Search Results for author:
Found 7 papers, 3 papers with code
MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment
We propose Continual AQA (CAQA) to refine models using sparse new data.
Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model
Text-driven human motion generation in computer vision is both significant and challenging.
Ranked #13 on
Motion Synthesis
on KIT Motion-Language
Tackling Data Bias in Painting Classification with Style Transfer
We propose a system to handle data bias in small paintings datasets like the Kaokore dataset while simultaneously accounting for domain adaptation in fine-tuning a model trained on real world images.
HSE: Hybrid Species Embedding for Deep Metric Learning
Deep metric learning is crucial for finding an embedding function that can generalize to training and testing data, including unknown test classes.
Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos
Consider that geometric features such as human pose and object position provide meaningful information to understand HOIs, we argue to combine the benefits of both visual and geometric features in HOI recognition, and propose a novel Two-level Geometric feature-informed Graph Convolutional Network (2G-GCN).
Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing
In this paper, we propose a novel parsing framework, Multi-Scale Dual-Branch Fully Convolutional Network (MSDB-FCN), for hand parsing tasks.
DOOBNet: Deep Object Occlusion Boundary Detection from an Image
Object occlusion boundary detection is a fundamental and crucial research problem in computer vision.
