Discriminativeness is a desirable feature of image captions: captions should
describe the characteristic details of input images. However, recent
high-performing captioning models, which are trained with reinforcement
learning (RL), tend to generate overly generic captions despite their high
performance in various other criteria. First, we investigate the cause of the
unexpectedly low discriminativeness and show that RL has a deeply rooted side
effect of limiting the output words to high-frequency words. The limited
vocabulary is a severe bottleneck for discriminativeness as it is difficult for
a model to describe the details beyond its vocabulary. Then, based on this
identification of the bottleneck, we drastically recast discriminative image
captioning as a much simpler task of encouraging low-frequency word generation.
Hinted by long-tail classification and debiasing methods, we propose methods
that easily switch off-the-shelf RL models to discriminativeness-aware models
with only a single-epoch fine-tuning on the part of the parameters. Extensive
experiments demonstrate that our methods significantly enhance the
discriminativeness of off-the-shelf RL models and even outperform previous
discriminativeness-aware methods with much smaller computational costs.
Detailed analysis and human evaluation also verify that our methods boost the
discriminativeness without sacrificing the overall quality of captions.

本文探讨了针对图像的生成式字幕生成模型具有过高泛化性质的问题，分析了词汇量限制造成的字幕不够详细的原因，并提出了一种鼓励低频词汇生成的简单方法，使得模型能够生成更具独创性的字幕。实验表明，这种方法有效提高了模型的字幕生成质量。

通过缓解增强学习的瓶颈，实现切换到判别式图像字幕生成

Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning

Face recognition has advanced considerably with the availability of
large-scale labeled datasets. However, how to further improve the performance
with the easily accessible unlabeled dataset remains a challenge. In this
paper, we propose the novel Unknown Identity Rejection (UIR) loss to utilize
the unlabeled data. We categorize identities in unconstrained environment into
the known set and the unknown set. The former corresponds to the identities
that appear in the labeled training dataset while the latter is its
complementary set. Besides training the model to accurately classify the known
identities, we also force the model to reject unknown identities provided by
the unlabeled dataset via our proposed UIR loss. In order to 'reject' faces of
unknown identities, centers of the known identities are forced to keep enough
margin from centers of unknown identities which are assumed to be approximated
by the features of their samples. By this means, the discriminativeness of the
face representations can be enhanced. Experimental results demonstrate that our
approach can provide obvious performance improvement by utilizing the unlabeled
data.

本文提出 “未知身份拒绝”（UIR）损失，通过利用未标记的数据进一步提高人脸识别性能。对于被归类于未受约束的环境中的身份，我们将其分为已知集合和未知集合。除了训练模型以准确分类已知身份，我们还通过所提出的 UIR 损失使模型拒绝未标记数据提供的未知身份。利用所提出的方法，可以增强人脸表示的区分度，实验结果表明，我们的方法可以明显提高利用未标记数据的人脸识别性能。

未知身份拒绝损失：利用无标签数据进行人脸识别

Unknown Identity Rejection Loss: Utilizing Unlabeled Data for Face  Recognition

In this work, we develop a technique to produce counterfactual visual
explanations. Given a 'query' image $I$ for which a vision system predicts
class $c$, a counterfactual visual explanation identifies how $I$ could change
such that the system would output a different specified class $c'$. To do this,
we select a 'distractor' image $I'$ that the system predicts as class $c'$ and
identify spatial regions in $I$ and $I'$ such that replacing the identified
region in $I$ with the identified region in $I'$ would push the system towards
classifying $I$ as $c'$. We apply our approach to multiple image classification
datasets generating qualitative results showcasing the interpretability and
discriminativeness of our counterfactual explanations. To explore the
effectiveness of our explanations in teaching humans, we present machine
teaching experiments for the task of fine-grained bird classification. We find
that users trained to distinguish bird species fare better when given access to
counterfactual explanations in addition to training examples.

本文提出了一种生成反事实视觉解释的方法，用于多个图像分类数据集，结果表明该方法具有可解释性和区分性；同时，机器教学实验表明在鸟类分类任务中，提供反事实解释的人更容易区分鸟类物种。