MIVPG and Instance Correlation: Enhanced Multi-Instance Learning
Table of Links
Abstract and 1 Introduction
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Related Work
2.1. Multimodal Learning
2.2. Multiple Instance Learning
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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
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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
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Conclusion and References
\ Supplementary Material
A. Detailed Architecture of QFormer
B. Proof of Proposition
C. More Experiments
3.4. Unveiling Instance Correlation in MIVPG for Enhanced Multi-instance Scenarios
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\ Subsequently, the aggregated low-rank matrix can be reintegrated with the original embeddings, as shown in Equation 9. This low-rank projection effectively reduces the time complexity to O(MM′).
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\ Proposition 2. MIVPG, when equipped with the CSA (Correlated Self-Attention) module, continues to fulfill the essential properties of MIL
\ We prove the proposition 2 in the supplementary B.
\ In summary, as depicted in Figure 2a, we establish that QFormer falls under the MIL category and is a specialized instance of our proposed MIVPG. The latter extends to visual inputs with multiple dimensions, accounting for instance correlation.
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:::info 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]).
:::
:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
:::
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