End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning
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Abstract (arXiv)
We introduce End2You -- the Imperial College London toolkit for multimodal profiling by end-to-end deep learning. End2You is an open-source toolkit implemented in Python and is based on Tensorflow. It provides capabilities to train and evaluate models in an end-to-end manner, i.e., using raw input. It supports input from raw audio, visual, physiological or other types of information or combination of those, and the output can be of an arbitrary representation, for either classification or regression tasks. To our knowledge, this is the first toolkit that provides generic end-to-end learning for profiling capabilities in either unimodal or multimodal cases. To test our toolkit, we utilise the RECOLA database as was used in the AVEC 2016 challenge. Experimental results indicate that End2You can provide comparable results to state-of-the-art methods despite no need of expert-alike feature representations, but self-learning these from the data "end to end".
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2018.
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Citing papers per year: 2021: 4; 2022: 6; 2023: 0; 2024: 1; 2025: 1.
12 papers.
- 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
- 2021The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primatescited by 32
- 2022The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stresscited by 23
- 2022The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Burstscited by 16
- 2021DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Datacited by 12
- 2022Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognitioncited by 7
- 2022The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expressioncited by 7
- 2021Use of Affective Visual Information for Summarization of Human-Centric Videoscited by 3
- 2022A Summary of the ComParE COVID-19 Challengescited by 2
- 2022Fine-tuning Wav2vec for Vocal-burst Emotion Recognitioncited by 1
- 2021Facial Emotion Recognition using Deep Residual Networks in Real-World Environmentscited by 0
- 2025Lightweight Models for Emotional Analysis in Videocited by 0
- 2025Lightweight Models for Emotional Analysis in Video13 Mar 2025
- 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition29 Feb 2024
- 2022Fine-tuning Wav2vec for Vocal-burst Emotion Recognition1 Oct 2022
- 2022The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression7 Jul 2022
- 2022The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress23 Jun 2022
- 2022The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts3 May 2022
- 2022Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognition24 Mar 2022
- 2022A Summary of the ComParE COVID-19 Challenges17 Feb 2022
- 2021Facial Emotion Recognition using Deep Residual Networks in Real-World Environments4 Nov 2021
- 2021Use of Affective Visual Information for Summarization of Human-Centric Videos8 Jul 2021
- 2021DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data23 Apr 2021
- 2021The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates24 Feb 2021