118 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
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
Person Re-identification (ReID) is to identify the same person across
different cameras. It is a challenging task due to the large variations in
person pose, occlusion, background clutter, etc How to extract powerful
features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn
powerful features over full body and body parts, which can well capture the
local context knowledge by stacking multi-scale convolutions in each layer.
Moreover, instead of using predefined rigid parts, we propose to learn and
localize deformable pedestrian parts using Spatial Transformer Networks (STN)
with novel spatial constraints. The learned body parts can release some
difficulties, eg pose variations and background clutters, in part-based
representation. Finally, we integrate the representation learning processes of
full body and body parts into a unified framework for person ReID through
multi-class person identification tasks. Extensive evaluations on current
challenging large-scale person ReID datasets, including the image-based
Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed
method achieves the state-of-the-art results.Comment: Accepted by CVPR 201
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