Search Results for author:
Found 19 papers, 4 papers with code
Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss
In document-level event argument extraction, an argument is likely to appear multiple times in different expressions in the document.
GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a Gradient-Aware Mask and Semantic Constraints
Self-supervised depth estimation has evolved into an image reconstruction task that minimizes a photometric loss.
Heuristic-Driven Link-of-Analogy Prompting: Enhancing Large Language Models for Document-Level Event Argument Extraction
In this study, we investigate in-context learning (ICL) in document-level event argument extraction (EAE) to alleviate the dependency on large-scale labeled data for this task.
Feature-aware conditional GAN for category text generation
To address these issues, this paper proposes a novel GAN framework, the feature-aware conditional GAN (FA-GAN), for controllable category text generation.
Generative Adversarial Network
Multi-class Classification
+4
Particle swarm optimization with state-based adaptive velocity limit strategy
Several adaptive VL strategies have been introduced with which the performance of PSO can be improved.
Artificial-Intelligence-Based Triple Phase Shift Modulation for Dual Active Bridge Converter with Minimized Current Stress
However, to minimize the current stress when the DAB converter is under TPS modulation, two difficulties exist in analysis process and realization process, respectively.
Artificial-Intelligence-Based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter with Full ZVS Range and Optimal Efficiency
ZVS range and efficiency are two significant performance indicators for DAB converter.
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data
More specifically, a feature-domain discriminator is employed to align teacher's and student's representations for universal knowledge transfer.
Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation
It utilizes a novel class weight calibration method to alleviate the negative transfer caused by incorrect class weights.
Self-Supervised Video Representation Learning by Video Incoherence Detection
This paper introduces a novel self-supervised method that leverages incoherence detection for video representation learning.
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network
For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem.
Aligning Correlation Information for Domain Adaptation in Action Recognition
Yet correlation features of the same action would differ across domains due to domain shift.
Effective Action Recognition with Embedded Key Point Shifts
Temporal feature extraction is an essential technique in video-based action recognition.
PNL: Efficient Long-Range Dependencies Extraction with Pyramid Non-Local Module for Action Recognition
Empirical results prove the effectiveness and efficiency of our PNL module, which achieves state-of-the-art performance of 83. 09% on the Mini-Kinetics dataset, with decreased computation cost compared to the non-local block.
ARID: A New Dataset for Recognizing Action in the Dark
We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset.
Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition
Temporal feature extraction is an important issue in video-based action recognition.
Improving Relation Extraction with Knowledge-attention
While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven.
Deep Learning and Its Applications to Machine Health Monitoring: A Survey
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation.
Supervised Fine Tuning for Word Embedding with Integrated Knowledge
Inspired by deep learning, the authors propose a supervised framework for learning vector representation of words to provide additional supervised fine tuning after unsupervised learning.
