1 citations · 1 across the 10 of their papers we have counts for
11 papers
RAGMark: A Comprehensive Framework for Benchmarking Retrieval-Augmented Generation Systems
Zlatan Feric, Amir Taherin, Bin Ren +3
We present RAGMark, a modular benchmarking framework for advanced Retrieval-Augmented Generation (RAG) systems targeting small-scale multi-GPU environments. RAGMark evaluates diver…
LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing
Masih Eskandar, Miquel Sirera Perelló, Stratis Ioannidis +1
Lifelong knowledge editing aims to efficiently and sequentially update language models over time, as new knowledge becomes available or when the model makes mistakes, while preserv…
CD-RCM: Generalizable Continuous-Depth Novel View Synthesis for Reflectance Confocal Microscopy
Tooba Imtiaz, Milind Rajadhyaksha, Kivanc Kose +1
Reflectance confocal microscopy (RCM) provides noninvasive, cellular-resolution "optical biopsies" of human skin \emph{in vivo} by acquiring en-face images at successive depths, fo…
PanoWorld: Geometry-Consistent Panoramic Video World Modeling
Le Jiang, Xiangyu Bai, Bishoy Galoaa +7
We present PanoWorld, a panoramic video world model that generates geometry-consistent 360 video from a single image and a caption. Existing panoramic video methods optimi…
PhyGround: Benchmarking Physical Reasoning in Generative World Models
Juyi Lin, Arash Akbari, Yumei He +13
Generative world models are increasingly used for video generation, where learned simulators are expected to capture the physical rules that govern real-world dynamics. However, ev…
ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models
Arash Akbari, Arman Akbari, Masih Eskandar +11
Vision-Language-Action (VLA) models exhibit remarkable action generation for embodied intelligence, but their heavy compute make deployment on edge platforms impractical. Aggressiv…
LVT: Large-Scale Scene Reconstruction via Local View Transformers
Tooba Imtiaz, Lucy Chai, Kathryn Heal +4
Large transformer models are proving to be a powerful tool for 3D vision and novel view synthesis. However, the standard Transformer's well-known quadratic complexity makes it diff…
DISCO: Disentangled Communication Steering for Large Language Models
Max Torop, Aria Masoomi, Masih Eskandar +1
A variety of recent methods guide large language model outputs via the inference-time addition of steering vectors to residual-stream or attention-head representations. In contrast…
Grounding Multimodal Large Language Models with Quantitative Skin Attributes: A Retrieval Study
Max Torop, Masih Eskandar, Nicholas Kurtansky +6
Artificial Intelligence models have demonstrated significant success in diagnosing skin diseases, including cancer, showing the potential to assist clinicians in their analysis. Ho…
STAR: Stability-Inducing Weight Perturbation for Continual Learning
Masih Eskandar, Tooba Imtiaz, Davin Hill +2
Humans can naturally learn new and varying tasks in a sequential manner. Continual learning is a class of learning algorithms that updates its learned model as it sees new data (on…
ADAPT to Robustify Prompt Tuning Vision Transformers
Masih Eskandar, Tooba Imtiaz, Zifeng Wang +1
The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks. Many existing defenses against these attacks, such as adversarial t…