Video Difference Captioning: Exploring Similarities and Differences in Dynamic Scenes

This article addresses the core question: What is the Video Difference Captioning task, and how does it enhance our understanding of video editing and multimodal model capabilities?

Video Difference Captioning (ViDiC) is a task where models generate natural language descriptions that precisely capture both static visual elements and temporal dynamics between two video clips, ensuring coherence and factual accuracy. It extends image difference captioning into the video realm, emphasizing motion, event progression, and stylistic shifts.

Introduction: The Importance of Understanding Video Differences

This section answers the core question: Why is perceiving differences in videos crucial for human-like visual reasoning?

Perceiving and describing differences between visual inputs is a fundamental human ability and key to visual reasoning. Current image difference captioning methods are limited to static pairs, overlooking temporal aspects like motion cues in real-world scenarios.

In dynamic environments, differences arise not just from frames but from action variations, event developments, camera motions, or style transitions. ViDiC bridges this by requiring models to describe similarities and differences in video content and dynamics, focusing on edit comprehension rather than creation.

For instance, in a background alteration scenario, two videos might share a fixed camera view of a rice paddy at sunset, but one shows a visible sun with strong reflections and a red-orange sky, while the other has a cloud-covered sun with soft, diffused light. The model must note: similarities in camera position, differences in sun visibility.

Reflection: Working with this task has taught me that video comprehension goes beyond still images; the temporal layer adds complexity, reminding me to prioritize real-world diversity when designing AI systems.

Related Works: Evolution of Visual Captioning

This section answers the core question: How does Video Difference Captioning differ from existing visual captioning tasks?

ViDiC builds on visual captioning but advances to comparative analysis. Modern multimodal models excel in single-input benchmarks like captioning static images or answering visual questions, yet struggle with differences between two videos or images.

Image Difference Captioning describes semantic shifts between images. Early datasets taught models to verbalize differences, while recent efforts focus on scalable synthetic data and preference selection. However, these rely on static pairs, missing temporal dynamics.

Video Editing Datasets face challenges in high-cost annotation and spatio-temporal consistency. Initial task-specific suites and competitions advanced text-guided edits, later expanding to AI-assisted tasks like reframing and color grading. Recent synthetic pipelines create massive edit pairs for training, but prioritize edit fidelity over describing differences.

ViDiC shifts emphasis from edit performance to understanding, evaluating models’ ability to verbalize fine-grained semantic variances in video pairs.

Image source: Pexels (depicting dynamic scene comparisons).

ViDiC-1K Dataset: Building the Benchmark

This section answers the core question: How was a benchmark dataset created for evaluating Video Difference Captioning?

The ViDiC-1K dataset includes 1,000 selected video pairs with over 4,000 comparative checklist items across seven categories: subject, style, background, cinematography, motion, position, and playback techniques. A dual-checklist framework assesses similarity and difference accuracy separately.

Data Collection

This subsection answers the core question: How were the video pairs gathered and generated?

To create a comprehensive benchmark, ViDiC-1K aggregates public sources and uses proprietary methods. External data comes from academic datasets and web platforms. For some, temporal bisection divides long takes into equal segments. All videos are filtered to eliminate duplicates, static content, or extreme differences.

Controlled synthesis via frame splicing stacks boundary frames, generates a composite video with a model, then splits it for precise variation control.

CV and rendering augmentation employs tools for modifications: (1) changing camera views; (2) altering styles; (3) adding/removing subjects with segmentation and inpainting; (4) re-animating actions in engines.

For example, in subject addition, one video shows a walking person, the other adds an extra figure via CV, requiring the model to describe count differences.

Image source: Unsplash (illustrating frame manipulation).

Image source: Pexels (showing video editing tools).

Annotation Pipeline

This subsection answers the core question: How is dataset annotation quality ensured?

The annotation uses a two-stage approach: automated drafting and human validation.

Stage 1: Automated Draft. A model generates detailed descriptions of differences and similarities. Another processes this as ground truth to create draft checklists.

Stage 2: Human Validation. Six trained annotators refine drafts. Each is reviewed by two based on criteria addressing errors, contradictions, or subjectivity. Disputes are resolved by a senior annotator. This retains only 16.32% of original items verbatim, ensuring accuracy and human alignment.

Reflection: This pipeline highlights that while AI speeds annotation, human oversight is essential for nuanced accuracy in temporal complexities, preventing model hallucinations.

Dataset Statistics

This subsection answers the core question: What is the scale and diversity of ViDiC-1K?

The benchmark has 1,000 pairs with 4,107 items (1,056 similarities, 3,051 differences). Distributions show variety in checklist lengths. Videos are diverse: durations 2-12 seconds typical for editing; varied resolutions; broad topics for generalizability.

Checklist taxonomy covers seven dimensions: 1) Subject (type, count, attributes like appearance/pose); 2) Style (objective descriptors like Anime/Oil Painting); 3) Background (location, atmosphere, lighting); 4) Camera Work (movement, scale); 5) Subject Motion (actions, interactions); 6) Positional Relationship (arrangements); 7) Playback Technique (slow-motion, reverse).

Use tables for comparisons:

Category	Subitem Examples	Count Examples
Subject	Clothing, Appearance, Color	278, 217, 198
Style	Realistic, Anime, Flat	33, 21, 19
Background	Objects, Lighting, Location	445, 259, 84
Camera	Scale, Movement, Orientation	110, 107, 86
Motion	Types, Interaction, Direction	264, 107, 78
Position	Layout, Interaction, Flip	143, 95, 8
Playback Tech.	Reverse, Slow, Fast	34, 17, 14

Image source: Pixabay (bar chart of category counts).

Video content hierarchy includes perspective, framing, angle, etc., ensuring coverage.

Image source: Gratisography (pie chart representation).

Distributions for items, durations, resolutions, and sources confirm balance.

Comparison with Other Benchmarks

This subsection answers the core question: What advantages does ViDiC-1K have over existing benchmarks?

Visual comparison benchmarks are fragmented, focusing on static images or isolated video tasks. ViDiC-1K is the first to unify difference and similarity evaluation in videos across broad scenarios.

Table comparison:

Benchmark	Source	Task	Category Count	Size	Evaluation
Spot-the-Diff	Real	Image Difference Captioning	1	1,400	Reference-based
CLEVR-Change	Syn.	Image Difference Captioning	5	7,970	Reference-based
OmniDiff	Real and Syn.	Image Difference Captioning	12	1,560	Reference-based
ViDi	Real	Image Difference Captioning	5	200	Reference-based
VidDiffBench	Real	Video Action Differencing	5	549	Checklist + LLM
ViDiC-1K	Real and Syn.	Video Difference Captioning	35	1,000	Checklist + LLM

ViDiC-1K overcomes single-score limits with granular checklists.

Evaluation Methodology: Dual-Checklist Framework

This section answers the core question: How is accuracy in Video Difference Captioning reliably assessed?

Traditional metrics measure text similarity, not facts. We propose a human-annotated checklist for direct factual quantification, with binary questions Q from dimensions, ground-truth AGT.

In evaluation, model M generates description D from video pair and dimensions. Judge J answers Q from D only, yielding AJ. Accuracy is AJ-AGT consistency.

Evaluation Metric

This subsection answers the core question: How are similarity and difference questions evaluated differently?

Accuracy = 1 / |Q| * sum I(AJ,i = AGT,i)

Similarity: Inverse framing penalizes hallucination (e.g., “Are videos at different locations?”). Correct if similarity confirmed or attribute omitted.

Difference: Verifiable propositions; must affirm true statements, penalizing failure or omission.

Example: Similarity – “Are videos filmed at different locations?” No. Difference – “Is sun unobstructed in A but covered in B?” Yes.

Reflection: Separating metrics reveals model balance, crucial for practical video editing applications.

Experiments: Insights into Model Performance

This section answers the core question: How do current models perform on Video Difference Captioning?

Evaluated 19 models, both proprietary and open-source. Results show significant gaps, highlighting comparative and difference perception weaknesses.

Main Results

This subsection answers the core question: What are the performance variations across dimensions?

Dataset scale reveals hierarchy. Proprietary lead, but open-source like Qwen3-VL-32B surpass some. Performance scales with size.

Models strong in Style, reasonable in Subject, Motion, Position, Background. Weak in Camera, Playback, especially open-source.

High Similarity (low hallucination), low Difference (weak perception). GPT-4o: 81.12% Similarity, 39.14% Difference.

Thinking mode boosts Difference but increases Similarity hallucinations.

Dual-video incompatibility in older models like LLaVA-v1.6-Vicuna-7B.

Table of results (partial):

Model	Param.	Avg.	Diff.	Sim.
Gemini-2.5-Pro	–	66.72	63.73	75.33
GPT-5	–	62.94	57.32	79.17
Qwen3-VL	32B	61.38	58.54	71.50
LLaVA-V1.6-Vicuna	7B	8.96	5.11	20.07

In video editing review, models must describe camera changes for consistency, but weaknesses cause omissions.

Further Analysis

This subsection answers the core question: How is evaluation reliability verified through judge consistency?

Human-model reliability analysis on 750 pairs. Subset assessed by humans and LLMs.

Concordance rates:

LLMs	Average	Similarities	Differences
GPT-5-mini	95.22	95.90	94.97
DeepSeek-V3	89.37	90.84	88.84
Qwen3-32B	87.23	88.98	86.60

Strong correlation, especially GPT-5-mini, supports scalable LLM evaluation.

Reflection: Calibration with human baselines prevents bias in fine-grained video tasks.

Conclusion: Future of Video Difference Captioning

ViDiC task and ViDiC-1K benchmark lay groundwork for robust, explainable video reasoning in multimodal models. Experiments expose temporal reasoning and edit interpretation gaps, even in top models.

Contributions: Task unification of description, comparison, temporality; benchmark with checklists; model weakness revelation.

In practice, for content verification, models detect manipulations, improving reliability.

Practical Summary / Action Checklist

• Dataset Building: Aggregate sources, filter quality; splice frames for synthesis; augment with CV.
• Annotation: Draft automatically, validate humanly for accuracy.
• Evaluation: Generate D; judge answers checklists; compute similarity/difference accuracy.
• Application: Use in forensics to spot changes like altered backgrounds.

One-Page Summary

• Task: ViDiC captures static/temporal differences.
• Dataset: 1,000 pairs, 4,107 items, 7 categories.
• Collection: Public + synthetic + augmented.
• Evaluation: Dual checklists + LLM judge, accuracy metric.
• Results: Gaps in camera/playback; tradeoff in thinking mode.
• Insights: Exposes temporal weaknesses, guides improvements.

FAQ

1. What distinguishes Video Difference Captioning from Image Difference Captioning?
   It includes temporal dynamics like motion and events.

2. How many video pairs are in ViDiC-1K?
   1,000.

3. What categories does the dataset cover?
   Subject, style, background, cinematography, motion, position, playback techniques.

4. How is model accuracy evaluated?
   Via dual checklists and LLM judge comparing answers.

5. What are common model weaknesses?
   Poor in camera and playback detection.

6. How does thinking mode affect performance?
   Improves differences but increases similarity hallucinations.

7. How is annotation quality maintained?
   Automated drafts with human validation, retaining 16.32% original.

8. What makes ViDiC-1K superior to other benchmarks?
   Unified video difference/similarity, 35 categories for granularity.