In this work, we propose Adversarial Complementary Learning (ACoL) to
automatically localize integral objects of semantic interest with weak
supervision. We first mathematically prove that class localization maps can be
obtained by directly selecting the class-specific feature maps of the last
convolutional layer, which paves a simple way to identify object regions. We
then present a simple network architecture including two parallel-classifiers
for object localization. Specifically, we leverage one classification branch to
dynamically localize some discriminative object regions during the forward
pass. Although it is usually responsive to sparse parts of the target objects,
this classifier can drive the counterpart classifier to discover new and
complementary object regions by erasing its discovered regions from the feature
maps. With such an adversarial learning, the two parallel-classifiers are
forced to leverage complementary object regions for classification and can
finally generate integral object localization together. The merits of ACoL are
mainly two-fold: 1) it can be trained in an end-to-end manner; 2) dynamically
erasing enables the counterpart classifier to discover complementary object
regions more effectively. We demonstrate the superiority of our ACoL approach
in a variety of experiments. In particular, the Top-1 localization error rate
on the ILSVRC dataset is 45.14%, which is the new state-of-the-art.

本文提出了一种名为 ACoL 的方法，可以利用弱监督自动定位语义感兴趣的物体，并且能够在端到端的方式下进行训练，并在 ILSVRC 数据集上达到了 45.14％的最佳定位误差率。

用于弱监督目标定位的对抗互补学习

Adversarial Complementary Learning for Weakly Supervised Object  Localization

Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of
a single dominant unseen object category in a test image. We hypothesize that
this setting is ill-suited for real-world applications where unseen objects
appear only as a part of a complex scene, warranting both the `recognition' and
`localization' of an unseen category. To address this limitation, we introduce
a new \emph{`Zero-Shot Detection'} (ZSD) problem setting, which aims at
simultaneously recognizing and locating object instances belonging to novel
categories without any training examples. We also propose a new experimental
protocol for ZSD based on the highly challenging ILSVRC dataset, adhering to
practical issues, e.g., the rarity of unseen objects. To the best of our
knowledge, this is the first end-to-end deep network for ZSD that jointly
models the interplay between visual and semantic domain information. To
overcome the noise in the automatically derived semantic descriptions, we
utilize the concept of meta-classes to design an original loss function that
achieves synergy between max-margin class separation and semantic space
clustering. Furthermore, we present a baseline approach extended from
recognition to detection setting. Our extensive experiments show significant
performance boost over the baseline on the imperative yet difficult ZSD
problem.

本文提出了用于同时识别和定位未见过类别的物体实例的 Zero-Shot Detection 问题，并引入基于 ILSVRC 数据集的新实验协议和元类概念来改善自动推导的语义描述的噪音，旨在在视觉和语义领域信息之间建立协同作用的新型 'Zero-Shot Detection' 深度神经网络。

零样本目标检测：学会同时识别和定位新概念

Zero-Shot Object Detection: Learning to Simultaneously Recognize and  Localize Novel Concepts

The DenseNet architecture is highly computationally efficient as a result of
feature reuse. However, a naive DenseNet implementation can require a
significant amount of GPU memory: If not properly managed, pre-activation batch
normalization and contiguous convolution operations can produce feature maps
that grow quadratically with network depth. In this technical report, we
introduce strategies to reduce the memory consumption of DenseNets during
training. By strategically using shared memory allocations, we reduce the
memory cost for storing feature maps from quadratic to linear. Without the GPU
memory bottleneck, it is now possible to train extremely deep DenseNets.
Networks with 14M parameters can be trained on a single GPU, up from 4M. A
264-layer DenseNet (73M parameters), which previously would have been
infeasible to train, can now be trained on a single workstation with 8 NVIDIA
Tesla M40 GPUs. On the ImageNet ILSVRC classification dataset, this large
DenseNet obtains a state-of-the-art single-crop top-1 error of 20.26%.

该技术报告介绍了一种减少 DenseNet 在训练期间内存消耗的策略，使得可以在单个 GPU 上训练非常深的网络，并在 ImageNet ILSVRC 分类数据集上获得了最先进的单剪裁 top-1 误差为 20.26%。

DenseNets 的内存高效实现

Memory-Efficient Implementation of DenseNets

We propose `Hide-and-Seek', a weakly-supervised framework that aims to
improve object localization in images and action localization in videos. Most
existing weakly-supervised methods localize only the most discriminative parts
of an object rather than all relevant parts, which leads to suboptimal
performance. Our key idea is to hide patches in a training image randomly,
forcing the network to seek other relevant parts when the most discriminative
part is hidden. Our approach only needs to modify the input image and can work
with any network designed for object localization. During testing, we do not
need to hide any patches. Our Hide-and-Seek approach obtains superior
performance compared to previous methods for weakly-supervised object
localization on the ILSVRC dataset. We also demonstrate that our framework can
be easily extended to weakly-supervised action localization.

通过在训练图像中随机隐藏补丁来激发神经网络查找对象的其他相关部分，从而提出了一个名为 “Hide-and-Seek” 的弱监督框架，可以在图像和视频中提高对象和操作定位的准确性。