با سلام. اسلایدهای ارائه هفته پژوهش در مورد مقاله نوریپس پذیرفته شده از RIML خدمت عزیزان تقدیم می‌شود. همینطور در این رشته توییت توضیحاتی در مورد مقاله داده‌ام: https://x.com/MhRohban/status/1867803097596338499

💠 Compositional Learning Journal Club
Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.

*✅ This Week's Presentation:*

*🔹 Title:* Counting Understanding in Visoin Lanugate Models

*🔸 Presenter:* Arash Marioriyad

*🌀 Abstract:*Counting-related challenges represent some of the most significant compositional understanding failure modes in vision-language models (VLMs) such as CLIP. While humans, even in early stages of development, readily generalize over numerical concepts, these models often struggle to accurately interpret numbers beyond three, with the difficulty intensifying as the numerical value increases. In this presentation, we explore the counting-related limitations of VLMs and examine the proposed solutions within the field to address these issues.

*📄 Papers:*- Teaching CLIP to Count to Ten (ICCV, 2023)
- CLIP-Count: Towards Text-Guided Zero-Shot Object Counting (ACM-MM, 2023)

Session Details:
- *📅 Date: Sunday
- *🕒 Time: 5:00 - 6:00 PM
- 🌐 Location:** Online at vc.sharif.edu/ch/rohban

We look forward to your participation! ✌️

🚨 Open Research Position: Visual Anomaly Detection

We announce that there is an open research position in the RIML lab at Sharif University of Technology, supervised by Dr. Rohban.

🔍 Project Description:
Industrial inspection and quality control are among the most prominent applications of visual anomaly detection. In this context, the model is given a training set of solely normal samples to learn their distribution. During inference, any sample that deviates from this established normal distribution, should be recognized as an anomaly.
This project aims to improve the capabilities of existing models, allowing them to detect intricate anomalies that extend beyond conventional defects.

Introductory Paper:
Deep Industrial Image Anomaly Detection: A Survey

Requirements:
- Good understanding of deep learning concepts
- Fluency in Python, PyTorch
- Willingness to dedicate significant time

Submit your application here:
Application Form

Application Deadline:
2024/11/22 (23:59 UTC+3:30)

If you have any questions, contact:
@sehbeygi79

🚨Open Position: Visual Compositional Generation Research 🚨

We are excited to announce an open research position for a project under Dr. Rohban at the RIML Lab (Sharif University of Technology). The project focuses on improving text-to-image generation in diffusion-based models by addressing compositional challenges.

🔍 Project Description:

Large-scale diffusion-based models excel at text-to-image (T2I) synthesis, but still face issues like object missing and improper attribute binding. This project aims to study and resolve these compositional failures to improve the quality of T2I models.

Key Papers:
- T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional T2I Generation
- Attend-and-Excite: Attention-Based Semantic Guidance for T2I Diffusion Models
- If at First You Don’t Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection
- ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization

🎯 Requirements:

- Must: PyTorch, Deep Learning,
- Recommended: Transformers and Diffusion Models.
- Able to dedicate significant time to the project.

🗓 Important Dates:

- Application Deadline: 2024/10/12 (23:59 UTC+3:30)

📌 Apply here:
Application Form

For questions:
📧 [email protected]
💬 @amirkasaei

@RIMLLab
#research_application
#open_position

💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: A semiotic methodology for assessing the compositional effectiveness of generative text-to-image models

🔸 Presenter: Amir Kasaei

🌀 Abstract:
A new methodology for evaluating text-to-image generation models is being proposed, addressing limitations in current evaluation techniques. Existing methods, which use metrics such as fidelity and CLIPScore, often combine criteria like position, action, and photorealism in their assessments. This new approach adapts model analysis from visual semiotics, establishing distinct visual composition criteria. It highlights three key dimensions: plastic categories, multimodal translation, and enunciation, each with specific sub-criteria. The methodology is tested on Midjourney and DALL·E, providing a structured framework that can be used for future quantitative analyses of generated images.

📄 Paper: A semiotic methodology for assessing the compositional effectiveness of generative text-to-image models

Session Details:
- 📅 Date: Sunday
- 🕒 Time: 5:00 - 6:00 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! ✌️

💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback

🔸 Presenter: Amir Kasaei

🌀 Abstract:
Recent advancements in text-conditioned image generation, particularly through latent diffusion models, have achieved significant progress. However, as text complexity increases, these models often struggle to accurately capture the semantics of prompts, and existing tools like CLIP frequently fail to detect these misalignments.

This presentation introduces a Decompositional-Alignment-Score, which breaks down complex prompts into individual assertions and evaluates their alignment with generated images using a visual question answering (VQA) model. These scores are then combined to produce a final alignment score. Experimental results show this method aligns better with human judgments compared to traditional CLIP and BLIP scores. Moreover, it enables an iterative process that improves text-to-image alignment by 8.7% over previous methods.

This approach not only enhances evaluation but also provides actionable feedback for generating more accurate images from complex textual inputs.

📄 Paper: Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback

Session Details:
- 📅 Date: Sunday
- 🕒 Time: 2:00 - 3:00 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! ✌️

Project Description:
This project is a collaborative effort between Dr. Rohban, Dr. Soleymani, and Dr. Asgari. Together, we aim to push the boundaries of language model evaluation for the Persian language. In this project, our primary goal is to benchmark and develop innovative methods for evaluating language models on the Persian language both robustly and comprehensively. Our approach will encompass both static and dynamic assessments to ensure thorough analysis. This initiative seeks to advance the field by addressing unique challenges posed by Persian language processing.

For more in-depth insights, please refer to the following papers:

"Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?"

Requirments:
Familiarity with LLM Concepts: Understanding the fundamentals and advancements in large language models.
Deep Learning Expertise: Practical knowledge and experience in deep learning techniques.
PyTorch Proficiency: Hands-on experience with the PyTorch framework is essential.
Commitment: Ability to dedicate significant time and maintain consistency throughout the project.

To apply for this position, please read the suggested papers and send your resume along with a brief summary of your research interests to [email protected]. We are eager to hear from motivated individuals who are passionate about advancing language model evaluation.

For any inquiries, feel free to reach out to us via the above email.

#open_position
#research_application

#اخبار_پژوهشی_آزمایشگاه

مقالات برتر چاپ شده از آغاز سال ۲۰۲۳ تحت نظارت آقای دکتر رهبان

Fake It Until You Make It: Towards Accurate Near-Distribution Novelty Detection
آقای حسین میرزایی دانشجوی کارشناسی‌ارشد آزمایشگاه RIML - چاپ شده در کنفرانس ICLR

Lagrangian objective function leads to improved unforeseen attack generalization
آقای محمد عزیزملایری دانشجوی دکترا آزمایشگاه RIML - چاپ شده در ژورنال Machine Learning

Compositions and methods for treating proliferative diseases
US Patent App.

Zerograd: Costless conscious remedies for catastrophic overfitting in the fgsm adversarial training
خانم زینب گلگونی دانشجوی دکترا آزمایشگاه RIML - چاپ شده در ژورنال Intelligent Systems with Applications

A deep learning framework to scale linear facial measurements to actual size using horizontal visible iris diameter: a study on an Iranian population
آقای دکتر حسین محمدرحیمی محقق در آزمایشگاه RIML - چاپ شده در ژورنال Scientific Reports

Weakly-Supervised Drug Efficiency Estimation with Confidence Score: Application to COVID-19 Drug Discovery
خانم نهال میرزایی و آقای محمد ولی‌ثانیان دانشجویان کارشناسی‌ارشد آزمایشگاه RIML - چاپ شده در کنفرانس MICCAI

Forecasting influenza hemagglutinin mutations through the lens of anomaly detection
آقای محمدرضا صالحی دانشجوی اسبق آزمایشگاه RIML - چاپ شده در ژورنال Scientific Reports

Borderless azerbaijani processing: Linguistic resources and a transformer-based approach for azerbaijani transliteration
خانم ریحانه زهرابی دانشجوی کارشناسی‌ارشد آزمایشگاه RIML - چاپ شده در کنفرانس ACL - تحت نظر دکتر بیگی، دکتر عسگری و دکتر رهبان

Examination of lemon bruising using different CNN-based classifiers and local spectral-spatial hyperspectral imaging
آقای دکتر سجاد سبزی پسادکترا آزمایشگاه RIML - چاپ شده در ژورنال Algorithms

A Robust Heterogeneous Offloading Setup Using Adversarial Training
آقای مهدی امیری دانشجوی کارشناسی‌ارشد آزمایشگاه RIML - چاپ شده در ژورنال IEEE Transactions on Mobile Computing - تحت نظر دکتر رهبان و دکتر حسابی

Universal Novelty Detection Through Adaptive Contrastive Learning
آقای حسین میرزایی و آقای مجتبی نافذ دانشجویان کارشناسی‌ارشد آزمایشگاه RIML - چاپ شده در کنفرانس CVPR

Killing It With Zero-Shot: Adversarially Robust Novelty Detection
آقای حسین میرزایی دانشجوی کارشناسی‌ارشد آزمایشگاه RIML - چاپ شده در کنفرانس IEEE ICASSP

User Voices, Platform Choices: Social Media Policy Puzzle with Decentralization Salt
جمعی از دانشجویان کارشناسی - چاپ شده در کنفرانس CHI

Comparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection
آقای دکتر سجاد سبزی پسادکترا و خانم ریحانه زهرابی دانشجوی کارشناسی‌ارشد آزمایشگاه RIML - چاپ شده در Journal of Food Science

RODEO: Robust Outlier Detection via Exposing Adaptive Outliers
آقای حسین میرزایی و آقای مجتبی نافذ دانشجویان کارشناسی‌ارشد آزمایشگاه RIML - چاپ شده در کنفرانس ICML

Virtual screening for small-molecule pathway regulators by image-profile matching
آقای دکتر رهبان و خانم دکتر Anne E. Carpenter - چاپ شده در ژورنال Cell systems

Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?
Coming Soon! :)

و ده‌ها مقاله دیگر که در Google Scholar دکتر رهبان می‌توانید مشاهده کنید.

تبریک خدمت تمامی اعضای آزمایشگاه به دلیل تلاش‌‌، کوشش و پژوهش در جهت رفع مشکلات جامعه و کشور و چاپ مقالات در برترین کنفرانس‌ها و مجلات AI