Search Results for author:
Found 14 papers, 3 papers with code
Principled Preferential Bayesian Optimization
We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions.
WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge
Sentiment analysis is rapidly advancing by utilizing various data modalities (e. g., text, image).
Dynamic Association Learning of Self-Attention and Convolution in Image Restoration
Thus, we propose to refine background textures with the predicted degradation prior in an association learning manner.
Generalizable Zero-Shot Speaker Adaptive Speech Synthesis with Disentangled Representations
However, most current approaches suffer from the degradation of naturalness and speaker similarity when synthesizing speech for unseen speakers (i. e., speakers not in the training dataset) due to the poor generalizability of the model in out-of-distribution data.
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model
As an emerging approach, deep learning plays an increasingly influential role in channel modeling.
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily.
AutoLV: Automatic Lecture Video Generator
We propose an end-to-end lecture video generation system that can generate realistic and complete lecture videos directly from annotated slides, instructor's reference voice and instructor's reference portrait video.
MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild
To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions.
Ranked #9 on
Dynamic Facial Expression Recognition
on MAFW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
Clip-aware expressive feature learning for video-based facial expression recognition
In this paper, we divide a video into several short clips for processing and propose a clip-aware emotion-rich feature learning network (CEFLNet) for robust video-based FER.
Ranked #16 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
Expression Snippet Transformer for Robust Video-based Facial Expression Recognition
To this end, we propose to decompose each video into a series of expression snippets, each of which contains a small number of facial movements, and attempt to augment the Transformer's ability for modeling intra-snippet and inter-snippet visual relations, respectively, obtaining the Expression snippet Transformer (EST).
Ranked #14 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
Topic Scene Graph Generation by Attention Distillation From Caption
To this end, we let the scene graph borrow the ability from the image caption so that it can be a specialist on the basis of remaining all-around, resulting in the so-called Topic Scene Graph.
Magnetoelectric coupling and decoupling in multiferroic hexagonal YbFeO3 thin films
The coupling between ferroelectric and magnetic orders in multiferroic materials and the nature of magnetoelectric (ME) effects are enduring experimental challenges.
Materials Science Other Condensed Matter
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation
Scene graph aims to faithfully reveal humans' perception of image content.
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation
Therefore, inspired by the successful application of context to object-oriented tasks, we especially construct context for relationships where all of them are gathered so that the recognition could benefit from their association.
