Multimodal Fusion: MIVPG's Hierarchical MIL Approach for Multi-Image Samples

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

3.3. MIVPG for Multiple Visual Inputs

\ When a sample comprises multiple images, it is imperative to consider MIL feature aggregation from different perspectives. In the context of individual images, each image can be treated as a ’bag,’ and each patch within the image as an ’instance.’ From the sample’s perspective, each sample can also be regarded as a ’bag,’ with each image within the sample as an ’instance.’ When a sample contains only a single image, we can focus primarily on the former perspective since the latter perspective involves a single instance per bag. However, in a more general context, it is essential to adopt a hierarchical approach when considering the utilization of MIL for feature aggregation. Without loss of generality, we now consider the input of the MIVPG to be a bag B containing multiple instances. Hence, the cross-attention can be expressed as Attention(Q = q, K = B, V = B).

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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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