It is challenging for artificial intelligence systems to achieve accurate
video recognition under the scenario of low computation costs. Adaptive
inference based efficient video recognition methods typically preview videos
and focus on salient parts to reduce computation costs. Most existing works
focus on complex networks learning with video classification based objectives.
Taking all frames as positive samples, few of them pay attention to the
discrimination between positive samples (salient frames) and negative samples
(non-salient frames) in supervisions. To fill this gap, in this paper, we
propose a novel Non-saliency Suppression Network (NSNet), which effectively
suppresses the responses of non-salient frames. Specifically, on the frame
level, effective pseudo labels that can distinguish between salient and
non-salient frames are generated to guide the frame saliency learning. On the
video level, a temporal attention module is learned under dual video-level
supervisions on both the salient and the non-salient representations. Saliency
measurements from both two levels are combined for exploitation of
multi-granularity complementary information. Extensive experiments conducted on
four well-known benchmarks verify our NSNet not only achieves the
state-of-the-art accuracy-efficiency trade-off but also present a significantly
faster (2.4~4.3x) practical inference speed than state-of-the-art methods. Our
project page is at this https URL .

该研究提出了一种有效的非显著性抑制网络 (NSNet)，该网络可以在硬件计算速度低下的情况下实现高精确度的视频识别。通过在帧级别生成伪标签，并在视频级别上学习时间注意，NSNet 有效地抑制了非关键帧的响应，并取得了显著的时间和效率改进。

NSNet：用于高效视频识别的非显著性抑制采样器

NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition

Most textual entailment models focus on lexical gaps between the premise text
and the hypothesis, but rarely on knowledge gaps. We focus on filling these
knowledge gaps in the Science Entailment task, by leveraging an external
structured knowledge base (KB) of science facts. Our new architecture combines
standard neural entailment models with a knowledge lookup module. To facilitate
this lookup, we propose a fact-level decomposition of the hypothesis, and
verifying the resulting sub-facts against both the textual premise and the
structured KB. Our model, NSnet, learns to aggregate predictions from these
heterogeneous data formats. On the SciTail dataset, NSnet outperforms a simpler
combination of the two predictions by 3% and the base entailment model by 5%.

本文介绍了一种使用外部结构化知识库填补科学知识差距的模型，将标准神经模型与知识查找模块相结合，在使用科学知识库和文本先验信息验证子事实后，NSnet 模型在 SciTail 数据集上比基准模型性能提高了 5%。