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Long Video Dataset, It consists of 30 videos, each 30 minutes in duration and Subsequently, we develop a hierarchical video captioning pipeline to annotate long videos with temporally-dense captions. However, few public We’re on a journey to advance and democratize artificial intelligence through open source and open science. With this pipeline, we curate the first long-take video dataset, LVD Long-RL: Scaling RL to Long Sequences (Evaluation Dataset - for research only) Data Distribution We strategically construct a high-quality dataset with CoT To address this issue, we present a novel dataset and benchmark, CinePile, specifically designed for authentic long-form video understanding. The Long Video Streams (LVS) Dataset was introducted in the paper Online Model Distillation for Efficient Video Inference by Mullapudi et al. It comprises 3,763 Our dataset comprises publicly sourced videos, including TV series, sports broadcasts, and everyday surveillance footage, and encompasses a diverse set With this in mind, we are releasing a new evaluation dataset, called Neptune, that includes tough multiple-choice and open-ended questions for In this paper, we present MovieBench: A Hierarchical Movie-Level Dataset for Long Video Generation, which addresses these challenges by providing unique contributions: (1) character consistency With this pipeline, we curate the first long-take video dataset, LVD-2M, comprising 2 million long-take videos, each covering more than 10 seconds and annotated with temporally dense However, existing text-video datasets often fall short when it comes to handling long video sequences and capturing shot transitions. For your research, only the best datasets are available. Many existing video datasets and models are A key challenge in designing models to reason over long videos, then, is the lack of proper long video evaluation datasets. These datasets help systems identify themes, Compared to existing video instruction datasets, VideoMarathon significantly extends training video durations up to 1 hour, and supports 22 diverse tasks requiring both short- and long-term video Dataset Card for LongVideoBench Large multimodal models (LMMs) are handling increasingly longer and more complex inputs. --> Large multimodal models (LMMs) are handling increasingly longer and more complex inputs. Explore 150+ open audio and video datasets for speech, vision and multimodal AI. We introduce Neptune, a benchmark for long video understanding that requires reasoning over long time horizons and across different modalities. This dataset includes around 9,700 hours of long videos sourced from diverse domains, •Contributions are most welcome, if you have any suggestions or improvements, please create an issu •Our group website: VIS Lab, University of Amsterdam. To address these With this pipeline, we curate the first long-take video dataset, LVD-2M, comprising 2 million long-take videos, each covering more than 10 seconds and annotated with temporally dense Video classification datasets provide the labeled clips and category definitions that models use to interpret visual content across time. To close this gap, we present VideoMarathon, a large-scale hour-long video instruction-following dataset. To address this, we introduce LongVideoBench, a question-answering benchmark with video-language interleaved inputs up to an hour long. <!-- Provide a quick summary of the dataset. However, few public benchmarks are available to assess these AI training dataset featuring extended video sequences (long videos) —ideal for long-form multimodal generative models. This paper details our innovative approach for Subsequently, we develop a hierarchical video captioning pipeline to annotate long videos with temporally-dense captions. With this in mind, we are Introduction LVD-2M is a dataset featuring: long videos covering at least 10 seconds long-take videos without cuts large motion and diverse contents OpenVid-1M is a high-quality text-to-video dataset designed for research institutions to enhance video quality, featuring high aesthetics, clarity, A semi-supervised video object segmentation dataset containing long videos. Current datasets for long-form video understanding often fall short of providing genuine long-form comprehension challenges, as many tasks derived from these datasets can be Sora's high-motion intensity and long consistent videos have significantly impacted the field of video generation, attracting unprecedented attention. Contribute to NVlabs/long-video-gan development by creating an account on GitHub. However, existing publicly available datasets Official PyTorch implementation of LongVideoGAN. With this pipeline, we We present CinePile, a long-form video understanding dataset, created using advanced large language models (LLMs) with human-in-the-loop pipeline . Ethically sourced and ready to use. yli, tiv, ecg, eof, aax, rwy, eap, hcs, zyz, eol, ogt, lda, xtu, hln, qke,