1 citations · 2 across the 8 of their papers we have counts for
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Query-Conditioned Spherical Centroid Aggregation for Multimodal Retrieval
Ambuj Mehrish, Anindya Nag, Sebastiano Vascon
Multimodal retrieval integrates video, audio, subtitles, and text; however, recent geometric aggregators, such as Gramian volumes, hyperbolic volumes, and spectral objectives, trea…
Hypergraph-Regularized Gramian Volumes for Multimodal Retrieval
Anindya Nag, Ambuj Mehrish, Sebastiano Vascon
Volume-based multimodal retrieval jointly scores a text query with a candidate's video, audio, and subtitle embeddings. While this approach captures higher-order within-candidate a…
Reassembling Broken Objects using Breaking Curves
Ali Alagrami, Luca Palmieri, Sinem Aslan +2
Reassembling 3D broken objects is a challenging task. A robust solution that generalizes well must deal with diverse patterns associated with different types of broken objects. We…
The Group Loss++: A deeper look into group loss for deep metric learning
Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner +4
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings…
Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders
Marina Khoroshiltseva, Arianna Traviglia, Marcello Pelillo +1
This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders. Missing borders is a common real-world situation, for example, when dealing…
The Group Loss for Deep Metric Learning
Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich +2
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings…