Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision
Visual reasoning is crucial for visual question answering (VQA). However, without labelled programs, implicit reasoning under natural supervision is still quite challenging and previous models are hard to interpret. In this paper, we rethink implicit reasoning process in VQA, and propose a new formulation which maximizes the log-likelihood of joint distribution for the observed question and predicted answer. Accordingly, we derive a Temporal Reasoning Network (TRN) framework which models the implicit reasoning process as sequential planning in latent space. Our model is interpretable on both model design in probabilist and reasoning process via visualization. We experimentally demonstrate that TRN can support implicit reasoning across various datasets. The experiment results of our model are competitive to existing implicit reasoning models and surpass baseline by large margin on complicated reasoning tasks without extra computation cost in forward stage.
Code (0)
등록된 구현이 없습니다.
Tasks
Question AnsweringVisual Question AnsweringVisual Question Answering (VQA)Visual ReasoningSimilar Papers 제목 키워드 기반
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering
We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formula…
counterfactualQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures
Abstract reasoning is a cornerstone of human intelligence, and replicating it with artificial intelligence (AI) presents an ongoing challenge. This study focuses on efficiently solving Raven's progressive matrices (RPM),…
AttributePRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario
Driving scene understanding is a critical real-world problem that involves interpreting and associating various elements of a driving environment, such as vehicles, pedestrians, and traffic signals. Despite advancements …
3D Object DetectionAutonomous DrivingObjectobject-detection+2On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning
Dictionary learning has long been studied from both optimization and probabilistic perspectives. While formulations with element-wise sparsity regularization (e.g., L1-based sparse coding) admit well-established probabil…
EM-NeSy: Expectation Maximization for Neurosymbolic Learning
Neurosymbolic (NeSy) models integrate neural networks and symbolic reasoning for robust and interpretable AI. State-of-the-art NeSy models require that the symbolic component is expressed in a differentiable way, often c…
Computational Efficiency