A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric

TL;DR

This paper critically examines the evaluation protocols for Temporal Sentence Grounding in Videos, revealing biases and proposing new dataset splits and metrics to improve benchmarking reliability and progress measurement.

Contribution

It introduces a reorganization of existing datasets with out-of-distribution splits and a new evaluation metric to address biases and better assess TSGV methods.

Findings

Existing benchmarks are biased and unreliable for evaluation.

The new dataset splits and metrics provide more accurate benchmarking.

Reorganized datasets and metrics better reflect true model performance.

Abstract

Temporal Sentence Grounding in Videos (TSGV), i.e., grounding a natural language sentence which indicates complex human activities in a long and untrimmed video sequence, has received unprecedented attentions over the last few years. Although each newly proposed method plausibly can achieve better performance than previous ones, current TSGV models still tend to capture the moment annotation biases and fail to take full advantage of multi-modal inputs. Even more incredibly, several extremely simple baselines without training can also achieve state-of-the-art performance. In this paper, we take a closer look at the existing evaluation protocols for TSGV, and find that both the prevailing dataset splits and evaluation metrics are the devils to cause unreliable benchmarking. To this end, we propose to re-organize two widely-used TSGV benchmarks (ActivityNet Captions and Charades-STA). Specifically, we deliberately make the ground-truth moment distribution different in the training and test splits, i.e., out-of-distribution (OOD) testing. Meanwhile, we introduce a new evaluation metric dR@n,IoU@m to calibrate the basic IoU scores by penalizing on the bias-influenced moment predictions and alleviate the inflating evaluations caused by the dataset annotation biases such as overlong ground-truth moments. Under our new evaluation protocol, we conduct extensive experiments and ablation studies on eight state-of-the-art TSGV methods. All the results demonstrate that the re-organized dataset splits and new metric can better monitor the progress in TSGV. Our reorganized datsets are available at https://github.com/yytzsy/grounding_changing_distribution.