Search Results for author:
Found 16 papers, 3 papers with code
LORD: Large Models based Opposite Reward Design for Autonomous Driving
Recently, large pretrained models have gained significant attention as zero-shot reward models for tasks specified with desired linguistic goals.
MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR
Motion prediction has been an essential component of autonomous driving systems since it handles highly uncertain and complex scenarios involving moving agents of different types.
PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification
Specifically, we design a Patch Message Passing (PMP) module based on the Message Passing mechanism to establish global interaction for pathological semantic features and to exploit the subtle differences further between different diseases.
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Macular Hole Reconstruction with Stochastic Retinal Defect Augmentation and Dynamic Weight Composition
Macular hole diagnosis and treatment rely heavily on spatial and quantitative data, yet the scarcity of such data has impeded the progress of deep learning techniques for effective segmentation and real-time 3D reconstruction.
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech
In contrast to conventional pipeline Spoken Language Understanding (SLU) which consists of automatic speech recognition (ASR) and natural language understanding (NLU), end-to-end SLU infers the semantic meaning directly from speech and overcomes the error propagation caused by ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph
We present a novel two-layer hierarchical reinforcement learning approach equipped with a Goals Relational Graph (GRG) for tackling the partially observable goal-driven task, such as goal-driven visual navigation.
Hierarchical Reinforcement Learning
Reinforcement Learning (RL)
+1
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling
Despite the significant success at enabling robots with autonomous behaviors makes deep reinforcement learning a promising approach for robotic object search task, the deep reinforcement learning approach severely suffers from the nature sparse reward setting of the task.
From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN)
Visual Indoor Navigation (VIN) task has drawn increasing attention from the data-driven machine learning communities especially with the recently reported success from learning-based methods.
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning
Multiple-choice VQA has drawn increasing attention from researchers and end-users recently.
ROS-HPL: Robotic Object Search with Hierarchical Policy Learning and Intrinsic-Extrinsic Modeling
Despite significant progress in Robotic Object Search (ROS) over the recent years with deep reinforcement learning based approaches, the sparsity issue in reward setting as well as the lack of interpretability of the previous ROS approaches leave much to be desired.
Incremental Classifier Learning Based on PEDCC-Loss and Cosine Distance
In this paper, we introduce an ensemble method of incremental classifier to alleviate this problem, which is based on the cosine distance between the output feature and the pre-defined center, and can let each task to be preserved in different networks.
A New Loss Function for CNN Classifier Based on Pre-defined Evenly-Distributed Class Centroids
With the development of convolutional neural networks (CNNs) in recent years, the network structure has become more and more complex and varied, and has achieved very good results in pattern recognition, image classification, object detection and tracking.
GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment
We study the problem of learning a generalizable action policy for an intelligent agent to actively approach an object of interest in an indoor environment solely from its visual inputs.
Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots
We study the problem of learning a navigation policy for a robot to actively search for an object of interest in an indoor environment solely from its visual inputs.
Crowd Counting Considering Network Flow Constraints in Videos
This paper, for the first time, introduces a quadratic programming model with the network flow constraints to improve the accuracy of crowd counting.
