6 research outputs found
Multitask Learning to Improve Egocentric Action Recognition
In this work we employ multitask learning to capitalize on the structure that
exists in related supervised tasks to train complex neural networks. It allows
training a network for multiple objectives in parallel, in order to improve
performance on at least one of them by capitalizing on a shared representation
that is developed to accommodate more information than it otherwise would for a
single task. We employ this idea to tackle action recognition in egocentric
videos by introducing additional supervised tasks. We consider learning the
verbs and nouns from which action labels consist of and predict coordinates
that capture the hand locations and the gaze-based visual saliency for all the
frames of the input video segments. This forces the network to explicitly focus
on cues from secondary tasks that it might otherwise have missed resulting in
improved inference. Our experiments on EPIC-Kitchens and EGTEA Gaze+ show
consistent improvements when training with multiple tasks over the single-task
baseline. Furthermore, in EGTEA Gaze+ we outperform the state-of-the-art in
action recognition by 3.84%. Apart from actions, our method produces accurate
hand and gaze estimations as side tasks, without requiring any additional input
at test time other than the RGB video clips.Comment: 10 pages, 3 figures, accepted at the 5th Egocentric Perception,
Interaction and Computing (EPIC) workshop at ICCV 2019, code repository:
https://github.com/georkap/hand_track_classificatio
Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (Damen in Scaling egocentric vision: ECCV, 2018), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection enables new challenges such as action detection and evaluating the “test of time”—i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later. The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics.Published versionResearch at Bristol is supported by Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Program (DTP), EPSRC Fellowship UMPIRE (EP/T004991/1). Research at Catania is sponsored by Piano della Ricerca 2016-2018 linea di Intervento 2 of DMI, by MISE - PON I&C 2014-2020, ENIGMA project (CUP: B61B19000520008) and by MIUR AIM - Attrazione e Mobilita Internazionale Linea 1 - AIM1893589 - CUP E64118002540007
Rescaling Egocentric Vision:Collection Pipeline and Challenges for EPIC-KITCHENS-100
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (Damen in Scaling egocentric vision: ECCV, 2018), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection enables new challenges such as action detection and evaluating the “test of time”—i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later. The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics.Published versionResearch at Bristol is supported by Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Program (DTP), EPSRC Fellowship UMPIRE (EP/T004991/1). Research at Catania is sponsored by Piano della Ricerca 2016-2018 linea di Intervento 2 of DMI, by MISE - PON I&C 2014-2020, ENIGMA project (CUP: B61B19000520008) and by MIUR AIM - Attrazione e Mobilita Internazionale Linea 1 - AIM1893589 - CUP E64118002540007
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Leveraging Depth for 3D Scene Perception
3D scene perception aims to understand the geometric and semantic information of the surrounding environment. It is crucial in many downstream applications, such as autonomous driving, robotics, AR/VR, and human-computer interaction. Despite its significance, understanding the 3D scene has been a challenging task, due to the complex interactions between objects, heavy occlusions, cluttered indoor environments, major appearance, viewpoint and scale changes, etc. The study of 3D scene perception has been significantly reshaped by the powerful deep learning models. These models are capable of leveraging large-scale training data to achieve outstanding performance. Learning-based models unlock new challenges and opportunities in the field.In this dissertation, we first present learning-based approaches to estimate depth maps, one of the crucial information in many 3D scene perception models. We describe two overlooked challenges in learning monocular depth estimators and present our proposed solutions. Specifically, we address the high-level domain gap between real and synthetic training data and the shift in camera pose distribution between training and testing data. Following that we present two application-driven works that leverage depth maps to achieve better 3D scene perception. We explore in detail the tasks of reference-based image inpainting and 3D object instance tracking in scenes from egocentric videos