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

Conferences Schedules

Conference Title Due Date Conferece Date Conference Location
ICIP Jan. 31st, 2019 Sep. 22nd-25th, 2019 Taipei, Taiwan
ICCV TBD Oct. 20th-26th, 2019 Seoul, South Korea