A Contrastive Learning Approach for Integrating Visuo-Tactile Representation in Textiles
Bibliographic record
Abstract
Vision and touch are fundamental sensory modalities that enable humans to perceive and interact with objects in their environment. Vision facilitates the perception of attributes such as shape, color, and texture from a distance, while touch provides detailed information at the contact level, including fine textures and material properties. Despite their distinct roles, the processing of visual and tactile information shares underlying similarities, presenting a unique opportunity to enhance artificial systems that integrate these modalities. However, existing methods for combining vision and touch often rely on data fusion at the decision level, requiring extensive labeled data and facing challenges in generalizing to novel situations. In this paper, we leverage contrastive learning to train a convolutional neural network on textile data using both visual and tactile inputs. Our objective is to develop a network capable of extracting unified representations from both modalities without the need for extensive labeled datasets. We explore using a contrastive loss function to optimize the learning process. Our results demonstrate that the shared representations effectively capture critical data structures and features from both sensory modalities, enabling successful differentiation between object classes based on both vision and touch. We validate our approach through a series of experiments, optimizing hyperparameters to maximize performance. The findings suggest that extracting shared representations for vision and touch not only enhances the integration of visual and tactile information but also provides a robust framework for multimodal perception in artificial systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".