Search Results for author:
Found 11 papers, 1 papers with code
MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition
Our approach takes spatial features of different scales extracted by CNN and feeds them into a Multi-scale Embedding Layer (MELayer).
SSP: A Simple and Safe automatic Prompt engineering method towards realistic image synthesis on LVM
Consequently, we propose a simple and safe prompt engineering method (SSP) to improve image generation quality by providing optimal camera descriptions.
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from Transformer
To mitigate potential negative transfer, we separate the item representations into market embeddings and item embeddings.
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey
Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.
Disentangling Prosody Representations with Unsupervised Speech Reconstruction
Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Energy Optimized Congestion Control-Based Temperature Aware Routing Algorithm for Software Defined Wireless Body Area Networks
The main contribution of this paper is to develop a novel Energy Optimized Congestion Control based on Temperature Aware Routing Algorithm (EOCC-TARA) using Enhanced Multi-objective Spider Monkey Optimization (EMSMO) for SDN-based WBAN.
BeSense: Leveraging WiFi Channel Data and Computational Intelligence for Behavior Analysis
The ever evolving informatics technology has gradually bounded human and computer in a compact way.
Two-level Attention with Two-stage Multi-task Learning for Facial Emotion Recognition
In this paper, a two-level attention with two-stage multi-task learning (2Att-2Mt) framework is proposed for facial emotion estimation on only static images.
