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. Methodology

3.1. Preliminaries and Notations

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).

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