24 citations · 36 across the 12 of their papers we have counts for
13 papers
The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +89
This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-wo…
FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback
Ashish Singh, Ashutosh Singh, Prateek Agarwal +10
Captions are crucial for understanding scientific visualizations and documents. Existing captioning methods for scientific figures rely on figure-caption pairs extracted from docum…
ShapeClipper: Scalable 3D Shape Learning from Single-View Images via Geometric and CLIP-based Consistency
Zixuan Huang, Varun Jampani, Anh Thai +3
We present ShapeClipper, a novel method that reconstructs 3D object shapes from real-world single-view RGB images. Instead of relying on laborious 3D, multi-view or camera pose ann…
Self-supervised Cloth Reconstruction via Action-conditioned Cloth Tracking
Zixuan Huang, Xingyu Lin, David Held
State estimation is one of the greatest challenges for cloth manipulation due to cloth's high dimensionality and self-occlusion. Prior works propose to identify the full state of c…
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
Stefan Stojanov, Anh Thai, Zixuan Huang +1
A hallmark of the deep learning era for computer vision is the successful use of large-scale labeled datasets to train feature representations for tasks ranging from object recogni…
Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation
Zixuan Huang, Xingyu Lin, David Held
Self-occlusion is challenging for cloth manipulation, as it makes it difficult to estimate the full state of the cloth. Ideally, a robot trying to unfold a crumpled or folded cloth…
Context Attention Network for Skeleton Extraction
Zixuan Huang, Yunfeng Wang, Zhiwen Chen +3
Skeleton extraction is a task focused on providing a simple representation of an object by extracting the skeleton from the given binary or RGB image. In recent years many attracti…
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues
Zixuan Huang, Stefan Stojanov, Anh Thai +2
We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image. Our approach uses a set of single-view images…
Simple Baseline for Single Human Motion Forecasting
Chenxi Wang, Yunfeng Wang, Zixuan Huang +1
Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction. Visual and social infor…
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction
Anh Thai, Stefan Stojanov, Zixuan Huang +2
Continual learning has been extensively studied for classification tasks with methods developed to primarily avoid catastrophic forgetting, a phenomenon where earlier learned conce…
Interpretable and Accurate Fine-grained Recognition via Region Grouping
Zixuan Huang, Yin Li
We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a d…
Style Mixer: Semantic-aware Multi-Style Transfer Network
Zixuan Huang, Jinghuai Zhang, Jing Liao
Recent neural style transfer frameworks have obtained astonishing visual quality and flexibility in Single-style Transfer (SST), but little attention has been paid to Multi-style T…
HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion
Zixuan Huang, Junming Fan, Shenggan Cheng +3
Dense depth cues are important and have wide applications in various computer vision tasks. In autonomous driving, LIDAR sensors are adopted to acquire depth measurements around th…