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20182026
most citedContrastive Language-Action Pre-training for Temporal Localization

5 citations · 6 across the 10 of their papers we have counts for

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14 papers · 1 filter

cs.CV2026

ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities

Aavash Chhetri, Bibek Niroula, Eduard Vazquez +4

In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or perform naive feature synthes…

cs.CV2026

Interactive Episodic Memory with User Feedback

Nikesh Subedi, Loris Bazzani, Ziad Al-Halah

In episodic memory with natural language queries (EM-NLQ), a user may ask a question (e.g., "Where did I place the mug?") that requires searching a long egocentric video, captured…

cs.CV2026

Benchmarking Interaction, Beyond Policy: a Reproducible Benchmark for Collaborative Instance Object Navigation

Edoardo Zorzi, Francesco Taioli, Yiming Wang +4

We propose Question-Asking Navigation (QAsk-Nav), the first reproducible benchmark for Collaborative Instance Object Navigation (CoIN) that enables an explicit, separate assessment…

cs.CV2026

Multi-Level Conditioning by Pairing Localized Text and Sketch for Fashion Image Generation

Ziyue Liu, Davide Talon, Federico Girella +5

Sketches offer designers a concise yet expressive medium for early-stage fashion ideation by specifying structure, silhouette, and spatial relationships, while textual descriptions…

cs.CV2026

Med-MMFL: A Multimodal Federated Learning Benchmark in Healthcare

Aavash Chhetri, Bibek Niroula, Pratik Shrestha +5

Federated learning (FL) enables collaborative model training across decentralized medical institutions while preserving data privacy. However, medical FL benchmarks remain scarce,…

cs.CV2025

ToFu: Visual Tokens Reduction via Fusion for Multi-modal, Multi-patch, Multi-image Task

Vittorio Pippi, Matthieu Guillaumin, Silvia Cascianelli +3

Large Multimodal Models (LMMs) are powerful tools that are capable of reasoning and understanding multimodal information beyond text and language. Despite their entrenched impact,…