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Found 14 papers, 7 papers with code
3D Points Splatting for Real-Time Dynamic Hand Reconstruction
This is followed by a self-adaptive deformation that deforms the hand from the canonical space to the target pose, adapting to the dynamic changing of canonical points which, in contrast to the common practice of subdividing the MANO model, offers greater flexibility and results in improved geometry fitting.
SMC-NCA: Semantic-guided Multi-level Contrast for Semi-supervised Temporal Action Segmentation
However, learning the representation of each frame by unsupervised contrastive learning for action segmentation remains an open and challenging problem.
A Probabilistic Attention Model with Occlusion-aware Texture Regression for 3D Hand Reconstruction from a Single RGB Image
The experimental results demonstrate our probabilistic model's state-of-the-art accuracy in 3D hand and texture reconstruction from a single image in both training schemes, including in the presence of severe occlusions.
Ranked #2 on
3D Hand Pose Estimation
on HO-3D
Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour
Furthermore, we design a novel Interaction-Aware Transformer (IAT) to dynamically learn the graph-level representation of social behaviours and update the node-level representation, guided by our proposed interaction-aware self-attention mechanism.
Graph-Context Attention Networks for Size-Varied Deep Graph Matching
Deep learning for graph matching has received growing interest and developed rapidly in the past decade.
Ranked #4 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Structured Context Enhancement Network for Mouse Pose Estimation
However, quantifying mouse behaviours from videos or images remains a challenging problem, where pose estimation plays an important role in describing mouse behaviours.
Muti-view Mouse Social Behaviour Recognition with Deep Graphical Model
To address this problem, we here propose a novel multiview latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures, where the former captures unique dynamics of each view whilst the latter encodes the interaction between the views.
Perceptual underwater image enhancement with deep learning and physical priors
Underwater image enhancement, as a pre-processing step to improve the accuracy of the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration.
Deep Learning Based Brain Tumor Segmentation: A Survey
Brain tumor segmentation is one of the most challenging problems in medical image analysis.
CANet: Context Aware Network for 3D Brain Glioma Segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.
A Benchmark dataset for both underwater image enhancement and underwater object detection
To investigate how the underwater image enhancement methods influence the following underwater object detection tasks, in this paper, we provide a large-scale underwater object detection dataset with both bounding box annotations and high quality reference images, namely OUC dataset.
Underwater object detection using Invert Multi-Class Adaboost with deep learning
In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet.
Detection and Tracking of Multiple Mice Using Part Proposal Networks
The study of mouse social behaviours has been increasingly undertaken in neuroscience research.
Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.
