Search Results for author:
Found 28 papers, 10 papers with code
Towards Understanding How Knowledge Evolves in Large Vision-Language Models
Large Vision-Language Models (LVLMs) are gradually becoming the foundation for many artificial intelligence applications.
Efficient Integration of Distributed Learning Services in Next-Generation Wireless Networks
Due to its intractability, a session-based optimization problem has been proposed assuming a large-scale coherence time.
High Dynamic Range Video Compression: A Large-Scale Benchmark Dataset and A Learned Bit-depth Scalable Compression Algorithm
However, due to absence of large and high-quality high dynamic range (HDR) video training data, LVC on HDR video is still unexplored.
A Sliding Layer Merging Method for Efficient Depth-Wise Pruning in LLMs
In particular, in the experiment with 35% pruning on the Vicuna-7B model, our method achieved a 1. 654% improvement in average performance on zero-shot tasks compared to the existing method.
An Efficient Framework for Enhancing Discriminative Models via Diffusion Techniques
Image classification serves as the cornerstone of computer vision, traditionally achieved through discriminative models based on deep neural networks.
Noise Diffusion for Enhancing Semantic Faithfulness in Text-to-Image Synthesis
Diffusion models have achieved impressive success in generating photorealistic images, but challenges remain in ensuring precise semantic alignment with input prompts.
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models
With the rapid development of deep learning, object detectors have demonstrated impressive performance; however, vulnerabilities still exist in certain scenarios.
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
Through in-depth understanding of the latest advancements and practical applications, this survey provides valuable resources for researchers, paving the way for sustained progress in this field.
GreenCOD: A Green Camouflaged Object Detection Method
Remarkably, our models are trained without backpropagation and achieve the best performance with fewer than 20G Multiply-Accumulate Operations (MACs).
Advancing Out-of-Distribution Detection through Data Purification and Dynamic Activation Function Design
Our work addresses this challenge by enhancing the detection and management of OOD samples in neural networks.
Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning
Hence, we propose fine-tuning the parameters of the attention pooling layer during the training process to encourage the model to focus on task-specific semantics.
COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts
To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts.
Green Steganalyzer: A Green Learning Approach to Image Steganalysis
A novel learning solution to image steganalysis based on the green learning paradigm, called Green Steganalyzer (GS), is proposed in this work.
SR-OOD: Out-of-Distribution Detection via Sample Repairing
Specifically, our framework consists of two components: a sample repairing module and a detection module.
Generative Adversarial Network
Out-of-Distribution Detection
+1
ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing
We also evaluate some robust models including both adversarially trained models and other robust trained models and find that some models show worse robustness against attribute changes than vanilla models.
Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems
We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection.
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective
Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method.
Boosting Out-of-distribution Detection with Typical Features
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios.
Enhance the Visual Representation via Discrete Adversarial Training
For borrowing the advantage from NLP-style AT, we propose Discrete Adversarial Training (DAT).
Ranked #1 on
Domain Generalization
on Stylized-ImageNet
A-PixelHop: A Green, Robust and Explainable Fake-Image Detector
A novel method for detecting CNN-generated images, called Attentive PixelHop (or A-PixelHop), is proposed in this work.
Rethinking Adversarial Transferability from a Data Distribution Perspective
Adversarial transferability enables attackers to generate adversarial examples from the source model to attack the target model, which has raised security concerns about the deployment of DNNs in practice.
Towards Understanding the Generative Capability of Adversarially Robust Classifiers
We find that adversarial training contributes to obtaining an energy function that is flat and has low energy around the real data, which is the key for generative capability.
SAD: Saliency Adversarial Defense without Adversarial Training
We justify the algorithm with a linear model that the added saliency maps pull data away from its closest decision boundary.
Relation-Aware Neighborhood Matching Model for Entity Alignment
Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations.
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning.
Ranked #3 on
Link Prediction
on FB15k
(MR metric)
An Interpretable Generative Model for Handwritten Digit Image Synthesis
An interpretable generative model for handwritten digits synthesis is proposed in this work.
A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares
We present a parallel algorithm for the undirected $s, t$-mincut problem with floating-point valued weights.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Numerical Analysis
