Composed Image Retrieval (CoIR) has recently gained popularity as a task that
considers both text and image queries together, to search for relevant images
in a database. Most CoIR approaches require manually annotated datasets,
comprising image-text-image triplets, where the text describes a modification
from the query image to the target image. However, manual curation of CoIR
triplets is expensive and prevents scalability. In this work, we instead
propose a scalable automatic dataset creation methodology that generates
triplets given video-caption pairs, while also expanding the scope of the task
to include composed video retrieval (CoVR). To this end, we mine paired videos
with a similar caption from a large database, and leverage a large language
model to generate the corresponding modification text. Applying this
methodology to the extensive WebVid2M collection, we automatically construct
our WebVid-CoVR dataset, resulting in 1.6 million triplets. Moreover, we
introduce a new benchmark for CoVR with a manually annotated evaluation set,
along with baseline results. Our experiments further demonstrate that training
a CoVR model on our dataset effectively transfers to CoIR, leading to improved
state-of-the-art performance in the zero-shot setup on both the CIRR and
FashionIQ benchmarks. Our code, datasets, and models are publicly available at
this https URL

通过自动数据集创建方法，我们提出了一个可扩展的 Composed Image Retrieval（CoIR）任务，使用由视频标题对生成的三元组，扩展任务范围到 Composed Video Retrieval（CoVR）。通过在庞大的数据库中挖掘具有相似标题的配对视频，并利用大型语言模型生成相应的修改文本，我们构建了 WebVid-CoVR 数据集，其中包含 1.6 百万个三元组。实验证明，在我们的数据集上训练 CoVR 模型可以有效迁移到 CoIR，提高了 CIRR 和 FashionIQ 基准测试中的最新性能。

CoVR：从网络视频字幕中学习组合视频检索

CoVR: Learning Composed Video Retrieval from Web Video Captions

Large pretrained language models have been performing increasingly well in a
variety of downstream tasks via prompting. However, it remains unclear from
where the model learns the task-specific knowledge, especially in a zero-shot
setup. In this work, we want to find evidence of the model's task-specific
competence from pretraining and are specifically interested in locating a very
small subset of pretraining data that directly supports the model in the task.
We call such a subset supporting data evidence and propose a novel method ORCA
to effectively identify it, by iteratively using gradient information related
to the downstream task. This supporting data evidence offers interesting
insights about the prompted language models: in the tasks of sentiment analysis
and textual entailment, BERT shows a substantial reliance on BookCorpus, the
smaller corpus of BERT's two pretraining corpora, as well as on pretraining
examples that mask out synonyms to the task verbalizers.

本文旨在探究预训练模型在零样本情况下的特定任务认知。通过对支持数据证据进行迭代使用，可以发现 BERT 在情感分析和文本蕴含任务中对 BookCorpus 预训练数据的大量依赖，以及对掩盖任务动词词汇同义词的预训练示例的依赖。

ORCA：通过定位预训练数据海洋中的支持数据证据解释提示型语言模型

ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data

We propose a morphology-based method for low-resource (LR) dependency
parsing. We train a morphological inflector for target LR languages, and apply
it to related rich-resource (RR) treebanks to create cross-lingual
(x-inflected) treebanks that resemble the target LR language. We use such
inflected treebanks to train parsers in zero- (training on x-inflected
treebanks) and few-shot (training on x-inflected and target language treebanks)
setups. The results show that the method sometimes improves the baselines, but
not consistently.

提出一种基于形态学的低资源依存解析方法，在目标低资源语言中训练形态变化器，并将其应用于相关的富资源树库以创建类似于目标低资源语言的跨语言折叠树库。使用这样的折叠树库在零（在折叠的树库上训练）和少量样本（在折叠和目标语言树库上训练）设置中训练解析器。结果表明该方法有时会改善基线，但不是一致的。