33 citations · 67 across the 23 of their papers we have counts for
24 papers
IRIS: Implicit Rendering Matters for Pose-Free Novel View Synthesis
Wenyu Li, Sidun Liu, Peng Qiao +2
Novel view synthesis from unposed multi-view images remains challenging, as the model must jointly learn scene representations and camera parameters without pose supervision. Exist…
LocusGS: Spatially Grounded Tokens for Feed-Forward 3D Gaussian Splatting
Wenyu Li, Sidun Liu, Tongrui Hu +2
Recent query-based feed-forward 3DGS methods represent a scene using learnable queries, each aggregating multi-view evidence and decoding a group of Gaussians. Ideally, different q…
Sparrow: Text-Anchored Window Attention with Visual-Semantic Glimpsing for Speculative Decoding in Video LLMs
Libo Zhang, Zhaoning Zhang, Wangyang Hong +2
Although speculative decoding is widely used to accelerate Vision-Language Models (VLMs) inference, it faces severe performance collapse when applied to Video Large Language Models…
Muskie: Multi-view Masked Image Modeling for 3D Vision Pre-training
Wenyu Li, Sidun Liu, Peng Qiao +2
We present Muskie, a native multi-view vision backbone designed for 3D vision tasks. Unlike existing models, which are frame-wise and exhibit limited multi-view consistency, Muskie…
Transolver is a Linear Transformer: Revisiting Physics-Attention through the Lens of Linear Attention
Wenjie Hu, Sidun Liu, Peng Qiao +2
Recent advances in Transformer-based Neural Operators have enabled significant progress in data-driven solvers for Partial Differential Equations (PDEs). Most current research has…
Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction
Wenyu Li, Sidun Liu, Peng Qiao +1
Recent advances in data-driven geometric multi-view 3D reconstruction foundation models (e.g., DUSt3R) have shown remarkable performance across various 3D vision tasks, facilitated…
Regist3R: Incremental Registration with Stereo Foundation Model
Sidun Liu, Wenyu Li, Peng Qiao +1
Multi-view 3D reconstruction has remained an essential yet challenging problem in the field of computer vision. While DUSt3R and its successors have achieved breakthroughs in 3D re…
Highly Parallelized Reinforcement Learning Training with Relaxed Assignment Dependencies
Zhouyu He, Peng Qiao, Rongchun Li +2
As the demands for superior agents grow, the training complexity of Deep Reinforcement Learning (DRL) becomes higher. Thus, accelerating training of DRL has become a major research…
Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey
Zhihong Liu, Xin Xu, Peng Qiao +1
Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size…
VoxNeuS: Enhancing Voxel-Based Neural Surface Reconstruction via Gradient Interpolation
Sidun Liu, Peng Qiao, Zongxin Ye +2
Neural Surface Reconstruction learns a Signed Distance Field~(SDF) to reconstruct the 3D model from multi-view images. Previous works adopt voxel-based explicit representation to i…
DistGrid: Scalable Scene Reconstruction with Distributed Multi-resolution Hash Grid
Sidun Liu, Peng Qiao, Zongxin Ye +2
Neural Radiance Field~(NeRF) achieves extremely high quality in object-scaled and indoor scene reconstruction. However, there exist some challenges when reconstructing large-scale…
AbsGS: Recovering Fine Details for 3D Gaussian Splatting
Zongxin Ye, Wenyu Li, Sidun Liu +2
3D Gaussian Splatting (3D-GS) technique couples 3D Gaussian primitives with differentiable rasterization to achieve high-quality novel view synthesis results while providing advanc…
TFDMNet: A Novel Network Structure Combines the Time Domain and Frequency Domain Features
Hengyue Pan, Yixin Chen, Zhiliang Tian +3
Convolutional neural network (CNN) has achieved impressive success in computer vision during the past few decades. The image convolution operation helps CNNs to get good performanc…
Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz
Tao Sun, Congliang Chen, Peng Qiao +3
Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates. However, the c…
PVP: Pre-trained Visual Parameter-Efficient Tuning
Zhao Song, Ke Yang, Naiyang Guan +3
Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models…
Towards Vision Transformer Unrolling Fixed-Point Algorithm: a Case Study on Image Restoration
Peng Qiao, Sidun Liu, Tao Sun +2
The great success of Deep Neural Networks (DNNs) has inspired the algorithmic development of DNN-based Fixed-Point (DNN-FP) for computer vision tasks. DNN-FP methods, trained by Ba…
Multi-Outputs Is All You Need For Deblur
Sidun Liu, Peng Qiao, Yong Dou
Image deblurring task is an ill-posed one, where exists infinite feasible solutions for blurry image. Modern deep learning approaches usually discard the learning of blur kernels a…
Visual Tree Convolutional Neural Network in Image Classification
Yuntao Liu, Yong Dou, Ruochun Jin +1
In image classification, Convolutional Neural Network(CNN) models have achieved high performance with the rapid development in deep learning. However, some categories in the image…
Exploring Frame Segmentation Networks for Temporal Action Localization
Ke Yang, Xiaolong Shen, Peng Qiao +3
Temporal action localization is an important task of computer vision. Though many methods have been proposed, it still remains an open question how to predict the temporal location…
IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition
Ke Yang, Peng Qiao, Dongsheng Li +1
Effective spatiotemporal feature representation is crucial to the video-based action recognition task. Focusing on discriminate spatiotemporal feature learning, we propose Informat…
Learning Generic Diffusion Processes for Image Restoration
Peng Qiao, Yong Dou, Yunjin Chen +1
Image restoration problems are typical ill-posed problems where the regularization term plays an important role. The regularization term learned via generative approaches is easy t…
Exploring Temporal Preservation Networks for Precise Temporal Action Localization
Ke Yang, Peng Qiao, Dongsheng Li +2
Temporal action localization is an important task of computer vision. Though a variety of methods have been proposed, it still remains an open question how to predict the temporal…
Learning Non-local Image Diffusion for Image Denoising
Peng Qiao, Yong Dou, Wensen Feng +1
Image diffusion plays a fundamental role for the task of image denoising. Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a simple but very effective…
Image Denoising via Multi-scale Nonlinear Diffusion Models
Wensen Feng, Peng Qiao, Xuanyang Xi +1
Image denoising is a fundamental operation in image processing and holds considerable practical importance for various real-world applications. Arguably several thousands of papers…