arXiv:2505. 00986v3 Announce Type: replace Abstract: Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data.
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LeanTTA is a backpropagation-free, stateless framework for quantized test-time adaptation designed for edge devices. It updates normalization statistics on the fly, avoiding large batches and historical data, and combines partial adaptation with quantized module fusion to reduce computational costs. Experiments across sensor modalities show a 15.7% error reduction, peak memory usage of only 11.2 MB for ResNet18, and fast adaptation within an order of magnitude of normal inference speeds on-device.
Open-Set Test-Time Adaptation (OSTTA) operates on non-stationary target streams where covariate and semantic shifts coexist. Existing TTA methods often obtain adaptation signals by updating model para...
Tail Cues, Principal Corrections: Plug-and-Play Rectification for Open-Set Test-Time Adaptation introduces TALOR, a lightweight module that rectifies features for non-stationary target streams by decomposing them into high-gain principal and low-gain tail coordinates. TALOR applies a soft-routed affine correction in the principal subspace, subtracts drift via weighted ridge regression, and reconstructs the feature for the source head. On ImageNet-C with six csOOD datasets, TALOR surpasses the next-best UniEnt by 2.2% H-score, runs 2.5× faster, and uses only 12% of its GPU memory, while also boosting COME to 68.4% H-score with minimal memory overhead.
FORGE is a forward‑only test‑time adaptation technique designed for integer‑only vision models running on microcontrollers. It restores batch‑normalization statistics after BN folding by re‑normalizing each convolution’s per‑channel output using only forward‑pass estimates, enabling adaptation on deployed, folded integer models. The method achieves accuracy gains comparable to gradient‑based TENT, requires adapting only a few layers, works with single‑sample streaming, and has been validated on an ESP32‑S3 with minimal energy and latency overhead.
arXiv:2606. 14716v1 Announce Type: cross Abstract: Edge object detection on embedded hardware requires balancing inference latency and detection quality under changing resource pressure.
arXiv:2608. 04428v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have emerged as a key component in embodied AI.