This content originally appeared on HackerNoon and was authored by Instancing
Table of Links
Related Work
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
\ Supplementary Material
A. Detailed Architecture of QFormer
4. Experiments
To assess the effectiveness of our proposed approach, we conduct evaluations across various scenarios:
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where each sample comprises a single image, and patches are naturally considered as instances;
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where each sample includes multiple instances, but we use a general embedding for each image;
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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.
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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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This content originally appeared on HackerNoon and was authored by Instancing
Instancing | Sciencx (2025-11-15T03:12:01+00:00) Evaluating Visual Adapters: MIVPG Performance on Single and Multi-Image Inputs. Retrieved from https://www.scien.cx/2025/11/15/evaluating-visual-adapters-mivpg-performance-on-single-and-multi-image-inputs/
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