LaSOT免费

xlsun 27 2021-08-24 公共数据集
点击图片放大查看

资源介绍

LaSOT 由 1,400 个序列组成，总共超过 3.5M 帧。 这些序列中的每一帧都用边界框仔细手动注释，使 LaSOT 成为最大的，密集注释的跟踪数据集

END
标签
上一篇
下一篇

发表评论

会员价:￥0推荐
原价:￥0
暂无演示 登录下载
登录

最新文章

更多>

热门文章

更多>

THUYG-20 维吾尔语语音数据

2021-08-24 292

MPI-INF-3DHP

2021-08-23 213

MIMIC-III（"重症监护医疗信息市场"）

2021-08-24 163

QASC: A Dataset for Question Answering via Sentence CompositionComposing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.

2021-08-24 146

帕尔默企鹅数据集

2023-03-11 144

标签云

猜你喜欢

THUYG-20 维吾尔语语音数据
MPI-INF-3DHP
MIMIC-III（"重症监护医疗信息市场"）
QASC: A Dataset for Question Answering via Sentence CompositionComposing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.
帕尔默企鹅数据集
TotalCapture 
UCI数据库
VGG-Sound
3DPW
Jeopardy! 问题数据集