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[CVPR 2024] Zero-shot method for Vision-Language Models based on a robust formulation of the MeanShift algorithm for Test-time Augmentation (MTA).

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# On the Test-Time Zero-Shot Generalization of Vision-Language Models: Do We Really Need Prompt Learning? [accepted at CVPR 2024]

Paper: [*On the Test-Time Zero-Shot Generalization of Vision-Language Models: Do We Really Need Prompt Learning?*](https://arxiv.org/abs/2405.02266).

Authors:
[Maxime Zanella](https://scholar.google.com/citations?user=FIoE9YIAAAAJ&hl=fr&oi=ao),
[Ismail Ben Ayed](https://scholar.google.com/citations?user=29vyUccAAAAJ&hl=fr&oi=ao).

This is the official GitHub repository for our paper accepted at CVPR '24. 
This work introduces the MeanShift Test-time Augmentation (MTA) method, leveraging Vision-Language models without the necessity for prompt learning. Our method randomly augments a single image into N augmented views, then alternates between two key steps (see [mta.py](mta.py) and [Details on the code](#details-on-the-code) section.):

### 1. Computing a Score for Each Augmented View

This step involves calculating a score for each augmented view to assess its relevance and quality (inlierness score).

<img src="inlierness.png" alt="Score computation for augmented views" width="500" height="150">
 
 Figure 1: Score computation for each augmented view.

### 2. Seeking for the Mode

Based on the scores computed in the previous step, we seek the mode of the data points (MeanShift).

<img src="mode.png" alt="Seeking the mode, weighted by score" width="500" height="180">
 
 Figure 2: Seeking the mode, weighted by inlierness scores.

## Dataset Preparation and Installation

We follow TPT installation and preprocessing. This ensures that your dataset is formatted appropriately. You can find their repository [here](https://github.com/azshue/TPT).
If more convenient you can change the folder names of each dataset in the dictionnary ID_to_DIRNAME in [data/datautils.py](data/datautils.py) (line 20).

## Quick Start Guide

### Running MTA

Execute MTA on the ImageNet dataset with a random seed of 1 and 'a photo of a' prompt by entering the following command:

```bash
python main.py --data /path/to/your/data --mta --test_sets I --seed 1
```

Or the 15 datasets at once:

```bash
python main.py --data /path/to/your/data --mta --test_sets I/A/R/V/K/DTD/Flower102/Food101/Cars/SUN397/Aircraft/Pets/Caltech101/UCF101/eurosat --seed 1
```

## Details on the code
More information on the procedure in [mta.py](mta.py).
### Function: `gaussian_kernel`
- **Purpose**: Computes the Gaussian kernel density estimate of data points relative to the mode.
- **Process**:
  - Computes the Euclidean distance between each datapoint and the mode.
  - Applies the Gaussian function to these distances to compute density values.

### Function: `solve_mta`
- **Purpose**: Solves the MTA alternate procedure.
- **Process**:
  1. **Feature Extraction**: Computes image and text features.
  2. **Initialization**:
     - Calculates pairwise distances among image features to determine the bandwidth for the Gaussian kernel.
     - Initializes the affinity matrix based on the softmax of scaled logits.
     - Sets initial values for inlierness scores (`y`) uniformly.
  3. **Iterative optimization**:
     - Alternates between updating inlierness scores and the mode.
  4. **Output Calculation**: Computes the final output by cosine similarity of the final mode with the text features.

## Citation

If you find this project useful, please cite it as follows:

```bibtex
@inproceedings{zanella2024test,
  title={On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?},
  author={Zanella, Maxime and Ben Ayed, Ismail},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={23783--23793},
  year={2024}
}
```

## Acknowledgement

We express our gratitude to the TPT authors for their open-source contribution. You can find their repository [here](https://github.com/azshue/TPT).

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