7 papers
ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models
Asif Hanif, Mohammad Yaqub
Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions. Although prompt learning improves accuracy on base classes thro…
Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models
Mashrafi Monon, Umaima Rahman, Asif Hanif +2
Recent advances in 3D medical vision-language models have enabled joint reasoning over volumetric images and text, showing strong performance in medical visual question-answering (…
DARK: Diagonal-Anchored Repulsive Knowledge Distillation for Vision-Language Models under Extreme Compression
Numan Saeed, Asif Hanif, Fadillah Adamsyah Maani +2
Compressing vision-language models for on-device deployment is increasingly important in clinical settings, but knowledge distillation (KD) degrades sharply when the teacher-studen…
Noise is an Efficient Learner for Zero-Shot Vision-Language Models
Raza Imam, Asif Hanif, Jian Zhang +3
Recently, test-time adaptation has garnered attention as a method for tuning models without labeled data. The conventional modus operandi for adapting pre-trained vision-language m…
PALM: Few-Shot Prompt Learning for Audio Language Models
Asif Hanif, Maha Tufail Agro, Mohammad Areeb Qazi +1
Audio-Language Models (ALMs) have recently achieved remarkable success in zero-shot audio recognition tasks, which match features of audio waveforms with class-specific text prompt…
BAPLe: Backdoor Attacks on Medical Foundational Models using Prompt Learning
Asif Hanif, Fahad Shamshad, Muhammad Awais +5
Medical foundation models are gaining prominence in the medical community for their ability to derive general representations from extensive collections of medical image-text pairs…
On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models
Hashmat Shadab Malik, Numan Saeed, Asif Hanif +4
Volumetric medical segmentation models have achieved significant success on organ and tumor-based segmentation tasks in recent years. However, their vulnerability to adversarial at…