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Found 10 papers, 7 papers with code
Multi-modal Generation via Cross-Modal In-Context Learning
Moreover, they often result in misaligned image generation for prompt sequences featuring multiple objects.
Distilling Local Texture Features for Colorectal Tissue Classification in Low Data Regimes
Multi-class colorectal tissue classification is a challenging problem that is typically addressed in a setting, where it is assumed that ample amounts of training data is available.
Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification
In this work, we propose a few-shot colorectal tissue image generation method for addressing the scarcity of histopathological training data for rare cancer tissues.
Generative Multiplane Neural Radiance for 3D-Aware Image Generation
We present a method to efficiently generate 3D-aware high-resolution images that are view-consistent across multiple target views.
Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings
Human sketch has already proved its worth in various visual understanding tasks (e. g., retrieval, segmentation, image-captioning, etc).
Transformers in Remote Sensing: A Survey
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade.
Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts
Handwritten documents are often characterized by dense and uneven layout.
Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition
In this paper, we argue that semantic information offers a complementary role in addition to visual only.
MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition
In this paper, we take a completely different perspective -- we work on the assumption that there is always a new style that is drastically different, and that we will only have very limited data during testing to perform adaptation.
UDBNET: Unsupervised Document Binarization Network via Adversarial Game
In this paper, we present a novel approach towards document image binarization by introducing three-player min-max adversarial game.
Ranked #2 on
Binarization
on DIBCO 2011
