Evaluating Visual Adapters: MIVPG Performance on Single and Multi-Image Inputs

Table of Links

Abstract and 1 Introduction

1. Related Work

   2.1. Multimodal Learning

   2.2. Multiple Instance Learning

2. Methodology

   3.1. Preliminaries and Notations

   3.2. Relations between Attention-based VPG and MIL

   3.3. MIVPG for Multiple Visual Inputs

   3.4. Unveiling Instance Correlation in MIVPG for Enhanced Multi-instance Scenarios

3. Experiments and 4.1. General Setup

   4.2. Scenario 1: Samples with Single Image

   4.3. Scenario 2: Samples with Multiple Images, with Each Image as a General Embedding

   4.4. Scenario 3: Samples with Multiple Images, with Each Image Having Multiple Patches to be Considered and 4.5. Case Study

4. Conclusion and References

\ Supplementary Material

A. Detailed Architecture of QFormer

B. Proof of Proposition

C. More Experiments

4. Experiments

To assess the effectiveness of our proposed approach, we conduct evaluations across various scenarios:

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1. where each sample comprises a single image, and patches are naturally considered as instances;

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2. where each sample includes multiple instances, but we use a general embedding for each image;

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3. where each sample contains multiple images, with each image containing multiple patches.

4.1. General Setup

We initialize our model using BLIP2 [22] with FLAN-T5- XL. MIVPG is initialized with weights from QFormer. The model consists of a frozen language model and a frozen visual model. During training, we only update the MIVPG. The visual encoder, ViT-G, is employed to encode images into patches of embeddings, and the images are resized to dimensions of 224 × 224. In our experiments, we observed that unfreezing the visual encoder does not lead to additional improvements in datasets with small sizes. Further details can be found in the supplementary C.1.

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:::info Authors:

(1) Wenliang Zhong, The University of Texas at Arlington (wxz9204@mavs.uta.edu);

(2) Wenyi Wu, Amazon (wenyiwu@amazon.com);

(3) Qi Li, Amazon (qlimz@amazon.com);

(4) Rob Barton, Amazon (rab@amazon.com);

(5) Boxin Du, Amazon (boxin@amazon.com);

(6) Shioulin Sam, Amazon (shioulin@amazon.com);

(7) Karim Bouyarmane, Amazon (bouykari@amazon.com);

(8) Ismail Tutar, Amazon (ismailt@amazon.com);

(9) Junzhou Huang, The University of Texas at Arlington (jzhuang@uta.edu).

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:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.

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