481 citations · 997 across the 45 of their papers we have counts for
20 papers · 1 filter
LLM as a Complementary Optimizer to Gradient Descent: A Case Study in Prompt Tuning
Zixian Guo, Ming Liu, Zhilong Ji +3
Mastering a skill generally relies on both hands-on experience from doers and insightful, high-level guidance by mentors. Will this strategy also work well for solving complex non-…
Black-Box Tuning of Vision-Language Models with Effective Gradient Approximation
Zixian Guo, Yuxiang Wei, Ming Liu +4
Parameter-efficient fine-tuning (PEFT) methods have provided an effective way for adapting large vision-language models to specific tasks or scenarios. Typically, they learn a very…
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models
Wan Xu, Tianyu Huang, Tianyu Qu +3
Few-shot class-incremental learning (FSCIL) aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data. However, many of these works l…
Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning
Qing Miao, Xiaohe Wu, Chao Xu +4
Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supe…
DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection
Zehao Wang, Yiwen Guo, Qizhang Li +2
Data augmentation is a dominant method for reducing model overfitting and improving generalization. Most existing data augmentation methods tend to find a compromise in augmenting…
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
Yuanze Li, Haolin Wang, Shihao Yuan +6
Due to the training configuration, traditional industrial anomaly detection (IAD) methods have to train a specific model for each deployment scenario, which is insufficient to meet…
Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models
Minheng Ni, Yabo Zhang, Kailai Feng +3
Zero-shot referring image segmentation is a challenging task because it aims to find an instance segmentation mask based on the given referring descriptions, without training on th…
Texts as Images in Prompt Tuning for Multi-Label Image Recognition
Zixian Guo, Bowen Dong, Zhilong Ji +3
Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited setti…
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Yichuan Mo, Dongxian Wu, Yifei Wang +2
Vision Transformers (ViTs) have recently achieved competitive performance in broad vision tasks. Unfortunately, on popular threat models, naturally trained ViTs are shown to provid…
On Steering Multi-Annotations per Sample for Multi-Task Learning
Yuanze Li, Yiwen Guo, Qizhang Li +2
The study of multi-task learning has drawn great attention from the community. Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously r…
An Intermediate-level Attack Framework on The Basis of Linear Regression
Yiwen Guo, Qizhang Li, Wangmeng Zuo +1
This paper substantially extends our work published at ECCV, in which an intermediate-level attack was proposed to improve the transferability of some baseline adversarial examples…
Deepfake Forensics via An Adversarial Game
Zhi Wang, Yiwen Guo, Wangmeng Zuo
With the progress in AI-based facial forgery (i.e., deepfake), people are increasingly concerned about its abuse. Albeit effort has been made for training classification (also know…
Practical No-box Adversarial Attacks against DNNs
Qizhang Li, Yiwen Guo, Hao Chen
The study of adversarial vulnerabilities of deep neural networks (DNNs) has progressed rapidly. Existing attacks require either internal access (to the architecture, parameters, or…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Yet Another Intermediate-Level Attack
Qizhang Li, Yiwen Guo, Hao Chen
The transferability of adversarial examples across deep neural network (DNN) models is the crux of a spectrum of black-box attacks. In this paper, we propose a novel method to enha…
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
Ziang Yan, Yiwen Guo, Changshui Zhang
Unlike the white-box counterparts that are widely studied and readily accessible, adversarial examples in black-box settings are generally more Herculean on account of the difficul…
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels
Yiwen Guo, Ming Lu, Wangmeng Zuo +2
Convolutional neural networks have been proven effective in a variety of image restoration tasks. Most state-of-the-art solutions, however, are trained using images with a single p…
Deep Defense: Training DNNs with Improved Adversarial Robustness
Ziang Yan, Yiwen Guo, Changshui Zhang
Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical syst…
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
Hao Zhao, Ming Lu, Anbang Yao +3
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is seman…
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
Aojun Zhou, Anbang Yao, Yiwen Guo +2
This paper presents incremental network quantization (INQ), a novel method, targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model…