Object-based reasoning in VQA
Visual Question Answering (VQA) is a novel problem domain where multi-modal inputs must be processed in order to solve the task given in the form of a natural language. As the solutions inherently require to combine visual and natural language processing with abstract reasoning, the problem is considered as AI-complete. Recent advances indicate that using high-level, abstract facts extracted from the inputs might facilitate reasoning. Following that direction we decided to develop a solution combining state-of-the-art object detection and reasoning modules. The results, achieved on the well-balanced CLEVR dataset, confirm the promises and show significant, few percent improvements of accuracy on the complex "counting" task.
Code (0)
등록된 구현이 없습니다.
Tasks
Objectobject-detectionObject DetectionQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Similar Papers 제목 키워드 기반
Evaluating the Meta- and Object-Level Reasoning of Large Language Models for Question Answering
Large Language Models (LLMs) excel in natural language tasks but still face challenges in Question Answering (QA) tasks requiring complex, multi-step reasoning. We outline the types of reasoning required in some of these…
Mathematical ReasoningObjectQuestion AnsweringChain of Reasoning for Visual Question Answering
Reasoning plays an essential role in Visual Question Answering (VQA). Multi-step and dynamic reasoning is often necessary for answering complex questions. For example, a question "What is placed next to the bus on the ri…
ObjectQuestion AnsweringRelationRelational Reasoning+2ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
Referring Multi-object tracking (RMOT) is an important research field in computer vision. Its task form is to guide the models to track the objects that conform to the language instruction. However, the RMOT task commonl…
Multi-Object TrackingObjectObject TrackingReferring Multi-Object TrackingSpatial Memory for Context Reasoning in Object Detection
Modeling instance-level context and object-object relationships is extremely challenging. It requires reasoning about bounding boxes of different classes, locations \etc. Above all, instance-level spatial reasoning inher…
ObjectObject DetectionSpatial ReasoningHopper: Multi-hop Transformer for Spatiotemporal Reasoning
This paper considers the problem of spatiotemporal object-centric reasoning in videos. Central to our approach is the notion of object permanence, i.e., the ability to reason about the location of objects as they move th…
ObjectVideo Object Tracking