Researchers commonly perform sentiment analysis on large collections of short
texts like tweets, Reddit posts or newspaper headlines that are all focused on
a specific topic, theme or event. Usually, general purpose sentiment analysis
methods are used which perform well on average but miss the variation in
meaning that happens across different contexts, for example, the word "active"
has a very different intention and valence in the phrase "active lifestyle"
versus "active volcano". This work presents a new approach, CIDER (Context
Informed Dictionary and sEntiment Reasoner), which performs context sensitive
sentiment analysis, where the valence of sentiment laden terms is inferred from
the whole corpus before being used to score the individual texts. In this paper
we detail the CIDER algorithm and demonstrate that it outperforms
state-of-the-art generalist sentiment analysis on a large collection of tweets
about the weather. We have made our implementation of CIDER available as a
python package: this https URL

该研究提出了一种新的情感分析方法 CIDER，利用上下文敏感的方式将情感计算应用于具有不同语境的一组文本，并证明其表现优于先前研究中的通用情感分析方法。

短文本上下文敏感情感分析

CIDER: Context sensitive sentiment analysis for short-form text

Many top-performing image captioning models rely solely on object features
computed with an object detection model to generate image descriptions.
However, recent studies propose to directly use scene graphs to introduce
information about object relations into captioning, hoping to better describe
interactions between objects. In this work, we thoroughly investigate the use
of scene graphs in image captioning. We empirically study whether using
additional scene graph encoders can lead to better image descriptions and
propose a conditional graph attention network (C-GAT), where the image
captioning decoder state is used to condition the graph updates. Finally, we
determine to what extent noise in the predicted scene graphs influence caption
quality. Overall, we find no significant difference between models that use
scene graph features and models that only use object detection features across
different captioning metrics, which suggests that existing scene graph
generation models are still too noisy to be useful in image captioning.
Moreover, although the quality of predicted scene graphs is very low in
general, when using high quality scene graphs we obtain gains of up to 3.3
CIDEr compared to a strong Bottom-Up Top-Down baseline. We open source code to
reproduce all our experiments in
this https URL

本文探讨了使用场景图在图像字幕生成中的应用，研究表明添加场景图编码器可以提高字幕描述效果，提出了一种 C-GAT 模型，该模型条件化图更新功能，使用高质量的场景图可与现有的底部 - 顶部基准模型相比获得 3.3 CIDEr 改进。

场景图是否足够好以改进图像字幕？

Are scene graphs good enough to improve Image Captioning?

A wide range of image captioning models has been developed, achieving
significant improvement based on popular metrics, such as BLEU, CIDEr, and
SPICE. However, although the generated captions can accurately describe the
image, they are generic for similar images and lack distinctiveness, i.e.,
cannot properly describe the uniqueness of each image. In this paper, we aim to
improve the distinctiveness of image captions through training with sets of
similar images. First, we propose a distinctiveness metric -- between-set CIDEr
(CIDErBtw) to evaluate the distinctiveness of a caption with respect to those
of similar images. Our metric shows that the human annotations of each image
are not equivalent based on distinctiveness. Thus we propose several new
training strategies to encourage the distinctiveness of the generated caption
for each image, which are based on using CIDErBtw in a weighted loss function
or as a reinforcement learning reward. Finally, extensive experiments are
conducted, showing that our proposed approach significantly improves both
distinctiveness (as measured by CIDErBtw and retrieval metrics) and accuracy
(e.g., as measured by CIDEr) for a wide variety of image captioning baselines.
These results are further confirmed through a user study.

通过 CIDErBtw 度量图片独特性，结合带权损失函数和强化学习奖励等多种训练策略，本文提出了一种改进的图像描述生成方法，实验证明该方法显著提高了描述独特性和准确性。

比较与重新加权：使用相似图像集进行独特的图像字幕

Compare and Reweight: Distinctive Image Captioning Using Similar Images  Sets

Image captioning is a challenging task that combines the field of computer
vision and natural language processing. A variety of approaches have been
proposed to achieve the goal of automatically describing an image, and
recurrent neural network (RNN) or long-short term memory (LSTM) based models
dominate this field. However, RNNs or LSTMs cannot be calculated in parallel
and ignore the underlying hierarchical structure of a sentence. In this paper,
we propose a framework that only employs convolutional neural networks (CNNs)
to generate captions. Owing to parallel computing, our basic model is around 3
times faster than NIC (an LSTM-based model) during training time, while also
providing better results. We conduct extensive experiments on MSCOCO and
investigate the influence of the model width and depth. Compared with
LSTM-based models that apply similar attention mechanisms, our proposed models
achieves comparable scores of BLEU-1,2,3,4 and METEOR, and higher scores of
CIDEr. We also test our model on the paragraph annotation dataset, and get
higher CIDEr score compared with hierarchical LSTMs

本文在图像字幕生成方面，提出了一种仅利用卷积神经网络生成字幕的框架，通过并行计算，训练速度比基于 LSTM 的模型快 3 倍，同时在 MSCOCO 图像字幕数据集上获得了比 LSTM 更高的评估得分。