Long-Term Ad Memorability: Understanding & Generating Memorable Ads

• Project Page
• Data (LAMBDA)
• Data (UltraLAMBDA)
• Paper

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Installation and Setup

Follow the steps below to install the required packages and set up the environment.

Step 1: Clone the Repository

Open your terminal and clone the repository using the following command:

git clone https://github.com/behavior-in-the-wild/ad-memorability.git

Step 2: Set Up the Conda Environment

Create and activate the Conda environment:

conda create -n admem python=3.10 -y
conda activate admem
pip install --upgrade pip  # Enable PEP 660 support
pip install -e .
pip install ninja
pip install flash-attn --no-build-isolation

Step 3: Set Up Model Zoo

Create directories and download the required models:

mkdir model_zoo
mkdir model_zoo/LAVIS
cd ./model_zoo/LAVIS
wget https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth

Step 4: Set Up LLaMA-VID

cd path/to/ad-memorability
mkdir work_dirs
cd work_dirs
git lfs install
git clone https://huggingface.co/YanweiLi/llama-vid-13b-full-224-video-fps-1

Step 5: Prepare Data Directory

cd path/to/ad-memorability
mkdir data
cd ./data

1. Create .npy files of your videos. A sample file is given in the sample folder.
2. Store them in as ./data/videos/video_scenes/{id}.npy

Training

1. Install the desired version of DeepSpeed.

2. Update the train.sh script: Replace the --data_path argument with one of the following options, depending on your training task: lambda_bs_train.json lambda_combine_train.json lambda_cs_train.json

3. If you're training on your own dataset, create a train.json file. Each entry should contain an id and a conversation. You can use lambda_bs_train.json as a reference for formatting.

bash train.sh

Inference

1. For predicting memorability scores:

bash eval_bs.sh

2. For generating memorable videos:

bash eval_cs.sh

Citation

If you find this repo useful for your research, please consider citing the paper

@misc{s2024longtermadmemorabilityunderstanding,
            title={Long-Term Ad Memorability: Understanding and Generating Memorable Ads}, 
            author={Harini S I au2 and Somesh Singh and Yaman K Singla and Aanisha Bhattacharyya and Veeky Baths and Changyou Chen and Rajiv Ratn Shah and Balaji Krishnamurthy},
            year={2024},
            eprint={2309.00378},
            archivePrefix={arXiv},
            primaryClass={cs.CL},
            url={https://arxiv.org/abs/2309.00378}}

Acknowledgement

We would like to thank the following repos for their great work:

• This work is built upon the LLaMA-VID
• This work is built upon the LLaVA.
• This work utilizes LLMs from Vicuna.
• This work utilizes pretrained weights from InstructBLIP.
• We perform video-based evaluation from Video-ChatGPT.

License

The data and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA-VID,LLaVA, LLaMA, Vicuna and GPT-4.