Project Info

AdJacent

This project did not submit a demo video on Devpost.

Inspiration

Creative teams guess which ads work; we'd much prefer fast, explainable signals across video, image, and audio to pick winners before spend.

What it does

Extracts features from images, videos, and audio. Merges video+audio per video; outputs JSON with interpret-able metrics (shots, motion, text/CTA timing, product reveal, end-card, thumbnails, faces, color, clutter, platform fit, loop-readiness, audio speech/music).

How we built it

We created a modular pipeline: image/: visual semantics, attention maps, OCR, optional LLM captioning. videos/: per-metric modules with parallel per-video execution and dependency waves. Sub-tasks are parallelize-able audio/: separation, ASR, speech/music/text analysis. Orchestrator that runs image, and in parallel runs video and audio, then merges by filename stem.

Challenges we ran into

Hitting sub‑second per-metric latency without GPUs. Aggregation can almost entirely be parallelized so the result will only take the longest of each metric, summed if dependencies exist. Avoiding redundant computation across steps (saliency/gray/MSER reuse).

Accomplishments we're proud of

End‑to‑end, explainable feature set with per‑metric breakdowns, parallel per‑video tasking and A/V concurrency, tweakable speed/quality knobs and weights, simple JSON outputs ready for ranking/ML.

What we learned

Lightweight CV proxies (saliency/MSER/flow) are strong baselines. Caching + downsampling matter more than we expected. Clear dependency waves simplify parallelism and debugging.

What's next

We still need to add lightweight logo/face detectors and text OCR keywords for CTAs. For full functionality, we should train a learned ranker on historical performance. We also need to add web UI for previews, breakdowns, and A/B comparisons. Integrating this with AppLovin would be the ultimate step!

Ad Intelligence

Setup

Run the setup script:

./setup.sh

Activate the virtual environment:

source .venv/bin/activate

The setup script will automatically download the ad assets and install all required dependencies.

Finally, install Tesseract:

brew install tesseract

TPP-Gaze Submodule Setup

Required for the attention map features.

Install brew dependencies:

brew install wget

Follow the setup instructions at https://github.com/phuselab/tppgaze:

git clone https://github.com/phuselab/tppgaze
cd tppgaze
pip install -r requirements.txt

Within the adintelligence folder, run the following.

bash download_models.sh

To run Audio/Video/Image Metrics

Checkout the relevant branch. Images are on the main branch.

git checkout [audio/videos/main]

Activate the virtual environment and run the setup:

source .venv/bin/activate
[repeat steps above]

The setup script will automatically download the ad assets and install all required dependencies.

Read each branch's README to see how to run the scripts. Details on what is run will be described in the provided markdown files.

Citation

This project uses the TPP-Gaze model for gaze dynamics and scanpath prediction.
If you use or build upon this component, please cite the following work:

@inproceedings{damelio2025tppgaze,
  title     = {TPP-Gaze: Modelling Gaze Dynamics in Space and Time with Neural Temporal Point Processes},
  author    = {D'Amelio, Alessandro and Cartella, Giuseppe and Cuculo, Vittorio and Lucchi, Manuele and Cornia, Marcella and Cucchiara, Rita and Boccignone, Giuseppe},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2025}
}

For more information, visit the official repository: https://github.com/phuselab/tppgaze

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Technology

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CSSIn code
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Codebase size

Source size

100 KB

Source files

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