51 citations · 69 across the 36 of their papers we have counts for
39 papers
RegVGGT: Sustainable Visual Geometry Grounding for Streaming via Regulated Memory
Hongbo Mao, Junjun Jiang, Youyu Chen +3
3D reconstruction from a lengthy video stream input poses a dilemma for feed-forward reconstruction models (FFRMs), that a whole-stream inference context cannot be retained under l…
ViP-Rig: Visual-Prompted Controllable Rigging
Zihan Qin, Mingze Sun, Yifan Mao +6
Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an i…
RainDancer: RGB-Event Video Deraining with Rain-Oriented Spiking Dynamics
Kui Jiang, Runzhe Li, Zhaocheng Yu +3
Video deraining aims to recover clean visual content from rainy videos for reliable perception under adverse weather. Existing methods mainly rely on RGB sequences and temporal red…
Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations
Youyu Chen, Junjun Jiang, Yueru Luo +4
With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However,…
Focus-Scan-Refine: From Human Visual Perception to Efficient Visual Token Pruning
Enwei Tong, Yuanchao Bai, Yao Zhu +2
Vision-language models (VLMs) often generate massive visual tokens that greatly increase inference latency and memory footprint; while training-free token pruning offers a practica…
UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing
Zengyuan Zuo, Junjun Jiang, Gang Wu +1
Image dehazing has witnessed significant advancements with the development of deep learning models. However, most existing methods focus solely on single-modal RGB features, neglec…
Semantics and Content Matter: Towards Multi-Prior Hierarchical Mamba for Image Deraining
Zhaocheng Yu, Kui Jiang, Junjun Jiang +3
Rain significantly degrades the performance of computer vision systems, particularly in applications like autonomous driving and video surveillance. While existing deraining method…
Variation-Bounded Loss for Noise-Tolerant Learning
Jialiang Wang, Xiong Zhou, Xianming Liu +4
Mitigating the negative impact of noisy labels has been aperennial issue in supervised learning. Robust loss functions have emerged as a prevalent solution to this problem. In this…
Rethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation
Daxin Li, Yuanchao Bai, Kai Wang +3
Autoregressive (AR) models, the theoretical performance benchmark for learned lossless image compression, are often dismissed as impractical due to prohibitive computational cost.…
-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise
Jialiang Wang, Xiong Zhou, Deming Zhai +3
Noisy labels pose a common challenge for training accurate deep neural networks. To mitigate label noise, prior studies have proposed various robust loss functions to achieve noise…
Boosting All-in-One Image Restoration via Self-Improved Privilege Learning
Gang Wu, Junjun Jiang, Kui Jiang +1
Unified image restoration models for diverse and mixed degradations often suffer from unstable optimization dynamics and inter-task conflicts. This paper introduces Self-Improved P…
The Fourth Monocular Depth Estimation Challenge
Anton Obukhov, Matteo Poggi, Fabio Tosi +54
This paper presents the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), which focuses on zero-shot generalization to the SYNS-Patches benchmark, a…
Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration
Gang Wu, Junjun Jiang, Kui Jiang +2
All-in-One Image Restoration (AiOIR), which addresses diverse degradation types with a unified model, presents significant challenges in designing task-aware prompts that effective…
Balancing Task-invariant Interaction and Task-specific Adaptation for Unified Image Fusion
Xingyu Hu, Junjun Jiang, Chenyang Wang +3
Unified image fusion aims to integrate complementary information from multi-source images, enhancing image quality through a unified framework applicable to diverse fusion tasks. W…
DSwinIR: Rethinking Window-based Attention for Image Restoration
Gang Wu, Junjun Jiang, Kui Jiang +2
Image restoration has witnessed significant advancements with the development of deep learning models. Transformer-based models, particularly those using window-based self-attentio…
COB-GS: Clear Object Boundaries in 3DGS Segmentation Based on Boundary-Adaptive Gaussian Splitting
Jiaxin Zhang, Junjun Jiang, Youyu Chen +2
Accurate object segmentation is crucial for high-quality scene understanding in the 3D vision domain. However, 3D segmentation based on 3D Gaussian Splatting (3DGS) struggles with…
DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds
Youyu Chen, Junjun Jiang, Kui Jiang +4
3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where the rendering resolution and the primitive number, concluded as the optimization complexity, d…
FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion
Pihai Sun, Junjun Jiang, Yuanqi Yao +4
Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability stemming from two factors: 1) limited an…
CALLIC: Content Adaptive Learning for Lossless Image Compression
Daxin Li, Yuanchao Bai, Kai Wang +3
Learned lossless image compression has achieved significant advancements in recent years. However, existing methods often rely on training amortized generative models on massive da…
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions
Zihan Qin, Jialei Xu, Wenbo Zhao +2
Depth estimation under adverse conditions remains a significant challenge. Recently, multi-spectral depth estimation, which integrates both visible light and thermal images, has sh…
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training
Yuanqi Yao, Gang Wu, Kui Jiang +4
Learning a self-supervised Monocular Depth Estimation (MDE) model with great generalization remains significantly challenging. Despite the success of adversarial augmentation in th…
Learning Lossless Compression for High Bit-Depth Volumetric Medical Image
Kai Wang, Yuanchao Bai, Daxin Li +3
Recent advances in learning-based methods have markedly enhanced the capabilities of image compression. However, these methods struggle with high bit-depth volumetric medical image…
A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends
Junjun Jiang, Zengyuan Zuo, Gang Wu +2
Image restoration (IR) seeks to recover high-quality images from degraded observations caused by a wide range of factors, including noise, blur, compression, and adverse weather. W…
MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields
Yuru Xiao, Deming Zhai, Wenbo Zhao +3
Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering. However, with sparse input views, the lack…