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Issue 1 • February-2025
Showing 1-13 of 13
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Review Article
Denoising diffusion models have demonstrated tremendous success in modeling data distributions and synthesizing high-quality samples. In the 2D image domain, they have become the state-of-the-art and are capable of generating photo-realistic images with high controllability. More recently, researchers have begun to explore how to utilize diffusion models to generate 3D data, as doing so has more p...Show More
Research Article
Mindstorms in Natural Language-Based Societies of Mind
Mingchen Zhuge;Haozhe Liu;Francesco Faccio;Dylan R. Ashley;Róbert Csordás;Anand Gopalakrishnan;Abdullah Hamdi;Hasan Abed Al Kader Hammoud;Vincent Herrmann;Kazuki Irie;Louis Kirsch;Bing Li;Guohao Li;Shuming Liu;Jinjie Mai;Piotr Piękos;Aditya A. Ramesh;Imanol Schlag;Weimin Shi;Aleksandar Stanić;Wenyi Wang;Yuhui Wang;Mengmeng Xu;Deng-Ping Fan;Bernard Ghanem;Jürgen Schmidhuber
Publication Year: 2025,Page(s):29 - 81
Inspired by Minsky's Society of Mind, Schmidhuber's Learning to Think, and other more recent works, this paper proposes and advocates for the concept of natural language-based societies of mind (NLSOMs). We imagine these societies as consisting of a collection of multimodal neural networks, including large language models, which engage in a “mindstorm” to solve problems using a shared natural lang...Show More
Mindstorms in Natural Language-Based Societies of Mind
Mingchen Zhuge;Haozhe Liu;Francesco Faccio;Dylan R. Ashley;Róbert Csordás;Anand Gopalakrishnan;Abdullah Hamdi;Hasan Abed Al Kader Hammoud;Vincent Herrmann;Kazuki Irie;Louis Kirsch;Bing Li;Guohao Li;Shuming Liu;Jinjie Mai;Piotr Piękos;Aditya A. Ramesh;Imanol Schlag;Weimin Shi;Aleksandar Stanić;Wenyi Wang;Yuhui Wang;Mengmeng Xu;Deng-Ping Fan;Bernard Ghanem;Jürgen Schmidhuber
Year: 2025 | Volume: 11 | Issue: 1
Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
Yu-Qi Yang;Yu-Xiao Guo;Jian-Yu Xiong;Yang Liu;Hao Pan;Peng-Shuai Wang;Xin Tong;Baining Guo
Publication Year: 2025,Page(s):83 - 101
Cited by: Papers (1)
The use of pretrained backbones with fine-tuning has shown success for 2D vision and natural language processing tasks, with advantages over task-specific networks. In this paper, we introduce a pretrained 3D backbone, called Swin3d, for 3D indoor scene understanding. We designed a 3D Swin Transformer as our backbone network, which enables efficient self-attention on sparse voxels with linear memo...Show More
Script-to-Storyboard: A New Contextual Retrieval Dataset and Benchmark
Publication Year: 2025,Page(s):103 - 122
Storyboards comprising key illustrations and images help filmmakers to outline ideas, key moments, and story events when filming movies. Inspired by this, we introduce the first contextual benchmark dataset Script-to-Storyboard (Sc2St) composed of storyboards to explicitly express story structures in the movie domain, and propose the contextual retrieval task to facilitate movie story understandin...Show More
Script-to-Storyboard: A New Contextual Retrieval Dataset and Benchmark
Year: 2025 | Volume: 11 | Issue: 1
Hybrid Mesh-Neural Representation for 3D Transparent Object Reconstruction
Publication Year: 2025,Page(s):123 - 140
In this study, we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting conditions. It combines the advantages of an explicit mesh and multi-layer perceptron (MLP) network as a hybrid representation to simplify the capture settings used in recent studies. After obtaining an initial shape through multi-view silhou...Show More
Hybrid Mesh-Neural Representation for 3D Transparent Object Reconstruction
Year: 2025 | Volume: 11 | Issue: 1
FACNet: Feature Alignment Fast Point Cloud Completion Network
Xinxing Yu;Jianyi Li;Chi-Chong Wong;Chi-Man Vong;Yanyan Liang
Publication Year: 2025,Page(s):141 - 157
Point cloud completion aims to infer complete point clouds based on partial 3D point cloud inputs. Various previous methods apply coarse-to-fine strategy networks for generating complete point clouds. However, such methods are not only relatively time-consuming but also cannot provide representative complete shape features based on partial inputs. In this paper, a novel feature alignment fast poin...Show More
FACNet: Feature Alignment Fast Point Cloud Completion Network
Xinxing Yu;Jianyi Li;Chi-Chong Wong;Chi-Man Vong;Yanyan Liang
Year: 2025 | Volume: 11 | Issue: 1
Exploring Contextual Priors for Real-World Image Super-Resolution
Publication Year: 2025,Page(s):159 - 177
Real-world blind image super-resolution is a challenging problem due to the absence of target high resolution images for training. Inspired by the recent success of the single image generation based method SinGAN, we tackle this challenging problem with a refined model SR-SinGAN, which can learn to perform single real image super-resolution. Firstly, we empirically find that downsampled LR input w...Show More
LucIE: Language-Guided Local Image Editing for Fashion Images
Publication Year: 2025,Page(s):179 - 194
Language-guided fashion image editing is challenging, as fashion image editing is local and requires high precision, while natural language cannot provide precise visual information for guidance. In this paper, we propose LucIE, a novel unsupervised language-guided local image editing method for fashion images. LucIE adopts and modifies recent text-to-image synthesis network, DF-GAN, as its backbo...Show More
FCDFusion: A Fast, Low Color Deviation Method for Fusing Visible and Infrared Image Pairs
Publication Year: 2025,Page(s):195 - 211
Visible and infrared image fusion (VIF) aims to combine information from visible and infrared images into a single fused image. Previous VIF methods usually employ a color space transformation to keep the hue and saturation from the original visible image. However, for fast VIF methods, this operation accounts for the majority of the calculation and is the bottleneck preventing faster processing. ...Show More
FCDFusion: A Fast, Low Color Deviation Method for Fusing Visible and Infrared Image Pairs
Year: 2025 | Volume: 11 | Issue: 1
Multi-Scale Enhancement and Aggregation Network for Single-Image Deraining
Rui Zhang;Yuetong Liu;Huijian Han;Yong Zheng;Tao Zhang;Yunfeng Zhang
Publication Year: 2025,Page(s):213 - 226
Rain streaks in an image appear in different sizes and orientations, resulting in severe blurring and visual quality degradation. Previous CNN-based algorithms have achieved encouraging deraining results although there are certain limitations in the description of rain streaks and the restoration of scene structures in different environments. In this paper, we propose an efficient multi-scale enha...Show More
Noise-Robust Few-Shot Classification via Variational Adversarial Data Augmentation
Renjie Xu;Baodi Liu;Kai Zhang;Honglong Chen;Dapeng Tao;Weifeng Liu
Publication Year: 2025,Page(s):227 - 239
Few-shot classification models trained with clean samples poorly classify samples from the real world with various scales of noise. To enhance the model for recognizing noisy samples, researchers usually utilize data augmentation or use noisy samples generated by adversarial training for model training. However, existing methods still have problems: (i) The effects of data augmentation on the robu...Show More
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