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Found 19 papers, 8 papers with code
When Emotional Stimuli meet Prompt Designing: An Auto-Prompt Graphical Paradigm
With the development of Large Language Models (LLM), numerous prompts have been proposed, each with a rich set of features and their own merits.
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Exploring the full spectrum of trade-offs between privacy, model utility, and runtime efficiency is critical for practical unlearning scenarios.
Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction
Our experimental findings demonstrate that our approach accelerates the training of vision-language models by a factor of 5 without a noticeable impact on overall performance.
Promoting Fairness in GNNs: A Characterization of Stability
Additionally, we theoretically analyze how the Lipschitz constant of a GNN model could constrain the output perturbations induced by biases learned from data for fairness training.
All in One: Exploring Unified Vision-Language Tracking with Multi-Modal Alignment
This approach achieves feature integration in a unified backbone, removing the need for carefully-designed fusion modules and resulting in a more effective and efficient VL tracking framework.
A Comprehensive Survey on Segment Anything Model for Vision and Beyond
As the first to comprehensively review the progress of segmenting anything task for vision and beyond based on the foundation model of SAM, this work focuses on its applications to various tasks and data types by discussing its historical development, recent progress, and profound impact on broad applications.
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
In this work, we pre-train DNNs on ultrasound (US) domains instead of ImageNet to reduce the domain gap in medical US applications.
Boosting Graph Neural Networks via Adaptive Knowledge Distillation
In this paper, we propose a novel adaptive KD framework, called BGNN, which sequentially transfers knowledge from multiple GNNs into a student GNN.
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
This work proposes a hierarchical contrastive learning (HiCo) method to improve the transferability for the US video model pretraining.
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
The effectiveness is further guaranteed and proved by the gradients' distance between the subset and the full set; (ii) empirically, we discover that during the learning process of a GNN, some samples in the training dataset are informative for providing gradients to update model parameters.
Contrastive Graph Few-Shot Learning
Later, the trained encoder is frozen as a teacher model to distill a student model with a contrastive loss.
Graph Contrastive Learning with Cross-view Reconstruction
In light of this, we introduce the Graph Contrastive Learning with Cross-View Reconstruction (GraphCV), which follows the information bottleneck principle to learn minimal yet sufficient representation from graph data.
Heterogeneous Graph Masked Autoencoders
In light of this, we study the problem of generative SSL on heterogeneous graphs and propose HGMAE, a novel heterogeneous graph masked autoencoder model to address these challenges.
Label-invariant Augmentation for Semi-Supervised Graph Classification
Recently, contrastiveness-based augmentation surges a new climax in the computer vision domain, where some operations, including rotation, crop, and flip, combined with dedicated algorithms, dramatically increase the model generalization and robustness.
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search
To further adapt to both various data distributions and different types of devices with heterogeneous embedded hardware platforms, inspired by meta-learning, a Cluster Federated Direct Neural Architecture Search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense that each device can learn a tailored deep learning model for its particular data distribution and hardware constraint.
WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking
In this work, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers.
Student Network Learning via Evolutionary Knowledge Distillation
Inspired by that, we propose an evolutionary knowledge distillation approach to improve the transfer effectiveness of teacher knowledge.
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning
To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain.
FDNAS: Improving Data Privacy and Model Diversity in AutoML
To further adapt for various data distributions of clients, inspired by meta-learning, a cluster Federated Direct Neural Architecture Search (CFDNAS) framework is proposed to achieve client-aware NAS, in the sense that each client can learn a tailored deep learning model for its particular data distribution.
