Temporal sentence grounding in videos~(TSGV), which aims to localize one target segment from an untrimmed video with respect to a given sentence query, has drawn increasing attentions in the research community over the past few years. Different from
the task of temporal action localization, TSGV is more flexible since it can locate complicated activities via natural languages, without restrictions from predefined action categories. Meanwhile, TSGV is more challenging since it requires both textual and visual understanding for semantic alignment between two modalities~(i.e., text and video). In this survey, we give a comprehensive overview for TSGV, which i) summarizes the taxonomy of existing methods, ii) provides a detailed description of the evaluation protocols~(i.e., datasets and metrics) to be used in TSGV, and iii) in-depth discusses potential problems of current benchmarking designs and research directions for further investigations. To the best of our knowledge, this is the first systematic survey on temporal sentence grounding. More specifically, we first discuss existing TSGV approaches by grouping them into four categories, i.e., two-stage methods, end-to-end methods, reinforcement learning-based methods, and weakly supervised methods. Then we present the benchmark datasets and evaluation metrics to assess current research progress. Finally, we discuss some limitations in TSGV through pointing out potential problems improperly resolved in the current evaluation protocols, which may push forwards more cutting edge research in TSGV. Besides, we also share our insights on several promising directions, including three typical tasks with new and practical settings based on TSGV.
Despite Temporal Sentence Grounding in Videos (TSGV) has realized impressive progress over the last few years, current TSGV models tend to capture the moment annotation biases and fail to take full advantage of multi-modal inputs. Miraculously, some
extremely simple TSGV baselines even without training can also achieve state-of-the-art performance. In this paper, we first take a closer look at the existing evaluation protocol, and argue that both the prevailing datasets and metrics are the devils to cause the unreliable benchmarking. To this end, we propose to re-organize two widely-used TSGV datasets (Charades-STA and ActivityNet Captions), and deliberately textbf{C}hange the moment annotation textbf{D}istribution of the test split to make it different from the training split, dubbed as Charades-CD and ActivityNet-CD, respectively. Meanwhile, we further introduce a new evaluation metric dR@$n$,IoU@$m$ to calibrate the basic IoU scores by penalizing more on the over-long moment predictions and reduce the inflating performance caused by the moment annotation biases. Under this new evaluation protocol, we conduct extensive experiments and ablation studies on eight state-of-the-art TSGV models. All the results demonstrate that the re-organized datasets and new metric can better monitor the progress in TSGV, which is still far from satisfactory. The repository of this work is at url{https://github.com/yytzsy/grounding_changing_distribution}.
