Search Results for author:
Found 15 papers, 8 papers with code
MeMSVD: Long-Range Temporal Structure Capturing Using Incremental SVD
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.
Multiscale Vision Transformers meet Bipartite Matching for efficient single-stage Action Localization
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.
ReGen: A good Generative Zero-Shot Video Classifier Should be Rewarded
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.
Bayesian Prompt Learning for Image-Language Model Generalization
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
REST: REtrieve & Self-Train for generative action recognition
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.
From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery
This paper proposes a novel paradigm for the unsupervised learning of object landmark detectors.
Subpixel Heatmap Regression for Facial Landmark Localization
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
Pre-training strategies and datasets for facial representation learning
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
Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition
Temporal context is key to the recognition of expressions of emotion.
Unsupervised Learning of Object Landmarks via Self-Training Correspondence
This paper addresses the problem of unsupervised discovery of object landmarks.
Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning
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.
A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation
Action Units (AUs) are geometrically-based atomic facial muscle movements known to produce appearance changes at specific facial locations.
A recurrent cycle consistency loss for progressive face-to-face synthesis
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.
Object landmark discovery through unsupervised adaptation
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.
Triple consistency loss for pairing distributions in GAN-based face synthesis
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.
