Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planning

• URL: http://arxiv.org/abs/2310.03325v2
• Date: Wed, 27 Mar 2024 08:54:35 GMT
• Title: Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planning
• Authors: Yilue Qian, Peiyu Yu, Ying Nian Wu, Yao Su, Wei Wang, Lifeng Fan,
• Abstract summary: Visual planning simulates how humans make decisions to achieve desired goals. We propose an interpretable and generalizable visual planning framework. We show that our framework can generalize to unseen task trajectories, unseen object categories, and real-world data.
• Score: 36.131648635051334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual planning simulates how humans make decisions to achieve desired goals in the form of searching for visual causal transitions between an initial visual state and a final visual goal state. It has become increasingly important in egocentric vision with its advantages in guiding agents to perform daily tasks in complex environments. In this paper, we propose an interpretable and generalizable visual planning framework consisting of i) a novel Substitution-based Concept Learner (SCL) that abstracts visual inputs into disentangled concept representations, ii) symbol abstraction and reasoning that performs task planning via the self-learned symbols, and iii) a Visual Causal Transition model (ViCT) that grounds visual causal transitions to semantically similar real-world actions. Given an initial state, we perform goal-conditioned visual planning with a symbolic reasoning method fueled by the learned representations and causal transitions to reach the goal state. To verify the effectiveness of the proposed model, we collect a large-scale visual planning dataset based on AI2-THOR, dubbed as CCTP. Extensive experiments on this challenging dataset demonstrate the superior performance of our method in visual task planning. Empirically, we show that our framework can generalize to unseen task trajectories, unseen object categories, and real-world data. Further details of this work are provided at https://fqyqc.github.io/ConTranPlan/.

Related papers

• Lifting Vision: Ground to Aerial Localization with Reasoning Guided Planning [0.0]
  We introduce Visual Reasoning for localization, or ViReLoc, which performs planning and localization using only visual representations.<n>The proposed framework learns spatial dependencies and geometric relations that text based reasoning often suffer to understand.<n> Experiments across diverse navigation and localization scenarios show consistent improvements in spatial reasoning accuracy and cross view retrieval performance.
  arXiv  Detail & Related papers  (2025-12-30T18:36:39Z)
• Latent Implicit Visual Reasoning [59.39913238320798]
  We propose a task-agnostic mechanism that trains LMMs to discover and use visual reasoning tokens without explicit supervision.<n>Our approach outperforms direct fine-tuning and achieves state-of-the-art results on a diverse range of vision-centric tasks.
  arXiv  Detail & Related papers  (2025-12-24T14:59:49Z)
• From Illusion to Intention: Visual Rationale Learning for Vision-Language Reasoning [19.84653798433995]
  We propose Visual Rationale Learning (ViRL), an end-to-end paradigm that grounds training in the visual rationale itself.<n>ViRL integrates (1) Process Supervision with ground-truth rationales, (2) Objective Alignment via step-level reward shaping, and (3) Fine-Grained Credit Assignment to distinguish correct, redundant, and erroneous actions.<n>This work establishes visual rationalization as a task-agnostic, process-grounded paradigm for building transparent, verifiable, and trustworthy vision-language models.
  arXiv  Detail & Related papers  (2025-11-28T09:52:56Z)
• Learning to See and Act: Task-Aware View Planning for Robotic Manipulation [85.65102094981802]
  Task-Aware View Planning (TAVP) is a framework designed to integrate active view planning with task-specific representation learning.<n>Our proposed TAVP model achieves superior performance over state-of-the-art fixed-view approaches.
  arXiv  Detail & Related papers  (2025-08-07T09:21:20Z)
• ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs [98.27348724529257]
  We introduce ViCrit (Visual Caption Hallucination Critic), an RL proxy task that trains VLMs to localize a subtle, synthetic visual hallucination injected into paragraphs of human-written image captions.<n>Models trained with the ViCrit Task exhibit substantial gains across a variety of vision-language models benchmarks.
  arXiv  Detail & Related papers  (2025-06-11T19:16:54Z)
• Visual Planning: Let's Think Only with Images [30.67065689757505]
  We argue that language may not always be the most natural or effective modality for reasoning, particularly in tasks involving spatial and geometrical information.<n>Motivated by this, we propose a new paradigm, Visual Planning, which enables planning through purely visual representations, independent of text.<n>In this paradigm, planning is executed via sequences of images that encode step-by-step inference in the visual domain, akin to how humans sketch or visualize future actions.
  arXiv  Detail & Related papers  (2025-05-16T16:17:22Z)
• What Makes a Maze Look Like a Maze? [92.80800000328277]
  We introduce Deep Grounding (DSG), a framework that leverages explicit structured representations of visual abstractions for grounding and reasoning. At the core of DSG are schemas--dependency graph descriptions of abstract concepts that decompose them into more primitive-level symbols. We show that DSG significantly improves the abstract visual reasoning performance of vision-language models.
  arXiv  Detail & Related papers  (2024-09-12T16:41:47Z)
• VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs [102.36953558562436]
  Vision language models (VLMs) are an exciting emerging class of language models (LMs) One understudied capability inVLMs is visual spatial planning. Our study introduces a benchmark that evaluates the spatial planning capability in these models in general.
  arXiv  Detail & Related papers  (2024-07-02T00:24:01Z)
• Selective Visual Representations Improve Convergence and Generalization for Embodied AI [44.33711781750707]
  Embodied AI models often employ off the shelf vision backbones like CLIP to encode their visual observations. This introduces noise within the learning process and distracts the agent's focus from task-relevant visual cues. Inspired by selective attention in humans-the process through which people filter their perception based on their experiences, knowledge, and the task at hand-we introduce a parameter-efficient approach to filter visual stimuli for embodied AI.
  arXiv  Detail & Related papers  (2023-11-07T18:34:02Z)
• Learning Differentiable Logic Programs for Abstract Visual Reasoning [18.82429807065658]
  Differentiable forward reasoning has been developed to integrate reasoning with gradient-based machine learning paradigms. NEUMANN is a graph-based differentiable forward reasoner, passing messages in a memory-efficient manner and handling structured programs with functors. We demonstrate that NEUMANN solves visual reasoning tasks efficiently, outperforming neural, symbolic, and neuro-symbolic baselines.
  arXiv  Detail & Related papers  (2023-07-03T11:02:40Z)
• Top-Down Visual Attention from Analysis by Synthesis [87.47527557366593]
  We consider top-down attention from a classic Analysis-by-Synthesis (AbS) perspective of vision. We propose Analysis-by-Synthesis Vision Transformer (AbSViT), which is a top-down modulated ViT model that variationally approximates AbS, and controllable achieves top-down attention.
  arXiv  Detail & Related papers  (2023-03-23T05:17:05Z)
• GAMR: A Guided Attention Model for (visual) Reasoning [7.919213739992465]
  Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes. We present a novel module for visual reasoning, the Guided Attention Model for (visual) Reasoning (GAMR) GAMR posits that the brain solves complex visual reasoning problems dynamically via sequences of attention shifts to select and route task-relevant visual information into memory.
  arXiv  Detail & Related papers  (2022-06-10T07:52:06Z)
• Visual Superordinate Abstraction for Robust Concept Learning [80.15940996821541]
  Concept learning constructs visual representations that are connected to linguistic semantics. We ascribe the bottleneck to a failure of exploring the intrinsic semantic hierarchy of visual concepts. We propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces.
  arXiv  Detail & Related papers  (2022-05-28T14:27:38Z)
• Interpretable Visual Reasoning via Induced Symbolic Space [75.95241948390472]
  We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images. We first design a new framework named object-centric compositional attention model (OCCAM) to perform the visual reasoning task with object-level visual features. We then come up with a method to induce concepts of objects and relations using clues from the attention patterns between objects' visual features and question words.
  arXiv  Detail & Related papers  (2020-11-23T18:21:49Z)

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.