Éric Granger

2024-04-28

ArXiv (prépublication)

Joint Multimodal Transformer for Dimensional Emotional Recognition in the Wild

Paul Waligora

Muhammad Osama Zeeshan

Muhammad Haseeb Aslam

Soufiane Belharbi

Alessandro Lameiras Koerich

Simon Bacon

Audiovisual emotion recognition (ER) in videos has immense potential over unimodal performance. It effectively leverages the inter-and intra… (voir plus)-modal dependencies between visual and auditory modalities. This work proposes a novel audio-visual emotion recognition system utilizing a joint multimodal transformer architecture with key-based cross-attention. This framework aims to exploit the complementary nature of audio and visual cues (facial expressions and vocal patterns) in videos, leading to superior performance compared to solely relying on a single modality. The proposed model leverages separate backbones for capturing intra-modal temporal dependencies within each modality (audio and visual). Subse-quently, a joint multimodal transformer architecture integrates the individual modality embeddings, enabling the model to effectively capture inter-modal (between audio and visual) and intra-modal (within each modality) relationships. Extensive evaluations on the challenging Affwild2 dataset demonstrate that the proposed model significantly outperforms baseline and state-of-the-art methods in ER tasks.

2024-02-29

arXiv (publié)

Domain Generalization by Rejecting Extreme Augmentations

Masih Aminbeidokhti

Fidel A. Guerrero Peña

Heitor Rapela Medeiros

Thomas Dubail

2024-01-02

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)

HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged Information

Heitor Rapela Medeiros

Fidel A. Guerrero Peña

Masih Aminbeidokhti

Thomas Dubail

A powerful way to adapt a visual recognition model to a new domain is through image translation. However, common image translation approache… (voir plus)s only focus on generating data from the same distribution as the target domain. Given a cross-modal application, such as pedestrian detection from aerial images, with a considerable shift in data distribution between infrared (IR) to visible (RGB) images, a translation focused on generation might lead to poor performance as the loss focuses on irrelevant details for the task. In this paper, we propose HalluciDet, an IR-RGB image translation model for object detection. Instead of focusing on reconstructing the original image on the IR modality, it seeks to reduce the detection loss of an RGB detector, and therefore avoids the need to access RGB data. This model produces a new image representation that enhances objects of interest in the scene and greatly improves detection performance. We empirically compare our approach against state-of-the-art methods for image translation and for fine-tuning on IR, and show that our HalluciDet improves detection accuracy in most cases by exploiting the privileged information encoded in a pre-trained RGB detector. Code: https://github.com/heitorrapela/HalluciDet.

2024-01-02

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)

Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean Teacher

Atif Belal

Akhil Meethal

Francisco Perdigon Romero

2024-01-02

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)

Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting

David Latortue

Moetez Kdayem

Fidel A. Guerrero Peña

Object detection models are commonly used for people counting (and localization) in many applications but require a dataset with costly boun… (voir plus)ding box annotations for training. Given the importance of privacy in people counting, these models rely more and more on infrared images, making the task even harder. In this paper, we explore how weaker levels of supervision affect the performance of deep person counting architectures for image classification and point-level localization. Our experiments indicate that counting people using a convolutional neural network with image-level annotation achieves a level of accuracy that is competitive with YOLO detectors and point-level localization models yet provides a higher frame rate and a simi-lar amount of model parameters. Our code is available at: https://github.com/tortueTortue/IRPeopleCounting.

2023-12-31

2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) (publié)

DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization

Shakeeb Murtaza

Soufiane Belharbi

Aydin Sarraf