19 citations · 97 across the 49 of their papers we have counts for
13 papers · 1 filter
Part-based Face Recognition with Vision Transformers
Zhonglin Sun, Georgios Tzimiropoulos
Holistic methods using CNNs and margin-based losses have dominated research on face recognition. In this work, we depart from this setting in two ways: (a) we employ the Vision Tra…
FS-DETR: Few-Shot DEtection TRansformer with prompting and without re-training
Adrian Bulat, Ricardo Guerrero, Brais Martinez +1
This paper is on Few-Shot Object Detection (FSOD), where given a few templates (examples) depicting a novel class (not seen during training), the goal is to detect all of its occur…
Bayesian Prompt Learning for Image-Language Model Generalization
Mohammad Mahdi Derakhshani, Enrique Sanchez, Adrian Bulat +4
Foundational image-language models have generated considerable interest due to their efficient adaptation to downstream tasks by prompt learning. Prompt learning treats part of the…
LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models
Adrian Bulat, Georgios Tzimiropoulos
Soft prompt learning has recently emerged as one of the methods of choice for adapting V&L models to a downstream task using a few training examples. However, current methods signi…
REST: REtrieve & Self-Train for generative action recognition
Adrian Bulat, Enrique Sanchez, Brais Martinez +1
This work is on training a generative action/video recognition model whose output is a free-form action-specific caption describing the video (rather than an action class label). A…
StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment
Stella Bounareli, Christos Tzelepis, Vasileios Argyriou +2
In this paper we address the problem of neural face reenactment, where, given a pair of a source and a target facial image, we need to transfer the target's pose (defined as the he…
Efficient Attention-free Video Shift Transformers
Adrian Bulat, Brais Martinez, Georgios Tzimiropoulos
This paper tackles the problem of efficient video recognition. In this area, video transformers have recently dominated the efficiency (top-1 accuracy vs FLOPs) spectrum. At the sa…
iBoot: Image-bootstrapped Self-Supervised Video Representation Learning
Fatemeh Saleh, Fuwen Tan, Adrian Bulat +2
Learning visual representations through self-supervision is an extremely challenging task as the network needs to sieve relevant patterns from spurious distractors without the acti…
ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences
Christos Tzelepis, James Oldfield, Georgios Tzimiropoulos +1
This work addresses the problem of discovering non-linear interpretable paths in the latent space of pre-trained GANs in a model-agnostic manner. In the proposed method, the discov…
From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery
Dimitrios Mallis, Enrique Sanchez, Matt Bell +1
This paper proposes a novel paradigm for the unsupervised learning of object landmark detectors. Contrary to existing methods that build on auxiliary tasks such as image generation…
Knowledge Distillation Meets Open-Set Semi-Supervised Learning
Jing Yang, Xiatian Zhu, Adrian Bulat +2
Existing knowledge distillation methods mostly focus on distillation of teacher's prediction and intermediate activation. However, the structured representation, which arguably is…
EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers
Junting Pan, Adrian Bulat, Fuwen Tan +5
Self-attention based models such as vision transformers (ViTs) have emerged as a very competitive architecture alternative to convolutional neural networks (CNNs) in computer visio…
Finding Directions in GAN's Latent Space for Neural Face Reenactment
Stella Bounareli, Vasileios Argyriou, Georgios Tzimiropoulos
This paper is on face/head reenactment where the goal is to transfer the facial pose (3D head orientation and expression) of a target face to a source face. Previous methods focus…