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20172024
most citedIncremental Classifier Learning with Generative Adversarial Networks

93 citations · 476 across the 42 of their papers we have counts for

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Showing 2023Show all

9 papers · 1 filter

cs.CV2023★ 4 cited

Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection

Lingchen Meng, Xiyang Dai, Jianwei Yang +7

Long-tailed object detection (LTOD) aims to handle the extreme data imbalance in real-world datasets, where many tail classes have scarce instances. One popular strategy is to expl…

cs.CV2023

Exploring Invariance in Images through One-way Wave Equations

Yinpeng Chen, Dongdong Chen, Xiyang Dai +5

In this paper, we empirically reveal an invariance over images-images share a set of one-way wave equations with latent speeds. Each image is uniquely associated with a solution to…

cs.CV2023★ 1 cited

Completing Visual Objects via Bridging Generation and Segmentation

Xiang Li, Yinpeng Chen, Chung-Ching Lin +6

This paper presents a novel approach to object completion, with the primary goal of reconstructing a complete object from its partially visible components. Our method, named MaskCo…

cs.CV2023

Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting

Qidong Huang, Xiaoyi Dong, Dongdong Chen +5

In this paper, we investigate the adversarial robustness of vision transformers that are equipped with BERT pretraining (e.g., BEiT, MAE). A surprising observation is that MAE has…

cs.CV2023★ 5 cited

Designing a Better Asymmetric VQGAN for StableDiffusion

Zixin Zhu, Xuelu Feng, Dongdong Chen +5

StableDiffusion is a revolutionary text-to-image generator that is causing a stir in the world of image generation and editing. Unlike traditional methods that learn a diffusion mo…

cs.CV2023★ 1 cited

PaintSeg: Training-free Segmentation via Painting

Xiang Li, Chung-Ching Lin, Yinpeng Chen +3

The paper introduces PaintSeg, a new unsupervised method for segmenting objects without any training. We propose an adversarial masked contrastive painting (AMCP) process, which cr…

cs.CV2023

Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance

Yinpeng Chen, Xiyang Dai, Dongdong Chen +4

This paper introduces a novel mathematical property applicable to diverse images, referred to as FINOLA (First-Order Norm+Linear Autoregressive). FINOLA represents each image in th…

cs.CV2023★ 5 cited

Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations

Ziyu Jiang, Yinpeng Chen, Mengchen Liu +5

Recently, both Contrastive Learning (CL) and Mask Image Modeling (MIM) demonstrate that self-supervision is powerful to learn good representations. However, naively combining them…

cs.CV2023★ 1 cited

Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression

Zhiyu Pan, Yinpeng Chen, Jiale Zhang +3

Automatic image cropping algorithms aim to recompose images like human-being photographers by generating the cropping boxes with improved composition quality. Cropping box regressi…