Search Results for author:
Found 16 papers, 13 papers with code
Controllable Group Choreography using Contrastive Diffusion
Music-driven group choreography poses a considerable challenge but holds significant potential for a wide range of industrial applications.
Music-Driven Group Choreography
The proposed dataset consists of 16. 7 hours of paired music and 3D motion from in-the-wild videos, covering 7 dance styles and 16 music genres.
Style Transfer for 2D Talking Head Animation
In this paper, we present a new method to generate talking head animation with learnable style references.
Uncertainty-aware Label Distribution Learning for Facial Expression Recognition
In this paper, we propose a new uncertainty-aware label distribution learning method to improve the robustness of deep models against uncertainty and ambiguity.
Facial Expression Recognition
Facial Expression Recognition (FER)
Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology
In this paper, we present a new multigraph topology for cross-silo federated learning.
Fine-Grained Visual Classification using Self Assessment Classifier
We show that by effectively addressing the ambiguity in the top-k prediction classes, our method achieves new state-of-the-art results on CUB200-2011, Stanford Dog, and FGVC Aircraft datasets.
Ranked #4 on
Fine-Grained Image Classification
on Stanford Dogs
Deep Federated Learning for Autonomous Driving
We design a new Federated Autonomous Driving network (FADNet) that can improve the model stability, ensure convergence, and handle imbalanced data distribution problems while is being trained with federated learning methods.
Coarse-to-Fine Reasoning for Visual Question Answering
Bridging the semantic gap between image and question is an important step to improve the accuracy of the Visual Question Answering (VQA) task.
Ranked #1 on
Visual Question Answering (VQA)
on GQA test-dev
Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledge
We design the student network such as it is light-weight and well suitable for deployment on a typical CPU.
Multiple Meta-model Quantifying for Medical Visual Question Answering
However, most of the existing medical VQA methods rely on external data for transfer learning, while the meta-data within the dataset is not fully utilized.
Ranked #5 on
Medical Visual Question Answering
on PathVQA
Graph-based Person Signature for Person Re-Identifications
In this paper, we propose a new method to effectively aggregate detailed person descriptions (attributes labels) and visual features (body parts and global features) into a graph, namely Graph-based Person Signature, and utilize Graph Convolutional Networks to learn the topological structure of the visual signature of a person.
Ranked #48 on
Person Re-Identification
on DukeMTMC-reID
Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering
Different approaches have been proposed to Visual Question Answering (VQA).
Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding
Based on pseudo labels, we propose a novel unsupervised metric loss which enforces the positive concentration and negative separation of samples in the embedding space.
Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Network
In this work, we propose a multimodal fusion approach to address the problem of autonomous navigation in complex environments such as collapsed cites, or natural caves.
Robotics
Compact Trilinear Interaction for Visual Question Answering
In Visual Question Answering (VQA), answers have a great correlation with question meaning and visual contents.
Ranked #2 on
Visual Question Answering (VQA)
on TDIUC
Overcoming Data Limitation in Medical Visual Question Answering
Traditional approaches for Visual Question Answering (VQA) require large amount of labeled data for training.
Ranked #13 on
Medical Visual Question Answering
on VQA-RAD
(using extra training data)
