Search Results for author:
Found 47 papers, 18 papers with code
Conditional Prototype Rectification Prompt Learning
Recent advancements in efficient transfer learning (ETL) have shown remarkable success in fine-tuning VLMs within the scenario of limited data, introducing only a few parameters to harness task-specific insights from VLMs.
Clean-image Backdoor Attacks
To gather a significant quantity of annotated training data for high-performance image classification models, numerous companies opt to enlist third-party providers to label their unlabeled data.
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
To our best knowledge, we are the first to (1) perform unsupervised domain-specific prompt-driven knowledge distillation for CLIP, and (2) establish a practical pre-storing mechanism of text features as shared class vectors between teacher and student.
Ranked #1 on
Prompt Engineering
on Oxford-IIIT Pet Dataset
TroubleLLM: Align to Red Team Expert
Not only are these methods labor-intensive and require large budget costs, but the controllability of test prompt generation is lacking for the specific testing domain of LLM applications.
On provable privacy vulnerabilities of graph representations
Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations.
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.
Boosting Audio-visual Zero-shot Learning with Large Language Models
Recent methods mainly focus on learning multi-modal features aligned with class names to enhance the generalization ability to unseen categories.
Ranked #1 on
GZSL Video Classification
on ActivityNet-GZSL (cls)
Privacy-preserving design of graph neural networks with applications to vertical federated learning
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM).
Self-supervision meets kernel graph neural models: From architecture to augmentations
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy.
ControlCom: Controllable Image Composition using Diffusion Model
To address these challenges, we propose a controllable image composition method that unifies four tasks in one diffusion model: image blending, image harmonization, view synthesis, and generative composition.
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer
For example, consider cross-domain fraud detection, where there are two types of transactions: credit and non-credit.
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients optimization toward their local optimums.
Backpropagation Path Search On Adversarial Transferability
Transfer-based attackers craft adversarial examples against surrogate models and transfer them to victim models deployed in the black-box situation.
End-to-end Remote Sensing Change Detection of Unregistered Bi-temporal Images for Natural Disasters
Deep networks have demonstrated significant success in detecting changes in bi-temporal remote sensing images and have found applications in various fields.
Compositional Prototypical Networks for Few-Shot Classification
We empirically demonstrate that the learned component prototypes have good class transferability and can be reused to construct compositional prototypes for novel classes.
DiffUTE: Universal Text Editing Diffusion Model
Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information.
Mobile User Interface Element Detection Via Adaptively Prompt Tuning
Recent object detection approaches rely on pretrained vision-language models for image-text alignment.
FlowText: Synthesizing Realistic Scene Text Video with Optical Flow Estimation
This paper introduces a novel video text synthesis technique called FlowText, which utilizes optical flow estimation to synthesize a large amount of text video data at a low cost for training robust video text spotters.
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks.
DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk
Graph representation plays an important role in the field of financial risk control, where the relationship among users can be constructed in a graph manner.
GRANDE: a neural model over directed multigraphs with application to anti-money laundering
The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently.
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention
With the frequent happening of privacy leakage and the enactment of privacy laws across different countries, data owners are reluctant to directly share their raw data and labels with any other party.
DiffusionInst: Diffusion Model for Instance Segmentation
Extensive experimental results on COCO and LVIS show that DiffusionInst achieves competitive performance compared to existing instance segmentation models with various backbones, such as ResNet and Swin Transformers.
Ranked #8 on
Instance Segmentation
on LVIS v1.0 val
Hierarchical Dynamic Image Harmonization
The MGD effectively applies distinct convolution to the foreground and background, learning the representations of foreground and background regions as well as their correlations to the global harmonization, facilitating local visual consistency for the images much more efficiently.
Ranked #2 on
Image Harmonization
on HAdobe5k(1024$\times$1024)
A2: Efficient Automated Attacker for Boosting Adversarial Training
In this paper, we propose an efficient automated attacker called A2 to boost AT by generating the optimal perturbations on-the-fly during training.
NEURAL MARIONETTE: A Transformer-based Multi-action Human Motion Synthesis System
Along with the novel system, we also present a new dataset dedicated to the multi-action motion synthesis task, which contains both action tags and their contextual information.
K-Order Graph-oriented Transformer with GraAttention for 3D Pose and Shape Estimation
We propose a novel attention-based 2D-to-3D pose estimation network for graph-structured data, named KOG-Transformer, and a 3D pose-to-shape estimation network for hand data, named GASE-Net.
Explore Faster Localization Learning For Scene Text Detection
Generally pre-training and long-time training computation are necessary for obtaining a good-performance text detector based on deep networks.
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding.
GUARD: Graph Universal Adversarial Defense
To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks.
XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding
Recently, various multimodal networks for Visually-Rich Document Understanding(VRDU) have been proposed, showing the promotion of transformers by integrating visual and layout information with the text embeddings.
document understanding
Optical Character Recognition (OCR)
+1
MT-GBM: A Multi-Task Gradient Boosting Machine with Shared Decision Trees
Despite the success of deep learning in computer vision and natural language processing, Gradient Boosted Decision Tree (GBDT) is yet one of the most powerful tools for applications with tabular data such as e-commerce and FinTech.
GraFormer: Graph-Oriented Transformer for 3D Pose Estimation
In 2D-to-3D pose estimation, it is important to exploit the spatial constraints of 2D joints, but it is not yet well modeled.
GraFormer: Graph Convolution Transformer for 3D Pose Estimation
Exploiting relations among 2D joints plays a crucial role yet remains semi-developed in 2D-to-3D pose estimation.
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data
In this work, we observe an interesting phenomenon that the variations of parameters are always AWPs when tuning the trained clean model to inject backdoors.
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.
A Vertical Federated Learning Framework for Graph Convolutional Network
Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data.
Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering
With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which hinders the development of video text-related applications.
Characters as Graphs: Recognizing Online Handwritten Chinese Characters via Spatial Graph Convolutional Network
Chinese is one of the most widely used languages in the world, yet online handwritten Chinese character recognition (OLHCCR) remains challenging.
Variational Policy Propagation for Multi-agent Reinforcement Learning
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Rethinking Object Detection in Retail Stores
In this paper, we propose a new task, ie, simultaneously object localization and counting, abbreviated as Locount, which requires algorithms to localize groups of objects of interest with the number of instances.
Guided Attention Network for Object Detection and Counting on Drones
Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background.
Learning Discriminative Features Via Weights-biased Softmax Loss
Loss functions play a key role in training superior deep neural networks.
Reinterpreting CTC training as iterative fitting
The outputs of a CTC-trained model tend to form a series of spikes separated by strongly predicted blanks, know as the spiky problem.
Egocentric Hand Detection Via Dynamic Region Growing
Egocentric videos, which mainly record the activities carried out by the users of the wearable cameras, have drawn much research attentions in recent years.
A New Hybrid-parameter Recurrent Neural Networks for Online Handwritten Chinese Character Recognition
And the proposed Memory Pool Unit is proved to be a simple hidden layer function and obtains a competitive recognition results.
