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Found 9 papers, 1 papers with code
Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization
Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at runtime.
Cross-Domain Style Mixing for Face Cartoonization
Cartoon domain has recently gained increasing popularity.
Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation
We propose a novel method that tackles the problem of unsupervised domain adaptation for semantic segmentation by maximizing the cosine similarity between the source and the target domain at the feature level.
WAN: Watermarking Attack Network
Multi-bit watermarking (MW) has been developed to improve robustness against signal processing operations and geometric distortions.
Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction
In the source domain, we fully train an object detector and the RRPN with full supervision of HOI.
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process are effective for TQA problems.
Vehicle Image Generation Going Well with The Surroundings
Using a subnetwork based on a precedent work of image completion, our model makes the shape of an object.
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams
In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way.
Where to Play: Retrieval of Video Segments using Natural-Language Queries
Through quantitative and qualitative evaluation, we show that our method is effective for retrieval of video segments using natural language queries.
