no code implementations • 11 Jun 2024 • Ioanna Ntinou, Enrique Sanchez, Georgios Tzimiropoulos
Our scheme has two advantages: (a) it reduces complexity by more than an order of magnitude, and (b) it is amenable to an efficient implementation for the calculation of the memory bases in an incremental fashion which does not require the storage of the whole feature bank in memory.
1 code implementation • CVPR 2024 • 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.
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.
1 code implementation • ICCV 2023 • Mohammad Mahdi Derakhshani, Enrique Sanchez, Adrian Bulat, Victor Guilherme Turrisi da Costa, Cees G. M. Snoek, Georgios Tzimiropoulos, Brais Martinez
Our approach regularizes the prompt space, reduces overfitting to the seen prompts and improves the prompt generalization on unseen prompts.
Ranked #1 on
Few-Shot Learning
on food101
no code implementations • 29 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.
2 code implementations • 31 May 2022 • Dimitrios Mallis, Enrique Sanchez, Matt Bell, Georgios Tzimiropoulos
This paper proposes a novel paradigm for the unsupervised learning of object landmark detectors.
no code implementations • 3 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 WFLW
2 code implementations • 30 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.)
Ranked #2 on
Facial Expression Recognition (FER)
on BP4D
no code implementations • CVPR 2021 • Enrique Sanchez, Mani Kumar Tellamekala, Michel Valstar, Georgios Tzimiropoulos
Temporal context is key to the recognition of expressions of emotion.
1 code implementation • NeurIPS 2020 • Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos
This paper addresses the problem of unsupervised discovery of object landmarks.
no code implementations • 3 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.
1 code implementation • 14 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.
no code implementations • 14 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.
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.
1 code implementation • 8 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.