Video Question Answering Research Articles

Harnessing Representative Spatial-Temporal Information for Video Question Answering

Video question answering, aiming to answer a natural language question related to the given video, has become prevalent in the past few years. Although remarkable improvements have been obtained, it is still exposed to the challenge of insufficient comprehension of video content. To this end, we propose a spatial-temporal representative visual exploitation network for video question answering, which enhances the understanding of the video by merely summarizing representative visual information. In order to explore representative object information, we advance adaptive attention based on uncertainty estimation. At the same time, to capture representative frame-level and clip-level visual information, we structure a much more compact set of representations iteratively in an expectation-maximization manner to deprecate noisy information. Both the quantitative and qualitative results on NExT-QA, TGIF-QA, MSRVTT-QA, and MSVD-QA datasets demonstrate the superiority of our model over several state-of-the-art approaches.

NExT-OOD: Overcoming Dual Multiple-Choice VQA Biases.

In recent years, multiple-choice Visual Question Answering (VQA) has become topical and achieved remarkable progress. However, most pioneer multiple-choice VQA models are heavily driven by statistical correlations in datasets, which cannot perform well on multimodal understanding and suffer from poor generalization. In this paper, we identify two kinds of spurious correlations, i.e., a Vision-Answer bias (VA bias) and a Question-Answer bias (QA bias). To systematically and scientifically study these biases, we construct a new video question answering (videoQA) benchmark NExT-OOD in OOD setting and propose a graph-based cross-sample method for bias reduction. Specifically, the NExT-OOD is designed to quantify models' generalizability and measure their reasoning ability comprehensively. It contains three sub-datasets including NExT-OOD-VA, NExT-OOD-QA, and NExT-OOD-VQA, which are designed for the VA bias, QA bias, and VA&QA bias, respectively. We evaluate several existing multiple-choice VQA models on our NExT-OOD, and illustrate that their performance degrades significantly compared with the results obtained on the original multiple-choice VQA dataset. Besides, to mitigate the VA bias and QA bias, we explicitly consider the cross-sample information and design a contrastive graph matching loss in our approach, which provides adequate debiasing guidance from the perspective of whole dataset, and encourages the model to focus on multimodal contents instead of spurious statistical regularities. Extensive experimental results illustrate that our method significantly outperforms other bias reduction strategies, demonstrating the effectiveness and generalizability of the proposed approach.

MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling

Video-and-language understanding has a variety of applications in the industry, such as video question answering, text-video retrieval, and multi-label classification. Existing video-and-language understanding methods generally adopt heavy multi-modal encoders and feature fusion modules, which consume high computational costs. Specially, they have difficulty dealing with dense video frames or long text prevalent in industrial applications. This paper proposes MuLTI, a highly accurate and efficient video-and-language understanding model that achieves efficient and effective feature fusion and rapid adaptation to downstream tasks. Specifically, we design a Text-Guided MultiWay-Sampler based on adapt-pooling residual mapping and self-attention modules to sample long sequences and fuse multi-modal features, which reduces the computational costs and addresses performance degradation caused by previous samplers. Therefore, MuLTI can handle longer sequences with limited computational costs. Then, to further enhance the model's performance and fill in the lack of pretraining tasks in the video question answering, we propose a new pretraining task named Multiple Choice Modeling. This task bridges the gap between pretraining and downstream tasks and improves the model's ability to align video and text features. Benefiting from the efficient feature fusion module and the new pretraining task, MuLTI achieves state-of-the-art performance on multiple datasets. Implementation and pretrained models will be released.

STAIR: Spatial-Temporal Reasoning with Auditable Intermediate Results for Video Question Answering

Recently we have witnessed the rapid development of video question answering models. However, most models can only handle simple videos in terms of temporal reasoning, and their performance tends to drop when answering temporal-reasoning questions on long and informative videos. To tackle this problem we propose STAIR, a Spatial-Temporal Reasoning model with Auditable Intermediate Results for video question answering. STAIR is a neural module network, which contains a program generator to decompose a given question into a hierarchical combination of several sub-tasks, and a set of lightweight neural modules to complete each of these sub-tasks. Though neural module networks are already widely studied on image-text tasks, applying them to videos is a non-trivial task, as reasoning on videos requires different abilities. In this paper, we define a set of basic video-text sub-tasks for video question answering and design a set of lightweight modules to complete them. Different from most prior works, modules of STAIR return intermediate outputs specific to their intentions instead of always returning attention maps, which makes it easier to interpret and collaborate with pre-trained models. We also introduce intermediate supervision to make these intermediate outputs more accurate. We conduct extensive experiments on several video question answering datasets under various settings to show STAIR's performance, explainability, compatibility with pre-trained models, and applicability when program annotations are not available. Code: https://github.com/yellow-binary-tree/STAIR

BDIQA: A New Dataset for Video Question Answering to Explore Cognitive Reasoning through Theory of Mind

As a foundational component of cognitive intelligence, theory of mind (ToM) can make AI more closely resemble human thought processes, thereby enhancing their interaction and collaboration with human. In particular, it can significantly improve a model's comprehension of videos in complex scenes. However, current video question answer (VideoQA) datasets focus on studying causal reasoning within events, few of them genuinely incorporating human ToM. Consequently, there is a lack of development in ToM reasoning tasks within the area of VideoQA. This paper presents BDIQA, the first benchmark to explore the cognitive reasoning capabilities of VideoQA models in the context of ToM. BDIQA is inspired by the cognitive development of children's ToM and addresses the current deficiencies in machine ToM within datasets and tasks. Specifically, it offers tasks at two difficulty levels, assessing Belief, Desire and Intention (BDI) reasoning in both simple and complex scenarios. We conduct evaluations on several mainstream methods of VideoQA and diagnose their capabilities with zero-shot, few-shot and supervised learning. We find that the performance of pre-trained models on cognitive reasoning tasks remains unsatisfactory. To counter this challenge, we undertake thorough analysis and experimentation, ultimately presenting two guidelines to enhance cognitive reasoning derived from ablation analysis.

Stitching Segments and Sentences towards Generalization in Video-Text Pre-training

Video-language pre-training models have recently achieved remarkable results on various multi-modal downstream tasks. However, most of these models rely on contrastive learning or masking modeling to align global features across modalities, neglecting the local associations between video frames and text tokens. This limits the model’s ability to perform fine-grained matching and generalization, especially for tasks that selecting segments in long videos based on query texts. To address this issue, we propose a novel stitching and matching pre-text task for video-language pre-training that encourages fine-grained interactions between modalities. Our task involves stitching video frames or sentences into longer sequences and predicting the positions of cross-model queries in the stitched sequences. The individual frame and sentence representations are thus aligned via the stitching and matching strategy, encouraging the fine-grained interactions between videos and texts. in the stitched sequences for the cross-modal query. We conduct extensive experiments on various benchmarks covering text-to-video retrieval, video question answering, video captioning, and moment retrieval. Our results demonstrate that the proposed method significantly improves the generalization capacity of the video-text pre-training models.

Video-Context Aligned Transformer for Video Question Answering

Video question answering involves understanding video content to generate accurate answers to questions. Recent studies have successfully modeled video features and achieved diverse multimodal interaction, yielding impressive outcomes. However, they have overlooked the fact that the video contains richer instances and events beyond the scope of the stated question. Extremely imbalanced alignment of information from both sides leads to significant instability in reasoning. To address this concern, we propose the Video-Context Aligned Transformer (V-CAT), which leverages the context to achieve semantic and content alignment between video and question. Specifically, the video and text are encoded into a shared semantic space initially. We apply contrastive learning to global video token and context token to enhance the semantic alignment. Then, the pooled context feature is utilized to obtain corresponding visual content. Finally, the answer is decoded by integrating the refined video and question features. We evaluate the effectiveness of V-CAT on MSVD-QA and MSRVTT-QA dataset, both achieving state-of-the-art performance. Extended experiments further analyze and demonstrate the effectiveness of each proposed module.

YTCommentQA: Video Question Answerability in Instructional Videos

Instructional videos provide detailed how-to guides for various tasks, with viewers often posing questions regarding the content. Addressing these questions is vital for comprehending the content, yet receiving immediate answers is difficult. While numerous computational models have been developed for Video Question Answering (Video QA) tasks, they are primarily trained on questions generated based on video content, aiming to produce answers from within the content. However, in real-world situations, users may pose questions that go beyond the video's informational boundaries, highlighting the necessity to determine if a video can provide the answer. Discerning whether a question can be answered by video content is challenging due to the multi-modal nature of videos, where visual and verbal information are intertwined. To bridge this gap, we present the YTCommentQA dataset, which contains naturally-generated questions from YouTube, categorized by their answerability and required modality to answer -- visual, script, or both. Experiments with answerability classification tasks demonstrate the complexity of YTCommentQA and emphasize the need to comprehend the combined role of visual and script information in video reasoning. The dataset is available at https://github.com/lgresearch/YTCommentQA.

Cascade transformers with dynamic attention for video question answering

Visual question answering (VQA) has become a hot study topic with challenging motivation of correctly answering the videos or images questions in recent years. However, the existing VQA model mostly aimed at answering questions about images and performed poorly in the video question answering (VideoQA) domain. VideoQA needs to simultaneously consider the correlations between video frames and the dynamic information of multiple objects in video. Therefore, we propose a novel Cascade Transformers with Dynamic Attention for Video Question Answering (CTDA-QA), which aims to simultaneously solve the above considerations. Specifically, the proposed CTDA-QA model utilizes multiple transformers structure to encode videos for reasoning complex spatial and temporal information, which is different from the previous recurrent neural network methods. Besides, in order to effectively capture the dynamic information from various scenarios in videos, a flexible attention module has been proposed to explore the essential relations between objects in a dynamic timeline. Finally, to avoid spurious answers and fully explore the cross-modal relationships, a mixed-supervised learning strategy is designed for optimizing the reasoning tasks. The experiments on several benchmark video question–answer datasets clearly verify the performance and effectiveness of CTDA-QA, which contains the results in contrast to the state-of-the-art methods. Besides, the provided ablation study and visualization results further reveal the potential of CTDA-QA.

Hierarchical synchronization with structured multi-granularity interaction for video question answering

Video Question Answering (VideoQA) requires a thorough comprehension of linguistic and visual modalities. However, recent methods confront two problems: (1) Synchronous modeling of object action and frame scene instead of a step-by-step manner, which can better mine potential semantic attributes of videos, lacks research; (2) The relationship between cross-modal alignments at different granularity of abstraction is not fully utilized. Based on these insights, we propose a novel method named hierarchical synchronization with structured multi-granularity interaction (HSSMI) for VideoQA. First, a hierarchical synchronous reasoning module is put forward to model objects’ relations and dynamics and synchronously capture their synergistic influences over time when analyzing whole frames. It is seen as multiple Object ConvLSTMs (O-CLSTMs) in isolation or a Frame ConvLSTM (F-CLSTM) in collectivity. Specifically, O-CLSTM learns the object-level action states under neighboring spatial interplays. Meanwhile, F-CLSTM learns the frame-level scene state, where action information from O-CLSTMs is selectively aggregated into a common memory cell of F-CLSTM as instructed by questions. Besides, a boundary detector is equipped to discover scene discontinuities, enabling F-CLSTM to alter its time connectivity and adapt its sequential encoding process to videos. Thereafter, we develop a conditional VLAD with topic constraints for discriminative modality summarization. Last, a structured multi-granularity interaction module is proposed to integrate complemented clues on the global alignment between scene summary and full question and the local alignments between action summaries and words. This module encourages useful information passing through compositional syntactical topologies of questions to predict answers. Experiments on three public benchmark datasets demonstrate the superiority of our HSSMI against other state-of-the-art methods. Codes will be publicly available at https://github.com/Qiss33/HSSMI.

Enhancing machine vision: the impact of a novel innovative technology on video question-answering

Enhancing machine vision: the impact of a novel innovative technology on video question-answering

Dynamic Spatio-Temporal Graph Reasoning for VideoQA With Self-Supervised Event Recognition.

Video question answering (VideoQA) requires the ability of comprehensively understanding visual contents in videos. Existing VideoQA models mainly focus on scenarios involving a single event with simple object interactions and leave event-centric scenarios involving multiple events with dynamically complex object interactions largely unexplored. These conventional VideoQA models are usually based on features extracted from the global visual signals, making it difficult to capture the object-level and event-level semantics. Although there exists a recent work utilizing a static spatio-temporal graph to explicitly model object interactions in videos, it ignores the dynamic impact of questions for graph construction and fails to exploit the implicit event-level semantic clues in questions. To overcome these limitations, we propose a Self-supervised Dynamic Graph Reasoning (SDGraphR) model for video question answering (VideoQA). Our SDGraphR model learns a question-guided spatio-temporal graph that dynamically encodes intra-frame spatial correlations and inter-frame correspondences between objects in the videos. Furthermore, the proposed SDGraphR model discovers event-level cues from questions to conduct self-supervised learning with an auxiliary event recognition task, which in turn helps to improve its VideoQA performances without using any extra annotations. We carry out extensive experiments to validate the substantial improvements of our proposed SDGraphR model over existing baselines.

Event Graph Guided Compositional Spatial-Temporal Reasoning for Video Question Answering.

Video question answering (VideoQA) is challenging since it requires the model to extract and combine multi-level visual concepts from local objects to global actions from complex events for compositional reasoning. Existing works represent the video with fixed-duration clip features that make the model struggle in capturing the crucial concepts in multiple granularities. To overcome this shortcoming, we propose to represent the video with an Event Graph in a hierarchical structure whose nodes correspond to visual concepts of different levels (object, relation, scene and action) and edges indicate their spatial-temporal relationships. We further propose a H ierarchical S patial- T emporal T ransformer (HSTT) which takes nodes from the graph as visual input to realize compositional reasoning guided by the event graph. To fully exploit the spatial-temporal context delivered from the graph structure, on the one hand, we encode the nodes in the order of their semantic hierarchy (depth) and occurrence time (breadth) with our improved graph search algorithm; On the other hand, we introduce edge-guided attention to combine the spatial-temporal context among nodes according to their edge connections. HSTT then performs QA by cross-modal interactions guaranteed by the hierarchical correspondence between the multi-level event graph and the cross-level question. Experiments on the recent challenging AGQA and STAR datasets show that the proposed method clearly outperforms the existing VideoQA models by a large margin, including those pre-trained with large-scale external data. Our code is available at https://github.com/ByZ0e/HSTT.

Transformer-Empowered Invariant Grounding for Video Question Answering.

Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is the understanding of the alignments between video scenes and question semantics to yield the answer. In leading VideoQA models, the typical learning objective, empirical risk minimization (ERM), tends to over-exploit the spurious correlations between question-irrelevant scenes and answers, instead of inspecting the causal effect of question-critical scenes, which undermines the prediction with unreliable reasoning. In this work, we take a causal look at VideoQA and propose a modal-agnostic learning framework, named Invariant Grounding for VideoQA (IGV), to ground the question-critical scene, whose causal relations with answers are invariant across different interventions on the complement. With IGV, leading VideoQA models are forced to shield the answering from the negative influence of spurious correlations, which significantly improves their reasoning ability. To unleash the potential of this framework, we further provide a Transformer-Empowered Invariant Grounding for VideoQA (TIGV), a substantial instantiation of IGV framework that naturally integrates the idea of invariant grounding into a transformer-style backbone. Experiments on four benchmark datasets validate our design in terms of accuracy, visual explainability, and generalization ability over the leading baselines. Our code is available at https://github.com/yl3800/TIGV.

Multi-Granularity Contrastive Cross-Modal Collaborative Generation for End-to-End Long-Term Video Question Answering.

Long-term Video Question Answering (VideoQA) is a challenging vision-and-language bridging task focusing on semantic understanding of untrimmed long-term videos and diverse free-form questions, simultaneously emphasizing comprehensive cross-modal reasoning to yield precise answers. The canonical approaches often rely on off-the-shelf feature extractors to detour the expensive computation overhead, but often result in domain-independent modality-unrelated representations. Furthermore, the inherent gradient blocking between unimodal comprehension and cross-modal interaction hinders reliable answer generation. In contrast, recent emerging successful video-language pre-training models enable cost-effective end-to-end modeling but fall short in domain-specific ratiocination and exhibit disparities in task formulation. Toward this end, we present an entirely end-to-end solution for long-term VideoQA: Multi-granularity Contrastive cross-modal collaborative Generation (MCG) model. To derive discriminative representations possessing high visual concepts, we introduce Joint Unimodal Modeling (JUM) on a clip-bone architecture and leverage Multi-granularity Contrastive Learning (MCL) to harness the intrinsically or explicitly exhibited semantic correspondences. To alleviate the task formulation discrepancy problem, we propose a Cross-modal Collaborative Generation (CCG) module to reformulate VideoQA as a generative task instead of the conventional classification scheme, empowering the model with the capability for cross-modal high-semantic fusion and generation so as to rationalize and answer. Extensive experiments conducted on six publicly available VideoQA datasets underscore the superiority of our proposed method.

Multi-Granularity Relational Attention Network for Audio-Visual Question Answering

Recent methods for video question answering (VideoQA), aiming to generate answers based on given questions and video content, have made significant progress in cross-modal interaction. From the perspective of video understating, these existing frameworks concentrate on the various levels of visual content, partially assisted by subtitles. However, audio information is also instrumental in helping get correct answers, especially in videos with real-life scenarios. Indeed, in some cases, both audio and visual contents are required and complement each other to answer questions, which is defined as audio-visual question answering (AVQA). In this paper, we focus on importing raw audio for AVQA and contribute in three ways. Firstly, due to no dataset annotating QA pairs for raw audio, we introduce E-AVQA, a manually annotated and large-scale dataset involving multiple modalities. E-AVQA consists of 34,033 QA pairs on 33,340 clips of 18,786 videos from the e-commerce scenarios. Secondly, we propose a multi-granularity relational attention method with contrastive constraints between audio and visual features after the interaction, named MGN, which captures local sequential representation by leveraging the pairwise potential attention mechanism and obtains global multi-modal representation via designing the novel ternary potential attention mechanism. Thirdly, our proposed MGN outperforms the baseline on dataset E-AVQA, achieving 20.73% on WUPS@0.0 and 19.81% on BLEU@1, demonstrating its superiority with at least 1.02 improvement on WUPS@0.0 and about 10% on timing complexity over the baseline.

Collaborative Aware Bidirectional Semantic Reasoning for Video Question Answering

Collaborative Aware Bidirectional Semantic Reasoning for Video Question Answering

Locate Before Answering: Answer Guided Question Localization for Video Question Answering

Locate Before Answering: Answer Guided Question Localization for Video Question Answering

Prompting Video-Language Foundation Models with Domain-specific Fine-grained Heuristics for Video Question Answering

Prompting Video-Language Foundation Models with Domain-specific Fine-grained Heuristics for Video Question Answering

TLNet: Temporal Span Localization Network With Collaborative Graph Reasoning for Video Question Answering

TLNet: Temporal Span Localization Network With Collaborative Graph Reasoning for Video Question Answering