# When Sensing Varies with Contexts: Context Probing for Tactile Few-Shot Class-Incremental Learning

arXiv: https://arxiv.org/abs/2603.25115 · HTML: https://mlsanity.com/paper/2603.25115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 54 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 28 of 28)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2004.10956/) (2020)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [Pengi: An Audio Language Model for Audio Tasks](https://mlsanity.com/paper/2305.11834/) (2023)
- [Few-Shot Incremental Learning with Continually Evolved Classifiers](https://mlsanity.com/paper/2104.03047/) (2021)
- [Forward Compatible Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2203.06953/) (2022)
- [Touch and Go: Learning from Human-Collected Vision and Touch](https://mlsanity.com/paper/2211.12498/) (2022)
- [Few-Shot Class-Incremental Learning from an Open-Set Perspective](https://mlsanity.com/paper/2208.00147/) (2022)
- [A Touch, Vision, and Language Dataset for Multimodal Alignment](https://mlsanity.com/paper/2402.13232/) (2024)
- [Connecting Touch and Vision via Cross-Modal Prediction](https://mlsanity.com/paper/1906.06322/) (2019)
- [Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2304.00426/) (2023)
- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners](https://mlsanity.com/paper/2404.02117/) (2024)
- [Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation](https://mlsanity.com/paper/2406.03813/) (2024)
- [OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2403.18550/) (2024)
- [Compositional Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2405.17022/) (2024)
- [Learnable Distribution Calibration for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2210.00232/) (2022)
- [Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes](https://mlsanity.com/paper/2305.19539/) (2023)
- [An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2411.06659/) (2024)
- [Multimodal Zero-Shot Learning for Tactile Texture Recognition](https://mlsanity.com/paper/2306.12705/) (2023)
- [Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes](https://mlsanity.com/paper/2305.18045/) (2023)
- [Adaptive Decision Boundary for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2504.10976/) (2025)
- [Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning](https://mlsanity.com/paper/2501.05862/) (2025)
- [Cluster Haptic Texture Dataset: Haptic Texture Dataset with Varied Velocity-Direction Sliding Contacts](https://mlsanity.com/paper/2407.16206/) (2024)
- [Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification](https://mlsanity.com/paper/2509.14958/) (2025)
- [CNN-based Methods for Object Recognition with High-Resolution Tactile Sensors](https://mlsanity.com/paper/2305.12417/) (2023)
- [Visuo-Tactile Zero-Shot Object Recognition with Vision-Language Model](https://mlsanity.com/paper/2409.09276/) (2024)
- [Can Synthetic Images Conquer Forgetting? Beyond Unexplored Doubts in Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2507.13739/) (2025)

## Abstract (arXiv)

Few-shot class-incremental learning (FSCIL) aims to recognize novel classes from only a few labeled samples while retaining previously learned knowledge. Although recent FSCIL methods have achieved substantial progress on visual benchmarks, they remain limited in tactile sensing, where the same material may produce markedly different observations under different acquisition contexts, such as sensing devices, contact states, scanning trajectories, and interaction conditions. In tactile FSCIL, the challenges of few-shot learning and class-incremental learning are further amplified by acquisition context: the limited support samples may not only be scarce, but also carry context-induced biases. Once the resulting biased prototypes are inserted into the classifier, they may affect the decision boundaries in subsequent sessions. To address this problem, we propose Context-Probing Few-Shot Class-Incremental Learning (CoP-FSCIL), a context-aware framework for tactile FSCIL. CoP-FSCIL first employs Context-Probing Intervention (CPI) to diagnose local context-sensitive variations in tactile representations. It then introduces a Probe-Conditioned Quotient Adapter (PCQA) to suppress context-sensitive components identified by the probes. Finally, Probe-Stability Prototype Calibration (PSPC) estimates support sample reliability from probe-induced embedding fluctuations and calibrates stochastic prototypes accordingly. Experiments on HapTex and LMT108 show that CoP-FSCIL consistently outperforms representative FSCIL baselines, and extended experiments on audio FSCIL further demonstrate the generality of the proposed context probing mechanism. The source code is currently being prepared and will be released soon.