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Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions
Hongwei Xue, Tiankai Hang, Yanhong Zeng, Yuchong Sun, Bei Liu, Huan Yang, Jianlong Fu, Baining Guo
TL;DR
Video-language pre-training is constrained by limited video features and text semantics, motivating richer cross-modality learning. HD-VILA introduces a large, high-resolution, diversified dataset and an end-to-end hybrid Transformer framework. It reports strong results across understanding and text-to-visual generation tasks, while noting computational, bias, and misuse concerns.
Problem
Existing video-language methods either use low-quality video features or limited text embeddings, restricting the available cross-modality learning signal.
Method
HD-VILA combines HD-VILA-100M with hybrid HR/LR video encoding and multimodal Transformer optimization for joint video-language representation learning.
Results
HD-VILA achieves state-of-the-art results on 10 video-language understanding and 2 text-to-visual generation tasks.
Takeaways & Limitations
The results support using diversified categories and high-resolution video data for video-language pre-training.
Takeaways & Limitations
The paper notes substantial computation requirements, possible bias in user-generated data, and malicious-use risks for visual generation.
Abstract
from arXiv · showhide
We study joint video and language (VL) pre-training to enable cross-modality learning and benefit plentiful downstream VL tasks. Existing works either extract low-quality video features or learn limited text embedding, while neglecting that high-resolution videos and diversified semantics can significantly improve cross-modality learning. In this paper, we propose a novel High-resolution and Diversified VIdeo-LAnguage pre-training model (HD-VILA) for many visual tasks. In particular, we collect a large dataset with two distinct properties: 1) the first high-resolution dataset including 371.5k hours of 720p videos, and 2) the most diversified dataset covering 15 popular YouTube categories. To enable VL pre-training, we jointly optimize the HD-VILA model by a hybrid Transformer that learns rich spatiotemporal features, and a multimodal Transformer that enforces interactions of the learned video features with diversified texts. Our pre-training model achieves new state-of-the-art results in 10 VL understanding tasks and 2 more novel text-to-visual generation tasks. For example, we outperform SOTA models with relative increases of 40.4% R@1 in zero-shot MSR-VTT text-to-video retrieval task and 55.4% in high-resolution dataset LSMDC. The learned VL embedding is also effective in generating visually pleasing and semantically relevant results in text-to-visual editing and super-resolution tasks.
1. Introduction
HD-VILA addresses limitations in video-language pre-training by combining a large, high-resolution, diversified dataset with end-to-end hybrid video representation learning. The framework targets joint spatiotemporal and multimodal learning for broad video-language tasks.
- Existing datasets are limited by expensive manual annotation and by descriptive sentences with restricted complexity and variability.
- HD-VILA-100M contains 100M clip-sentence pairs from 3.3 million videos spanning 371.5K hours.The dataset has 2.8× the video hours and 8× the average sentence length of HowTo100M.
- The dataset uses 720p videos across 15 popular, balanced YouTube categories, including sports, music, and autos.
- HD-VILA learns spatiotemporal representations from hybrid sequences containing a few high-resolution frames and more low-resolution neighboring frames.High-resolution frames are randomly sampled, while low-resolution frames are uniformly sampled.
- Experiments evaluate the learned cross-modality embedding on 10 video-understanding and 2 text-to-visual generation tasks.
2. Related Work
Prior video-language work commonly relies on conventional visual encoders or pre-extracted features, while multimodal pre-training has recently followed advances in image-language learning.
- Video Representation: Video representations are typically built with 2D/3D CNNs or Transformers.
- Video Representation: Early video-language pre-training methods commonly use pre-extracted S3D or I3D video features.
- Video-Language Pre-Training: Vision-language pre-training has attracted extensive attention following the success of image-language pre-training.
3. Dataset
HD-VILA-100M is a large, high-resolution, diversified video-language dataset built from YouTube videos and transcriptions, with balanced coverage across domains.
- HD-VILA-100M is collected to support multimodal representation learning with large-scale, high-resolution, diversified video-language data.
- YouTube provides videos from professional channels and ordinary users across diverse topics.
- Figure 2 reports category distributions separately for videos and video clips.
- The dataset uses video transcriptions rather than traditional manually annotated descriptions to obtain large quantities of richer information.An off-the-shelf tool is used to split subtitles into complete sentences, with 75.7% test-set accuracy.
- HD-VILA-100M covers a wide range of domains with relatively balanced category representation, and all videos are 720p.The paper links broader domains and balanced categories to generalization, while higher quality preserves detailed information for representation learning.
4. Approach
HD-VILA combines hybrid HR/LR video encoding with language and multimodal Transformers to learn spatiotemporal and cross-modal representations. Contrastive video-language matching and masked language modeling support pre-training, yielding strong video QA performance.
- Model architecture: HD-VILA comprises a hybrid video encoder, language encoder, and multimodal joint-learning module.The video encoder uses HR and LR processing, while the language and multimodal components model unimodal and cross-modal relationships.
- Hybrid video encoder: Hybrid image sequences combine sparsely sampled HR frames with more LR neighboring frames for efficient spatiotemporal learning.Segments are sparsely sampled from long videos, with one randomly selected HR frame and surrounding LR frames.
- Hybrid video encoder: The HR and LR encoders produce features for a Hybrid Transformer, which learns spatiotemporal features through divided space-time attention.The HR encoder uses a four-stage ResNet and adapter, while the LR encoder uses a three-stage ResNet; both encoders are learnable.
- Hybrid video encoder: HR spatial features are fused with hybrid spatiotemporal features after channel adjustment and 2 × 2 max-pooling.A linear layer forms the segment representation from the adapted HR feature and the Hybrid Transformer output.
- Pre-training tasks: Contrastive video-language matching strengthens cross-modal matching, while masked language modeling aligns visual and language tokens at a fine-grained level.Masked-token logits are aggregated across segments to compute video-level MLM loss.
- Results: 97.1 Acc is reported on one video question-answering comparison, while 40.0 Acc is reported on another.The tables compare HD-VILA with prior methods, including ClipBERT PT at 88.2 and 37.4 Acc, respectively.
5. Experiments
HD-VILA is evaluated across video understanding, retrieval, text-guided generation, and ablation settings. Results show strong gains from its diversified high-resolution data and hybrid video encoding.
- Video Question and Answering: HD-VILA outperforms existing methods on five video QA tasks across MSRVTT-QA, MSR-VTT multiple-choice, and TGIF-QA.Absolute improvements are 2.6 and 5.0 on the two MSR-VTT tasks, and 1.5, 2.2, and 0.2 on TGIF-QA Action, Trans, and Frame.
- Video-Text Retrieval: 40.4% relative R@1 gains over VideoCLIP occur in zero-shot MSR-VTT retrieval, while LSMDC shows 55.4% relative gains under fair comparison.HD-VILA also achieves better performance on DiDeMo and ActivityNet retrieval.
- Text-to-Visual Generation: HD-VILA edits target facial attributes more accurately than StyleCLIP and TediGAN in text-guided manipulation.The comparison uses 1024 × 1024 inputs and includes complex descriptions.
- Text-to-Visual Generation: HD-VILA reconstructs text-specified facial attributes more accurately than SR3 and pSp for 16 × 16-to-1024 × 1024 super-resolution.The reported examples include lipstick and straight hair, which visual-only methods fail to reconstruct accurately.
- Ablation Studies: 66.7% relative R@1 gains show that diversified pre-training data outperform a HowTo-only subset on zero-shot MSR-VTT retrieval.The comparison uses two-million clip-text-pair subsets.
- Ablation Studies: The 1-HR & 10-LR setting performs best, while high-resolution frames improve performance over low-resolution-only inputs.This supports jointly modeling spatial and temporal video features with hybrid frame sequences.
6. Conclusion
The paper introduces HD-VILA-100M and HD-VILA to learn high-resolution, diversified video-language representations from large-scale video-language pairs. Across 12 video-language understanding and text-to-visual generation tasks, the experiments demonstrate the dataset and model’s effectiveness.
- Conclusion: HD-VILA-100M is introduced as a large-scale, high-resolution, diversified video-language dataset for multimodal representation learning.The paper positions the dataset as supporting both video-language understanding and generation tasks.
- Conclusion: HD-VILA uses a hybrid Transformer to learn spatiotemporal information from high- and low-resolution frames during video-language pre-training.The model is designed to employ richer information in videos through hybrid image sequences.
- Conclusion: Experiments cover 12 video-language understanding and text-to-visual generation tasks and demonstrate the capability of HD-VILA-100M and effectiveness of HD-VILA.
B. Limitation and Social Impact
The paper identifies computation, data bias, and malicious-use risks associated with large-scale video-language pre-training and visual generation. It notes that balancing categories helps address bias, but does not eliminate biased content.
- Large-scale pre-training requires substantial computation, motivating future work on reducing model size and computing effort.
- User-generated videos may contain biased content despite balancing video categories during dataset construction.
- The authors identify malicious use of visual generation techniques for conscious attacks as a critical concern.
C. HD-VILA-100M Dataset Details
HD-VILA-100M contains mostly short video clips paired with moderately long transcriptions, whose n-gram and part-of-speech statistics indicate richer semantic diversity than HowTo100M.
- Most HD-VILA-100M clips last 5–15 seconds, with an average duration of 13.4 seconds.
- Most transcriptions contain 15–50 words, with an average length of 32.5 words.
- HD-VILA-100M transcriptions have more unique n-grams and part-of-speech tags than HowTo100M, indicating richer and more diverse semantics.
C.3. More Examples of HD-VILA-100M Dataset
The supplementary material illustrates HD-VILA-100M examples, video-QA settings, and the text-to-visual generation pipeline, including its encoders, mapper modules, and StyleGAN synthesis.
- More Examples of HD-VILA-100M Dataset: HD-VILA-100M examples pair video frames with automatically generated transcriptions and manually highlighted relevant words across diverse categories.
- More Examples of HD-VILA-100M Dataset: TGIF-QA experiments sample temporal segments differently for Action, Transition, and FrameQA, reflecting their distinct video-question answering settings.
- Model Design: The text-guided generation framework uses multimodal encoders, visual and text mapper modules, and a pre-trained StyleGAN to synthesize images.
- Dataset Details: The FDVD dataset contains 613 high-resolution human-face video-description pairs, 74,803 frames, and 6,130 descriptive sentences.
D.3.4 Experiments
Experiments show that HD-VILA pre-training improves text-guided editing and super-resolution relative to non-pre-trained and competing generation systems. The supplementary video demo also reports promising frame-by-frame text-guided video editing.
- Text-Guided Editing: HD-VILA attends to multiple attributes and edits images accurately, including complex descriptions containing eyeglasses and lipstick.
- Text-Guided Editing: The video demo generates edited videos frame-by-frame from an input clip and target description, showing promising text-guided video editing performance.
- Text-Guided Super-Resolution: In super-resolution, the pre-trained model produces realistic outputs with more textual attributes than the from-scratch model and competing baselines SR3 and pSp.
- Text-Guided Super-Resolution: Super-resolution remains limited because low-resolution images can correspond to multiple high-resolution images, while details such as hair are difficult to preserve.
D.4. Ablation Study on Data Domain
The ablation evaluates whether pre-training data domain affects YouCook2 retrieval. In-domain pre-training can be especially beneficial, although the advantage depends on training duration and dataset choice.
- D.4. Ablation Study on Data Domain: In-domain pre-training benefits YouCook2 retrieval substantially.The authors use YouCook2 to test whether matching the downstream domain helps.
- D.4. Ablation Study on Data Domain: HD-VILA-100M pre-training outperforms the published HowTo100M model on YouCook2 retrieval.
- D.4. Ablation Study on Data Domain: The HowTo100M-pretrained model performs best when training is limited to few epochs.This indicates that the observed ranking varies with the available training budget.
E.2. Composition
HD-VILA-100M combines videos with time-aligned ASR transcripts and samples high-quality content across 15 YouTube categories. Its scale and reuse are broad, but noisy transcripts, public-video churn, and restricted release conditions bound future use.
- Composition: Each instance contains a short video clip paired with an ASR transcript aligned over time.The average clip lasts 13.4 seconds and the average transcript contains 32.5 words.
- Composition: The dataset includes 3.3 million videos and 103 million extracted video clips with ASR transcripts.
- Composition: The collection is a sample restricted to videos with quality at least 720p and covers 15 popular categories.Probabilistic sampling was used to broaden category coverage and improve balance.
- Composition: ASR transcripts remain noisy despite cleaning, with mistakes that cannot all be fixed.
- Composition: The dataset supports general video-language pre-training and transfer to retrieval, video QA, and video captioning.
- Composition: Only video URLs and metadata are released, so deleted YouTube videos may become unavailable and raw data cannot be redistributed.Raw data is withheld because of copyright and privacy concerns.