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Matan Levy

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Multi-Agent Reinforcement Learning for Modeling, Simulating, and Optimizing Energy Markets

Matan Levy
Itay Segev
Alexander Tuisov
Sarah Keren

The objective of this study is to advance the optimization of hybrid electricity markets using multi-agent reinforcement learning (MARL). The transition from centralized systems to public–private models introduces significant challenges, including the emergence of independent market players and the increasing integration of renewable energy sources (RESs). These challenges are further intensified by rapidly shifting demand patterns, driven both by energy-intensive data centers and AI inference workloads, as well as by political and societal instabilities. To address these complexities, we develop a formal model of market participants’ behavior and propose a MARL-based framework for optimizing system operator strategies. This framework incorporates dynamic pricing and dispatch scheduling to minimize operational costs, maintain grid stability, and align market incentives. We also present a new, adaptable simulation environment compatible with state-of-the-art MARL methods. Empirical evaluations in increasingly complex scenarios demonstrate the effectiveness of our approach in capturing the dynamic and decentralized nature of modern electricity markets.

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ICLR Conference 2025 Conference Paper

EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition

Issar Tzachor
Boaz Lerner
Matan Levy
Michael Green
Tal Berkovitz Shalev
Gavriel Habib
Dvir Samuel
Noam Korngut Zailer

The task of Visual Place Recognition (VPR) is to predict the location of a query image from a database of geo-tagged images. Recent studies in VPR have highlighted the significant advantage of employing pre-trained foundation models like DINOv2 for the VPR task. However, these models are often deemed inadequate for VPR without further fine-tuning on VPR-specific data. In this paper, we present an effective approach to harness the potential of a foundation model for VPR. We show that features extracted from self-attention layers can act as a powerful re-ranker for VPR, even in a zero-shot setting. Our method not only outperforms previous zero-shot approaches but also introduces results competitive with several supervised methods. We then show that a single-stage approach utilizing internal ViT layers for pooling can produce global features that achieve state-of-the-art performance, with impressive feature compactness down to 128D. Moreover, integrating our local foundation features for re-ranking further widens this performance gap. Our method also demonstrates exceptional robustness and generalization, setting new state-of-the-art performance, while handling challenging conditions such as occlusion, day-night transitions, and seasonal variations.

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NeurIPS Conference 2025 Conference Paper

Find your Needle: Small Object Image Retrieval via Multi-Object Attention Optimization

Michael Green
Matan Levy
Issar Tzachor
Dvir Samuel
Nir Darshan
Rami Ben-Ari

We address the challenge of Small Object Image Retrieval (SoIR), where the goal is to retrieve images containing a specific small object, in a cluttered scene. The key challenge in this setting is constructing a single image descriptor, for scalable and efficient search, that effectively represents all objects in the image. In this paper, we first analyze the limitations of existing methods on this challenging task and then introduce new benchmarks to support SoIR evaluation. Next, we introduce \ours (\oursMI), a novel retrieval framework which incorporates a dedicated multi-object pre-training phase. This is followed by a refinement process that leverages attention-based feature extraction with object masks, integrating them into a single unified image descriptor. Our \oursMI approach significantly outperforms existing retrieval methods and strong baselines, achieving notable improvements in both zero-shot and lightweight multi-object fine-tuning. We hope this work will pave the way and inspire further research to enhance retrieval performance for this highly practical task.

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AAAI Conference 2024 Conference Paper

Data Roaming and Quality Assessment for Composed Image Retrieval

Matan Levy
Rami Ben-Ari
Nir Darshan
Dani Lischinski

The task of Composed Image Retrieval (CoIR) involves queries that combine image and text modalities, allowing users to express their intent more effectively. However, current CoIR datasets are orders of magnitude smaller compared to other vision and language (V&L) datasets. Additionally, some of these datasets have noticeable issues, such as queries containing redundant modalities. To address these shortcomings, we introduce the Large Scale Composed Image Retrieval (LaSCo) dataset, a new CoIR dataset which is ten times larger than existing ones. Pre-training on our LaSCo, shows a noteworthy improvement in performance, even in zero-shot. Furthermore, we propose a new approach for analyzing CoIR datasets and methods, which detects modality redundancy or necessity, in queries. We also introduce a new CoIR baseline, the Cross-Attention driven Shift Encoder (CASE). This baseline allows for early fusion of modalities using a cross-attention module and employs an additional auxiliary task during training. Our experiments demonstrate that this new baseline outperforms the current state-of-the-art methods on established benchmarks like FashionIQ and CIRR.

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NeurIPS Conference 2024 Conference Paper

Where's Waldo: Diffusion Features For Personalized Segmentation and Retrieval

Dvir Samuel
Rami Ben-Ari
Matan Levy
Nir Darshan
Gal Chechik

Personalized retrieval and segmentation aim to locate specific instances within a dataset based on an input image and a short description of the reference instance. While supervised methods are effective, they require extensive labeled data for training. Recently, self-supervised foundation models have been introduced to these tasks showing comparable results to supervised methods. However, a significant flaw in these models is evident: they struggle to locate a desired instance when other instances within the same class are presented. In this paper, we explore text-to-image diffusion models for these tasks. Specifically, we propose a novel approach called PDM for Personalized Diffusion Features Matching, that leverages intermediate features of pre-trained text-to-image models for personalization tasks without any additional training. PDM demonstrates superior performance on popular retrieval and segmentation benchmarks, outperforming even supervised methods. We also highlight notable shortcomings in current instance and segmentation datasets and propose new benchmarks for these tasks.

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NeurIPS Conference 2023 Conference Paper

Chatting Makes Perfect: Chat-based Image Retrieval

Matan Levy
Rami Ben-Ari
Nir Darshan
Dani Lischinski

Chats emerge as an effective user-friendly approach for information retrieval, and are successfully employed in many domains, such as customer service, healthcare, and finance. However, existing image retrieval approaches typically address the case of a single query-to-image round, and the use of chats for image retrieval has been mostly overlooked. In this work, we introduce ChatIR: a chat-based image retrieval system that engages in a conversation with the user to elicit information, in addition to an initial query, in order to clarify the user's search intent. Motivated by the capabilities of today's foundation models, we leverage Large Language Models to generate follow-up questions to an initial image description. These questions form a dialog with the user in order to retrieve the desired image from a large corpus. In this study, we explore the capabilities of such a system tested on a large dataset and reveal that engaging in a dialog yields significant gains in image retrieval. We start by building an evaluation pipeline from an existing manually generated dataset and explore different modules and training strategies for ChatIR. Our comparison includes strong baselines derived from related applications trained with Reinforcement Learning. Our system is capable of retrieving the target image from a pool of 50K images with over 78% success rate after 5 dialogue rounds, compared to 75% when questions are asked by humans, and 64% for a single shot text-to-image retrieval. Extensive evaluations reveal the strong capabilities and examine the limitations of CharIR under different settings. Project repository is available at https: //github. com/levymsn/ChatIR.

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