activity
20232026
collaborators

8 papers

cs.CV2026

SAMPLe: SAM-based Optimizer for Prompt Learning in VLMs

Hossein Rajoli, Fatemeh Lotfi, Niloufar Alipour Talemi +3

Pre-trained Vision-Language Models (VLMs) like CLIP have proven highly effective as foundation models for various downstream applications. However, prompt learning in VLMs encounte…

cs.LG2026

ZO-SAM: Zero-Order Sharpness-Aware Minimization for Efficient Sparse Training

Jie Ji, Gen Li, Kaiyuan Deng +2

Deep learning models, despite their impressive achievements, suffer from high computational costs and memory requirements, limiting their usability in resource-constrained environm…

cs.CV2025

Modality-Aware SAM: Sharpness-Aware-Minimization Driven Gradient Modulation for Harmonized Multimodal Learning

Hossein R. Nowdeh, Jie Ji, Xiaolong Ma +1

In multimodal learning, dominant modalities often overshadow others, limiting generalization. We propose Modality-Aware Sharpness-Aware Minimization (M-SAM), a model-agnostic frame…

cs.LG2025

FairSAM: Fair Classification on Corrupted Image Data Through Sharpness-Aware Minimization

Yucong Dai, Jie Ji, Xiaolong Ma +1

Image classification models trained on clean data often degrade sharply when exposed to corrupted test or deployment data, such as images with impulse noise, Gaussian noise, or env…

cs.LG2024

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning

Mingyu Cao, Gen Li, Jie Ji +6

Mixture-of-Experts (MoE) has garnered significant attention for its ability to scale up neural networks while utilizing the same or even fewer active parameters. However, MoE does…

cs.CV2024

Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design

Gen Li, Zhihao Shu, Jie Ji +4

Deep neural networks (DNNs) are frequently employed in a variety of computer vision applications. Nowadays, an emerging trend in the current video distribution system is to take ad…

cs.CV2024

FlameFinder: Illuminating Obscured Fire through Smoke with Attentive Deep Metric Learning

Hossein Rajoli, Sahand Khoshdel, Fatemeh Afghah +1

FlameFinder is a deep metric learning (DML) framework designed to accurately detect flames, even when obscured by smoke, using thermal images from firefighter drones during wildfir…

cs.LG2023

Integrating Fairness and Model Pruning Through Bi-level Optimization

Yucong Dai, Gen Li, Feng Luo +2

Deep neural networks have achieved exceptional results across a range of applications. As the demand for efficient and sparse deep learning models escalates, the significance of mo…