VISREAS: Complex Visual Reasoning with Unanswerable Questions

Khái niệm cốt lõi

Addressing the need for complex visual reasoning, VISREAS dataset introduces unanswerable questions to enhance model performance.

Tóm tắt

The VISREAS dataset focuses on multihop reasoning, clustering objects based on attributes and relations. It challenges existing models by introducing unanswerable queries, emphasizing validation of question-image alignment before answering. LOGIC2VISION outperforms generative models in VISREAS by reasoning with pseudocode without external modules.

Thống kê

VISREAS contains 2.07M semantically diverse queries generated automatically using Visual Genome scene graphs.
LOGIC2VISION outperforms generative models in VISREAS (+4.82% over LLaVA-1.5; +12.23% over InstructBLIP).
The average number of reasoning hops for VISREAS is 1.42, significantly higher than GQA (mean: 0.52) and CLEVR (mean: 0.84).
The average objects per question for VISREAS is 3.91, higher than GQA (1.12) and CLEVR (1.63).
Models trained on VISREAS struggle in Compare, Count, and Query questions requiring grounding and clustering multiple objects.

Trích dẫn

"The unique feature of this task, validating question answerability with respect to an image before answering..." - Content
"A reliable and responsible system should be able to question the validity of the instruction it receives before acting upon it." - Content
"Our dataset makes the first step towards developing reliable VLM adaptable to real-world scenarios where user instructions may not always be impeccable." - Content
"We anticipate that this dataset and model will catalyze advancements in VQA research..." - Content
"LOGIC2VISION shows a promising result in Query questions." - Content
"GPT-4V excels at identifying problematic questions that involve an object not present in the image or an object with a false attribute." - Content

Thông tin chi tiết chính được chắt lọc từ

VISREAS

by Syeda Nahida... lúc arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10534.pdf

Yêu cầu sâu hơn

How can the introduction of unanswerable questions improve model performance in visual reasoning tasks?

Introducing unanswerable questions in visual reasoning tasks serves to challenge models by requiring them to validate the question's relevance with the image before providing an answer. This approach compels models to not only focus on generating accurate responses but also on verifying the consistency of the question text with the image. By including unanswerable questions, models are pushed to exhibit a deeper understanding of both textual queries and visual content, leading to improved performance in handling complex and nuanced scenarios where traditional datasets may fall short.

What are the implications of introducing pseudocode-guided reasoning for VQA tasks beyond the VISREAS dataset?

The introduction of pseudocode-guided reasoning for Visual Question Answering (VQA) tasks beyond the VISREAS dataset has significant implications for enhancing model capabilities. By deconstructing questions into structured pseudocodes that outline sequential reasoning steps, models gain a systematic approach to processing information from both textual queries and images. This method enables models to consider each object's attributes and relations before arriving at an answer, promoting more comprehensive understanding and logical inference. Beyond VISREAS, incorporating pseudocode-guided reasoning can lead to more robust VQA systems capable of handling diverse spatial reasoning challenges across various domains.

How might incorporating other visual-language tasks into the dataset enhance overall model generalization capabilities?

Incorporating other visual-language tasks into a dataset like VISREAS can significantly enhance overall model generalization capabilities by diversifying training data and expanding task complexity. By including additional tasks such as image captioning, visual storytelling, or scene description alongside VQA challenges, models are exposed to a broader range of cognitive skills and semantic comprehension requirements. This exposure helps in developing more versatile AI systems that excel not only in answering specific questions but also in understanding contextual relationships between objects, attributes, and scenes within images. The incorporation of multiple visual-language tasks fosters holistic learning experiences for models, enabling them to generalize better across different real-world applications involving vision-based interactions.