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<h2 itemprop="name">Idan Schwartz</h2>
<h3 itemprop="jobTitle">Assistant Professor</h3>
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<h1>Research & Bio</h1>
<p>I'm an Assistant Professor at Bar-Ilan University, leading the Multimodal Lab.</p>
<p>My research focuses on multimodal problems, primarily generative, with challenges including modal alignment and efficient inference-time solutions.</p>
<p>I also have a strong interest in connecting ideas from cognitive science decision making to deep learning concepts, such as model perceptiveness, representation as comprehension, attention, and programming as system 2 problem solving.</p>
<p>I completed my postdoc with Prof. Lior Wolf at Tel Aviv University. Before that, I earned my PhD in Computer Science from the Technion, under the supervision of Prof. Tamir Hazan and Prof. Alexander G. Schwing from UIUC. My thesis focused on Cognitive Models in Deep Learning. You can find my thesis <a href="/files/thesis_compress.pdf" target="_blank">here</a>.</p>
<p>My experience as a researcher in industry includes work on eBay's catalog (vision and language), Microsoft's Assistant (meeting insights, transcript-based), and at Spot for cloud workload optimization platform (time-series prediction). I currently serve as Chief Scientist at Aigency.ai, where I'm helping revolutionize the internet in the age of intelligent agents.</p>
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<li>Artificial Intelligence & Cognition</li>
<li>Attention Models</li>
<li>Multimodal Learning</li>
<li>Computer Vision</li>
<li>Natural Language Processing</li>
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<p class="course">Postdoc in Computer Science, 2023</p>
<p class="institution">Tel-Aviv University</p>
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<p class="course">PhD in Computer Science, 2022</p>
<p class="institution">Technion</p>
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<p class="course">BSc in Computer Science, 2015</p>
<p class="institution">Technion</p>
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<h1>Featured Publications</h1>
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<meta content="2023-04-20 00:00:00 +0000 UTC" itemprop="datePublished">
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<span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/i.-schwartz/">I. Schwartz</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person">V. Snæbjarnarson</span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/s.-benaim/">S. Benaim</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/h.-chefer/">H. Chefer</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/r.-cotterell/">R. Cotterell</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/l.-wolf/">L. Wolf</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/s.-belongie/">S. Belongie</a></span>
</div>
<span class="article-date">
<time>April 2023</time>
</span>
<span class="middot-divider"></span>
<span class="pub-publication">
<em>ICCV'23</em>
</span>
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<h3 class="article-title mb-1 mt-3" itemprop="name">
<a href="/publication/schwartz2023discriminative/" itemprop="url">Discriminative Class Tokens for Text-to-Image Diffusion Models</a>
</h3>
<div class="article-style" itemprop="articleBody">
Recent advances in text-to-image diffusion models have enabled the generation of diverse and high-quality images. However, generated images often fall short of depicting subtle details and are susceptible to errors due to ambiguity in the input text. One way of alleviating these issues is to train diffusion models on class-labeled datasets. This comes with a downside, doing so limits their expressive power: (i) supervised datasets are generally small compared to large-scale scraped text-image datasets on which text-to-image models are trained, and so the quality and diversity of generated images are severely affected, or (ii) the input is a hard-coded label, as opposed to free-form text, which limits the control over the generated images. In this work, we propose a non-invasive fine-tuning technique that capitalizes on the expressive potential of free-form text while achieving high accuracy through discriminative signals from a pretrained classifier, which guides the generation. This is done by iteratively modifying the embedding of a single input token of a text-to-image diffusion model, using the classifier, by steering generated images toward a given target class. Our method is fast compared to prior fine-tuning methods and does not require a collection of in-class images or retraining of a noise-tolerant classifier. We evaluate our method extensively, showing that the generated images are: (i) more accurate and of higher quality than standard diffusion models, (ii) can be used to augment training data in a low-resource setting, and (iii) reveal information about the data used to train the guiding classifier.
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PDF
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Cite
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<span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/h.-chefer/">H. Chefer</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/i.-schwartz/">I. Schwartz</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/l.-wolf/">L. Wolf</a></span>
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<span class="article-date">
<time>July 2022</time>
</span>
<span class="middot-divider"></span>
<span class="pub-publication">
<em>Neruips'22</em>
</span>
</div>
<h3 class="article-title mb-1 mt-3" itemprop="name">
<a href="/publication/chefer2022optimizing/" itemprop="url">Optimizing Relevance Maps of Vision Transformers Improves Robustness</a>
</h3>
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It has been observed that visual classification models often rely mostly on spurious cues such as the image background, which hurts their robustness to distribution changes. To alleviate this shortcoming, we propose to monitor the model’s relevancy signal and direct the model to base its prediction on the foreground object.This is done as a finetuning step, involving relatively few samples consisting of pairs of images and their associated foreground masks. Specifically, we encourage the model’s relevancy map (i) to assign lower relevance to background regions, (ii) to consider as much information as possible from the foreground, and (iii) we encourage the decisions to have high confidence. When applied to Vision Transformer (ViT) models, a marked improvement in robustness to domain-shifts is observed. Moreover, the foreground masks can be obtained automatically, from a self-supervised variant of the ViT model itself; therefore no additional supervision is required.
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<div class="card-simple" itemscope itemtype="http://schema.org/ScholarlyArticle">
<meta content="2022-06-20 00:00:00 +0000 UTC" itemprop="datePublished">
<meta content="2022-06-20 00:00:00 +0000 UTC" itemprop="dateModified">
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<span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/y.-tewel/">Y. Tewel</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/y.-shalev/">Y. Shalev</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/i.-schwartz/">I. Schwartz</a></span>, <span itemprop="author name" itemtype="http://schema.org/Person"><a href="/authors/l.-wolf/">L. Wolf</a></span>
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<span class="article-date">
<time>June 2022</time>
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<span class="pub-publication">
<em>CVPR'22</em>