6 citations · 54 across the 65 of their papers we have counts for
18 papers · 1 filter
Improving Image Restoration through Removing Degradations in Textual Representations
Jingbo Lin, Zhilu Zhang, Yuxiang Wei +3
In this paper, we introduce a new perspective for improving image restoration by removing degradation in the textual representations of a given degraded image. Intuitively, restora…
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…
VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
Chun-Mei Feng, Yang Bai, Tao Luo +6
Albeit progress has been made in Composed Image Retrieval (CIR), we empirically find that a certain percentage of failure retrieval results are not consistent with their relative c…
Decoupled Textual Embeddings for Customized Image Generation
Yufei Cai, Yuxiang Wei, Zhilong Ji +3
Customized text-to-image generation, which aims to learn user-specified concepts with a few images, has drawn significant attention recently. However, existing methods usually suff…
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…
DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior
Tianyu Huang, Yihan Zeng, Zhilu Zhang +5
3D generation has raised great attention in recent years. With the success of text-to-image diffusion models, the 2D-lifting technique becomes a promising route to controllable 3D…
Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
The adversarial vulnerability of deep neural networks (DNNs) has drawn great attention due to the security risk of applying these models in real-world applications. Based on transf…
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…
A cross Transformer for image denoising
Chunwei Tian, Menghua Zheng, Wangmeng Zuo +3
Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural informa…
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…
Sentence-level Prompts Benefit Composed Image Retrieval
Yang Bai, Xinxing Xu, Yong Liu +5
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. Most existing CIR models adop…
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…
Learning Real-World Image De-Weathering with Imperfect Supervision
Xiaohui Liu, Zhilu Zhang, Xiaohe Wu +4
Real-world image de-weathering aims at removing various undesirable weather-related artifacts. Owing to the impossibility of capturing image pairs concurrently, existing real-world…
Toward Understanding BERT-Like Pre-Training for DNA Foundation Models
Chaoqi Liang, Lifeng Qiao, Peng Ye +9
With the success of large-scale pre-training in language tasks, there is an increasing trend of applying it to the domain of life sciences. In particular, pre-training methods base…
Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes
Zhilu Zhang, Haoyu Wang, Shuai Liu +3
Merging multi-exposure images is a common approach for obtaining high dynamic range (HDR) images, with the primary challenge being the avoidance of ghosting artifacts in dynamic sc…
Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition
Xiaoyu Liu, Ming Liu, Junyi Li +4
For improving image composition and aesthetic quality, most existing methods modulate the captured images by striking out redundant content near the image borders. However, such im…
TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields
Tianyu Huang, Yihan Zeng, Bowen Dong +4
Recent works learn 3D representation explicitly under text-3D guidance. However, limited text-3D data restricts the vocabulary scale and text control of generations. Generators may…
Rethinking Client Drift in Federated Learning: A Logit Perspective
Yunlu Yan, Chun-Mei Feng, Mang Ye +5
Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. However, the real-world non-IID data will lead to c…