Video Question Answering

TutorialVQA is a new type of dataset used to find answer spans in tutorial videos. The dataset includes about 6,000 triples, comprised of videos, questions, and answer spans manually collected from screencast tutorial videos with spoken narratives for a photo-editing software. Source: TutorialVQA: Question Answering Dataset for Tutorial Videos

Large multimodal models (LMMs) are processing increasingly longer and richer inputs. Albeit the progress, few public benchmark is available to measure such development. To mitigate this gap, we introduce LongVideoBench, a question-answering benchmark that features video-language interleaved inputs up to an hour long. Our benchmark includes 3,763 varying-length web-collected videos with their subtitles across diverse themes, designed to comprehensively evaluate LMMs on long-term multimodal understanding. To achieve this, we interpret the primary challenge as to accurately retrieve and reason over detailed multimodal information from long inputs. As such, we formulate a novel video question-answering task termed referring reasoning. Specifically, as part of the question, it contains a referring query that references related video contexts, called referred context. The model is then required to reason over relevant video details from the referred context. Following the paradigm of referring reasoning, we curate 6,678 human-annotated multiple-choice questions in 17 fine-grained categories, establishing one of the most comprehensive benchmarks for long-form video understanding. Evaluations suggest that the LongVideoBench presents significant challenges even for the most advanced proprietary models (e.g. GPT-4o, Gemini-1.5-Pro, GPT-4-Turbo), while their open-source counterparts show an even larger performance gap. In addition, our results indicate that model performance on the benchmark improves only when they are capable of processing more frames, positioning LongVideoBench as a valuable benchmark for evaluating future-generation long-context LMMs.

Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness. Scene text recognition in motion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason over time. To address this issue, we introduce RoadTextVQA, a new dataset for the task of video question answering (VideoQA) in the context of driver assistance. RoadTextVQA consists of 3,222 driving videos collected from multiple countries, annotated with 10,500 questions, all based on text or road signs present in the driving videos. We assess the performance of state-of-the-art video question answering models on our RoadTextVQA dataset, highlighting the significant potential for improvement in this domain and the usefulness of the dataset in advancing research on in-vehicle support systems and text-aware multimodal question answering.

A short clip of video may contain progression of multiple events and an interesting story line. A human needs to capture both the event in every shot and associate them together to understand the story behind it. In this work, we present a new multi-shot video understanding benchmark Shot2Story with detailed shot-level captions and comprehensive video summaries. To facilitate better semantic understanding of videos, we provide captions for both visual signals and human narrations. We design several distinct tasks including single-shot video and narration captioning, multi-shot video summarization, and video retrieval with shot descriptions. Preliminary experiments show some challenges to generate a long and comprehensive video summary. Nevertheless, the generated imperfect summaries can already significantly boost the performance of existing video understanding tasks such as video question-answering, promoting an underexplored setting of video understanding with detailed summaries.

Video Localized Narratives is a new form of multimodal video annotations connecting vision and language. The annotations are created from videos with Localized Narratives, capturing even complex events involving multiple actors interacting with each other and with several passive objects. It contains annotations of 20k videos of the OVIS, UVO, and Oops datasets, totalling 1.7M words. Source: Connecting Vision and Language with Video Localized Narratives Image Source: https://arxiv.org/pdf/2302.11217v1.pdf

CRIPP-VQA is a video question answering dataset for reasoning about the implicit physical properties of objects in a scene. It contains videos of object in motion, annotated with questions that involve counterfactual reasoning about actions, questions about planning in order to reach a goal, and descriptive questions about visible properties of objects. Source: CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering Image Source: https://arxiv.org/pdf/2211.03779v1.pdf

CausalChaos! is a dataset for causal video question answering. It is based on Tom and Jerry cartoons. It features longer causal chains embedded in dynamic visual scenes. It also features challenging incorrect options, especially, Causal Confusion set which contains causally confounding incorrect options. All these factors prove to be challenging for current VLMs and other traditional Video Question Answering models.

CinePile is a question-answering-based, long-form video understanding dataset. It has been created using advanced large language models (LLMs) with human-in-the-loop pipeline leveraging existing human-generated raw data. It consists of approximately 300,000 training data points and 5,000 test data points.

Short-Films 20K (SF20K) is the largest publicly available movie dataset. SF20K is composed of 20,143 amateur films and offers long-term video tasks in the form of multiple-choice and open-ended question answering.

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Video-ChatGPT introduces the VideoInstruct100K dataset, which employs a semi-automatic annotation pipeline to generate 75K instruction-tuning QA pairs. To address the limitations of this annotation process, we present \ourdata~dataset developed through an improved annotation pipeline. Our approach improves the accuracy and quality of instruction tuning pairs by improving keyframe extraction, leveraging SoTA large multimodal models (LMMs) for detailed descriptions, and refining the instruction generation strategy.

WildQA is a video understanding dataset of videos recorded in outside settings. The dataset can be used to evaluate models for video question answering.

We construct a fine-grained video-text dataset with 12K annotated high-resolution videos (~400k clips). The annotation of this dataset is inspired by the video script. If we want to make a video, we have to first write a script to organize how to shoot the scenes in the videos. To shoot a scene, we need to decide the content, shot type (medium shot, close-up, etc), and how the camera moves (panning, tilting, etc). Therefore, we extend video captioning to video scripting by annotating the videos in the format of video scripts. Different from the previous video-text datasets, we densely annotate the entire videos without discarding any scenes and each scene has a caption with ~145 words. Besides the vision modality, we transcribe the voice-over into text and put it along with the video title to give more background information for annotating the videos. In addition, we propose Vript-Bench, a new benchmark consisting of three challenging video understanding tasks (which are carefully double-checked by humans): - Vript-CAP (Caption): A benchmark with detailed captions rather than short captions. - Vript-RR (Retrieve then Reason): A video reasoning benchmark by first giving a detailed description of the scene as a hint and then asking questions about details in the scene. - Vript-ERO (Event Re-ordering): A benchmark that tests the temporal understanding by offering the descriptions of scenes located in two/four different timelines of the same video, and asks the model to give the right temporal order of the scenes.