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Vision Transformer Adapters for Generalizable Multitask Learning [PDF]
, 2023 We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains.
Bhattacharjee, Deblina +2 more
core +3 more sources
LAVT: Language-Aware Vision Transformer for referring image segmentation
, 2022 Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image.
Zhao, H +12 more
core +2 more sources
Transformer-based ripeness segmentation for tomatoes
Smart Agricultural Technology, 2023 With the recent development of computer vision technology, various computer vision techniques have been applied to agriculture. Recently, the Transformer network has been introduced to image recognition, which allows a different approach to extracting ...
Risa Shinoda +3 more
doaj +1 more source
Reversible Vision Transformers
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory requirement from the depth of the model, Reversible Vision Transformers enable scaling up architectures with efficient memory usage.
Karttikeya Mangalam +6 more
openaire +2 more sources
Transformer architectures for computer vision: A comprehensive review and future research directions [PDF]
EPJ Web of ConferencesLong-range dependencies and contextual relationships in videos were captured by using Convolutional Neural Networks (CNNs) in past. Recently the use of Transformers is started for capturing the long-range dependencies and contextual relationships in ...
Ugile Tukaram, Uke Nilesh
doaj +1 more source
Diverse features discovery transformer for pedestrian attribute recognition
, 2023 Recently, Swin Transformer has been widely explored as a general backbone for computer vision, which helps to improve the performance of vision tasks due to the ability to establish associations for long-range dependencies of different spatial locations.
Hussain, Amir +5 more
core +2 more sources
Machine Learning and Knowledge Extraction, 2023 This study introduces an optimal topology of vision transformers for real-time video action recognition in a cloud-based solution. Although model performance is a key criterion for real-time video analysis use cases, inference latency plays a more ...
Saman Sarraf, Milton Kabia
doaj +1 more source
Vision Transformers Are Robust Learners
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy. What remains largely unexplored is their robustness evaluation and attribution.
Sayak Paul, Pin-Yu Chen
openaire +2 more sources
Art authentication with vision transformers
Neural Computing and Applications, 2023 AbstractIn recent years, transformers, initially developed for language, have been successfully applied to visual tasks. Vision transformers have been shown to push the state of the art in a wide range of tasks, including image classification, object detection, and semantic segmentation.
Schaerf, Ludovica +2 more
openaire +3 more sources
Mix-ViT : mixing attentive vision transformer for ultra-fine-grained visual categorization
, 2023 Ultra-fine-grained visual categorization (ultra-FGVC) moves down the taxonomy level to classify sub-granularity categories of fine-grained objects. This inevitably poses a challenge, i.e., classifying highly similar objects with limited samples, which ...
Yu, Xiaohan +3 more
core +1 more source