Search Results for author: Enrique Sanchez

Found 14 papers, 7 papers with code

Multiscale Vision Transformers meet Bipartite Matching for efficient single-stage Action Localization

no code implementations29 Dec 2023 Ioanna Ntinou, Enrique Sanchez, Georgios Tzimiropoulos

These methods build on adding a DETR head with learnable queries that after cross- and self-attention can be sent to corresponding MLPs for detecting a person's bounding box and action.

Action Localization

ReGen: A good Generative Zero-Shot Video Classifier Should be Rewarded

no code implementations ICCV 2023 Adrian Bulat, Enrique Sanchez, Brais Martinez, Georgios Tzimiropoulos

Specifically, we propose ReGen, a novel reinforcement learning based framework with a three-fold objective and reward functions: (1) a class-level discrimination reward that enforces the generated caption to be correctly classified into the corresponding action class, (2) a CLIP reward that encourages the generated caption to continue to be descriptive of the input video (i. e. video-specific), and (3) a grammar reward that preserves the grammatical correctness of the caption.

Action Classification Action Recognition +4

REST: REtrieve & Self-Train for generative action recognition

no code implementations29 Sep 2022 Adrian Bulat, Enrique Sanchez, Brais Martinez, Georgios Tzimiropoulos

We evaluate REST on the problem of zero-shot action recognition where we show that our approach is very competitive when compared to contrastive learning-based methods.

Action Recognition Caption Generation +5

Subpixel Heatmap Regression for Facial Landmark Localization

no code implementations3 Nov 2021 Adrian Bulat, Enrique Sanchez, Georgios Tzimiropoulos

Deep Learning models based on heatmap regression have revolutionized the task of facial landmark localization with existing models working robustly under large poses, non-uniform illumination and shadows, occlusions and self-occlusions, low resolution and blur.

 Ranked #1 on Face Alignment on WFW (Extra Data) (using extra training data)

Face Alignment regression

Pre-training strategies and datasets for facial representation learning

2 code implementations30 Mar 2021 Adrian Bulat, Shiyang Cheng, Jing Yang, Andrew Garbett, Enrique Sanchez, Georgios Tzimiropoulos

Recent work on Deep Learning in the area of face analysis has focused on supervised learning for specific tasks of interest (e. g. face recognition, facial landmark localization etc.)

3D Face Reconstruction 3D Facial Landmark Localization +11

Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning

no code implementations3 Nov 2020 Enrique Sanchez, Adrian Bulat, Anestis Zaganidis, Georgios Tzimiropoulos

The second stage uses another dataset of randomly chosen labeled frames to train a regressor on top of our spatio-temporal model for estimating the AU intensity.

Contrastive Learning Unsupervised Pre-training

A recurrent cycle consistency loss for progressive face-to-face synthesis

1 code implementation14 Apr 2020 Enrique Sanchez, Michel Valstar

To the best of our knowledge, we are the first to propose a loss to overcome the limitation of the cycle consistency loss, and the first to propose an ''in-the-wild'' landmark guided synthesis approach.

Face Generation

A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation

no code implementations14 Apr 2020 Ioanna Ntinou, Enrique Sanchez, Adrian Bulat, Michel Valstar, Georgios Tzimiropoulos

Action Units (AUs) are geometrically-based atomic facial muscle movements known to produce appearance changes at specific facial locations.

Face Alignment regression +1

Object landmark discovery through unsupervised adaptation

1 code implementation NeurIPS 2019 Enrique Sanchez, Georgios Tzimiropoulos

Contrary to previous works, we do however assume that a landmark detector, which has already learned a structured representation for a given object category in a fully supervised manner, is available.

Object Unsupervised Landmark Detection

Triple consistency loss for pairing distributions in GAN-based face synthesis

1 code implementation8 Nov 2018 Enrique Sanchez, Michel Valstar

To show this is effective, we incorporate the triple consistency loss into the training of a new landmark-guided face to face synthesis, where, contrary to previous works, the generated images can simultaneously undergo a large transformation in both expression and pose.

Attribute Face Generation +2

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