Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Darshan, Nir"'
Autor:
Tzachor, Issar, Lerner, Boaz, Levy, Matan, Green, Michael, Shalev, Tal Berkovitz, Habib, Gavriel, Samuel, Dvir, Zailer, Noam Korngut, Shimshi, Or, Darshan, Nir, Ben-Ari, Rami
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 t
Externí odkaz:
http://arxiv.org/abs/2405.18065
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 train
Externí odkaz:
http://arxiv.org/abs/2405.18025
Autor:
Samuel, Dvir, Meiri, Barak, Maron, Haggai, Tewel, Yoad, Darshan, Nir, Avidan, Shai, Chechik, Gal, Ben-Ari, Rami
Diffusion inversion is the problem of taking an image and a text prompt that describes it and finding a noise latent that would generate the exact same image. Most current deterministic inversion techniques operate by approximately solving an implici
Externí odkaz:
http://arxiv.org/abs/2312.12540
With such a massive growth in the number of images stored, efficient search in a database has become a crucial endeavor managed by image retrieval systems. Image Retrieval with Relevance Feedback (IRRF) involves iterative human interaction during the
Externí odkaz:
http://arxiv.org/abs/2312.11078
Gait Recognition is a computer vision task aiming to identify people by their walking patterns. Although existing methods often show high performance on specific datasets, they lack the ability to generalize to unseen scenarios. Unsupervised Domain A
Externí odkaz:
http://arxiv.org/abs/2307.06751
Text-to-image diffusion models show great potential in synthesizing a large variety of concepts in new compositions and scenarios. However, the latent space of initial seeds is still not well understood and its structure was shown to impact the gener
Externí odkaz:
http://arxiv.org/abs/2306.08687
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 o
Externí odkaz:
http://arxiv.org/abs/2305.20062
Text-to-image diffusion models can synthesize high-quality images, but they have various limitations. Here we highlight a common failure mode of these models, namely, generating uncommon concepts and structured concepts like hand palms. We show that
Externí odkaz:
http://arxiv.org/abs/2304.14530
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
Externí odkaz:
http://arxiv.org/abs/2303.09429
Text-to-image diffusion models can synthesize a large variety of concepts in new compositions and scenarios. However, they still struggle with generating uncommon concepts, rare unusual combinations, or structured concepts like hand palms. Their limi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96219413fb13adb6494df886015b7132