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Found 6 papers, 0 papers with code
Leaf Cultivar Identification via Prototype-enhanced Learning
Plant leaf identification is crucial for biodiversity protection and conservation and has gradually attracted the attention of academia in recent years.
MUVA: A New Large-Scale Benchmark for Multi-View Amodal Instance Segmentation in the Shopping Scenario
To bridge this gap, we propose a new task called Multi-view Amodal Instance Segmentation (MAIS) and introduce the MUVA dataset, the first MUlti-View AIS dataset that takes the shopping scenario as instantiation.
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?
In this paper, we investigate the role of self-supervised representation learning in the context of CDFSL via a thorough evaluation of existing methods.
Adaptive Semantic-Visual Tree for Hierarchical Embeddings
To solve this new problem, we propose a hierarchical adaptive semantic-visual tree (ASVT) to depict the architecture of merchandise categories, which evaluates semantic similarities between different semantic levels and visual similarities within the same semantic class simultaneously.
Deep Semantic Parsing of Freehand Sketches with Homogeneous Transformation, Soft-Weighted Loss, and Staged Learning
First, we propose a homogeneous transformation method to address the problem of domain adaptation.
Sketch-Specific Data Augmentation for Freehand Sketch Recognition
Conventional methods for this task often rely on the availability of the temporal order of sketch strokes, additional cues acquired from different modalities and supervised augmentation of sketch datasets with real images, which also limit the applicability and feasibility of these methods in real scenarios.
