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 ([email protected]);

(2) Wenyi Wu, Amazon ([email protected]);

(3) Qi Li, Amazon ([email protected]);

(4) Rob Barton, Amazon ([email protected]);

(5) Boxin Du, Amazon ([email protected]);

(6) Shioulin Sam, Amazon ([email protected]);

(7) Karim Bouyarmane, Amazon ([email protected]);

(8) Ismail Tutar, Amazon ([email protected]);

(9) Junzhou Huang, The University of Texas at Arlington ([email protected]).

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