Amin Karimi Monsefi — accepted AI-conference papers

2026

FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Models

Amin Karimi Monsefi, Nikhil Bhendawade, Manuel Rafael Ciosici, Dominic Culver, Yizhe Zhang, Irina Belousova

ICLR 2026poster

Autoregressive language models (ARMs) deliver strong likelihoods, but are inherently serial: they generate one token per forward pass, which limits throughput and inflates latency for long sequences. Diffusion Language Models (DLMs) parallelize across positions and thus appear promising for language…

Cited by 0 Source ↗code ↗Scholar ↗

2025

Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning

Amin Karimi Monsefi, Mengxi Zhou, Nastaran Karimi Monsefi, Ser-Nam Lim, Wei-Lun Chao, Rajiv Ramnath

ICLR 2025poster

We present a novel frequency-based Self-Supervised Learning (SSL) approach that significantly enhances its efficacy for pre-training. Prior work in this direction masks out pre-defined frequencies in the input image and employs a reconstruction loss to pre-train the model. While achieving promising…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2025

TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation

Amin Karimi Monsefi, Mridul Khurana, Rajiv Ramnath, Anuj Karpatne, Wei-Lun Chao, Cheng Zhang

ICCV 2025poster

We propose TaxaDiffusion, a taxonomy-informed training framework for diffusion models to generate fine-grained animal images with high morphological and identity accuracy. Unlike standard approaches that treat each species as an independent category, TaxaDiffusion incorporates domain knowledge that…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗