Source-linked AI summary
CLIP-It! Language-Guided Video Summarization
Medhini Narasimhan, Anna Rohrbach, Trevor Darrell
TL;DR
Video summarization must capture important scenes while accommodating subjective user interests, but generic and query-focused settings have been treated separately. CLIP-It unifies them with language-guided multimodal frame scoring, using captions for generic summaries and queries for focused ones. It reports large improvements over prior methods, especially in transfer evaluation, while noting that pretrained captioning and vision-language models may propagate inappropriate biases.
Problem
Video-scene importance is subjective, users need natural-language customization, and generic summarization has not exploited available language models as saliency priors.
Method
CLIP-It uses a language-guided multimodal Transformer to score frames relative to one another and their correlation with user queries or automatically generated dense video captions.
Results
CLIP-It outperforms baselines and prior work across TVSum, SumMe, and QFVS, with particularly large improvements in transfer evaluation.
Takeaways & Limitations
A single framework can support both generic and query-focused summarization, including open-ended natural-language guidance and unsupervised training.
Takeaways & Limitations
The off-the-shelf captioning and CLIP models may encode inappropriate biases that propagate to CLIP-It.
Abstract
from arXiv · showhide
A generic video summary is an abridged version of a video that conveys the whole story and features the most important scenes. Yet the importance of scenes in a video is often subjective, and users should have the option of customizing the summary by using natural language to specify what is important to them. Further, existing models for fully automatic generic summarization have not exploited available language models, which can serve as an effective prior for saliency. This work introduces CLIP-It, a single framework for addressing both generic and query-focused video summarization, typically approached separately in the literature. We propose a language-guided multimodal transformer that learns to score frames in a video based on their importance relative to one another and their correlation with a user-defined query (for query-focused summarization) or an automatically generated dense video caption (for generic video summarization). Our model can be extended to the unsupervised setting by training without ground-truth supervision. We outperform baselines and prior work by a significant margin on both standard video summarization datasets (TVSum and SumMe) and a query-focused video summarization dataset (QFVS). Particularly, we achieve large improvements in the transfer setting, attesting to our method's strong generalization capabilities.
1 Introduction
CLIP-It unifies generic and query-focused video summarization in a language-guided framework that uses captions or user queries to condition summaries. Its multimodal Transformer scores frames while preserving their ordering and attending across the video.
- CLIP-It addresses generic and query-focused video summarization within one language-guided framework.
- Generic summaries use system-generated dense video captions, whereas query-focused summaries use open-ended natural language queries supplied by users.
- The Transformer attends to all frames while positional encoding preserves their ordering for score prediction.
- The model fuses image and language representations with Language-Guided Attention and assigns frame scores using a Frame-Scoring Transformer.
- Nearly 3% F1 improvement is achieved in the supervised setting on generic summarization benchmarks, with larger gains reported in transfer evaluation.
2 Related Work
Prior work developed generic and query-focused summarization through largely separate approaches, while CLIP-It builds on advances in supervised learning, video-text modeling, query-focused datasets, and Transformers.
- Generic Video Summarization: Generic video summarization evolved from hand-designed and heuristic methods toward supervised approaches trained on TVSum and SumMe annotations.
- Video-Text Summarization: Existing video-text summarization methods either evaluate in the text domain or require supplied text, whereas CLIP-It uses off-the-shelf captions and generic and query-focused benchmarks.
- Query-Focused Video Summarization: Query-focused summarization was introduced with QFVS and initially centered on user-defined summaries for predefined concepts.
- Transformers: CLIP-It adapts Transformers through a Language-Guided Attention block and uses CLIP image and text features for video summarization.
3 CLIP-It: Language-Guided Video Summarization
CLIP-It unifies generic and query-focused video summarization in a language-guided framework that conditions frame relevance on user queries or generated video descriptions. It fuses visual and language representations, models dependencies across frames, and selects high-scoring shots for the final summary.
- Framework: CLIP-It constructs summaries from a video and either a user-defined query or a system-generated dense video caption.Generic summarization uses automatically generated descriptions, while query-focused summarization uses natural-language user input.
- Summary Construction: At inference, CLIP-It converts frame scores into shot scores and applies a 0/1 knapsack algorithm to select key shots within the summary duration.This produces a timed summary containing high-scoring shots.
- Language-Guided Attention: Language-Guided Attention fuses image and language embeddings so captions can attend to video frames across modalities.The module modifies multi-head attention to accept visual and language inputs and infer long-term cross-modal dependencies.
- Frame-Scoring Transformer: The Frame-Scoring Transformer jointly models interactions across frames and outputs one relevance score per frame.Positional encoding preserves frame-order information while contextualizing frame representations relative to one another.
- Learning: Supervised training combines classification, diversity, and reconstruction losses, whereas unsupervised training omits classification and retains diversity and reconstruction losses.The supervised objective is Lsup = α · Lc + β · Ld + λ · Lr; the unsupervised objective is Lunsup = β · Ld + λ · Lr.
- Learning: The diversity regularizer discourages redundancy among selected keyframes, while reconstruction compares reconstructed and original selected-keyframe features.Reconstruction uses mean squared error, and diversity uses pairwise cosine similarity.
4 Experiments
CLIP-It is evaluated on generic and query-focused video summarization across standard, augmented, transfer, and unsupervised settings. It outperforms prior methods, with language guidance and Transformer-based scoring improving summaries and transfer performance.
- Experimental Setup: The three data configurations are Standard, Augment, and Transfer, with Transfer training on three datasets and evaluation on a fourth unseen dataset.Augment combines one dataset with three others, while Transfer is the most challenging setting.
- Generic Video Summarization: 5% on TVSum and 3% on SumMe: CLIP-It exceeds SumGraph in the Transfer setting, indicating stronger out-of-distribution generalization.The full CLIP-Image+Video Caption+Transformer model outperforms state-of-the-art across all three data settings.
- Qualitative Results: Language conditioning improves frame selection by reducing irrelevant high-scoring frames, while the predicted scores qualitatively align with ground-truth scores and key actions.The qualitative examples show captions focusing scores on important actions and objects, and CLIP-It selecting clear recipe steps.
- Generic Video Summarization: 2%: adding captions improves generic QFVS summaries over the CLIP Image + Transformer baseline, while replacing CLIP features or the Transformer reduces performance.Generated-caption CLIP-It outperforms supervised and unsupervised baselines on all four QFVS videos.
- Query-Focused Video Summarization: 54.55% avg F1 versus 44.19% for the best baseline: CLIP-It improves query-focused summarization on QFVS by 10%.Different natural-language queries applied to the same egocentric video produce different summaries.
5 Discussion and Broader Impacts
CLIP-It unifies generic and query-focused video summarization while allowing users to guide summaries with open-ended natural-language queries. The authors caution that biases from the captioning and vision-language models may propagate into the system.
- CLIP-It is presented as a unified language-guided framework for generic and query-focused video summarization.
- Open-ended natural-language queries give users greater flexibility in guiding video summarization.
- Biases encoded in the off-the-shelf captioning model and CLIP may propagate to CLIP-It.
- Practitioners should consider these bias sources in relation to their specific use cases.
A Appendix
The appendix begins by organizing the implementation details of the method. It then introduces the implementation-details subsection.
- The appendix states that its section is organized as follows.
- The supplied appendix passages provide the section heading but no additional implementation content.
- The first listed appendix subsection is titled Implementation Details.
A.1 Implementation Details
The implementation combines multimodal attention, a frame-scoring Transformer, CLIP-based image and text encodings, and shot-construction procedures. It also reports fixed training components, optimization settings, computational cost, and evaluation conversions.
- Language-Guided Multi-head Attention: The language-guided attention uses 4 heads, with image encoding as query and text encoding as key and value.
- Frame-Scoring Transformer: The frame-scoring Transformer uses 8 heads, 6 encoder layers, 6 decoder layers, and an input sequence length of 256.
- Encodings: CLIP encodes images into fimg(F) ∈R512, while query text is encoded into a 512-dimensional CLIP text embedding.
- Encodings: For generic summaries, dense captions are generated with BMT, seven captions are sampled, concatenated, and projected into ftext(C) ∈R512.
- Training: The caption generator and CLIP encoders remain fixed, while the attention network and frame-scoring Transformer are trained with Adam.
- Computational Resources: Training runs for 20 epochs with batch size 100 and takes about 2–3 hours on 5 NVIDIA RTX 2080 GPUs per dataset and setting.
A.2 Additional Results
Additional experiments examine ranking metrics, loss components, and cross-modal attention. They show strong rank-based performance, benefits from reconstruction and diversity losses, a supervised advantage, and a measurable cost when replacing multimodal attention.
- Rank-Based Metrics: Kendall’s τ and Spearman’s ρ compare predicted and ground-truth frame rankings, and CLIP-It outperforms all baselines on these TVSum metrics.
- Loss Ablations: The loss ablation evaluates CLIP-It on the TVSum and SumMe datasets using classification, reconstruction, and diversity losses.
- Loss Ablations: Adding reconstruction or diversity loss improves performance over classification loss alone, while the full supervised combination performs best.
- Loss Ablations: Removing classification loss for unsupervised training causes a slight performance drop because ground-truth annotations are unavailable.
- Cross-Modal Attention: Replacing language-guided attention with a basic MLP reduces performance by 4%.
A.3 Limitations
CLIP-It relies on large-scale language models for video captioning and CLIP feature extraction, whose biases may propagate into frame scoring. In particular, web-sourced training data may introduce inappropriate biases or stereotypes.
- Large-scale language models for video captioning and CLIP feature extraction may encode inappropriate biases that propagate into CLIP-It.These biases may affect how video frames are scored.