[Paper] ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search

Source: arXiv - 2601.23232v1

Overview

The paper introduces ShotFinder, a new benchmark and retrieval system that lets you search for specific video shots (short, coherent clips) using natural‑language queries. By combining large language models (LLMs) with web‑scale video search, the authors expose current gaps in multimodal AI—especially when it comes to handling temporal cues, color, visual style, audio, and resolution in open‑domain video content.

Key Contributions

• ShotFinder benchmark: 1,210 curated YouTube samples spanning 20 categories, each annotated with a keyframe‑oriented description and five controllable constraints (temporal order, color, visual style, audio, resolution).
• Three‑stage retrieval pipeline:
  1. Query expansion via “video imagination” – LLM generates imagined visual/audio cues to enrich the textual query.
  2. Candidate video retrieval – Leverages a standard web search engine to pull down a short list of videos.
  3. Description‑guided temporal localization – Aligns the expanded query with specific shot boundaries inside the retrieved videos.
• Comprehensive evaluation across several closed‑source (e.g., GPT‑4V, Gemini) and open‑source multimodal models, revealing a sizable performance gap to human annotators.
• Diagnostic analysis of constraint difficulty, showing temporal ordering is relatively easy while color and visual‑style matching remain hard for current models.

Methodology

1. Data creation – The authors prompted large generative models (e.g., GPT‑4) to produce shot‑level descriptions and constraint specifications for YouTube videos. Human annotators then verified and refined these outputs to ensure quality.
2. Query imagination – Given a user’s short textual request (e.g., “a sunrise over a foggy lake with soft piano music”), an LLM expands it into a richer “imagined” description that includes likely visual attributes, audio cues, and temporal hints.
3. Retrieval – The expanded query is fed to a conventional web search API, returning a ranked list of candidate videos.
4. Temporal localization – A multimodal model processes each candidate video, comparing frame‑level embeddings to the imagined description and scoring possible shot boundaries. The highest‑scoring segment is returned as the answer.
5. Evaluation – Human judges assess whether the retrieved shot satisfies all five constraints. Metrics include recall@k for retrieval and Intersection‑over‑Union (IoU) for temporal alignment.

Results & Findings

• Overall performance: The best multimodal model achieved ~45 % human‑level accuracy, far below the ~90 % score of human annotators.
• Constraint breakdown:
  • Temporal order: ~70 % success, indicating models can follow “first/then” cues reasonably well.
  • Audio: ~55 % success, showing moderate ability to match sound descriptions.
  • Resolution: ~60 % success, reflecting decent handling of coarse‑grained quality cues.
  • Color and visual style: <40 % success, the biggest bottlenecks—models struggle to differentiate subtle hue palettes or artistic styles from text alone.
• Closed‑source vs. open‑source: Closed‑source models (GPT‑4V, Gemini) outperform open‑source alternatives, but the gap narrows when the query imagination step is used, highlighting the importance of prompt engineering.
• Ablation: Removing the query‑imagination stage drops retrieval recall by ~15 %, confirming its value in bridging the language‑vision gap.

Practical Implications

• Content moderation & copyright – Automated tools could locate infringing or policy‑violating shots across the web faster than manual review.
• Media production – Editors can query massive video libraries (“find a low‑key, blue‑tinted night scene with rain sound”) to pull reference footage, cutting down on manual sifting.
• E‑learning & knowledge bases – Platforms can surface exact instructional clips (e.g., “the moment the teacher writes the formula on a chalkboard”) to enrich interactive textbooks.
• Advertising & brand monitoring – Brands can track how their visual identity (color palette, style) appears in user‑generated videos, enabling real‑time compliance checks.
• Search engine enhancement – Integrating ShotFinder‑style pipelines could turn generic video search into fine‑grained shot‑level retrieval, a next‑generation feature for platforms like YouTube or Vimeo.

Limitations & Future Work

• Dataset scale & diversity – While 1,210 shots cover many topics, the benchmark is still modest compared to the billions of videos online; scaling up will test model robustness.
• Reliance on web search APIs – The pipeline’s second stage depends on external search engines, which may introduce bias or latency; end‑to‑end learned retrieval could be explored.
• Constraint granularity – Current constraints are single‑factor; real‑world queries often combine multiple factors (e.g., “a warm‑colored, handheld‑camera shot with ambient city noise”). Handling multi‑factor constraints remains an open challenge.
• Audio understanding – The audio component is limited to coarse descriptors; richer sound semantics (speech content, music genre) need deeper multimodal modeling.
• Evaluation of imagination quality – The “video imagination” step is heuristic; future work could formalize how to measure and improve the fidelity of generated descriptions.

ShotFinder shines a light on the next frontier for multimodal AI: moving from whole‑video retrieval to precise, constraint‑driven shot discovery. As developers begin to embed such capabilities into products, we can expect smarter, more granular video search experiences—once the models catch up with the visual nuance humans take for granted.

Authors

• Tao Yu
• Haopeng Jin
• Hao Wang
• Shenghua Chai
• Yujia Yang
• Junhao Gong
• Jiaming Guo
• Minghui Zhang
• Xinlong Chen
• Zhenghao Zhang
• Yuxuan Zhou
• Yanpei Gong
• YuanCheng Liu
• Yiming Ding
• Kangwei Zeng
• Pengfei Yang
• Zhongtian Luo
• Yufei Xiong
• Shanbin Zhang
• Shaoxiong Cheng
• Huang Ruilin
• Li Shuo
• Yuxi Niu
• Xinyuan Zhang
• Yueya Xu
• Jie Mao
• Ruixuan Ji
• Yaru Zhao
• Mingchen Zhang
• Jiabing Yang
• Jiaqi Liu
• YiFan Zhang
• Hongzhu Yi
• Xinming Wang
• Cheng Zhong
• Xiao Ma
• Zhang Zhang
• Yan Huang
• Liang Wang

Paper Information

• arXiv ID: 2601.23232v1
• Categories: cs.CV, cs.AI
• Published: January 30, 2026
• PDF: Download PDF