Multimodal Material Classification Using Visual Attention
Bibliographic record
Abstract
The material of an object is an inherent property that can be perceived through various sensory modalities, yet the integration of multisensory information substantially improves the accuracy of these perceptions. For example, differentiating between a ceramic and a plastic cup with similar visual properties may be difficult when relying solely on visual cues. However, the integration of touch and audio feedback when interacting with these objects can significantly clarify these distinctions. Similarly, combining audio and touch exploration with visual guidance can optimize the sensory examination process. In this study, we introduce a multisensory approach for categorizing object materials by integrating visual, audio, and touch perceptions. The main contribution of this paper is the exploration of a computational model of visual attention that directs the sampling of touch and audio data. We conducted experiments using a subset of 63 household objects from a publicly available dataset, the ObjectFolder dataset. Our findings indicate that incorporating a visual attention model enhances the ability to generalize material classifications to new objects and achieves superior performance compared to a baseline approach, where data are gathered through random interactions with an object's surface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".