Curriculum multi-negative augmentation for debiased video grounding
Video Grounding (VG) aims to locate the desired segment from a video given a sentence
query. Recent studies have found that current VG models are prone to over-rely the …
query. Recent studies have found that current VG models are prone to over-rely the …
Curriculum graph machine learning: A survey
Graph machine learning has been extensively studied in both academia and industry.
However, in the literature, most existing graph machine learning models are designed to …
However, in the literature, most existing graph machine learning models are designed to …
Data-augmented curriculum graph neural architecture search under distribution shifts
Graph neural architecture search (NAS) has achieved great success in designing
architectures for graph data processing. However, distribution shifts pose great challenges …
architectures for graph data processing. However, distribution shifts pose great challenges …
Large Language Model with Curriculum Reasoning for Visual Concept Recognition
Visual concept recognition aims to capture the basic attributes of an image and reason
about the relationships among them to determine whether the image satisfies a certain …
about the relationships among them to determine whether the image satisfies a certain …
Curriculum Learning for Multimedia in the Era of Large Language Models
This tutorial focuses on curriculum learning (CL), an important topic in machine learning,
which gains an increasing amount of attention in the research community. CL is a learning …
which gains an increasing amount of attention in the research community. CL is a learning …
Neighbor Does Matter: Curriculum Global Positive-Negative Sampling for Vision-Language Pre-training
Sampling strategies have been widely adopted in Vision-Language Pre-training (VLP) and
have achieved great success recently. However, the sampling strategies adopted by current …
have achieved great success recently. However, the sampling strategies adopted by current …
Curriculum Learning: Theories, Approaches, Applications, Tools, and Future Directions in the Era of Large Language Models
This tutorial focuses on curriculum learning (CL), an important topic in machine learning,
which gains an increasing amount of attention in the research community. CL is a learning …
which gains an increasing amount of attention in the research community. CL is a learning …
CurBench: Curriculum Learning Benchmark
Curriculum learning is a training paradigm where machine learning models are trained in a
meaningful order, inspired by the way humans learn curricula. Due to its capability to …
meaningful order, inspired by the way humans learn curricula. Due to its capability to …
[PDF][PDF] Application of Self-Paced learning for noisy meta-learning
A Aszalós - 2024 - repository.tudelft.nl
Meta-learning is an important emerging paradigm in machine learning, aimed at improving
dataefficiency and generalization performance across learning tasks. Challenges caused by …
dataefficiency and generalization performance across learning tasks. Challenges caused by …
[PDF][PDF] DATA-EFFICIENT LOW-COMPLEXITY ACOUSTIC SCENE CLASSIFICATION WITH CURRICULUM LEARNING AND SE-LAYER
X Chen, W **e - dcase.community
This technical report presents a data-efficient and low-complexity acoustic scene
classification (ASC) system developed for Task 1 of the DCASE2024 Challenge. The …
classification (ASC) system developed for Task 1 of the DCASE2024 Challenge. The …