Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction
TASKER algorithm improves video understanding by task-driven and scene-aware keyframe extraction, achieving a 2.0% gain on EgoSchema.
Key Findings
Methodology
The study introduces TASKER, a task-driven and scene-aware keyframe extraction algorithm. TASKER combines task relevance and scene dynamics using graph search algorithms to select informative frames, enhancing performance in video QA and guided tasks.
Key Results
- TASKER achieved 63.1% accuracy on the EgoSchema dataset, surpassing the best baseline by 2.0%. On the NExT-QA dataset, TASKER's average accuracy was 77.4%, exceeding the best baseline by 1.8%.
- In the VG-GUI-Bench benchmark, TASKER significantly improved the execution ability of multimodal large models on long-horizon GUI tasks.
- TASKER also demonstrated excellent frame efficiency, processing only about 15% of total frames to achieve high accuracy.
Significance
By introducing TASKER, the study significantly enhances video understanding tasks, especially in long-horizon tasks. It not only improves model accuracy but also reduces computational burden, offering new possibilities for deploying multimodal large models in practical applications.
Technical Contribution
TASKER provides a unified mechanism for temporal information selection by combining task-driven and scene-aware strategies. It achieves higher accuracy and frame efficiency compared to existing methods without relying on training.
Novelty
TASKER uniquely combines task-driven and scene-aware strategies for keyframe extraction, differing from traditional methods that focus solely on visual features or scene changes.
Limitations
- TASKER may face efficiency issues in frame selection when handling extremely long videos.
- The algorithm might not accurately identify all keyframes in complex scenes.
Future Work
Future research could explore TASKER's application in other video understanding tasks, such as video generation and editing. Additionally, optimizing the frame selection algorithm for improved efficiency is a potential direction.
AI Executive Summary
Video understanding is a fundamental capability for multimodal intelligence. Existing multimodal large models excel in video QA benchmarks but primarily evaluate shallow visual cues, lacking the ability to learn deeper knowledge and procedural skills. To address this gap, this paper introduces VG-GUI-Bench, a new benchmark to evaluate whether multimodal large models can follow video tutorials to complete corresponding GUI tasks. Additionally, the study finds that model performance in video QA and guided tasks critically depends on effective keyframe extraction. Based on this observation, the paper proposes TASKER, a keyframe extraction algorithm that considers task relevance and scene dynamics. Experimental results demonstrate significant performance improvements with TASKER, achieving a 2.0% gain on EgoSchema and 1.8% on NExT-QA. These results highlight the potential of generalized keyframe extraction methods for video understanding tasks.
Deep Analysis
Background
Video understanding is a core task in multimodal intelligence. With the development of multimodal large models, video QA tasks have made significant progress. However, existing benchmarks mainly focus on shallow visual cues, neglecting the ability to learn and generalize deeper knowledge and procedural skills. Video tutorials are widely used to teach complex procedures, from software operations to device configuration and daily life skills.
Core Problem
Existing video QA benchmarks primarily evaluate models' ability to perceive shallow visual cues, lacking the ability to learn and generalize deeper knowledge and procedural skills. This limitation is particularly evident in real-world learning scenarios, where video tutorials are widely used to teach complex procedures.
Innovation
The paper introduces TASKER, a task-driven and scene-aware keyframe extraction method. TASKER uses graph search algorithms to select informative frames, enhancing performance in video QA and guided tasks. Unlike traditional methods, TASKER considers both task relevance and scene dynamics.
Methodology
- �� TASKER combines task-driven and scene-aware strategies, using graph search algorithms to select informative frames. • In TASKER, videos are divided into segments, each representing a node. • By evaluating frames' information and task relevance, TASKER selects the optimal node for expansion. • TASKER achieves efficient frame selection without relying on training.
Experiments
Experiments were conducted on the EgoSchema and NExT-QA datasets using the TASKER algorithm for keyframe extraction. Baseline methods included VideoTree and VideoAgent. Results showed TASKER outperformed existing methods in accuracy and frame efficiency, particularly in long-horizon tasks.
Results
TASKER achieved 63.1% accuracy on the EgoSchema dataset, surpassing the best baseline by 2.0%. On the NExT-QA dataset, TASKER's average accuracy was 77.4%, exceeding the best baseline by 1.8%. Additionally, TASKER demonstrated excellent frame efficiency, processing only about 15% of total frames to achieve high accuracy.
Applications
TASKER can be applied to video QA, guided tasks, and other video understanding tasks requiring efficient frame selection. Its efficient frame selection capability makes it widely applicable in practical scenarios.
Limitations & Outlook
While TASKER performs well in video QA and guided tasks, it may face efficiency issues in frame selection when handling extremely long videos. Additionally, the algorithm might not accurately identify all keyframes in complex scenes. Future research could explore optimizing the frame selection algorithm for improved efficiency.
Plain Language Accessible to non-experts
Imagine watching a movie with many scenes and details. The TASKER algorithm is like a smart viewer who can quickly find the most important parts of the movie and ignore the less important ones. This way, it can understand the movie's content faster and answer questions about it. Just like when you watch a movie, you focus on the parts that interest you and ignore the less important details.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super complex game with lots of levels and tasks. TASKER is like a super smart helper that can find the most important hints in the game, helping you clear levels faster! It's like in school, where you only need to remember the key points the teacher tells you, not every single detail. Isn't that cool?
Glossary
TASKER
A task-driven and scene-aware keyframe extraction algorithm to enhance video understanding tasks.
TASKER uses graph search algorithms to select informative frames.
VideoQA
Video question answering tasks that evaluate a model's ability to understand and reason over video content.
TASKER excels in VideoQA tasks.
VG-GUI-Bench
A benchmark to evaluate multimodal large models' performance on long-horizon GUI tasks.
TASKER significantly improves model performance in VG-GUI-Bench.
Keyframe Extraction
Selecting informative frames from videos to enhance the efficiency of video understanding tasks.
TASKER improves video QA accuracy through keyframe extraction.
Multimodal Large Model
A model that combines multiple modalities for learning and reasoning.
TASKER achieves efficient keyframe selection on multimodal large models.
Open Questions Unanswered questions from this research
- 1 How to maintain TASKER's frame selection efficiency in extremely long videos?
- 2 How to improve TASKER's keyframe identification in complex scenes?
Applications
Immediate Applications
Video QA
TASKER can enhance the accuracy and efficiency of video QA systems, helping users quickly access key information in videos.
Long-term Vision
Intelligent Video Editing
TASKER can be used for intelligent video editing, automatically selecting and clipping key segments in videos to improve production efficiency.
Abstract
Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks. However, existing benchmarks primarily evaluate whether models can perceive shallow visual cues, while rarely examining whether MLLMs can learn deeper knowledge or procedural skills from video tutorials and generalize them to downstream long-horizon agentic tasks. To address this gap, we introduce VG-GUIBench (Video-Guided GUI Benchmark), a new benchmark designed to evaluate whether MLLM-based GUI agents can follow video tutorials to complete corresponding GUI interactive tasks. Furthermore, we observe that the performance of models on both VideoQA and video-guided agentic tasks critically depends on effective keyframe extraction. Based on this observation, we propose TASKER (Task-driven And Scene-aware Keyframe searchER), a keyframe extraction algorithm that jointly considers task relevance and scene dynamics to identify informative frames. Experimental results demonstrate that TASKER achieves significant performance improvements on both VideoQA and video-guided agentic task benchmarks, outperforming the best baseline by 2.0% on the EgoSchema fullset and 1.8% on the NExT-QA dataset, respectively. These results further highlight the potential of generalized keyframe extraction methods for video understanding tasks. Our code and data are available at https://github.com/VG-GUI-TASKER/VG-GUI-TASKER.