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20242026
most citedCogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detection

16 citations · 24 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Understanding Multimodal Complementarity for Single-Frame Action Anticipation

Manuel Benavent-Lledo, Konstantinos Bacharidis, Konstantinos Papoutsakis +2

Human action anticipation is commonly treated as a video understanding problem, implicitly assuming that dense temporal information is required to reason about future actions. In t…

cs.CV2025

Action Anticipation at a Glimpse: To What Extent Can Multimodal Cues Replace Video?

Manuel Benavent-Lledo, Konstantinos Bacharidis, Victoria Manousaki +3

Anticipating actions before they occur is a core challenge in action understanding research. While conventional methods rely on extracting and aggregating temporal information from…

cs.CV2025★ 2 cited

Text-driven Online Action Detection

Manuel Benavent-Lledo, David Mulero-Pérez, David Ortiz-Perez +1

Detecting actions as they occur is essential for applications like video surveillance, autonomous driving, and human-robot interaction. Known as online action detection, this task…

cs.CV2025

Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos

Javier Rodriguez-Juan, David Ortiz-Perez, Manuel Benavent-Lledo +5

The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and info…

cs.CV2024

Detecting Facial Image Manipulations with Multi-Layer CNN Models

Alejandro Marco Montejano, Angela Sanchez Perez, Javier Barrachina +3

The rapid evolution of digital image manipulation techniques poses significant challenges for content verification, with models such as stable diffusion and mid-journey producing h…

cs.CV2024★ 3 cited

Enhancing Action Recognition by Leveraging the Hierarchical Structure of Actions and Textual Context

Manuel Benavent-Lledo, David Mulero-Pérez, David Ortiz-Perez +2

We propose a novel approach to improve action recognition by exploiting the hierarchical organization of actions and by incorporating contextualized textual information, including…