pwc-archive/papers-with-abstracts huggingface dataset:
PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models
Authors: Antonio Alliegro, Yawar Siddiqui, Tatiana Tommasi, Matthias Nießner
Date: 2023-12-18 00:00:00
Tasks: Avg, Denoising

We introduce PolyDiff, the first diffusion-based approach capable of directly generating realistic and diverse 3D polygonal meshes. In contrast to methods that use alternate 3D shape representations (e.g. implicit representations), our approach is a discrete denoising diffusion probabilistic model that operates natively on the polygonal mesh data structure. This enables learning of both the geometric properties of vertices and the topological characteristics of faces. Specifically, we treat meshes as quantized triangle soups, progressively corrupted with categorical noise in the forward diffusion phase. In the reverse diffusion phase, a transformer-based denoising network is trained to revert the noising process, restoring the original mesh structure. At inference, new meshes can be generated by applying this denoising network iteratively, starting with a completely noisy triangle soup. Consequently, our model is capable of producing high-quality 3D polygonal meshes, ready for integration into downstream 3D workflows. Our extensive experimental analysis shows that PolyDiff achieves a significant advantage (avg. FID and JSD improvement of 18.2 and 5.8 respectively) over current state-of-the-art methods.
Warping the Residuals for Image Editing with StyleGAN
Authors: Ahmet Burak Yildirim, Hamza Pehlivan, Aysegul Dundar
Date: 2023-12-18 00:00:00

StyleGAN models show editing capabilities via their semantically interpretable latent organizations which require successful GAN inversion methods to edit real images. Many works have been proposed for inverting images into StyleGAN's latent space. However, their results either suffer from low fidelity to the input image or poor editing qualities, especially for edits that require large transformations. That is because low-rate latent spaces lose many image details due to the information bottleneck even though it provides an editable space. On the other hand, higher-rate latent spaces can pass all the image details to StyleGAN for perfect reconstruction of images but suffer from low editing qualities. In this work, we present a novel image inversion architecture that extracts high-rate latent features and includes a flow estimation module to warp these features to adapt them to edits. The flows are estimated from StyleGAN features of edited and unedited latent codes. By estimating the high-rate features and warping them for edits, we achieve both high-fidelity to the input image and high-quality edits. We run extensive experiments and compare our method with state-of-the-art inversion methods. Qualitative metrics and visual comparisons show significant improvements.
Language-Assisted 3D Scene Understanding
Authors: Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang
Date: 2023-12-18 00:00:00
Tasks: 3D Object Detection, 3D Semantic Segmentation, Dimensionality Reduction, feature selection, object-detection, Object Detection, Scene Classification, Scene Understanding, Semantic Segmentation, Triplet

The scale and quality of point cloud datasets constrain the advancement of point cloud learning. Recently, with the development of multi-modal learning, the incorporation of domain-agnostic prior knowledge from other modalities, such as images and text, to assist in point cloud feature learning has been considered a promising avenue. Existing methods have demonstrated the effectiveness of multi-modal contrastive training and feature distillation on point clouds. However, challenges remain, including the requirement for paired triplet data, redundancy and ambiguity in supervised features, and the disruption of the original priors. In this paper, we propose a language-assisted approach to point cloud feature learning (LAST-PCL), enriching semantic concepts through LLMs-based text enrichment. We achieve de-redundancy and feature dimensionality reduction without compromising textual priors by statistical-based and training-free significant feature selection. Furthermore, we also delve into an in-depth analysis of the impact of text contrastive training on the point cloud. Extensive experiments validate that the proposed method learns semantically meaningful point cloud features and achieves state-of-the-art or comparable performance in 3D semantic segmentation, 3D object detection, and 3D scene classification tasks.
GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis
Authors: Yiqing Liang, Numair Khan, Zhengqin Li, Thu Nguyen-Phuoc, Douglas Lanman, James Tompkin, Lei Xiao
Date: 2023-12-18 00:00:00
Tasks: GPU, Inductive Bias, NeRF, Novel View Synthesis

We propose a method that achieves state-of-the-art rendering quality and efficiency on monocular dynamic scene reconstruction using deformable 3D Gaussians. Implicit deformable representations commonly model motion with a canonical space and time-dependent backward-warping deformation field. Our method, GauFRe, uses a forward-warping deformation to explicitly model non-rigid transformations of scene geometry. Specifically, we propose a template set of 3D Gaussians residing in a canonical space, and a time-dependent forward-warping deformation field to model dynamic objects. Additionally, we tailor a 3D Gaussian-specific static component supported by an inductive bias-aware initialization approach which allows the deformation field to focus on moving scene regions, improving the rendering of complex real-world motion. The differentiable pipeline is optimized end-to-end with a self-supervised rendering loss. Experiments show our method achieves competitive results and higher efficiency than both previous state-of-the-art NeRF and Gaussian-based methods. For real-world scenes, GauFRe can train in ~20 mins and offer 96 FPS real-time rendering on an RTX 3090 GPU. Project website: https://lynl7130.github.io/gaufre/index.html
VolumeDiffusion: Flexible Text-to-3D Generation with Efficient Volumetric Encoder
Authors: Zhicong Tang, Shuyang Gu, Chunyu Wang, Ting Zhang, Jianmin Bao, Dong Chen, Baining Guo
Date: 2023-12-18 00:00:00
Tasks: 3D Generation, Object, Text to 3D

This paper introduces a pioneering 3D volumetric encoder designed for text-to-3D generation. To scale up the training data for the diffusion model, a lightweight network is developed to efficiently acquire feature volumes from multi-view images. The 3D volumes are then trained on a diffusion model for text-to-3D generation using a 3D U-Net. This research further addresses the challenges of inaccurate object captions and high-dimensional feature volumes. The proposed model, trained on the public Objaverse dataset, demonstrates promising outcomes in producing diverse and recognizable samples from text prompts. Notably, it empowers finer control over object part characteristics through textual cues, fostering model creativity by seamlessly combining multiple concepts within a single object. This research significantly contributes to the progress of 3D generation by introducing an efficient, flexible, and scalable representation methodology.
Appearance-Based Refinement for Object-Centric Motion Segmentation
Authors: Junyu Xie, Weidi Xie, Andrew Zisserman
Date: 2023-12-18 00:00:00
Tasks: Motion Segmentation, Object, Optical Flow Estimation, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

The goal of this paper is to discover, segment, and track independently moving objects in complex visual scenes. Previous approaches have explored the use of optical flow for motion segmentation, leading to imperfect predictions due to partial motion, background distraction, and object articulations and interactions. To address this issue, we introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars, and an object-centric architecture that refines problematic masks based on exemplar information. The model is pre-trained on synthetic data and then adapted to real-world videos in a self-supervised manner, eliminating the need for human annotations. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTubeVOS, SegTrackv2, and FBMS-59. We achieve competitive performance on single-object segmentation, while significantly outperforming existing models on the more challenging problem of multi-object segmentation. Finally, we investigate the benefits of using our model as a prompt for the per-frame Segment Anything Model.
Algorithm Selection for Deep Active Learning with Imbalanced Datasets
Authors: Jifan Zhang, Shuai Shao, Saurabh Verma, Robert Nowak
Date: 2023-02-14 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Active Learning

Label efficiency has become an increasingly important objective in deep learning applications. Active learning aims to reduce the number of labeled examples needed to train deep networks, but the empirical performance of active learning algorithms can vary dramatically across datasets and applications. It is difficult to know in advance which active learning strategy will perform well or best in a given application. To address this, we propose the first adaptive algorithm selection strategy for deep active learning. For any unlabeled dataset, our (meta) algorithm TAILOR (Thompson ActIve Learning algORithm selection) iteratively and adaptively chooses among a set of candidate active learning algorithms. TAILOR uses novel reward functions aimed at gathering class-balanced examples. Extensive experiments in multi-class and multi-label applications demonstrate TAILOR's effectiveness in achieving accuracy comparable or better than that of the best of the candidate algorithms. Our implementation of TAILOR is open-sourced at https://github.com/jifanz/TAILOR.
Deep Learning-based MRI Reconstruction with Artificial Fourier Transform Network (AFTNet)
Authors: Yanting Yang, Yiren Zhang, Zongyu Li, Jeffery Siyuan Tian, Matthieu Dagommer, Jia Guo
Date: 2023-12-18 00:00:00
Tasks: MRI Reconstruction

Deep complex-valued neural networks (CVNNs) provide a powerful way to leverage complex number operations and representations and have succeeded in several phase-based applications. However, previous networks have not fully explored the impact of complex-valued networks in the frequency domain. Here, we introduce a unified complex-valued deep learning framework-Artificial Fourier Transform Network (AFTNet)-which combines domain-manifold learning and CVNNs. AFTNet can be readily used to solve image inverse problems in domain transformation, especially for accelerated magnetic resonance imaging (MRI) reconstruction and other applications. While conventional methods typically utilize magnitude images or treat the real and imaginary components of k-space data as separate channels, our approach directly processes raw k-space data in the frequency domain, utilizing complex-valued operations. This allows for a mapping between the frequency (k-space) and image domain to be determined through cross-domain learning. We show that AFTNet achieves superior accelerated MRI reconstruction compared to existing approaches. Furthermore, our approach can be applied to various tasks, such as denoised magnetic resonance spectroscopy (MRS) reconstruction and datasets with various contrasts. The AFTNet presented here is a valuable preprocessing component for different preclinical studies and provides an innovative alternative for solving inverse problems in imaging and spectroscopy. The code is available at: https://github.com/yanting-yang/AFT-Net.
Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation
Authors: Yousuf Babiker M. Osman, Cheng Li, Weijian Huang, Shanshan Wang
Date: 2023-12-18 00:00:00
Tasks: Image Segmentation, Left Atrium Segmentation, Segmentation, Self-Supervised Learning, Semantic Segmentation

Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating 3D MR images is tedious and time-consuming, requiring experts with rich domain knowledge and experience. Purpose: To build a deep learning method exploring sparse annotations, namely only a single 2D slice label for each 3D training MR image. Population: 3D MR images of 150 subjects from two publicly available datasets were included. Among them, 50 (1,377 image slices) are for prostate segmentation. The other 100 (8,800 image slices) are for left atrium segmentation. Five-fold cross-validation experiments were carried out utilizing the first dataset. For the second dataset, 80 subjects were used for training and 20 were used for testing. Assessment: A collaborative learning method by integrating the strengths of semi-supervised and self-supervised learning schemes was developed. The method was trained using labeled central slices and unlabeled non-central slices. Segmentation performance on testing set was reported quantitatively and qualitatively. Results: Compared to FS-LCS, MT, UA-MT, DCT-Seg, ICT, and AC-MT, the proposed method achieved a substantial improvement in segmentation accuracy, increasing the mean B-IoU significantly by more than 10.0% for prostate segmentation (proposed method B-IoU: 70.3% vs. ICT B-IoU: 60.3%) and by more than 6.0% for left atrium segmentation (proposed method B-IoU: 66.1% vs. ICT B-IoU: 60.1%).
DataElixir: Purifying Poisoned Dataset to Mitigate Backdoor Attacks via Diffusion Models
Authors: Jiachen Zhou, Peizhuo Lv, Yibing Lan, Guozhu Meng, Kai Chen, Hualong Ma
Date: 2023-12-18 00:00:00

Dataset sanitization is a widely adopted proactive defense against poisoning-based backdoor attacks, aimed at filtering out and removing poisoned samples from training datasets. However, existing methods have shown limited efficacy in countering the ever-evolving trigger functions, and often leading to considerable degradation of benign accuracy. In this paper, we propose DataElixir, a novel sanitization approach tailored to purify poisoned datasets. We leverage diffusion models to eliminate trigger features and restore benign features, thereby turning the poisoned samples into benign ones. Specifically, with multiple iterations of the forward and reverse process, we extract intermediary images and their predicted labels for each sample in the original dataset. Then, we identify anomalous samples in terms of the presence of label transition of the intermediary images, detect the target label by quantifying distribution discrepancy, select their purified images considering pixel and feature distance, and determine their ground-truth labels by training a benign model. Experiments conducted on 9 popular attacks demonstrates that DataElixir effectively mitigates various complex attacks while exerting minimal impact on benign accuracy, surpassing the performance of baseline defense methods.
QDA$^2$: A principled approach to automatically annotating charge stability diagrams
Authors: Brian Weber, Justyna P. Zwolak
Date: 2023-12-18 00:00:00
Tasks: Benchmarking

Gate-defined semiconductor quantum dot (QD) arrays are a promising platform for quantum computing. However, presently, the large configuration spaces and inherent noise make tuning of QD devices a nontrivial task and with the increasing number of QD qubits, the human-driven experimental control becomes unfeasible. Recently, researchers working with QD systems have begun putting considerable effort into automating device control, with a particular focus on machine-learning-driven methods. Yet, the reported performance statistics vary substantially in both the meaning and the type of devices used for testing. While systematic benchmarking of the proposed tuning methods is necessary for developing reliable and scalable tuning approaches, the lack of openly available standardized datasets of experimental data makes such testing impossible. The QD auto-annotator -- a classical algorithm for automatic interpretation and labeling of experimentally acquired data -- is a critical step toward rectifying this. QD auto-annotator leverages the principles of geometry to produce state labels for experimental double-QD charge stability diagrams and is a first step towards building a large public repository of labeled QD data.
FAKEPCD: Fake Point Cloud Detection via Source Attribution
Authors: Yiting Qu, Zhikun Zhang, Yun Shen, Michael Backes, Yang Zhang
Date: 2023-12-18 00:00:00
Tasks: Attribute, Cloud Detection

To prevent the mischievous use of synthetic (fake) point clouds produced by generative models, we pioneer the study of detecting point cloud authenticity and attributing them to their sources. We propose an attribution framework, FAKEPCD, to attribute (fake) point clouds to their respective generative models (or real-world collections). The main idea of FAKEPCD is to train an attribution model that learns the point cloud features from different sources and further differentiates these sources using an attribution signal. Depending on the characteristics of the training point clouds, namely, sources and shapes, we formulate four attribution scenarios: close-world, open-world, single-shape, and multiple-shape, and evaluate FAKEPCD's performance in each scenario. Extensive experimental results demonstrate the effectiveness of FAKEPCD on source attribution across different scenarios. Take the open-world attribution as an example, FAKEPCD attributes point clouds to known sources with an accuracy of 0.82-0.98 and to unknown sources with an accuracy of 0.73-1.00. Additionally, we introduce an approach to visualize unique patterns (fingerprints) in point clouds associated with each source. This explains how FAKEPCD recognizes point clouds from various sources by focusing on distinct areas within them. Overall, we hope our study establishes a baseline for the source attribution of (fake) point clouds.
Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring
Authors: Jérémy Scanvic, Mike Davies, Patrice Abry, Julián Tachella
Date: 2023-12-18 00:00:00
Tasks: Deblurring, Image Super-Resolution, Self-Supervised Learning, Super-Resolution

Self-supervised methods have recently proved to be nearly as effective as supervised ones in various imaging inverse problems, paving the way for learning-based approaches in scientific and medical imaging applications where ground truth data is hard or expensive to obtain. These methods critically rely on invariance to translations and/or rotations of the image distribution to learn from incomplete measurement data alone. However, existing approaches fail to obtain competitive performances in the problems of image super-resolution and deblurring, which play a key role in most imaging systems. In this work, we show that invariance to roto-translations is insufficient to learn from measurements that only contain low-frequency information. Instead, we propose scale-equivariant imaging, a new self-supervised approach that leverages the fact that many image distributions are approximately scale-invariant, enabling the recovery of high-frequency information lost in the measurement process. We demonstrate throughout a series of experiments on real datasets that the proposed method outperforms other self-supervised approaches, and obtains performances on par with fully supervised learning.
Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)
Authors: Sabrina Toro, Anna V Anagnostopoulos, Sue Bello, Kai Blumberg, Rhiannon Cameron, Leigh Carmody, Alexander D Diehl, Damion Dooley, William Duncan, Petra Fey, Pascale Gaudet, Nomi L Harris, Marcin Joachimiak, Leila Kiani, Tiago Lubiana, Monica C Munoz-Torres, Shawn O'Neil, David Osumi-Sutherland, Aleix Puig, Justin P Reese, Leonore Reiser, Sofia Robb, Troy Ruemping, James Seager, Eric Sid, Ray Stefancsik, Magalie Weber, Valerie Wood, Melissa A Haendel, Christopher J Mungall
Date: 2023-12-18 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Background: Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources. Results: We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues. Conclusions: These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.
Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making
Authors: Jingqing Ruan, Kaishen Wang, Qingyang Zhang, Dengpeng Xing, Bo Xu
Date: 2023-12-18 00:00:00
Tasks: Decision Making, Representation Learning

Many complicated real-world tasks can be broken down into smaller, more manageable parts, and planning with prior knowledge extracted from these simplified pieces is crucial for humans to make accurate decisions. However, replicating this process remains a challenge for AI agents and naturally raises two questions: How to extract discriminative knowledge representation from priors? How to develop a rational plan to decompose complex problems? Most existing representation learning methods employing a single encoder structure are fragile and sensitive to complex and diverse dynamics. To address this issue, we introduce a multiple-encoder and individual-predictor regime to learn task-essential representations from sufficient data for simple subtasks. Multiple encoders can extract adequate task-relevant dynamics without confusion, and the shared predictor can discriminate the task characteristics. We also use the attention mechanism to generate a top-k subtask planning tree, which customizes subtask execution plans in guiding complex decisions on unseen tasks. This process enables forward-looking and globality by flexibly adjusting the depth and width of the planning tree. Empirical results on a challenging platform composed of some basic simple tasks and combinatorially rich synthetic tasks consistently outperform some competitive baselines and demonstrate the benefits of our design.