Search Results for author:
Found 17 papers, 5 papers with code
ShapeMoiré: Channel-Wise Shape-Guided Network for Image Demoiréing
Interestingly, we find that the Shape information effectively captures the moir\'e patterns in artifact images.
Aircraft Landing Time Prediction with Deep Learning on Trajectory Images
Aircraft landing time (ALT) prediction is crucial for air traffic management, especially for arrival aircraft sequencing on the runway.
SOGDet: Semantic-Occupancy Guided Multi-view 3D Object Detection
This indicates that the combination of 3D object detection and 3D semantic occupancy leads to a more comprehensive perception of the 3D environment, thereby aiding build more robust autonomous driving systems.
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection
Early detection of dysplasia of the cervix is critical for cervical cancer treatment.
Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials
Street-view imagery provides us with novel experiences to explore different places remotely.
Ranked #3 on
Image-Based Localization
on cvact
Emotionally Enhanced Talking Face Generation
To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more realistic and convincing.
Ranked #1 on
Talking Face Generation
on CREMA-D
CrossMatch: Source-Free Domain Adaptive Semantic Segmentation via Cross-Modal Consistency Training
Source-free domain adaptive semantic segmentation has gained increasing attention recently.
Emotional Talking Faces: Making Videos More Expressive and Realistic
Lip synchronization and talking face generation have gained a specific interest from the research community with the advent and need of digital communication in different fields.
Mix-up Self-Supervised Learning for Contrast-agnostic Applications
Contrastive self-supervised learning has attracted significant research attention recently.
Motion Prediction via Joint Dependency Modeling in Phase Space
In this paper, we introduce a novel convolutional neural model to effectively leverage explicit prior knowledge of motion anatomy, and simultaneously capture both spatial and temporal information of joint trajectory dynamics.
Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns.
Zero-Shot Multi-View Indoor Localization via Graph Location Networks
In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization.
"Notic My Speech" -- Blending Speech Patterns With Multimedia
To close the gap between speech understanding and multimedia video applications, in this paper, we show the initial experiments by modelling the perception on visual speech and showing its use case on video compression.
COBRA: Contrastive Bi-Modal Representation Algorithm
In this paper, we present a novel framework COBRA that aims to train two modalities (image and text) in a joint fashion inspired by the Contrastive Predictive Coding (CPC) and Noise Contrastive Estimation (NCE) paradigms which preserve both inter and intra-class relationships.
Lipper: Synthesizing Thy Speech using Multi-View Lipreading
The model takes silent videos as input and produces speech as the output.
Harnessing GANs for Zero-shot Learning of New Classes in Visual Speech Recognition
To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases.
A Multimodal Approach to Predict Social Media Popularity
In this work, we propose a multimodal dataset consisiting of content, context, and social information for popularity prediction.
