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Found 5 papers, 2 papers with code
CaLa: Complementary Association Learning for Augmenting Composed Image Retrieval
Firstly, we introduce the concept of text-bridged image alignment, where the query text serves as a bridge between the query image and the target image.
Generative Plant Growth Simulation from Sequence-Informed Environmental Conditions
A plant growth simulation can be characterized as a reconstructed visual representation of a plant or plant system.
Dual Relation Alignment for Composed Image Retrieval
Unlike the general image-text retrieval problem with only one alignment relation, i. e., image-text, we argue for the existence of two types of relations in composed image retrieval.
Eff-3DPSeg: 3D organ-level plant shoot segmentation using annotation-efficient point clouds
Second, a weakly-supervised deep learning method was proposed for plant organ segmentation.
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training, to allow training larger models or to accelerate training.
