Stratified Rule-Aware Network for Abstract Visual Reasoning

• URL: http://arxiv.org/abs/2002.06838v3
• Date: Tue, 7 Jun 2022 11:49:44 GMT
• Title: Stratified Rule-Aware Network for Abstract Visual Reasoning
• Authors: Sheng Hu, Yuqing Ma, Xianglong Liu, Yanlu Wei, Shihao Bai
• Abstract summary: Raven's Progressive Matrices (RPM) test is typically used to examine the capability of abstract reasoning. Recent studies, taking advantage of Convolutional Neural Networks (CNNs), have achieved encouraging progress to accomplish the RPM test. We propose a Stratified Rule-Aware Network (SRAN) to generate the rule embeddings for two input sequences.
• Score: 46.015682319351676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Abstract reasoning refers to the ability to analyze information, discover rules at an intangible level, and solve problems in innovative ways. Raven's Progressive Matrices (RPM) test is typically used to examine the capability of abstract reasoning. The subject is asked to identify the correct choice from the answer set to fill the missing panel at the bottom right of RPM (e.g., a 3$\times$3 matrix), following the underlying rules inside the matrix. Recent studies, taking advantage of Convolutional Neural Networks (CNNs), have achieved encouraging progress to accomplish the RPM test. However, they partly ignore necessary inductive biases of RPM solver, such as order sensitivity within each row/column and incremental rule induction. To address this problem, in this paper we propose a Stratified Rule-Aware Network (SRAN) to generate the rule embeddings for two input sequences. Our SRAN learns multiple granularity rule embeddings at different levels, and incrementally integrates the stratified embedding flows through a gated fusion module. With the help of embeddings, a rule similarity metric is applied to guarantee that SRAN can not only be trained using a tuplet loss but also infer the best answer efficiently. We further point out the severe defects existing in the popular RAVEN dataset for RPM test, which prevent from the fair evaluation of the abstract reasoning ability. To fix the defects, we propose an answer set generation algorithm called Attribute Bisection Tree (ABT), forming an improved dataset named Impartial-RAVEN (I-RAVEN for short). Extensive experiments are conducted on both PGM and I-RAVEN datasets, showing that our SRAN outperforms the state-of-the-art models by a considerable margin.

Related papers

• Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures [22.12114509953737]
  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), a visual test for assessing abstract reasoning abilities. Instead of hard-coding the rule formulations associated with RPMs, our approach can learn the VSA rule formulations with just one pass through the training data.
  arXiv  Detail & Related papers  (2024-01-29T10:17:18Z)
• Towards Generative Abstract Reasoning: Completing Raven's Progressive Matrix via Rule Abstraction and Selection [52.107043437362556]
  Raven's Progressive Matrix (RPM) is widely used to probe abstract visual reasoning in machine intelligence. Participators of RPM tests can show powerful reasoning ability by inferring and combining attribute-changing rules. We propose a deep latent variable model for answer generation problems through Rule AbstractIon and SElection.
  arXiv  Detail & Related papers  (2024-01-18T13:28:44Z)
• Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning [53.445068584013896]
  We study matrix estimation problems arising in reinforcement learning (RL) with low-rank structure. In low-rank bandits, the matrix to be recovered specifies the expected arm rewards, and for low-rank Markov Decision Processes (MDPs), it may for example characterize the transition kernel of the MDP. We show that simple spectral-based matrix estimation approaches efficiently recover the singular subspaces of the matrix and exhibit nearly-minimal entry-wise error.
  arXiv  Detail & Related papers  (2023-10-10T17:06:41Z)
• Semi-DETR: Semi-Supervised Object Detection with Detection Transformers [105.45018934087076]
  We analyze the DETR-based framework on semi-supervised object detection (SSOD) We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector. Our method outperforms all state-of-the-art methods by clear margins.
  arXiv  Detail & Related papers  (2023-07-16T16:32:14Z)
• Effective Abstract Reasoning with Dual-Contrast Network [10.675709291797535]
  We aim to solve Raven's Progressive Matrices ( RPM) puzzles with neural networks. We design a simple yet effective Dual-Contrast Network (DCNet) to exploit the inherent structure of RPM puzzles. Experimental results on the RAVEN and PGM datasets show that DCNet outperforms the state-of-the-art methods by a large margin of 5.77%.
  arXiv  Detail & Related papers  (2022-05-27T02:26:52Z)
• Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization [52.7137956951533]
  We argue that devising simpler methods for learning predictors on existing features is a promising direction for future research. We introduce Domain-Adjusted Regression (DARE), a convex objective for learning a linear predictor that is provably robust under a new model of distribution shift. Under a natural model, we prove that the DARE solution is the minimax-optimal predictor for a constrained set of test distributions.
  arXiv  Detail & Related papers  (2022-02-14T16:42:16Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• Distributionally Robust Multi-Output Regression Ranking [3.9318191265352196]
  We introduce a new listwise listwise learning-to-rank model called Distributionally Robust Multi-output Regression Ranking (DRMRR) DRMRR uses a Distributionally Robust Optimization framework to minimize a multi-output loss function under the most adverse distributions in the neighborhood of the empirical data distribution. Our experiments were conducted on two real-world applications, medical document retrieval, and drug response prediction.
  arXiv  Detail & Related papers  (2021-09-27T05:19:27Z)
• Unsupervised Abstract Reasoning for Raven's Problem Matrices [9.278113063631643]
  Raven's Progressive Matrices ( RPM) is highly correlated with human intelligence. We propose the first unsupervised learning method for solving RPM problems. Our method even outperforms some of the supervised approaches.
  arXiv  Detail & Related papers  (2021-09-21T07:44:58Z)
• Solving Raven's Progressive Matrices with Neural Networks [10.675709291797535]
  Raven's Progressive Matrices (RPM) have been widely used for Intelligence Quotient (IQ) test of humans. In this paper, we aim to solve RPM with neural networks in both supervised and unsupervised manners.
  arXiv  Detail & Related papers  (2020-02-05T05:18:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.