This paper focuses on open-ended video question answering, which aims to find
the correct answers from a large answer set in response to a video-related
question. This is essentially a multi-label classification task, since a
question may have multiple answers. However, due to annotation costs, the
labels in existing benchmarks are always extremely insufficient, typically one
answer per question. As a result, existing works tend to directly treat all the
unlabeled answers as negative labels, leading to limited ability for
generalization. In this work, we introduce a simple yet effective ranking
distillation framework (RADI) to mitigate this problem without additional
manual annotation. RADI employs a teacher model trained with incomplete labels
to generate rankings for potential answers, which contain rich knowledge about
label priority as well as label-associated visual cues, thereby enriching the
insufficient labeling information. To avoid overconfidence in the imperfect
teacher model, we further present two robust and parameter-free ranking
distillation approaches: a pairwise approach which introduces adaptive soft
margins to dynamically refine the optimization constraints on various pairwise
rankings, and a listwise approach which adopts sampling-based partial listwise
learning to resist the bias in teacher ranking. Extensive experiments on five
popular benchmarks consistently show that both our pairwise and listwise RADIs
outperform state-of-the-art methods. Further analysis demonstrates the
effectiveness of our methods on the insufficient labeling problem.

本文针对开放式视频问题回答进行了研究，提出了一种简单而有效的排名蒸馏框架 (RADI)，用于解决现有标注问题的缺陷，并且在五个流行基准测试中与最先进的方法相比，表现出色。

不足标签的开放式视频问题回答的排名蒸馏

Ranking Distillation for Open-Ended Video Question Answering with  Insufficient Labels

Unsupervised sentence representation learning is one of the fundamental
problems in natural language processing with various downstream applications.
Recently, contrastive learning has been widely adopted which derives
high-quality sentence representations by pulling similar semantics closer and
pushing dissimilar ones away. However, these methods fail to capture the
fine-grained ranking information among the sentences, where each sentence is
only treated as either positive or negative. In many real-world scenarios, one
needs to distinguish and rank the sentences based on their similarities to a
query sentence, e.g., very relevant, moderate relevant, less relevant,
irrelevant, etc. In this paper, we propose a novel approach, RankCSE, for
unsupervised sentence representation learning, which incorporates ranking
consistency and ranking distillation with contrastive learning into a unified
framework. In particular, we learn semantically discriminative sentence
representations by simultaneously ensuring ranking consistency between two
representations with different dropout masks, and distilling listwise ranking
knowledge from the teacher. An extensive set of experiments are conducted on
both semantic textual similarity (STS) and transfer (TR) tasks. Experimental
results demonstrate the superior performance of our approach over several
state-of-the-art baselines.

本文提出了一种新方法 RankCSE，将排名一致性和排名蒸馏与对比学习相结合，用于无监督学习句子表示，实现了语义区分的句子表示，该方法在语义文本相似性任务和转移任务中表现出优异的性能。