Search Results for author:
Found 28 papers, 12 papers with code
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation
When applied to zero-shot cross-lingual abstractive summarization, it produces an average performance gain of 12. 3 ROUGE-L over mBART-ft. We conduct detailed analyses to understand the key ingredients of SixT+, including multilinguality of the auxiliary parallel data, positional disentangled encoder, and the cross-lingual transferability of its encoder.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+3
End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps
In this paper, we aim to forecast a future trajectory distribution of a moving agent in the real world, given the social scene images and historical trajectories.
High-resolution Face Swapping via Latent Semantics Disentanglement
Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space.
Autofocus for Event Cameras
Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios.
Visual-tactile sensing for Real-time liquid Volume Estimation in Grasping
We propose a deep visuo-tactile model for realtime estimation of the liquid inside a deformable container in a proprioceptive way. We fuse two sensory modalities, i. e., the raw visual inputs from the RGB camera and the tactile cues from our specific tactile sensor without any extra sensor calibrations. The robotic system is well controlled and adjusted based on the estimation model in real time.
An Intelligent Self-driving Truck System For Highway Transportation
Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry.
DiffSRL: Learning Dynamic-aware State Representation for Deformable Object Control with Differentiable Simulator
We propose DiffSRL, a dynamic state representation learning pipeline utilizing differentiable simulation that can embed complex dynamics models as part of the end-to-end training.
Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
When applied to zero-shot cross-lingual abstractive summarization, it produces an average performance gain of 12. 3 ROUGE-L over mBART-ft. We conduct detailed analyses to understand the key ingredients of SixT+, including multilinguality of the auxiliary parallel data, positional disentangled encoder, and the cross-lingual transferability of its encoder.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+3
Modular Lagrangian Neural Networks: Designing Structures of Networks with Physical Inductive Biases
At the same time, we directly applied our trained models to predict the motion of multi-pendulum and multi-body systems, demonstrating the intriguing performance in the extrapolation of our method.
Learning Selective Communication for Multi-Agent Path Finding
Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF).
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View Transformation
Furthermore, our model runs at 35 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
Autonomous Driving
Monocular Cross-View Road Scene Parsing(Road)
+1
Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders
In this paper, we focus on a zero-shot cross-lingual transfer task in NMT.
USTC-NELSLIP System Description for DIHARD-III Challenge
This system description describes our submission system to the Third DIHARD Speech Diarization Challenge.
Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping
This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping.
Robotics
A Noise-Aware Memory-Attention Network Architecture for Regression-Based Speech Enhancement
We propose a novel noise-aware memory-attention network (NAMAN) for regression-based speech enhancement, aiming at improving quality of enhanced speech in unseen noise conditions.
Over-crowdedness Alert! Forecasting the Future Crowd Distribution
In this paper, we formulate a novel crowd analysis problem, in which we aim to predict the crowd distribution in the near future given sequential frames of a crowd video without any identity annotations.
Keyfilter-Aware Real-Time UAV Object Tracking
Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency.
Rigid-Soft Interactive Learning for Robust Grasping
We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden and exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects.
A Configuration-Space Decomposition Scheme for Learning-based Collision Checking
Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution.
Learning Resilient Behaviors for Navigation Under Uncertainty
Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically.
DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance
We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL).
Augmented Memory for Correlation Filters in Real-Time UAV Tracking
The outstanding computational efficiency of discriminative correlation filter (DCF) fades away with various complicated improvements.
Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery
We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles.
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
We built neural networks as our policy to map sensor readings to control signals on the UAV.
Robust Shape Estimation for 3D Deformable Object Manipulation
Existing shape estimation methods for deformable object manipulation suffer from the drawbacks of being off-line, model dependent, noise-sensitive or occlusion-sensitive, and thus are not appropriate for manipulation tasks requiring high precision.
Robotics
Safe Navigation with Human Instructions in Complex Scenes
In this paper, we present a robotic navigation algorithm with natural language interfaces, which enables a robot to safely walk through a changing environment with moving persons by following human instructions such as "go to the restaurant and keep away from people".
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system.
Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation
We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations.
