Search Results for author:
Found 18 papers, 7 papers with code
Hallucinating Visual Instances in Total Absentia
This seemingly minor difference in fact makes the HVITA a much challenging task, as the restoration algorithm would have to not only infer the category of the object in total absentia, but also hallucinate an object of which the appearance is consistent with the background.
Deployment Prior Injection for Run-time Calibratable Object Detection
With a strong alignment between the training and test distributions, object relation as a context prior facilitates object detection.
UGG: Unified Generative Grasping
Generation-based methods that generate grasping postures conditioned on the object can often produce diverse grasping, but they are insufficient for high grasping success due to lack of discriminative information.
Deep Graph Reprogramming
In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming".
Learning Graph Neural Networks for Image Style Transfer
To this end, we develop an elaborated GNN model with content and style local patches as the graph vertices.
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks
In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs).
Turning Frequency to Resolution: Video Super-Resolution via Event Cameras
To this end, we propose an Event-based VSR framework (E-VSR), of which the key component is an asynchronous interpolation (EAI) module that reconstructs a high-frequency (HF) video stream with uniform and tiny pixel displacements between neighboring frames from an event stream.
Amalgamating Knowledge From Heterogeneous Graph Neural Networks
In this paper, we study a novel knowledge transfer task in the domain of graph neural networks (GNNs).
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking
In this paper, we propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame, and hence may serve as a robust estimation even in challenging scenarios including occlusion.
Multi-Person Pose Estimation
Multi-Person Pose Estimation and Tracking
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VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals
We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences.
SPAGAN: Shortest Path Graph Attention Network
SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further {a} more effective aggregation of information from distant neighbors into the center node, as compared to node-based GCN methods.
Overcoming Catastrophic Forgetting in Graph Neural Networks
Catastrophic forgetting refers to the tendency that a neural network "forgets" the previous learned knowledge upon learning new tasks.
Learning Propagation Rules for Attribution Map Generation
Prior gradient-based attribution-map methods rely on handcrafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the input and then the attribution map.
Factorizable Graph Convolutional Networks
In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network(FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph.
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Distilling Knowledge from Graph Convolutional Networks
To enable the knowledge transfer from the teacher GCN to the student, we propose a local structure preserving module that explicitly accounts for the topological semantics of the teacher.
Dual Teaching: A Practical Semi-supervised Wrapper Method
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data.
Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification
It is theoretically proved that Boost Picking could train a supervised model mainly by un-labeled data as effectively as the same model trained by 100% labeled data, only if recalls of the two weak classifiers are all greater than zero and the sum of precisions is greater than one.
