This paper focuses on activity retrieval from a video query in an imbalanced
scenario. In current query-by-activity-video literature, a common assumption is
that all activities have sufficient labelled examples when learning an
embedding. This assumption does however practically not hold, as only a portion
of activities have many examples, while other activities are only described by
few examples. In this paper, we propose a visual-semantic embedding network
that explicitly deals with the imbalanced scenario for activity retrieval. Our
network contains two novel modules. The visual alignment module performs a
global alignment between the input video and fixed-sized visual bank
representations for all activities. The semantic module performs an alignment
between the input video and fixed-sized semantic activity representations. By
matching videos with both visual and semantic activity representations that are
of equal size over all activities, we no longer ignore infrequent activities
during retrieval. Experiments on a new imbalanced activity retrieval benchmark
show the effectiveness of our approach for all types of activities.

本文针对非平衡场景中的视频查询中的活动检索进行研究，提出了一种视觉 - 语义嵌入网络，该网络包含两个新模块：视觉对齐模块通过全局对齐输入视频和所有活动的固定大小视觉库表示之间的关系，语义模块在输入视频和固定大小的语义活动表示之间进行对齐。通过匹配具有相等规模的视觉和语义活动表示的视频，我们不再在检索过程中忽略不常见的活动。实验结果表明，我们的方法在各类活动的非平衡活动检索基准上具有有效性。

野外活动视频查询

Query by Activity Video in the Wild

In this paper, we focus on generating training examples for few-shot intents
in the realistic imbalanced scenario. To build connections between existing
many-shot intents and few-shot intents, we consider an intent as a combination
of a domain and an action, and propose a composed variational natural language
generator (CLANG), a transformer-based conditional variational autoencoder.
CLANG utilizes two latent variables to represent the utterances corresponding
to two different independent parts (domain and action) in the intent, and the
latent variables are composed together to generate natural examples.
Additionally, to improve the generator learning, we adopt the contrastive
regularization loss that contrasts the in-class with the out-of-class utterance
generation given the intent. To evaluate the quality of the generated
utterances, experiments are conducted on the generalized few-shot intent
detection task. Empirical results show that our proposed model achieves
state-of-the-art performances on two real-world intent detection datasets.

本文提出了一种组合变分自然语言生成器（CLANG），它是基于变压器的条件变分自编码器，利用两个潜在变量分别表达意图中两个独立部分（领域和操作），并采取对比正则化损失来提高生成器学习，并在广义的少样本意图检测任务上进行了实验证明表明，我们的模型在两个真实世界的意图检测数据集上均达到了最先进的性能。