ShortVid-Bench: Short Video Benchmark

• ShortVid-Bench is a benchmark designed for structured comprehension of fast-paced, user-generated short videos, integrating temporal, visual, audio, and textual signals.
• It evaluates tasks such as timestamped captioning, open-ended QA, and temporal grounding to rigorously measure multimodal reasoning and narrative understanding.
• The framework provides a quantitative basis for comparing multimodal models, supporting both academic research and real-world video content analysis.

ShortVid-Bench is a benchmark and evaluation suite designed for structured comprehension of real-world user-generated short videos. It addresses challenges unique to short video content—such as high information density, multimodal complexity, and rapid temporal progression—by offering a comprehensive framework for assessing and advancing video understanding models. The benchmark emphasizes temporally-structured, multimodal reasoning, covering tasks including timestamped video captioning, open-ended question answering, temporal video grounding, and narrative comprehension. ShortVid-Bench also establishes a quantitative basis for comparing multimodal model performance, reflecting both academic advancements and practical deployment considerations.

1. Motivation and Benchmark Scope

ShortVid-Bench was conceptualized to reflect the distinct characteristics and challenges of short-form user-generated video prevalent on platforms such as WeChat Channel and TikTok (Ge et al., 28 Jul 2025). Unlike conventional benchmarks targeted at long-form or isolated video tasks, ShortVid-Bench is tailored for videos with high event density, rapid pacing, emotion-centric storytelling, and multi-granular narrative structures. The need for fine-grained temporal localization, explicit multimodal integration (visual, audio, text), and robust reasoning over creator intent and storyline is central to the benchmark's design. It offers a unified evaluation platform that quantifies a model's capacity to comprehend nuanced short video content at multiple levels of granularity.

2. Technical Design and Annotation Methodology

ShortVid-Bench's technical framework leverages an automated annotation pipeline to generate high-quality, timestamped datasets that include synchronized visual, audio, and ASR-derived textual signals (Ge et al., 28 Jul 2025). The pipeline utilizes:

• Timestamp Overlay Mechanism: Every frame (sampled at 1 fps, up to 150 frames per video) is embedded with a rendered timestamp, ensuring temporal cues are retained throughout the video input stream.
• Multimodal Synchronization: Audio (processed via Whisper and segmented for synchronization) and visual features (from a Vision Transformer backbone) are fused per frame/timestamp.

$S_i = V_i + A_i,\quad i = 1, 2, \ldots, n$

where $V_i$ and $A_i$ represent visual and audio token embeddings, respectively; $S_i$ is the synchronized multimodal token passed to the LLM.

• Data Sourcing and Granular Supervision: The dataset incorporates millions of real-world short videos annotated with event-level and chapter-level captions, ASR transcripts, and QA pairs. Video-level annotation covers a diverse taxonomy of creator intent, affective categories, and narrative events. Temporal supervision is provided by aligning captions and QA pairs with precise time intervals.

3. Benchmark Task Suite

ShortVid-Bench comprises multiple tasks, each designed to test a different dimension of structured short video comprehension:

Task	Input Modalities	Evaluation Focus
Timestamped Captioning	Visual, Audio, Text	Multi-granularity alignment to temporal intervals
Open-Ended QA	Visual, Audio, Text	Reasoning over events, emotions, and intent
Temporal Grounding	Visual, Audio, Text	Localization of queried events to time ranges
Multiple-Choice Reasoning	Visual, Audio, Text	Taxonomy classification, narrative consistency

The benchmark's evaluation protocol uses metrics such as accuracy for multiple-choice QA, mean temporal Intersection-over-Union (tIoU) for temporal grounding, and strict alignment of generated captions to annotated time slices.

4. Reference Model: ARC-Hunyuan-Video-7B

The ARC-Hunyuan-Video-7B model serves as a reference architecture and establishes a high-performance baseline on ShortVid-Bench (Ge et al., 28 Jul 2025). The model is a multimodal transformer that processes synchronized visual, audio, and (optionally) text signals. Its key architectural features include:

• Vision Transformer (ViT) Backbone: Extracts dense frame-level embeddings with overlaid temporal information.
• Audio Encoder (Whisper-based): Processes segmented raw waveforms with frame-wise alignment and MLP projection.
• Token Synchronization: Audio and visual tokens are aligned at the frame level via zero-padding and direct addition before passing to the LLM.
• Instruction Fine-Tuning and RL: Following pre-training, the model undergoes multiple rounds of supervised fine-tuning (including chain-of-thought style instruction data), cold start initialization, and reinforcement learning (using Gradient Regularized Policy Optimization [GRPO]) to maximize structured reasoning performance.

Performance on ShortVid-Bench:

• ARC-Hunyuan-Video-7B achieves 74.3% accuracy on the benchmark's multiple-choice QA suite, outperforming other multimodal models including Qwen2.5-VL-7B-Instruct (~67.8%), Qwen2.5-Omni-7B (~68.3%), and Keye-VL-8B (~53.5%). This demonstrates the impact of explicit temporal and multimodal integration.

5. Structured Comprehension Capabilities

ShortVid-Bench and the ARC-Hunyuan-Video-7B model together support a wide array of structured video understanding tasks:

• Multi-Granularity Captioning: The timestamp overlay mechanism enables both event-level and broader chapter-level summaries, aligning descriptive text precisely to time intervals.
• Open-Ended and Multiple-Choice QA: Models are required to reason about creator intent, affective meaning, narrative structure, and event causality.
• Temporal Grounding: Explicit temporal alignment of query responses (e.g., grounding a described event to a specific sequence of frames) is integral, enforced by data design and evaluation metrics.
• Zero-Shot and Few-Shot Generalization: The benchmark and reference model permit evaluation of generalization to diverse and unseen short video tasks without extensive re-training.

6. Deployment and Impact

ShortVid-Bench extends beyond academic evaluation by supporting direct application in production systems (Ge et al., 28 Jul 2025). Deployed ARC-Hunyuan-Video-7B instances have enabled:

• Rapid Inference: Approximately 10 seconds per one-minute video when using the vLLM framework on an H20 GPU, while generating up to 500 output tokens.
• Enhanced User Engagement: Production deployment has yielded measurable increases in click-through rates, longer session durations, and higher reported user satisfaction, attributed to improvements in tagging, retrieval, and recommendation accuracy.
• Versatility: The model and benchmark enable zero-shot deployment for diverse downstream tasks, including real-time content tagging, video search, event localization, and creator intent inference.

7. Future Directions

ShortVid-Bench paves the way for continued research in structured short video understanding by:

• Stimulating advances in multimodal fusion, particularly for aligning high-density short video narratives and rapidly shifting visual/audio content.
• Enabling systematic benchmarking and analysis of both open-source and closed-source multimodal models for real-world applications.
• Supporting further developments in efficient annotation, temporally-aware reasoning, and robust handling of heterogeneous audio-visual-textual data.
• Facilitating broader comparisons and standardization in model evaluation for short-form video comprehension across diverse cultural, linguistic, and content domains.

ShortVid-Bench establishes a foundation for next-generation research and deployment of video comprehension models tailored to user-generated short video ecosystems, offering both rigorous empirical benchmarks and real-world impact.