Learning to ground natural language queries to target objects or regions in 3D point clouds is quite essential for 3D scene understanding. Nevertheless, existing 3D visual grounding approaches require a substantial number of bounding box annotations for text queries, which is time-consuming and labor-intensive to obtain. In this paper, we propose3D-VLA, a weakly supervised approach for3Dvisual grounding based onVisualLanguageAlignment. Our 3D-VLA exploits the superior ability of current large-scale vision-language models (VLMs) on aligning the semantics between texts and 2D images, as well as the naturally existing correspondences between 2D images and 3D point clouds, and thus implicitly constructs correspondences between texts and 3D point clouds with no need for fine-grained box annotations in the training procedure. During the inference stage, the learned text-3D correspondence will help us ground the text queries to the 3D target objects even without 2D images. To the best of our knowledge, this is the first work to investigate 3D visual grounding in a weakly supervised manner by involving large scale vision-language models, and extensive experiments on ReferIt3D and ScanRefer datasets demonstrate that our 3D-VLA achieves comparable and even superior results over the fully supervised methods.

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.

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.

Temporal sentence grounding in videos aims to localize one target video segment, which semantically corresponds to a given sentence. Unlike previous methods mainly focusing on matching semantics between the sentence and different video segments, in this paper, we propose a novel semantic conditioned dynamic modulation (SCDM) mechanism, which leverages the sentence semantics to modulate the temporal convolution operations for better correlating and composing the sentence-relevant video contents over time. The proposed SCDM also performs dynamically with respect to the diverse video contents so as to establish a precise semantic alignment between sentence and video. By coupling the proposed SCDM with a hierarchical temporal convolutional architecture, video segments with various temporal scales are composed and localized. Besides, more fine-grained clip-level actionness scores are also predicted with the SCDM-coupled temporal convolution on the bottom layer of the overall architecture, which are further used to adjust the temporal boundaries of the localized segments and thereby lead to more accurate grounding results. Experimental results on benchmark datasets demonstrate that the proposed model can improve the temporal grounding accuracy consistently, and further investigation experiments also illustrate the advantages of SCDM on stabilizing the model training and associating relevant video contents for temporal sentence grounding. Our code for this paper is available at https://github.com/yytzsy/SCDM-TPAMI.

Enhancing the diversity of sentences to describe video contents is an important problem arising in recent video captioning research. In this paper, we explore this problem from a novel perspective of customizing video captions by imitating exemplar sentence syntaxes. Specifically, given a video and any syntax-valid exemplar sentence, we introduce a new task of Syntax Customized Video Captioning (SCVC) aiming to generate one caption which not only semantically describes the video contents but also syntactically imitates the given exemplar sentence. To tackle the SCVC task, we propose a novel video captioning model, where a hierarchical sentence syntax encoder is first designed to extract the syntactic structure of the exemplar sentence, then a syntax conditioned caption decoder is devised to generate the syntactically structured caption expressing video semantics. As there is no available syntax customized groundtruth video captions, we tackle such a challenge by proposing a new training strategy, which leverages the traditional pairwise video captioning data and our collected exemplar sentences to accomplish the model learning. Extensive experiments, in terms of semantic, syntactic, fluency, and diversity evaluations, clearly demonstrate our model capability to generate syntax-varied and semantics-coherent video captions that well imitate different exemplar sentences with enriched diversities. Code is available at https://github.com/yytzsy/Syntax-Customized-Video-Captioning.

In this paper, we investigate a novel and challenging task, namely controllable video captioning with an exemplar sentence. Formally, given a video and a syntactically valid exemplar sentence, the task aims to generate one caption which not only describes the semantic contents of the video, but also follows the syntactic form of the given exemplar sentence. In order to tackle such an exemplar-based video captioning task, we propose a novel Syntax Modulated Caption Generator (SMCG) incorporated in an encoder-decoder-reconstructor architecture. The proposed SMCG takes video semantic representation as an input, and conditionally modulates the gates and cells of long short-term memory network with respect to the encoded syntactic information of the given exemplar sentence. Therefore, SMCG is able to control the states for word prediction and achieve the syntax customized caption generation. We conduct experiments by collecting auxiliary exemplar sentences for two public video captioning datasets. Extensive experimental results demonstrate the effectiveness of our approach on generating syntax controllable and semantic preserved video captions. By providing different exemplar sentences, our approach is capable of producing different captions with various syntactic structures, thus indicating a promising way to strengthen the diversity of video captioning. Code for this paper is available at https://github.com/yytzsy/SMCG.

We have witnessed the tremendous growth of videos over the Internet, where most of these videos are typically paired with abundant sentence descriptions, such as video titles, captions and comments. Therefore, it has been increasingly crucial to associate specific video segments with the corresponding informative text descriptions, for a deeper understanding of video content. This motivates us to explore an overlooked problem in the research community — temporal sentence localization in video, which aims to automatically determine the start and end points of a given sentence within a paired video. For solving this problem, we face three critical challenges: (1) preserving the intrinsic temporal structure and global context of video to locate accurate positions over the entire video sequence; (2) fully exploring the sentence semantics to give clear guidance for localization; (3) ensuring the efficiency of the localization method to adapt to long videos. To address these issues, we propose a novel Attention Based Location Regression (ABLR) approach to localize sentence descriptions in videos in an efficient end-to-end manner. Specifically, to preserve the context information, ABLR first encodes both video and sentence via Bi-directional LSTM networks. Then, a multi-modal co-attention mechanism is presented to generate both video and sentence attentions. The former reflects the global video structure, while the latter highlights the sentence details for temporal localization. Finally, a novel attention based location prediction network is designed to regress the temporal coordinates of sentence from the previous attentions. We evaluate the proposed ABLR approach on two public datasets ActivityNet Captions and TACoS. Experimental results show that ABLR significantly outperforms the existing approaches in both effectiveness and efficiency.

Automatic content generation has become an attractive while challenging topic in the past decade. Generating videos from sentences particularly poses great challenges to the multimedia community due to its multi-modal characteristics in essence, e.g., difficulties in semantic alignment, and the temporal dependencies in video contents. Existing works resort to Variational AutoEncoder (VAE) or Generative Adversary Network (GAN) for generating videos given sentences, which may suffer from either blurry generated videos or unstable training processes as well as difficulties in converging to optimal solutions. In this paper, we propose a cross-modal dual learning (CMDL) algorithm to tackle the challenges in sentence-to-video generation and address the weaknesses in existing works. The proposed CMDL model adopts a dual learning mechanism to simultaneously learn the bidirectional mappings between sentences and videos such that it is able to generate realistic videos which maintain semantic consistencies with their corresponding textual descriptions. By further capturing both global and contextual structures, CMDL employs a multi-scale sentence-to-visual encoder to produce more sequentially consistent and plausible videos. Extensive experiments on various datasets validate the advantages of our proposed CMDL model against several state-of-the-art benchmarks both visually and quantitatively.

With the tremendous growth of videos over the Internet, video thumbnails, providing video content previews, are becoming increasingly crucial to influencing users' online searching experiences. Conventional video thumbnails are generated once purely based on the visual characteristics of videos, and then displayed as requested. Hence, such video thumbnails, without considering the users' searching intentions, cannot provide a meaningful snapshot of the video contents that users concern. In this paper, we define a distinctively new task, namely sentence specified dynamic video thumbnail generation, where the generated thumbnails not only provide a concise preview of the original video contents but also dynamically relate to the users' searching intentions with semantic correspondences to the users' query sentences. To tackle such a challenging task, we propose a novel graph convolved video thumbnail pointer (GTP). Specifically, GTP leverages a sentence specified video graph convolutional network to model both the sentence-video semantic interaction and the internal video relationships incorporated with the sentence information, based on which a temporal conditioned pointer network is then introduced to sequentially generate the sentence specified video thumbnails. Moreover, we annotate a new dataset based on ActivityNet Captions for the proposed new task, which consists of 10,000+ video-sentence pairs with each accompanied by an annotated sentence specified video thumbnail. We demonstrate that our proposed GTP outperforms several baseline methods on the created dataset, and thus believe that our initial results along with the release of the new dataset will inspire further research on sentence specified dynamic video thumbnail generation. Dataset and code are available at https://github.com/yytzsy/GTP

Temporal sentence grounding in videos aims to detect and localize one target video segment, which semantically corresponds to a given sentence. Existing methods mainly tackle this task via matching and aligning semantics between a sentence and candidate video segments, while neglect the fact that the sentence information plays an important role in temporally correlating and composing the described contents in videos. In this paper, we propose a novel semantic conditioned dynamic modulation (SCDM) mechanism, which relies on the sentence semantics to modulate the temporal convolution operations for better correlating and composing the sentence related video contents over time. More importantly, the proposed SCDM performs dynamically with respect to the diverse video contents so as to establish a more precise matching relationship between sentence and video, thereby improving the temporal grounding accuracy. Extensive experiments on three public datasets demonstrate that our proposed model outperforms the state-of-the-arts with clear margins, illustrating the ability of SCDM to better associate and localize relevant video contents for temporal sentence grounding. Our code for this paper is available at https://github.com/yytzsy/SCDM.

With the rapid growth of video content, video summarization, which focuses on automatically selecting important and informative parts from videos, is becoming increasingly crucial. However, the problem is challenging due to its subjectiveness. Previous research, which predominantly relies on manually designed criteria or resourcefully expensive human annotations, often fails to achieve satisfying results. We observe that the side information associated with a video (e.g., surrounding text such as titles, queries, descriptions, comments, and so on) represents a kind of human-curated semantics of video content. This side information, although valuable for video summarization, is overlooked in existing approaches. In this paper, we present a novel deep side semantic embedding (DSSE) model to generate video summaries by leveraging the freely available side information. The DSSE constructs a latent subspace by correlating the hidden layers of the two uni-modal autoencoders, which embed the video frames and side information, respectively. Specifically, by interactively minimizing the semantic relevance loss and the feature reconstruction loss of the two uni-modal autoencoders, the comparable common information between video frames and side information can be more completely learned. Therefore, their semantic relevance can be more effectively measured. Finally, semantically meaningful segments are selected from videos by minimizing their distances to the side information in the constructed latent subspace. We conduct experiments on two datasets (Thumb1K and TVSum50) and demonstrate the superior performance of DSSE to the several state-of-the-art approaches to video summarization.