204 citations · 213 across the 25 of their papers we have counts for
16 papers · 1 filter
C-PTQ: Fisher-weighted Channel-wise Sensitivity for Post-training Quantization of MLLMs
Jiameng Li, Han Zhou, Matthew B. Blaschko
Multimodal large language models (MLLMs) require huge memory and computational costs, which limits their practical deployment. Post-training quantization (PTQ) techniques offer an…
EchoPrune: Interpreting Redundancy as Temporal Echoes for Efficient VideoLLMs
Jiameng Li, Minye Wu, Jiezhang Cao +2
Long-form video understanding remains challenging for Video Large Language Models (VideoLLMs), as the dense frame sampling introduces massive visual tokens while sparse sampling ri…
MI-Pruner: Crossmodal Mutual Information-guided Token Pruner for Efficient MLLMs
Jiameng Li, Aleksei Tiulpin, Matthew B. Blaschko
For multimodal large language models (MLLMs), visual information is relatively sparse compared with text. As a result, research on visual pruning emerges for efficient inference. C…
Spectrum Matching: a Unified Perspective for Superior Diffusability in Latent Diffusion
Mang Ning, Mingxiao Li, Le Zhang +4
In this paper, we study the diffusability (learnability) of variational autoencoders (VAEs) in latent diffusion. We first derive distribution-free bounds on the maximum achievable…
When normalization hallucinates: unseen risks in AI-powered whole slide image processing
Karel Moens, Matthew B. Blaschko, Tinne Tuytelaars +3
Whole slide image (WSI) normalization remains a vital preprocessing step in computational pathology. Increasingly driven by deep learning, these models learn to approximate data di…
CLASH: A Benchmark for Cross-Modal Contradiction Detection
Teodora Popordanoska, Jiameng Li, Matthew B. Blaschko
Contradictory multimodal inputs are common in real-world settings, yet existing benchmarks typically assume input consistency and fail to evaluate cross-modal contradiction detecti…
SoftCFG: Uncertainty-guided Stable Guidance for Visual Autoregressive Model
Dongli Xu, Aleksei Tiulpin, Matthew B. Blaschko
Autoregressive (AR) models have emerged as powerful tools for image generation by modeling images as sequences of discrete tokens. While Classifier-Free Guidance (CFG) has been ado…
Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles
Zifu Wang, Junyi Zhu, Bo Tang +4
The application of rule-based reinforcement learning (RL) to multimodal large language models (MLLMs) introduces unique challenges and potential deviations from findings in text-on…
Revisiting Reweighted Risk for Calibration: AURC, Focal, and Inverse Focal Loss
Han Zhou, Sebastian G. Gruber, Teodora Popordanoska +1
Several variants of reweighted risk functionals, such as focal loss, inverse focal loss, and the Area Under the Risk Coverage Curve (AURC), have been proposed for improving model c…
CARE: Confidence-aware Ratio Estimation for Medical Biomarkers
Jiameng Li, Teodora Popordanoska, Aleksei Tiulpin +3
Ratio-based biomarkers (RBBs), such as the proportion of necrotic tissue within a tumor, are widely used in clinical practice to support diagnosis, prognosis, and treatment plannin…
DAVE: Diagnostic benchmark for Audio Visual Evaluation
Gorjan Radevski, Teodora Popordanoska, Matthew B. Blaschko +1
Audio-visual understanding is a rapidly evolving field that seeks to integrate and interpret information from both auditory and visual modalities. Despite recent advances in multi-…
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications
Jordy Van Landeghem, Subhajit Maity, Ayan Banerjee +4
This work explores knowledge distillation (KD) for visually-rich document (VRD) applications such as document layout analysis (DLA) and document image classification (DIC). While V…
Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting
Omar Hamed, Souhail Bakkali, Marie-Francine Moens +2
This work addresses the need for a balanced approach between performance and efficiency in scalable production environments for visually-rich document understanding (VDU) tasks. Cu…
The Common Stability Mechanism behind most Self-Supervised Learning Approaches
Abhishek Jha, Matthew B. Blaschko, Yuki M. Asano +1
Last couple of years have witnessed a tremendous progress in self-supervised learning (SSL), the success of which can be attributed to the introduction of useful inductive biases i…
Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection
Teodora Popordanoska, Aleksei Tiulpin, Matthew B. Blaschko
Despite their impressive predictive performance in various computer vision tasks, deep neural networks (DNNs) tend to make overly confident predictions, which hinders their widespr…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…