Beginner Explanation
Imagine you’re playing a game where you look at pictures and then come up with fun questions about them. The Proposer is like a clever friend who helps you think of lots of different questions based on what you see in the pictures. This way, you can have interesting conversations and learn more about the images together. It’s all about being creative and asking the right questions to make the most out of the pictures!Technical Explanation
In the EvoLMM framework, the Proposer is an agent designed to generate diverse, image-grounded questions. It utilizes deep learning techniques, particularly convolutional neural networks (CNNs) for image processing and natural language processing (NLP) models for question generation. The Proposer takes an input image, extracts features using a CNN, and then employs a transformer-based model to formulate questions based on the extracted features. For example, using PyTorch, the Proposer might look like this: “`python import torch from transformers import VisionEncoderDecoderModel, ViTImageProcessor model = VisionEncoderDecoderModel.from_pretrained(‘model_name’) image_processor = ViTImageProcessor.from_pretrained(‘model_name’) image = image_processor(images=image_path, return_tensors=’pt’) output = model.generate(**image) questions = model.tokenizer.batch_decode(output, skip_special_tokens=True) “` This code processes an image and generates relevant questions, demonstrating the Proposer’s function in generating diverse inquiries based on visual input.Academic Context
The Proposer in the EvoLMM framework is grounded in research related to multimodal learning, where models are trained to understand and generate content across different modalities, such as images and text. Key papers include ‘Visual Question Answering’ by Antol et al. (2015), which explores how images can be paired with questions to enhance understanding, and ‘Transformers for Image Captioning’ by Chen et al. (2020), which discusses the application of transformer models for generating descriptive text from images. The mathematical foundations involve understanding embeddings, attention mechanisms, and loss functions that optimize the question generation process in conjunction with visual data.Code Examples
Example 1:
import torch
from transformers import VisionEncoderDecoderModel, ViTImageProcessor
model = VisionEncoderDecoderModel.from_pretrained('model_name')
image_processor = ViTImageProcessor.from_pretrained('model_name')
image = image_processor(images=image_path, return_tensors='pt')
output = model.generate(**image)
questions = model.tokenizer.batch_decode(output, skip_special_tokens=True)Example 2:
import torch
from transformers import VisionEncoderDecoderModel, ViTImageProcessor
model = VisionEncoderDecoderModel.from_pretrained('model_name')
image_processor = ViTImageProcessor.from_pretrained('model_name')Example 3:
from transformers import VisionEncoderDecoderModel, ViTImageProcessor
model = VisionEncoderDecoderModel.from_pretrained('model_name')
image_processor = ViTImageProcessor.from_pretrained('model_name')
View Source: https://arxiv.org/abs/2511.16672v1