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InfoScan: Information-Efficient Visual Scanning via Resource-Adaptive Walks

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Vision Model; Scan Strategy; Markov Decision Processes; Information Scoring
Abstract:

High-resolution visual representation learning remains challenging due to the quadratic complexity of Vision Transformers and the limitations of existing efficient approaches, where fixed scanning patterns in recent Mamba-based models hinder content-adaptive perception. To address these limitations, a novel Information-aware Scanning mechanism (InfoScan) tailored for state-space visual backbones is proposed, which dynamically allocates computational resources to the most salient regions of an image. Specifically, InfoScan rigorously assesses the informativeness of image patches by integrating entropy with local structural analyses, formulates a joint optimization objective balancing fine-grained detail preservation and broader contextual coherence, and learns an adaptive scanning policy via reinforcement learning. Built upon the innovative Visual Information State Space (VISS) block, InfoScan establishes a new family of models that achieve superior efficiency-accuracy trade-offs across diverse tasks. Extensive empirical evaluation in different downstream vision tasks demonstrates that our information-driven dynamic scanning paradigm offers a robust and principled alternative to fixed or global-first traversal methods. Collectively, our work positions adaptive, content-aware processing as a promising and effective new paradigm for efficient high-resolution visual representation.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes InfoScan, a reinforcement learning-driven mechanism for content-adaptive scanning in state-space visual models, addressing efficiency challenges in high-resolution image processing. According to the taxonomy, this work resides in the 'Content-Adaptive Scanning with Reinforcement Learning' leaf under 'Adaptive Scanning and State-Space Models'. Notably, this leaf contains only the original paper itself, with no sibling papers identified, suggesting this represents a relatively sparse and emerging research direction within the broader field of adaptive visual scanning.

The taxonomy reveals that neighboring work explores related but distinct approaches: sibling leaves include adaptive scanning for restoration tasks, change detection with frequency-domain guidance, and multimodal fusion with state-space models. These directions share the use of state-space architectures but differ in their adaptation mechanisms and application domains. The broader 'Efficient High-Resolution Processing Architectures' branch addresses similar efficiency goals through alternative strategies like continuous-scale super-resolution and 3D medical imaging optimization, highlighting that InfoScan's reinforcement learning-based scan order optimization represents one of several complementary approaches to handling high-resolution visual data.

Among seventeen candidates examined across three contributions, no clearly refuting prior work was identified. The core InfoScan mechanism examined ten candidates with zero refutations, the joint optimization framework examined two candidates with zero refutations, and the reinforcement learning policy examined five candidates with zero refutations. This limited search scope suggests that within the top semantic matches analyzed, no substantial overlap with existing methods was detected. The absence of sibling papers in the same taxonomy leaf further indicates that the specific combination of information-theoretic patch assessment, joint optimization, and RL-based scanning policy appears relatively unexplored in the examined literature.

Based on the limited search of seventeen candidates, the work appears to occupy a novel position combining entropy-based informativeness assessment with reinforcement learning for adaptive scanning in state-space models. However, the analysis scope is constrained to top semantic matches and does not constitute an exhaustive survey of all related work in adaptive visual processing or state-space architectures. The sparse taxonomy leaf and absence of refuting candidates suggest potential novelty, though broader literature may contain relevant precedents not captured in this focused search.

Taxonomy

Core-task Taxonomy Papers
5
3
Claimed Contributions
17
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: information-aware dynamic scanning for efficient high-resolution visual representation learning. The field addresses the challenge of processing high-resolution images efficiently by adapting how visual data is scanned and encoded. The taxonomy reveals two main branches: Adaptive Scanning and State-Space Models, which focuses on learning content-dependent scan orders and leveraging state-space architectures like Mamba for sequential processing, and Efficient High-Resolution Processing Architectures, which encompasses methods that reduce computational costs through multi-scale representations, selective attention, or hierarchical designs. Works such as Vision Mamba Alzheimer[2], VAMamba[4], and AGFNet[3] illustrate how state-space models can be tailored to vision tasks, while Dynamic Scale Awareness[1] and LIDAR Crack Segmentation[5] demonstrate alternative strategies for handling resolution and spatial detail. These branches are complementary: one emphasizes the order and mechanism of scanning, the other the architectural efficiency needed to handle large inputs. A particularly active line of work explores content-adaptive scanning with reinforcement learning, where the goal is to learn scan paths that prioritize informative regions rather than following fixed raster orders. InfoScan[0] sits squarely within this cluster, proposing a reinforcement learning framework to dynamically determine scanning strategies based on image content. This contrasts with approaches like VAMamba[4], which applies state-space models with more conventional scanning patterns, and AGFNet[3], which integrates adaptive gating mechanisms but does not explicitly optimize scan order through RL. The central trade-off in this area is between the flexibility and potential gains of learned, data-driven scanning versus the simplicity and stability of fixed or heuristic scan strategies. InfoScan[0] emphasizes the former, aiming to maximize information capture per scan step, a direction that remains relatively underexplored compared to the broader adoption of state-space models with standard scan orders.

Claimed Contributions

Information-aware Scanning Mechanism (InfoScan)

The authors propose InfoScan, a mechanism that adaptively allocates computation based on image patch informativeness. It assesses patch significance by integrating Shannon entropy with local structural analyses, enabling dynamic resource allocation to salient regions rather than uniform scanning.

10 retrieved papers
Joint Optimization Framework for Adaptive Scanning

The authors develop a mathematical framework that jointly optimizes patch information content, information loss, and scanning step size. This formulation provides a principled approach to determine traversal strategies that outperform fixed scanning patterns like raster or Hilbert curves.

2 retrieved papers
Reward-Driven Dynamic Scanning Policy via Reinforcement Learning

The authors design a scanning policy formulated as a Markov decision process and learned via reinforcement learning. This policy dynamically determines the next patch to attend based on contextual information density, balancing local detail preservation with global context integration.

5 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Information-aware Scanning Mechanism (InfoScan)

The authors propose InfoScan, a mechanism that adaptively allocates computation based on image patch informativeness. It assesses patch significance by integrating Shannon entropy with local structural analyses, enabling dynamic resource allocation to salient regions rather than uniform scanning.

Contribution

Joint Optimization Framework for Adaptive Scanning

The authors develop a mathematical framework that jointly optimizes patch information content, information loss, and scanning step size. This formulation provides a principled approach to determine traversal strategies that outperform fixed scanning patterns like raster or Hilbert curves.

Contribution

Reward-Driven Dynamic Scanning Policy via Reinforcement Learning

The authors design a scanning policy formulated as a Markov decision process and learned via reinforcement learning. This policy dynamically determines the next patch to attend based on contextual information density, balancing local detail preservation with global context integration.

Table of Contents