Search Results for author:
Found 9 papers, 2 papers with code
Spatial-Temporal Synergic Residual Learning for Video Person Re-Identification
We present a novel method - Spatial-Temporal Synergic Residual Network (STSRN) for this problem.
Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks
In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties.
Hierarchical Meta Learning
Extensive experiments on few-shot classification and regression problems clearly demonstrate the superiority of HML over fine-tuning and state-of-the-art meta learning approaches in terms of generalization across heterogeneous tasks.
Panoptic Edge Detection
In this work, we address a new finer-grained task, termed panoptic edge detection (PED), which aims at predicting semantic-level boundaries for stuff categories and instance-level boundaries for instance categories, in order to provide more comprehensive and unified scene understanding from the perspective of edges. We then propose a versatile framework, Panoptic Edge Network (PEN), which aggregates different tasks of object detection, semantic and instance edge detection into a single holistic network with multiple branches.
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.
Ranked #67 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Image-to-Video Generation via 3D Facial Dynamics
Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks.
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate
In this paper, we study the effect of the adaptation learning rate in meta-learning with mixed linear regression.
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization.
