22 citations · 62 across the 50 of their papers we have counts for
7 papers · 2 filters
A Decision-Aware Evaluation Protocol for Uncertainty in Medical Image Segmentation
Saket Maganti, Shreyank N Gowda
Medical segmentation uncertainty is often evaluated with calibration error or pixel-level error-detection scores, but these metrics do not show whether uncertainty improves downstr…
Explanation Stability of Test-Time Adaptation in Computational Pathology: A Large-Scale Benchmark
R. G. Bahumanya, Harshith V. M., Shreyank N. Gowda +1
Test-time adaptation (TTA) has become a practical way to adapt deployed models to unlabeled target data, a setting that is especially relevant in computational pathology where stai…
Erasing Without Collateral Damage: Precise Concept Removal in Diffusion Models
Parth Upman, Nishita Jain, Shreyank N Gowda
Training-free concept erasure is an attractive mechanism for controlling text-to-image diffusion models, but precise erasure often comes at the cost of damaging semantically relate…
SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning
Hezhao Liu, Jiacheng Yang, Junlong Gao +4
In open-world semi-supervised learning (OWSSL), a model learns from labeled data and unlabeled data containing both known and novel classes. In practical OWSSL applications, models…
Compression as an Adversarial Amplifier Through Decision Space Reduction
Lewis Evans, Harkrishan Jandu, Zihan Ye +2
Image compression is a ubiquitous component of modern visual pipelines, routinely applied by social media platforms and resource-constrained systems prior to inference. Despite its…
Low-Effort Jailbreak Attacks Against Text-to-Image Safety Filters
Ahmed B Mustafa, Zihan Ye, Yang Lu +2
Text-to-image generative models are widely deployed in creative tools and online platforms. To mitigate misuse, these systems rely on safety filters and moderation pipelines that a…
ZeroDiff++: Substantial Unseen Visual-semantic Correlation in Zero-shot Learning
Zihan Ye, Shreyank N Gowda, Kaile Du +2
Zero-shot Learning (ZSL) enables classifiers to recognize classes unseen during training, commonly via generative two stage methods: (1) learn visual semantic correlations from see…