1.5k citations · 2.6k across the 50 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…