arXiv subjects

Qihang Yao

Publications and source records attributed to Qihang Yao.

4 recordsLinked to original sources

arXiv2026

Tac-DINO: Learning Vision-Tactile Features with Patch Alignment

Touch is the primary medium through which humans interact with the environment. Currently, tactile learning mainly focuses on image-level pretraining or alignment. However, tactile signals correspond to local object contact, while research into scale alignment and holographic matching remains limited and proper datasets and benchmarks also lack. To bridge this gap, we first construct a data collection system to acquire a large-scale tactile dataset, with over 20 K tactile contacts from 505 real-world objects. Building on this dataset, we design a Vis-Tac Holographic Matching Benchmark to evaluate vision-tactile local-to-global alignment ability. Then we propose Vision-Tactile Patch Alignment (VTPA) methods for vision-tactile representation learning. Experiments demonstrate that these exceed the performance of methods without alignment and align with whole-object images.

cs.CV↗

arXiv2026

MoDora: Tree-Based Semi-Structured Document Analysis System

Semi-structured documents integrate diverse interleaved data elements (e.g., tables, charts, hierarchical paragraphs) arranged in various and often irregular layouts. These documents are widely observed across domains and account for a large portion of real-world data. However, existing methods struggle to support natural language question answering over these documents due to three main technical challenges: (1) The elements extracted by techniques like OCR are often fragmented and stripped of their original semantic context, making them inadequate for analysis. (2) Existing approaches lack effective representations to capture hierarchical structures within documents (e.g., associating tables with nested chapter titles) and to preserve layout-specific distinctions (e.g., differentiating sidebars from main content). (3) Answering questions often requires retrieving and aligning relevant information scattered across multiple regions or pages, such as linking a descriptive paragraph to table cells located elsewhere in the document. To address these issues, we propose MoDora, an LLM-powered system for semi-structured document analysis. First, we adopt a local-alignment aggregation strategy to convert OCR-parsed elements into layout-aware components, and conduct type-specific information extraction for components with hierarchical titles or non-text elements. Second, we design the Component-Correlation Tree (CCTree) to hierarchically organize components, explicitly modeling inter-component relations and layout distinctions through a bottom-up cascade summarization process. Finally, we propose a question-type-aware retrieval strategy that supports (1) layout-based grid partitioning for location-based retrieval and (2) LLM-guided pruning for semantic-based retrieval. Experiments show MoDora outperforms baselines by 5.97%-61.07% in accuracy. The code is at https://github.com/weAIDB/MoDora.

cs.IR↗

arXiv2025

Gradual Capacity Growth for Sparse Network Discovery

Sparse neural network methods typically assume that the target sparsity (or density) is fixed in advance, even though the relationship between network capacity and performance is generally unknown and task-dependent. Existing approaches (including iterative pruning, dynamic sparse training, and pruning at initialization) either rely on dense pretraining, incur substantial retraining cost, or require a preset sparsity budget. We propose Gradual Capacity Growth (GCG), a constructive sparse-to-dense training framework that allocates network capacity progressively during training. Starting from a sparse seed network, GCG grows new connections in stages using PathGrow, a probabilistic path-based growth rule that biases additions toward high-signal input-output pathways while preserving structural diversity. Growth is interleaved with limited training, and a lightweight performance-density extrapolation rule is used to estimate the smallest density beyond which further capacity increases yield diminishing accuracy gains. Experiments on CIFAR, TinyImageNet, and ImageNet show that GCG efficiently identifies sparse networks that achieve near-dense performance at moderate densities, without requiring dense pretraining or exhaustive retraining at each sparsity level. While growth-only methods do not reach the extreme sparsity achievable by pruning or dynamic reallocation techniques, GCG substantially reduces total training cost compared to iterative magnitude pruning and provides a practical mechanism for exploring the accuracy-density tradeoff under limited optimization budgets.

cs.LG↗

arXiv2022

Root-Cause Analysis of Activation Cascade Differences in Brain Networks

Diffusion MRI imaging and tractography algorithms have enabled the mapping of the macro-scale connectome of the entire brain. At the functional level, probably the simplest way to study the dynamics of macro-scale brain activity is to compute the "activation cascade" that follows the artificial stimulation of a source region. Such cascades can be computed using the Linear Threshold model on a weighted graph representation of the connectome. The question we focus on is: if we are given such activation cascades for two groups, say A and B (e.g. Controls versus a mental disorder), what is the smallest set of brain connectivity (graph edge weight) changes that are sufficient to explain the observed differences in the activation cascades between the two groups? We have developed and computationally validated an efficient algorithm, TRACED, to solve the previous problem. We argue that this approach to compare the connectomes of two groups, based on activation cascades, is more insightful than simply identifying "static" network differences (such as edges with large weight or centrality differences). We have also applied the proposed method in the comparison between a Major Depressive Disorder (MDD) group versus healthy controls and briefly report the resulting set of connections that cause most of the observed cascade differences.

q-bio.NC↗