Video Grounding (VG) aims to locate the desired segment from a video given a sentence query. Recent studies have found that current VG models are prone to over-rely the groundtruth moment annotation distribution biases in the training set. To discourage the standard VG model's behavior of exploiting such temporal annotation biases and improve the model generalization ability, we propose multiple negative augmentations in a hierarchical way, including cross-video augmentations from clip-/video-level, and self-shuffled augmentations with masks. These augmentations can effectively diversify the data distribution so that the model can make more reasonable predictions instead of merely fitting the temporal biases. However, directly adopting such data augmentation strategy may inevitably carry some noise shown in our cases, since not all of the handcrafted augmentations are semantically irrelevant to the groundtruth video. To further denoise and improve the grounding accuracy, we design a multi-stage curriculum strategy to adaptively train the standard VG model from easy to hard negative augmentations. Experiments on newly collected Charades-CD and ActivityNet-CD datasets demonstrate our proposed strategy can improve the performance of the base model on both i.i.d and o.o.d scenarios.

Video Grounding (VG), has drawn widespread attention over the past few years, and numerous studies have been devoted to improving performance on various VG benchmarks. Nevertheless, the label annotation procedures in VG produce imbalanced query-moment-label distributions in the datasets, which severely deteriorate the learning model's capability of truly understanding the video contents. Existing works on debiased VG either focus on adjusting the learning model or conducting video-level augmentation, failing to handle the temporal bias issue caused by imbalanced query-moment-label distributions. In this paper, we propose a Disentangled Feature Mixup (DFM) framework for debiased VG, which is capable of performing unbiased grounding to tackle the temporal bias issue. Specifically, a feature-mixup augmentation strategy is designed to generate new (text, location) pairs with diverse temporal distributions via jointly augmenting the representation of text queries and the location labels. This strategy encourages making prediction based on more diverse data samples with balanced query-moment-label distributions. Furthermore, we also design a content-location disentanglement module to disentangle the representations of the temporal information and content information in videos, which is able to remove the spurious effect of temporal biases on video representation. Given that our proposed DFM framework conducts feature-level augmentation and disentanglement, it is model-agnostic and can be applied to most baselines simply yet effectively. Extensive experiments show that our proposed DFM framework is able to significantly outperform baseline models in various metrics under both independent identical distribution (i.i.d.) and out-of-distribution (o.o.d.) scenes, especially in scenarios with annotation distribution changes.

Temporal Sentence Grounding aims to retrieve a video moment given a natural language query. Most existing literature merely focuses on visual information in videos without considering the naturally accompanied audio which may contain rich semantics. The few works considering audio simply regard it as an additional modality, overlooking that: i) it's non-trivial to explore consistency and complementarity between audio and visual; ii) such exploration requires handling different levels of information densities and noises in the two modalities. To tackle these challenges, we propose Adaptive Dual-branch Promoted Network (ADPN) to exploit such consistency and complementarity: i) we introduce a dual-branch pipeline capable of jointly training visual-only and audio-visual branches to simultaneously eliminate inter-modal interference; ii) we design Text-Guided Clues Miner (TGCM) to discover crucial locating clues via considering both consistency and complementarity during audio-visual interaction guided by text semantics; iii) we propose a novel curriculum-based denoising optimization strategy, where we adaptively evaluate sample difficulty as a measure of noise intensity in a self-aware fashion. Extensive experiments show the state-of-the-art performance of our method.

Temporal sentence grounding in videos (TSGV), which aims at localizing 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, single-stage 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 four typical tasks with new and practical settings based on TSGV.

Temporal Sentence Grounding in Videos (TSGV) , which aims to ground a natural language sentence that indicates complex human activities in an untrimmed video, has drawn widespread attention over the past few years. However, recent studies have found that current benchmark datasets may have obvious moment annotation biases, enabling several simple baselines even without training to achieve state-of-the-art (SOTA) performance. In this paper, we take a closer look at existing evaluation protocols for TSGV, and find that both the prevailing dataset splits and evaluation metrics are the devils that lead to untrustworthy benchmarking. Therefore, we propose to re-organize the two widely-used datasets, making the ground-truth moment distributions different in the training and test splits, i.e., out-of-distribution (OOD) test. Meanwhile, we introduce a new evaluation metric “dR@ n ,IoU= m ” that discounts the basic recall scores especially with small IoU thresholds, so as to alleviate the inflating evaluation caused by biased datasets with a large proportion of long ground-truth moments. New benchmarking results indicate that our proposed evaluation protocols can better monitor the research progress in TSGV. Furthermore, we propose a novel causality-based Multi-branch Deconfounding Debiasing (MDD) framework for unbiased moment prediction. Specifically, we design a multi-branch deconfounder to eliminate the effects caused by multiple confounders with causal intervention. In order to help the model better align the semantics between sentence queries and video moments, we enhance the representations during feature encoding. Specifically, for textual information, the query is parsed into several verb-centered phrases to obtain a more fine-grained textual feature. For visual information, the positional information has been decomposed from the moment features to enhance the representations of moments with diverse locations. Extensive experiments demonstrate that our proposed approach can achieve competitive results among existing SOTA approaches and outperform the base model with great gains.

Video grounding aims to ground a sentence query in a video by determining the start and end timestamps of the semantically matched segment. It is a fundamental and essential vision-and-language problem widely investigated by the research community, and it also has potential values applied in industrial domains. This tutorial will give a detailed introduction about the development and evolution of this task, point out the limitations of existing benchmarks, and extend such a text-based grounding task to more general scenarios, especially how it guides the learning of other video-language tasks like video question answering based on event grounding. This topic is at the core of the scope of ACM Multimedia, and is attractive to MM audience from both academia and industry.

Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications.Cross-lingual KG alignment is the task of matching entities with their counterparts in different languages, which is an important way to enrich the crosslingual links in multilingual KGs.In this paper, we propose a novel approach for crosslingual KG alignment via graph convolutional networks (GCNs).Given a set of pre-aligned entities, our approach trains GCNs to embed entities of each language into a unified vector space.Entity alignments are discovered based on the distances between entities in the embedding space.Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments.In the experiments on aligning real multilingual KGs, our approach gets the best performance compared with other embedding-based KG alignment approaches.