318 citations · 405 across the 31 of their papers we have counts for
32 papers
Towards UAV Detection in the Real World: A New Multispectral Dataset UAVNet-MS and a New Method
Yihang Luo, Jun Chen, Chao Xiao +13
The proliferation of unmanned aerial vehicles (UAVs) has created urgent demand for precise UAV monitoring. Existing RGB-based systems rely on spatial cues that degrade at small sca…
Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models
Yexing Xu, Wei Feng, Shen Zhang +15
Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models driven by click-through-rate (CTR)…
Deep Lookup Network
Yulan Guo, Longguang Wang, Wendong Mao +4
Convolutional neural networks are constructed with massive operations with different types and are highly computationally intensive. Among these operations, multiplication operatio…
Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better
Ruojing Li, Wei An, Yingqian Wang +6
Infrared small target (IRST) detection is challenging in simultaneously achieving precise, robust, and efficient performance due to extremely dim targets and strong interference. C…
A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios
Zhuo Song, Ye Zhang, Kunhong Li +2
Cross-view geo-localization is a promising solution for large-scale localization problems, requiring the sequential execution of retrieval and metric localization tasks to achieve…
DropoutGS: Dropping Out Gaussians for Better Sparse-view Rendering
Yexing Xu, Longguang Wang, Minglin Chen +3
Although 3D Gaussian Splatting (3DGS) has demonstrated promising results in novel view synthesis, its performance degrades dramatically with sparse inputs and generates undesirable…
Pluggable Style Representation Learning for Multi-Style Transfer
Hongda Liu, Longguang Wang, Weijun Guan +2
Due to the high diversity of image styles, the scalability to various styles plays a critical role in real-world applications. To accommodate a large amount of styles, previous mul…
SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer
Hongda Liu, Longguang Wang, Ye Zhang +2
Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transfor…
Layout2Scene: 3D Semantic Layout Guided Scene Generation via Geometry and Appearance Diffusion Priors
Minglin Chen, Longguang Wang, Sheng Ao +3
3D scene generation conditioned on text prompts has significantly progressed due to the development of 2D diffusion generation models. However, the textual description of 3D scenes…
VideoDirector: Precise Video Editing via Text-to-Video Models
Yukun Wang, Longguang Wang, Zhiyuan Ma +3
Despite the typical inversion-then-editing paradigm using text-to-image (T2I) models has demonstrated promising results, directly extending it to text-to-video (T2V) models still s…
NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results
Longguang Wang, Yulan Guo, Juncheng Li +6
This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-…
Preserving Full Degradation Details for Blind Image Super-Resolution
Hongda Liu, Longguang Wang, Ye Zhang +3
The performance of image super-resolution relies heavily on the accuracy of degradation information, especially under blind settings. Due to the absence of true degradation models…
Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation
Minglin Chen, Weihao Yuan, Yukun Wang +5
Recently, text-to-3D approaches have achieved high-fidelity 3D content generation using text description. However, the generated objects are stochastic and lack fine-grained contro…
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos
Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao +3
We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Previous methods commonly conduct representation learning at th…
Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos
Zhiqiang Shen, Xiaoxiao Sheng, Hehe Fan +5
Recently, the community has made tremendous progress in developing effective methods for point cloud video understanding that learn from massive amounts of labeled data. However, a…
PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos
Zhiqiang Shen, Xiaoxiao Sheng, Longguang Wang +3
Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost. In this…
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
Yingqian Wang, Longguang Wang, Zhengyu Liang +3
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradati…
Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection
Boyang Li, Yingqian Wang, Longguang Wang +5
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images. Recently, deep learning based methods have achiev…
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution
Zhengyu Liang, Yingqian Wang, Longguang Wang +3
Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities…
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
Longguang Wang, Yulan Guo, Yingqian Wang +3
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new sol…
Occlusion-Aware Cost Constructor for Light Field Depth Estimation
Yingqian Wang, Longguang Wang, Zhengyu Liang +3
Matching cost construction is a key step in light field (LF) depth estimation, but was rarely studied in the deep learning era. Recent deep learning-based LF depth estimation metho…
Disentangling Light Fields for Super-Resolution and Disparity Estimation
Yingqian Wang, Longguang Wang, Gaochang Wu +4
Light field (LF) cameras record both intensity and directions of light rays, and encode 3D scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have been p…
Decoupling Makes Weakly Supervised Local Feature Better
Kunhong Li, Longguang Wang, Li Liu +3
Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences. However, since weak supe…
Unsupervised Degradation Representation Learning for Blind Super-Resolution
Longguang Wang, Yingqian Wang, Xiaoyu Dong +4
Most existing CNN-based super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic downsampling). However, these meth…