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Found 19 papers, 3 papers with code
Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing
Abstract In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive models.
DMNR: Unsupervised De-noising of Point Clouds Corrupted by Airborne Particles
However, airborne particles, such as fog, rain, snow, and dust, will degrade its performance and it is inevitable to encounter these inclement environmental conditions outdoors.
Multi-Sample Consensus Driven Unsupervised Normal Estimation for 3D Point Clouds
Comprehensive experiments demonstrate that the two proposed unsupervised methods are noticeably superior to some supervised deep normal estimators on the most common synthetic dataset.
Pose-Controllable 3D Facial Animation Synthesis using Hierarchical Audio-Vertex Attention
In this paper, a novel pose-controllable 3D facial animation synthesis method is proposed by utilizing hierarchical audio-vertex attention.
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents
Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i. e., semantic entity), while the relations in-between are largely unexplored.
Ranked #4 on
Entity Linking
on FUNSD
Singlino-dominated dark matter in $Z_3$-NMSSM
Approximate analytical formulas describing the dark matter abundance and cross section in the scattering with nucleons are used to illustrate a dependence on theoretical parameters in neutralino and Higgs sectors.
High Energy Physics - Phenomenology
Singlino-dominated dark matter in general NMSSM
Under such a theoretical structure, the DM achieves the correct abundance by annihilating into a pair of singlet-dominated Higgs bosons by adjusting $\kappa$'s value.
High Energy Physics - Phenomenology
HILONet: Hierarchical Imitation Learning from Non-Aligned Observations
In this work, we propose a new imitation learning approach called Hierarchical Imitation Learning from Observation(HILONet), which adopts a hierarchical structure to choose feasible sub-goals from demonstrated observations dynamically.
Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together
We also deploy and validate our method in a real world scenario.
Robotics Multiagent Systems
Semantic Parsing for English as a Second Language
This paper is concerned with semantic parsing for English as a second language (ESL).
SMPR: Single-Stage Multi-Person Pose Regression
Existing multi-person pose estimators can be roughly divided into two-stage approaches (top-down and bottom-up approaches) and one-stage approaches.
Ranked #7 on
Multi-Person Pose Estimation
on COCO test-dev
SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT
Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.
A Comparative Analysis of Knowledge-Intensive and Data-Intensive Semantic Parsers
We present a phenomenon-oriented comparative analysis of the two dominant approaches in task-independent semantic parsing: classic, knowledge-intensive and neural, data-intensive models.
Multi-Normal Estimation via Pair Consistency Voting
This paper presents a unified definition for point cloud normals of feature and non-feature points, which allows feature points to possess multiple normals.
Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis
We present experiments for cross-domain semantic dependency analysis with a neural Maximum Subgraph parser.
Quasi-Second-Order Parsing for 1-Endpoint-Crossing, Pagenumber-2 Graphs
We propose a new Maximum Subgraph algorithm for first-order parsing to 1-endpoint-crossing, pagenumber-2 graphs.
Semantic Dependency Parsing via Book Embedding
We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing.
Parsing to 1-Endpoint-Crossing, Pagenumber-2 Graphs
We study the Maximum Subgraph problem in deep dependency parsing.
Adaptive Partial Differential Equation Learning for Visual Saliency Detection
Then by optimizing a discrete submodular function constrained with this LESD and a uniform matroid, the saliency seeds (i. e., boundary conditions) can be learnt for this image, thus achieving an optimal PDE system to model the evolution of visual saliency.
