training.
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition
When sampling tasks in MML-ASR, AMS adaptively determines the task sampling probability for each source language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Photometric Multi-View Mesh Refinement for High-Resolution Satellite Images
Modern high-resolution satellite sensors collect optical imagery with ground sampling distances (GSDs) of 30-50cm, which has sparked a renewed interest in photogrammetric 3D surface reconstruction from satellite data.
Bidirectional Graph Reasoning Network for Panoptic Segmentation
We introduce a Bidirectional Graph Reasoning Network (BGRNet), which incorporates graph structure into the conventional panoptic segmentation network to mine the intra-modular and intermodular relations within and between foreground things and background stuff classes.
Layout-Graph Reasoning for Fashion Landmark Detection
Each Layout-Graph Reasoning(LGR) layer aims to map feature representations into structural graph nodes via a Map-to-Node module, performs reasoning over structural graph nodes to achieve global layout coherency via a layout-graph reasoning module, and then maps graph nodes back to enhance feature representations via a Node-to-Map module.
Graphonomy: Universal Human Parsing via Graph Transfer Learning
By distilling universal semantic graph representation to each specific task, Graphonomy is able to predict all levels of parsing labels in one system without piling up the complexity.
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
Besides the challenges for conversational dialogue systems (e. g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations.
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis
Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations.
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
Beyond the existing single-person and multiple-person human parsing tasks in static images, this paper makes the first attempt to investigate a more realistic video instance-level human parsing that simultaneously segments out each person instance and parses each instance into more fine-grained parts (e. g., head, leg, dress).
Instance-level Human Parsing via Part Grouping Network
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
Ranked #6 on
Human Part Segmentation
on CIHP
Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark
To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality.
Ranked #10 on
Semantic Segmentation
on LIP val
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Human parsing has recently attracted a lot of research interests due to its huge application potentials.
Ranked #13 on
Semantic Segmentation
on LIP val
