arXiv AI

How Do Diffusion Classifiers Decide? A Bias-Centric Evaluation

arXiv:2607. 03831v1 Announce Type: cross Abstract: Diffusion models have recently been repurposed for zero-shot classification, giving rise to diffusion classifiers that identify the best-matching text prompt by minimizing the noise-prediction error.

arXiv AI

Jul 7

Self-Improving Diffusion Classifiers with Minority Preference Optimization

arXiv:2607. 03770v1 Announce Type: cross Abstract: Prior studies have demonstrated that diffusion classifiers achieve robust zero-shot classification performance.

By Hyunsoo Kim, Jungmyung Wi, Soobin Um, Donghyun Kim, Suhyun Kim

diffusion fine-tuning safety

arXiv Machine Learning

1d ago

Discrete Diffusion Language Models Are Training-Free Multi-Label Classifiers

arXiv:2608. 14649v1 Announce Type: new Abstract: We present dLLM-SetScore, a training-free method that uses discrete masked-diffusion language models for multi-label text classification.

By Pawan Kumar

llms diffusion nlp fine-tuning

arXiv Machine Learning

Jul 15

Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification

arXiv:2607. 12464v1 Announce Type: cross Abstract: When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task.

By Jeeyung Kim, Erfan Esmaeili, Qiang Qiu

diffusion computer-vision reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search

arXiv:2512. 08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gender, race and age.

By Manos Plitsis, Giorgos Bouritsas, Vassilis Katsouros, Yannis Panagakis

llms diffusion benchmarks safety

arXiv Machine Learning

Jun 2

RAIGen: Rare Attribute Identification in Text-to-Image Generative Models

arXiv:2602. 06806v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attributes.

By Silpa Vadakkeeveetil Sreelatha, Dan Wang, Serge Belongie, Muhammad Awais, Anjan Dutta

diffusion safety

arXiv AI

Jul 28

Concept-based Visual Counterfactual Explanations with Diffusion Models

arXiv:2607. 22544v1 Announce Type: new Abstract: Visual counterfactual explanations aim to answer "what minimal change to this image would flip the model's prediction?

By Yassine Oueslati, Daniil Kirilenko, Martin Gjoreski, Marc Langheinrich

diffusion benchmarks safety

arXiv AI

Jul 22

Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models

arXiv:2607. 18695v1 Announce Type: cross Abstract: A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the resulting descriptors.

By Gautam Rajendrakumar Gare, Jia Shi, Zhiqiu Lin, Deepak Pathak, John Galeotti, Deva Ramanan

llms rag multimodal

arXiv AI

Jun 30

On the Faithfulness of Post-Hoc Concept Bottleneck Models

arXiv:2606. 30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color.

By Laines Schmalwasser, Jan Blunk, Niklas Penzel, Julia Niebling, Joachim Denzler

llms multimodal benchmarks

arXiv AI

5d ago

DiffImaginE: Imagine to Verify Entity Types with Diffusion

arXiv:2608. 03025v3 Announce Type: replace Abstract: Multimodal named entity recognition (MNER) determines whether each candidate span and entity-type hypothesis is supported by joint textual and visual evidence.

By Feng Zhang, Feiyu Han, Rongxin Yang, Yang Liu, Yancheng Chen, Rui Wang, Yingguang Yang, Tian Xueyun, Chongyang Zhang, Hao Zheng, Xu Kefu, Congjing Ran, Fuhai Chen, Bin Chong

diffusion nlp multimodal

arXiv Machine Learning

Jul 2

AdaBoosting Text Prompts for Vision-Language Models

arXiv:2607. 00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts.

By Seokhee Jin, Changhwan Sung, Sunung Mun, Hoyoung Kim, Jungseul Ok

llms multimodal benchmarks

arXiv Machine Learning

Jun 18

When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift

arXiv:2606. 19184v1 Announce Type: cross Abstract: Recent advances in generative AI, such as diffusion models and face-swapping tools, have enabled the creation of highly realistic deepfakes, leading to real-world harms including financial fraud and non-consensual explicit content.

By Dat Nguyen, Cosmin Radoi, Romain Hermary, Marcella Astrid, Nesryne Mejri, Enjie Ghorbel, Djamila Aouada

diffusion benchmarks

arXiv AI

Jun 15

Mirage Probes: How Vision Models Fake Visual Understanding

arXiv:2606. 13870v1 Announce Type: cross Abstract: Vision-language models (VLMs) can answer image-based questions confidently, and often correctly, even when no image is provided.

By Daniel Ben-Levi, Judah Goldfeder, Weiliang Zhao, Raz Lapid, Amit LeVi, Allen G. Roush, Ravid Shwartz-Ziv, Hod Lipson

llms diffusion multimodal benchmarks

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