Search Results for author:
Found 14 papers, 4 papers with code
Alignment-free Cross-lingual Semantic Role Labeling
Cross-lingual semantic role labeling (SRL) aims at leveraging resources in a source language to minimize the effort required to construct annotations or models for a new target language.
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models
In this paper, we propose the Confidence-aware KG-based Recommendation Framework with LLM Augmentation (CKG-LLMA), a novel framework that combines KGs and LLMs for recommendation task.
Dynamic Adapter with Semantics Disentangling for Cross-lingual Cross-modal Retrieval
As a general parameter-efficient way, a common solution is to utilize adapter modules to transfer the vision-language alignment ability of Vision-Language Pretraining (VLP) models from a source language to a target language.
Zero-Shot Relational Learning for Multimodal Knowledge Graphs
Relational learning is an essential task in the domain of knowledge representation, particularly in knowledge graph completion (KGC).
Masked Face Dataset Generation and Masked Face Recognition
In the post-pandemic era, wearing face masks has posed great challenge to the ordinary face recognition.
Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning
In this paper, we propose a noise-robust cross-lingual cross-modal retrieval method for low-resource languages.
Semi-Supervised Semantic Role Labeling with Cross-View Training
The successful application of neural networks to a variety of NLP tasks has provided strong impetus to develop end-to-end models for semantic role labeling which forego the need for extensive feature engineering.
Syntax-aware Semantic Role Labeling without Parsing
In this paper we focus on learning dependency aware representations for semantic role labeling without recourse to an external parser.
Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation
This layer can learn to adjust the contributions of RGB and depth over each pixel for high-performance object recognition.
Ranked #91 on
Semantic Segmentation
on NYU Depth v2
Joint Multiview Segmentation and Localization of RGB-D Images Using Depth-Induced Silhouette Consistency
In this paper, we propose an RGB-D camera localization approach which takes an effective geometry constraint, i. e. silhouette consistency, into consideration.
Query Adaptive Similarity Measure for RGB-D Object Recognition
The reasons are in two-fold: (1) existing similarity measures are sensitive to object pose and scale changes, as well as intra-class variations; and (2) effectively fusing RGB and depth cues is still an open problem.
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation
We present a novel global stereo model designed for view interpolation.
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition
3D model-based object recognition has been a noticeable research trend in recent years.
