22 citations · 87 across the 17 of their papers we have counts for
17 papers
ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
Jiaxiang Cheng, Pan Xie, Xin Xia +7
Recent advancement in text-to-image models (e.g., Stable Diffusion) and corresponding personalized technologies (e.g., DreamBooth and LoRA) enables individuals to generate high-qua…
DiffusionAgent: Navigating Expert Models for Agentic Image Generation
Jie Qin, Jie Wu, Weifeng Chen +1
In the accelerating era of human-instructed visual content creation, diffusion models have demonstrated remarkable generative potential. Yet their deployment is constrained by a du…
AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration
Lijiang Li, Huixia Li, Xiawu Zheng +7
Diffusion models are emerging expressive generative models, in which a large number of time steps (inference steps) are required for a single image generation. To accelerate such t…
UGC: Unified GAN Compression for Efficient Image-to-Image Translation
Yuxi Ren, Jie Wu, Peng Zhang +6
Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on pond…
DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection
Manlin Zhang, Jie Wu, Yuxi Ren +7
Data is the cornerstone of deep learning. This paper reveals that the recently developed Diffusion Model is a scalable data engine for object detection. Existing methods for scalin…
DLIP: Distilling Language-Image Pre-training
Huafeng Kuang, Jie Wu, Xiawu Zheng +5
Vision-Language Pre-training (VLP) shows remarkable progress with the assistance of extremely heavy parameters, which challenges deployment in real applications. Knowledge distilla…
AlignDet: Aligning Pre-training and Fine-tuning in Object Detection
Ming Li, Jie Wu, Xionghui Wang +6
The paradigm of large-scale pre-training followed by downstream fine-tuning has been widely employed in various object detection algorithms. In this paper, we reveal discrepancies…
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation
Jie Qin, Jie Wu, Pengxiang Yan +8
Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more gen…
Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic Segmentation
Jie Qin, Jie Wu, Ming Li +3
Albeit with varying degrees of progress in the field of Semi-Supervised Semantic Segmentation, most of its recent successes are involved in unwieldy models and the lightweight solu…
Parallel Pre-trained Transformers (PPT) for Synthetic Data-based Instance Segmentation
Ming Li, Jie Wu, Jinhang Cai +6
Recently, Synthetic data-based Instance Segmentation has become an exceedingly favorable optimization paradigm since it leverages simulation rendering and physics to generate high-…
TRT-ViT: TensorRT-oriented Vision Transformer
Xin Xia, Jiashi Li, Jie Wu +4
We revisit the existing excellent Transformers from the perspective of practical application. Most of them are not even as efficient as the basic ResNets series and deviate from th…
Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation
Jie Qin, Jie Wu, Xuefeng Xiao +2
Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet challenging computer vision task facilitating scene understanding and automatic driving. Most existi…
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme
Shaojie Li, Jie Wu, Xuefeng Xiao +3
Recently, a series of algorithms have been explored for GAN compression, which aims to reduce tremendous computational overhead and memory usages when deploying GANs on resource-co…
Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movements
Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini +1
Telegram is one of the most used instant messaging apps worldwide. Some of its success lies in providing high privacy protection and social network features like the channels -- vi…
Online Multi-Granularity Distillation for GAN Compression
Yuxi Ren, Jie Wu, Xuefeng Xiao +1
Generative Adversarial Networks (GANs) have witnessed prevailing success in yielding outstanding images, however, they are burdensome to deploy on resource-constrained devices due…
Robust Sequence Submodular Maximization
Gamal Sallam, Zizhan Zheng, Jie Wu +1
Submodularity is an important property of set functions and has been extensively studied in the literature. It models set functions that exhibit a diminishing returns property, whe…
PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs
Jie Wu, Ying Peng, Chenghao Zheng +2
Recently, generative adversarial networks (GANs) have shown great advantages in synthesizing images, leading to a boost of explorations of using faked images to augment data. This…