# Test-time Distribution Learning Adapter for Cross-modal Visual Reasoning

arXiv: https://arxiv.org/abs/2403.06059 · HTML: https://mlsanity.com/paper/2403.06059/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 10 Mar 2024.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Builds on (most cited first, 1 of 1)

- [CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models](https://mlsanity.com/paper/2109.11797/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multi-Point Positional Insertion Tuning for Small Object Detection](https://mlsanity.com/paper/2412.18090/) (2024)

## Abstract (arXiv)

Vision-Language Pre-Trained (VLP) models, such as CLIP, have demonstrated remarkable effectiveness in learning generic visual representations. Several approaches aim to efficiently adapt VLP models to downstream tasks with limited supervision, aiming to leverage the acquired knowledge from VLP models. However, these methods suffer from either introducing biased representations or requiring high computational complexity, which hinders their effectiveness in fine-tuning the CLIP model. Moreover, when a model is trained on data specific to a particular domain, its ability to generalize to uncharted domains diminishes. In this work, we propose Test-Time Distribution LearNing Adapter (TT-DNA) which directly works during the testing period. Specifically, we estimate Gaussian distributions to model visual features of the few-shot support images to capture the knowledge from the support set. The cosine similarity between query image and the feature distribution of support images is used as the prediction of visual adapter. Subsequently, the visual adapter's prediction merges with the original CLIP prediction via a residual connection, resulting in the final prediction. Our extensive experimental results on visual reasoning for human object interaction demonstrate that our proposed TT-DNA outperforms existing state-of-the-art methods by large margins.