33 research outputs found
MSC: A Dataset for Macro-Management in StarCraft II
Macro-management is an important problem in StarCraft, which has been studied
for a long time. Various datasets together with assorted methods have been
proposed in the last few years. But these datasets have some defects for
boosting the academic and industrial research: 1) There're neither standard
preprocessing, parsing and feature extraction procedures nor predefined
training, validation and test set in some datasets. 2) Some datasets are only
specified for certain tasks in macro-management. 3) Some datasets are either
too small or don't have enough labeled data for modern machine learning
algorithms such as deep neural networks. So most previous methods are trained
with various features, evaluated on different test sets from the same or
different datasets, making it difficult to be compared directly. To boost the
research of macro-management in StarCraft, we release a new dataset MSC based
on the platform SC2LE. MSC consists of well-designed feature vectors,
pre-defined high-level actions and final result of each match. We also split
MSC into training, validation and test set for the convenience of evaluation
and comparison. Besides the dataset, we propose a baseline model and present
initial baseline results for global state evaluation and build order
prediction, which are two of the key tasks in macro-management. Various
downstream tasks and analyses of the dataset are also described for the sake of
research on macro-management in StarCraft II. Homepage:
https://github.com/wuhuikai/MSC.Comment: Homepage: https://github.com/wuhuikai/MS
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping
Image cropping aims at improving the aesthetic quality of images by adjusting
their composition. Most weakly supervised cropping methods (without bounding
box supervision) rely on the sliding window mechanism. The sliding window
mechanism requires fixed aspect ratios and limits the cropping region with
arbitrary size. Moreover, the sliding window method usually produces tens of
thousands of windows on the input image which is very time-consuming. Motivated
by these challenges, we firstly formulate the aesthetic image cropping as a
sequential decision-making process and propose a weakly supervised Aesthetics
Aware Reinforcement Learning (A2-RL) framework to address this problem.
Particularly, the proposed method develops an aesthetics aware reward function
which especially benefits image cropping. Similar to human's decision making,
we use a comprehensive state representation including both the current
observation and the historical experience. We train the agent using the
actor-critic architecture in an end-to-end manner. The agent is evaluated on
several popular unseen cropping datasets. Experiment results show that our
method achieves the state-of-the-art performance with much fewer candidate
windows and much less time compared with previous weakly supervised methods.Comment: Accepted by CVPR 201
YOLOrtho -- A Unified Framework for Teeth Enumeration and Dental Disease Detection
Detecting dental diseases through panoramic X-rays images is a standard
procedure for dentists. Normally, a dentist need to identify diseases and find
the infected teeth. While numerous machine learning models adopting this
two-step procedure have been developed, there has not been an end-to-end model
that can identify teeth and their associated diseases at the same time. To fill
the gap, we develop YOLOrtho, a unified framework for teeth enumeration and
dental disease detection. We develop our model on Dentex Challenge 2023 data,
which consists of three distinct types of annotated data. The first part is
labeled with quadrant, and the second part is labeled with quadrant and
enumeration and the third part is labeled with quadrant, enumeration and
disease. To further improve detection, we make use of Tufts Dental public
dataset. To fully utilize the data and learn both teeth detection and disease
identification simultaneously, we formulate diseases as attributes attached to
their corresponding teeth. Due to the nature of position relation in teeth
enumeration, We replace convolution layer with CoordConv in our model to
provide more position information for the model. We also adjust the model
architecture and insert one more upsampling layer in FPN in favor of large
object detection. Finally, we propose a post-process strategy for teeth layout
that corrects teeth enumeration based on linear sum assignment. Results from
experiments show that our model exceeds large Diffusion-based model