Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Related Work
2.1. Multimodal Learning
2.2. Multiple Instance Learning


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


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


Conclusion and References Related Work
2.1. Multimodal Learning
2.2. Multiple Instance Learning Related Work 2.1. Multimodal Learning 2.1. Multimodal Learning 2.2. Multiple Instance Learning 2.2. Multiple Instance Learning 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 Methodology 3.1. Preliminaries and Notations 3.1. Preliminaries and Notations 3.2. Relations between Attention-based VPG and MIL 3.2. Relations between Attention-based VPG and MIL 3.3. MIVPG for Multiple Visual Inputs 3.3. MIVPG for Multiple Visual Inputs 3.4. Unveiling Instance Correlation in MIVPG for Enhanced Multi-instance Scenarios 3.4. Unveiling Instance Correlation in MIVPG for Enhanced Multi-instance Scenarios 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 Experiments and 4.1. General Setup Experiments and 4.1. General Setup 4.2. Scenario 1: Samples with Single Image 4.2. Scenario 1: Samples with Single Image 4.3. Scenario 2: Samples with Multiple Images, with Each Image as a General Embedding 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 4.4. Scenario 3: Samples with Multiple Images, with Each Image Having Multiple Patches to be Considered and 4.5. Case Study Conclusion and References Conclusion and References Conclusion and References Supplementary Material Supplementary Material A. Detailed Architecture of QFormer A. Detailed Architecture of QFormer B. Proof of Proposition B. Proof of Proposition C. More Experiments C. More Experiments 4. Experiments To assess the effectiveness of our proposed approach, we conduct evaluations across various scenarios: where each sample comprises a single image, and patches are naturally considered as instances;


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


where each sample contains multiple images, with each image containing multiple patches. where each sample comprises a single image, and patches are naturally considered as instances; where each sample comprises a single image, and patches are naturally considered as instances; where each sample includes multiple instances, but we use a general embedding for each image; where each sample includes multiple instances, but we use a general embedding for each image; where each sample contains multiple images, with each image containing multiple patches. 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. 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). Authors: 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. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

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

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