279 citations · 364 across the 24 of their papers we have counts for
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Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval
Giacomo Pacini, Fabio Carrara, Nicola Messina +3
Query suggestion, a technique widely adopted in information retrieval, enhances system interactivity and the browsing experience of document collections. In cross-modal retrieval,…
Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a Baseline
Nicola Messina, Lucia Vadicamo, Leo Maltese +1
Recent advancements in deep learning have significantly enhanced content-based retrieval methods, notably through models like CLIP that map images and texts into a shared embedding…
Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation
Luca Barsellotti, Lorenzo Bianchi, Nicola Messina +5
Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP…
Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting
Luca Ciampi, Nicola Messina, Matteo Pierucci +3
Recently, object counting has shifted towards class-agnostic counting (CAC), which counts instances of arbitrary object classes never seen during model training. With advancements…
Joint-Dataset Learning and Cross-Consistent Regularization for Text-to-Motion Retrieval
Nicola Messina, Jan Sedmidubsky, Fabrizio Falchi +1
Pose-estimation methods enable extracting human motion from common videos in the structured form of 3D skeleton sequences. Despite great application opportunities, effective conten…
Is CLIP the main roadblock for fine-grained open-world perception?
Lorenzo Bianchi, Fabio Carrara, Nicola Messina +1
Modern applications increasingly demand flexible computer vision models that adapt to novel concepts not encountered during training. This necessity is pivotal in emerging domains…