68 citations · 277 across the 25 of their papers we have counts for
28 papers
A Bayesian Approach to Data Point Selection
Xinnuo Xu, Minyoung Kim, Royson Lee +2
Data point selection (DPS) is becoming a critical topic in deep learning due to the ease of acquiring uncurated training data compared to the difficulty of obtaining curated or pro…
Aligned Unsupervised Pretraining of Object Detectors with Self-training
Ioannis Maniadis Metaxas, Adrian Bulat, Ioannis Patras +2
The unsupervised pretraining of object detectors has recently become a key component of object detector training, as it leads to improved performance and faster convergence during…
Black Box Few-Shot Adaptation for Vision-Language models
Yassine Ouali, Adrian Bulat, Brais Martinez +1
Vision-Language (V-L) models trained with contrastive learning to align the visual and language modalities have been shown to be strong few-shot learners. Soft prompt learning is t…
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…
Effective Self-supervised Pre-training on Low-compute Networks without Distillation
Fuwen Tan, Fatemeh Saleh, Brais Martinez
Despite the impressive progress of self-supervised learning (SSL), its applicability to low-compute networks has received limited attention. Reported performance has trailed behind…
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…
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…
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…
SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition
Victor Escorcia, Ricardo Guerrero, Xiatian Zhu +1
Learning an egocentric action recognition model from video data is challenging due to distractors (e.g., irrelevant objects) in the background. Further integrating object informati…
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…
SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021
Swathikiran Sudhakaran, Adrian Bulat, Juan-Manuel Perez-Rua +5
This report presents the technical details of our submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021. To participate in the challenge we deployed spatio-temporal…
Space-time Mixing Attention for Video Transformer
Adrian Bulat, Juan-Manuel Perez-Rua, Swathikiran Sudhakaran +2
This paper is on video recognition using Transformers. Very recent attempts in this area have demonstrated promising results in terms of recognition accuracy, yet they have been al…
Few-shot Action Recognition with Prototype-centered Attentive Learning
Xiatian Zhu, Antoine Toisoul, Juan-Manuel Perez-Rua +3
Few-shot action recognition aims to recognize action classes with few training samples. Most existing methods adopt a meta-learning approach with episodic training. In each episode…
Boundary-sensitive Pre-training for Temporal Localization in Videos
Mengmeng Xu, Juan-Manuel Perez-Rua, Victor Escorcia +5
Many video analysis tasks require temporal localization thus detection of content changes. However, most existing models developed for these tasks are pre-trained on general video…
High-Capacity Expert Binary Networks
Adrian Bulat, Brais Martinez, Georgios Tzimiropoulos
Network binarization is a promising hardware-aware direction for creating efficient deep models. Despite its memory and computational advantages, reducing the accuracy gap between…
Egocentric Action Recognition by Video Attention and Temporal Context
Juan-Manuel Perez-Rua, Antoine Toisoul, Brais Martinez +4
We present the submission of Samsung AI Centre Cambridge to the CVPR2020 EPIC-Kitchens Action Recognition Challenge. In this challenge, action recognition is posed as the problem o…
Towards Practical Lipreading with Distilled and Efficient Models
Pingchuan Ma, Brais Martinez, Stavros Petridis +1
Lipreading has witnessed a lot of progress due to the resurgence of neural networks. Recent works have placed emphasis on aspects such as improving performance by finding the optim…
Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention
Juan-Manuel Perez-Rua, Brais Martinez, Xiatian Zhu +3
Attentive video modeling is essential for action recognition in unconstrained videos due to their rich yet redundant information over space and time. However, introducing attention…
Training Binary Neural Networks with Real-to-Binary Convolutions
Brais Martinez, Jing Yang, Adrian Bulat +1
This paper shows how to train binary networks to within a few percent points () of the full precision counterpart. We first show how to build a strong baseline, which…
Knowledge distillation via adaptive instance normalization
Jing Yang, Brais Martinez, Adrian Bulat +1
This paper addresses the problem of model compression via knowledge distillation. To this end, we propose a new knowledge distillation method based on transferring feature statisti…
BATS: Binary ArchitecTure Search
Adrian Bulat, Brais Martinez, Georgios Tzimiropoulos
This paper proposes Binary ArchitecTure Search (BATS), a framework that drastically reduces the accuracy gap between binary neural networks and their real-valued counterparts by me…
Lipreading using Temporal Convolutional Networks
Brais Martinez, Pingchuan Ma, Stavros Petridis +1
Lip-reading has attracted a lot of research attention lately thanks to advances in deep learning. The current state-of-the-art model for recognition of isolated words in-the-wild c…