  Understanding human emotions is a crucial ability for intelligent robots to
provide better human-robot interactions. The existing works are limited to
trimmed video-level emotion classification, failing to locate the temporal
window corresponding to the emotion. In this paper, we introduce a new task,
named Temporal Emotion Localization in videos~(TEL), which aims to detect human
emotions and localize their corresponding temporal boundaries in untrimmed
videos with aligned subtitles. TEL presents three unique challenges compared to
temporal action localization: 1) The emotions have extremely varied temporal
dynamics; 2) The emotion cues are embedded in both appearances and complex
plots; 3) The fine-grained temporal annotations are complicated and
labor-intensive. To address the first two challenges, we propose a novel
dilated context integrated network with a coarse-fine two-stream architecture.
The coarse stream captures varied temporal dynamics by modeling
multi-granularity temporal contexts. The fine stream achieves complex plots
understanding by reasoning the dependency between the multi-granularity
temporal contexts from the coarse stream and adaptively integrates them into
fine-grained video segment features. To address the third challenge, we
introduce a cross-modal consensus learning paradigm, which leverages the
inherent semantic consensus between the aligned video and subtitle to achieve
weakly-supervised learning. We contribute a new testing set with 3,000
manually-annotated temporal boundaries so that future research on the TEL
problem can be quantitatively evaluated. Extensive experiments show the
effectiveness of our approach on temporal emotion localization. The repository
of this work is at
https://github.com/YYJMJC/Temporal-Emotion-Localization-in-Videos.
