Journal Special Issues Schedules

Journal Title Topic Due Date
Springer IJCV Looking At People: Analyzing Human Behavior from Social Media Data
  • · Human behavior analysis from visual and multimodal information, with emphasis on unconscious behaviors, including, but not limited to: personality analysis, deception detection, social behavior analysis, etc
  • · All aspects of human behavior analysis in the context of social networks by using multimodal information. Including but not limited to gesture/action, emotion recognition, personality analysis and human-computer interaction
  • · Personality analysis and deception detection from multimodal information. Including textual, visual, and audible information
  • · Information retrieval, categorization and clustering of social networks data, including images, text, and videos for the analysis of human behavior
  • · Analysis of human intention from social networks data involving multimodal information
  • · New tasks, data sets and benchmarks on human behavior analysis from multimodal information
  • · Multimodal machine learning, deep learning, active learning, and transfer learning for human behaviour analysis in social media
  • · Multimodal zero-shot learning, and unsupervised learning for the analysis of unconscious human behaviors
  • · Crowdsourcing, community contributions, and social multimedia
  • · Information fusion for the analysis of human behavior in the context of social networks
  • · Large-scale and web-scale multimodal analysis of social media
  • · Explainability and fairness in multimodal AI systems for human behavior analysis
  • · Applications of unconscious behavior analysis methods, e.g., medicine, sports, commerce, lifelogs, travel, security, environment.
Mar. 15th, 2019
Springer IJCV Efficient Visual Recognition
  • · Hashing/binary coding and its related applications
  • · Compact and efficient convolutional neural networks
  • · Efficient handcrafted feature design
  • · Fast features tailored to wearable/mobile devices
  • · Efficient dimensionality reduction and feature selection
  • · Sparse representation and its related applications
  • · Evaluations of current handcrafted descriptors and deep learning based features
  • · DCNN compression/quantization/binarization
  • · Hybrid methods combining strengths of handcrafted and learning based approaches
  • · Efficient feature learning for applications with limited amounts of annotated training data
  • · Efficient approaches to increase the invariance of DCNN
Feb. 15th, 2019
Springer IJCV Generative Adversarial Networks for Computer Vision
  • · Theoretical analysis of GANs and its variants.
  • · Evaluation of implicit generative models.
  • · New objective functions and formulations for GANs.
  • · New network structures and training schema for GANs.
  • · Unsupervised, semi-supervised and self-supervised feature learning with GANs.
  • · Adversarial learning for data generation and visual recognition.
  • · Image-to-Image translation.
  • · Video generation and future prediction.
  • · 3D generative modeling and editing.
  • · Adversarial domain adaptation and transfer learning.
  • · Image generation and photo manipulation.
  • · Image generation from textual descriptions.
  • · Low-level and middle-level vision: super-resolution, denoising, inpainting, etc.
  • · Adversarial cross-modal learning.
  • · Adversarial imitation learning and reinforcement learning.
  • · Image and video style transfer with adversarial learning.
Mar. 31st, 2019
IEEE TMM Multimedia Computing with Interpretable Machine Learning
  • · Open research challenges and directions for multimedia computing with interpretable machine learning
  • · New theories, models, and benchmarks for multimedia computing with interpretable machine learning
  • · Interpretable deep learning architectures and algorithms for large-scale multimedia data
  • · Interpretability in reinforcement learning for multimedia
  • · Quantifying and visualizing the interpretability of machine learning algorithms for multimedia
  • · Causality of predictive models for multimedia
  • · Verifying, diagnosing and debugging machine learning systems for multimedia
  • · Fairness, accountability, and transparency in multimedia machine learning
  • · Novel and inventive applications of interpretability multimedia machine leaning in various fields (e.g., social media, healthcare, smart city, and retail)
Mar. 31st, 2019

Conferences Schedules

Conference Title Due Date Conferece Date Conference Location
ICML Jan. 23rd, 2019 Jun. 10th-15th, 2019 Long Beach, United States
ICIP Jan. 31st, 2019 Sep. 22nd-25th, 2019 Taipei, Taiwan
ICCV Mar. 22nd, 2019 Oct. 20th-26th, 2019 Seoul, South Korea