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Beyond Similarity: Foundation Models as an Efficient Backbone for Training-Free Composed Video Retrieval
Dmitry Demidov, Muhammad Zaigham Zaheer, Omkar Thawakar +2
Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gallery scale, this creates a fu…
CoVR-R:Reason-Aware Composed Video Retrieval
Omkar Thawakar, Dmitry Demidov, Vaishnav Potlapalli +5
Composed Video Retrieval (CoVR) aims to find a target video given a reference video and a textual modification. Prior work assumes the modification text fully specifies the visual…
Thinking Beyond Labels: Vocabulary-Free Fine-Grained Recognition using Reasoning-Augmented LMMs
Dmitry Demidov, Zaigham Zaheer, Zongyan Han +2
Vocabulary-free fine-grained image recognition aims to distinguish visually similar categories within a meta-class without a fixed, human-defined label set. Existing solutions for…
Beyond Simple Edits: Composed Video Retrieval with Dense Modifications
Omkar Thawakar, Dmitry Demidov, Ritesh Thawkar +4
Composed video retrieval is a challenging task that strives to retrieve a target video based on a query video and a textual description detailing specific modifications. Standard r…
Vocabulary-free Fine-grained Visual Recognition via Enriched Contextually Grounded Vision-Language Model
Dmitry Demidov, Zaigham Zaheer, Omkar Thawakar +2
Fine-grained image classification, the task of distinguishing between visually similar subcategories within a broader category (e.g., bird species, car models, flower types), is a…
Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data Regimes
Dmitry Demidov, Abduragim Shtanchaev, Mihail Mihaylov +1
The emerging task of fine-grained image classification in low-data regimes assumes the presence of low inter-class variance and large intra-class variation along with a highly limi…