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Found 7 papers, 3 papers with code
Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval
To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval.
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models
We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning.
Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection
Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and objects and predict HOI triplets.
Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification
Learning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision.
A SOM-based Gradient-Free Deep Learning Method with Convergence Analysis
As gradient descent method in deep learning causes a series of questions, this paper proposes a novel gradient-free deep learning structure.
STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection
To overcome these problems, a novel Spatio-Temporal Cross Network (STCNet) is proposed to recognize industrial smoke emissions.
Memory Group Sampling Based Online Action Recognition Using Kinetic Skeleton Features
First, we combine the spatial and temporal skeleton features to depict the actions, which include not only the geometrical features, but also multi-scale motion features, such that both the spatial and temporal information of the action are covered.
