Search Results for author:
Found 9 papers, 5 papers with code
PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation
However, the performance of pre-trained models on task-oriented dialog tasks is still under-explored.
A Unified Efficient Pyramid Transformer for Semantic Segmentation
Semantic segmentation is a challenging problem due to difficulties in modeling context in complex scenes and class confusions along boundaries.
A Comprehensive Study of Deep Video Action Recognition
Video action recognition is one of the representative tasks for video understanding.
Improving Semantic Segmentation via Self-Training
In the case of semantic segmentation, this means that large amounts of pixelwise annotations are required to learn accurate models.
A Tailored Pre-Training Model for Task-Oriented Dialog Generation
The recent success of large pre-trained language models such as BERT and GPT-2 has suggested the effectiveness of incorporating language priors in downstream dialog generation tasks.
ResNeSt: Split-Attention Networks
It is well known that featuremap attention and multi-path representation are important for visual recognition.
Ranked #8 on
Instance Segmentation
on COCO test-dev
(APM metric)
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds
We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds.
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
Instead, we model randomness under the framework of Bayesian Neural Network (BNN) to formally learn the posterior distribution of models in a scalable way.
Cruciform: Solving Crosswords with Natural Language Processing
We describe the results of several of our experiments with the system.
