Dual-path Collaborative Generation Network for Emotional Video Captioning

• URL: http://arxiv.org/abs/2408.03006v1
• Date: Tue, 6 Aug 2024 07:30:53 GMT
• Title: Dual-path Collaborative Generation Network for Emotional Video Captioning
• Authors: Cheng Ye, Weidong Chen, Jingyu Li, Lei Zhang, Zhendong Mao,
• Abstract summary: Emotional Video Captioning is an emerging task that aims to describe factual content with the intrinsic emotions expressed in videos. Existing emotional video captioning methods perceive global visual emotional cues at first, and then combine them with the video features to guide the emotional caption generation. We propose a dual-path collaborative generation network, which dynamically perceives visual emotional cues evolutions while generating emotional captions.
• Score: 33.230028098522254
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Emotional Video Captioning is an emerging task that aims to describe factual content with the intrinsic emotions expressed in videos. The essential of the EVC task is to effectively perceive subtle and ambiguous visual emotional cues during the caption generation, which is neglected by the traditional video captioning. Existing emotional video captioning methods perceive global visual emotional cues at first, and then combine them with the video features to guide the emotional caption generation, which neglects two characteristics of the EVC task. Firstly, their methods neglect the dynamic subtle changes in the intrinsic emotions of the video, which makes it difficult to meet the needs of common scenes with diverse and changeable emotions. Secondly, as their methods incorporate emotional cues into each step, the guidance role of emotion is overemphasized, which makes factual content more or less ignored during generation. To this end, we propose a dual-path collaborative generation network, which dynamically perceives visual emotional cues evolutions while generating emotional captions by collaborative learning. Specifically, in the dynamic emotion perception path, we propose a dynamic emotion evolution module, which first aggregates visual features and historical caption features to summarize the global visual emotional cues, and then dynamically selects emotional cues required to be re-composed at each stage. Besides, in the adaptive caption generation path, to balance the description of factual content and emotional cues, we propose an emotion adaptive decoder. Thus, our methods can generate emotion-related words at the necessary time step, and our caption generation balances the guidance of factual content and emotional cues well. Extensive experiments on three challenging datasets demonstrate the superiority of our approach and each proposed module.

Related papers

• MoEE: Mixture of Emotion Experts for Audio-Driven Portrait Animation [39.30784838378127]
  The generation of talking avatars has achieved significant advancements in precise audio synchronization. Current methods face fundamental challenges, including the lack of frameworks for modeling single basic emotional expressions. We propose the Mixture of Emotion Experts (MoEE) model, which decouples six fundamental emotions to enable the precise synthesis of both singular and compound emotional states. In conjunction with the DH-FaceEmoVid-150 dataset, we demonstrate that the MoEE framework excels in generating complex emotional expressions and nuanced facial details.
  arXiv  Detail & Related papers  (2025-01-03T13:43:21Z)
• Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content [56.62027582702816]
  Multimodal Sentiment Analysis seeks to unravel human emotions by amalgamating text, audio, and visual data. Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge. We introduce DEVA, a progressive fusion framework founded on textual sentiment descriptions.
  arXiv  Detail & Related papers  (2024-12-12T11:30:41Z)
• EMOdiffhead: Continuously Emotional Control in Talking Head Generation via Diffusion [5.954758598327494]
  EMOdiffhead is a novel method for emotional talking head video generation. It enables fine-grained control of emotion categories and intensities. It achieves state-of-the-art performance compared to other emotion portrait animation methods.
  arXiv  Detail & Related papers  (2024-09-11T13:23:22Z)
• Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion [81.1492897350032]
  Emotional Voice Conversion aims to manipulate a speech according to a given emotion while preserving non-emotion components. We propose an Attention-based Interactive diseNtangling Network (AINN) that leverages instance-wise emotional knowledge for voice conversion.
  arXiv  Detail & Related papers  (2023-12-29T08:06:45Z)
• Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling [50.99252242917458]
  Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting. To address the issue of data scarcity, we meticulously create emotional labels in terms of category and intensity. Our model outperforms the baseline models in understanding and rendering emotions.
  arXiv  Detail & Related papers  (2023-12-19T08:47:50Z)
• Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach [22.00225071959289]
  EMPATHIC project aimed to design an emotionally expressive virtual coach capable of engaging healthy seniors to improve well-being and promote independent aging. This paper outlines the development of the emotion expression recognition module of the virtual coach, encompassing data collection, annotation design, and a first methodological approach.
  arXiv  Detail & Related papers  (2023-11-09T18:22:32Z)
• Emotion Intensity and its Control for Emotional Voice Conversion [77.05097999561298]
  Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In this paper, we aim to explicitly characterize and control the intensity of emotion. We propose to disentangle the speaker style from linguistic content and encode the speaker style into a style embedding in a continuous space that forms the prototype of emotion embedding.
  arXiv  Detail & Related papers  (2022-01-10T02:11:25Z)
• Audio-Driven Emotional Video Portraits [79.95687903497354]
  We present Emotional Video Portraits (EVP), a system for synthesizing high-quality video portraits with vivid emotional dynamics driven by audios. Specifically, we propose the Cross-Reconstructed Emotion Disentanglement technique to decompose speech into two decoupled spaces. With the disentangled features, dynamic 2D emotional facial landmarks can be deduced. Then we propose the Target-Adaptive Face Synthesis technique to generate the final high-quality video portraits.
  arXiv  Detail & Related papers  (2021-04-15T13:37:13Z)
• Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset [84.53659233967225]
  Emotional voice conversion aims to transform emotional prosody in speech while preserving the linguistic content and speaker identity. We propose a novel framework based on variational auto-encoding Wasserstein generative adversarial network (VAW-GAN) We show that the proposed framework achieves remarkable performance by consistently outperforming the baseline framework.
  arXiv  Detail & Related papers  (2020-10-28T07:16:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.