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From the 1 of 14 linked papers with an AI index.

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14 papers

cs.CV2026

Audio-Text Cross-Attention with Psycholinguistic Support Features for Ambivalence/Hesitancy Recognition

Luiz F. B. F. Martins, Rodrigo W. Pisaia, Matheus M. Girardi +5

The paper proposes a multimodal system that combines audio prosodic descriptors, RoBERTa text embeddings, and handcrafted psycholinguistic features via cross‑attention and multiple…

cs.CV2026

Robust Face Super-Resolution and Recognition Through Multi-Feature Aggregation in Diffusion Models

Marcelo dos Santos, Rayson Laroca, João Carlos Raposo Neves +1

Images acquired in surveillance environments often suffer from conditions such as low resolution, variations in pose, irregular illumination, and occlusions. Due to the low quality…

cs.CV2026

Computer Vision for MOBA Analytics: A Dataset and Baseline for Visibility Analysis in Dota 2

Ricardo da Rocha Carvalho, Eloísa Oliveira, Luiz Bernardo Martins Kummer +2

Introduction: Most Multiplayer Online Battle Arena (MOBA) analytics studies rely on structured data, which does not directly capture what each team could actually see during a matc…

cs.CV2026

Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach

Luan Marko Kujavski, Rayson Laroca, Paulo Lisboa de Almeida

As urban areas expand, automatic monitoring of parking lots becomes essential for efficient and sustainable cities. This work proposes a self-supervised approach for parking spot o…

cs.CV2026

Revisiting Vehicle Color Recognition in Long-Tailed Surveillance Scenarios

Vinícius Orrú, Bruno H. Foggiatto, Gabriel E. Lima +2

Vehicle color recognition is an important cue for vehicle identification in surveillance systems, especially when license plates are illegible due to low resolution, occlusion, mot…

cs.CV2026

CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition

Valter Estevam, Rayson Laroca, Helio Pedrini +1

This paper proposes a novel Zero-Shot Action Recognition~(ZSAR) method based on contrastive learning. In ZSAR, we aim to classify examples from classes that were missing during tra…