arXiv:2610.09849v1 Announce Type: new Abstract: Selecting synthetic degradations for dense prediction requires an estimate of their training utility, the generalization gain they bring under a finite...
arXiv:2607. 08794v1 Announce Type: cross Abstract: Sand boils on earthen levees are safety-critical defects, but pixel-level detection is limited by scarce annotations.
arXiv:2609.12078v1 Announce Type: new Abstract: Objects in post-fire environments often undergo irreversible physical transformations that change their geometry, material state, and visual appearance...
arXiv:2410.21582v4 Announce Type: replace-cross Abstract: Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of mainta...
arXiv:2610.07008v1 Announce Type: new Abstract: Few-shot class-incremental learning (FSCIL) aims to learn novel classes from limited annotations while preserving prior knowledge. Existing methods typ...
arXiv:2610.07698v1 Announce Type: new Abstract: Accurate building footprint extraction from high-resolution remote sensing imagery is essential for urban planning, disaster response, and environmenta...
Reference-Guided Machine Unlearning (ReGUn) is a vision unlearning framework that prioritizes distributional indistinguishability over degradation-based heuristics. It uses disjoint held-out data to create a class-conditioned reference distribution for distillation, guiding forget samples toward non-member behavior without explicitly degrading predictions. Experiments across various architectures and datasets show that ReGUn achieves a competitive forgetting–utility trade-off and closely matches retrain-like membership inference behavior.
arXiv:2607. 22705v1 Announce Type: cross Abstract: Object-centric learning aims to represent scenes as objects whose properties can be reused in new combinations.
The paper introduces the Real‑Calibrated Synthetic‑First Data Engine, a modular pipeline that integrates controllable diffusion‑based synthetic image generation with multi‑stage curation, filtering, and optional uncertainty‑driven selection and human verification. Designed as a CLI‑based framework, it allows independent configuration of generation, filtering, selection, and validation modules to enhance reproducibility and flexibility in real‑world data workflows. Empirical tests on human pose estimation demonstrate that synthetic data can boost a real‑data baseline when used as low‑cost augmentation, though synthetic‑only training still lags behind real‑only performance, underscoring the importance of data‑centric orchestration in low‑data regimes.
arXiv:2606. 15819v1 Announce Type: cross Abstract: The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high-fidelity text-to-image synthesis, while heightening concerns over the safety alignment of generated content.
arXiv:2606. 09601v1 Announce Type: new Abstract: Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimental factors.
arXiv:2608.29604v1 Announce Type: cross Abstract: Vision-language retrieval with CLIP-style dual encoders achieves strong cross-modal performance, yet practical accuracy often hinges on localized sem...