931 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
Mixed Supervised Object Detection with Robust Objectness Transfer
In this paper, we consider the problem of leveraging existing fully labeled
categories to improve the weakly supervised detection (WSD) of new object
categories, which we refer to as mixed supervised detection (MSD). Different
from previous MSD methods that directly transfer the pre-trained object
detectors from existing categories to new categories, we propose a more
reasonable and robust objectness transfer approach for MSD. In our framework,
we first learn domain-invariant objectness knowledge from the existing fully
labeled categories. The knowledge is modeled based on invariant features that
are robust to the distribution discrepancy between the existing categories and
new categories; therefore the resulting knowledge would generalize well to new
categories and could assist detection models to reject distractors (e.g.,
object parts) in weakly labeled images of new categories. Under the guidance of
learned objectness knowledge, we utilize multiple instance learning (MIL) to
model the concepts of both objects and distractors and to further improve the
ability of rejecting distractors in weakly labeled images. Our robust
objectness transfer approach outperforms the existing MSD methods, and achieves
state-of-the-art results on the challenging ILSVRC2013 detection dataset and
the PASCAL VOC datasets.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(2019). Together with Supplementary Materials. Note: The author list in
Google Scholar is INCORRECT. The right author list is 1) Yan Li, 2) Junge
Zhang, 3) Kaiqi Huang and 4) Jianguo Zhang. The official published version
can be found in https://ieeexplore.ieee.org/abstract/document/830462
Discriminative Learning of Latent Features for Zero-Shot Recognition
Zero-shot learning (ZSL) aims to recognize unseen image categories by
learning an embedding space between image and semantic representations. For
years, among existing works, it has been the center task to learn the proper
mapping matrices aligning the visual and semantic space, whilst the importance
to learn discriminative representations for ZSL is ignored. In this work, we
retrospect existing methods and demonstrate the necessity to learn
discriminative representations for both visual and semantic instances of ZSL.
We propose an end-to-end network that is capable of 1) automatically
discovering discriminative regions by a zoom network; and 2) learning
discriminative semantic representations in an augmented space introduced for
both user-defined and latent attributes. Our proposed method is tested
extensively on two challenging ZSL datasets, and the experiment results show
that the proposed method significantly outperforms state-of-the-art methods.Comment: CVPR 2018 (Oral
- …