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Found 21 papers, 6 papers with code
Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos
In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics.
Modality Compensation Network: Cross-Modal Adaptation for Action Recognition
In this work, we propose a Modality Compensation Network (MCN) to explore the relationships of different modalities, and boost the representations for human action recognition.
Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches
We present a sketch refinement strategy, as inspired by the coarse-to-fine drawing process of the artists, which we show can help our model well adapt to casual and varied sketches without the need for real sketch training data.
Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks
In particular, we cast the graph optimization problem as distance metric learning to capture pairwise similarities of features in each layer.
Feature Graph Learning for 3D Point Cloud Denoising
In this work, we assume instead the availability of a relevant feature vector $\mathbf{f}_i$ per node $i$, from which we compute an optimal feature graph via optimization of a feature metric.
Controllable Artistic Text Style Transfer via Shape-Matching GAN
In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter.
3D Dynamic Point Cloud Inpainting via Temporal Consistency on Graphs
With the development of 3D laser scanning techniques and depth sensors, 3D dynamic point clouds have attracted increasing attention as a representation of 3D objects in motion, enabling various applications such as 3D immersive tele-presence, gaming and navigation.
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification
In this paper, we propose Graph Learning Neural Networks (GLNNs), which exploit the optimization of graphs (the adjacency matrix in particular) from both data and tasks.
Feature Preserving and Uniformity-controllable Point Cloud Simplification on Graph
With the development of 3D sensing technologies, point clouds have attracted increasing attention in a variety of applications for 3D object representation, such as autonomous driving, 3D immersive tele-presence and heritage reconstruction.
TET-GAN: Text Effects Transfer via Stylization and Destylization
The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images.
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
In this paper, we represent skeletons naturally on graphs, and propose a graph regression based GCN (GR-GCN) for skeleton-based action recognition, aiming to capture the spatio-temporal variation in the data.
Ranked #2 on
Skeleton Based Action Recognition
on Florence 3D
Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks
Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks.
Context-Aware Text-Based Binary Image Stylization and Synthesis
The stylization is then followed by a context-aware layout design algorithm, where cues for both seamlessness and aesthetics are employed to determine the optimal layout of the shape in the background.
Local Frequency Interpretation and Non-Local Self-Similarity on Graph for Point Cloud Inpainting
Hence, leveraging on recent advances in graph signal processing, we propose an efficient point cloud inpainting method, exploiting both the local smoothness and the non-local self-similarity in point clouds.
RGCNN: Regularized Graph CNN for Point Cloud Segmentation
Leveraging on spectral graph theory, we treat features of points in a point cloud as signals on graph, and define the convolution over graph by Chebyshev polynomial approximation.
Erase or Fill? Deep Joint Recurrent Rain Removal and Reconstruction in Videos
In this paper, we address the problem of video rain removal by constructing deep recurrent convolutional networks.
Dual Recovery Network with Online Compensation for Image Super-Resolution
In particular, we infer the HF information based on both the LR image and similar HR references which are retrieved online.
Awesome Typography: Statistics-Based Text Effects Transfer
It allows our algorithm to produce artistic typography that fits for both local texture patterns and the global spatial distribution in the example.
Deep Joint Rain Detection and Removal from a Single Image
Based on the first model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output.
MARLow: A Joint Multiplanar Autoregressive and Low-Rank Approach for Image Completion
In this paper, we propose a novel multiplanar autoregressive (AR) model to exploit the correlation in cross-dimensional planes of a similar patch group collected in an image, which has long been neglected by previous AR models.
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual~(DEGREE) network to progressively recover the high-frequency details.
