pwc-archive/papers-with-abstracts huggingface dataset:
Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction
Authors: Maneesha Perera, Julian De Hoog, Kasun Bandara, Saman Halgamuge
Date: 2024-11-17 00:00:00
Tasks: Video Prediction

Accurate forecasts of distributed solar generation are necessary to reduce negative impacts resulting from the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this forecasting task difficult. To address this, using cloud images, which capture the second-to-second changes in cloud cover affecting solar generation, has shown promise. Recently, deep neural networks with "attention" that focus on important regions of an image have been applied with success in many computer vision applications. However, their use for forecasting cloud movement has not yet been extensively explored. In this work, we propose an attention-based convolutional long short-term memory network to forecast cloud movement and apply an existing self-attention-based method previously proposed for video prediction to forecast cloud movement. We investigate and discuss the impact of cloud forecasts from attention-based methods towards forecasting distributed solar generation, compared to cloud forecasts from non-attention-based methods. We further provide insights into the different solar forecast performances that can be achieved for high and low altitude clouds. We find that for clouds at high altitudes, the cloud predictions obtained using attention-based methods result in solar forecast skill score improvements of 5.86% or more compared to non-attention-based methods.
Generating Compositional Scenes via Text-to-image RGBA Instance Generation
Authors: Alessandro Fontanella, Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang, Sarah Parisot
Date: 2024-11-16 00:00:00
Tasks: Object, Prompt Engineering

Text-to-image diffusion generative models can generate high quality images at the cost of tedious prompt engineering. Controllability can be improved by introducing layout conditioning, however existing methods lack layout editing ability and fine-grained control over object attributes. The concept of multi-layer generation holds great potential to address these limitations, however generating image instances concurrently to scene composition limits control over fine-grained object attributes, relative positioning in 3D space and scene manipulation abilities. In this work, we propose a novel multi-stage generation paradigm that is designed for fine-grained control, flexibility and interactivity. To ensure control over instance attributes, we devise a novel training paradigm to adapt a diffusion model to generate isolated scene components as RGBA images with transparency information. To build complex images, we employ these pre-generated instances and introduce a multi-layer composite generation process that smoothly assembles components in realistic scenes. Our experiments show that our RGBA diffusion model is capable of generating diverse and high quality instances with precise control over object attributes. Through multi-layer composition, we demonstrate that our approach allows to build and manipulate images from highly complex prompts with fine-grained control over object appearance and location, granting a higher degree of control than competing methods.
Attention-based U-Net Method for Autonomous Lane Detection
Authors: Mohammadhamed Tangestanizadeh, Mohammad Dehghani Tezerjani, Saba Yousefian Jazi
Date: 2024-11-16 00:00:00
Tasks: Autonomous Driving, Lane Detection, Semantic Segmentation

Lane detection involves identifying lanes on the road and accurately determining their location and shape. This is a crucial technique for modern assisted and autonomous driving systems. However, several unique properties of lanes pose challenges for detection methods. The lack of distinctive features can cause lane detection algorithms to be confused by other objects with similar appearances. Additionally, the varying number of lanes and the diversity in lane line patterns, such as solid, broken, single, double, merging, and splitting lines, further complicate the task. To address these challenges, Deep Learning (DL) approaches can be employed in various ways. Merging DL models with an attention mechanism has recently surfaced as a new approach. In this context, two deep learning-based lane recognition methods are proposed in this study. The first method employs the Feature Pyramid Network (FPN) model, delivering an impressive 87.59% accuracy in detecting road lanes. The second method, which incorporates attention layers into the U-Net model, significantly boosts the performance of semantic segmentation tasks. The advanced model, achieving an extraordinary 98.98% accuracy and far surpassing the basic U-Net model, clearly showcases its superiority over existing methods in a comparative analysis. The groundbreaking findings of this research pave the way for the development of more effective and reliable road lane detection methods, significantly advancing the capabilities of modern assisted and autonomous driving systems.
Targeting Negative Flips in Active Learning using Validation Sets
Authors: Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
Date: 2024-11-16 00:00:00
Tasks: Active Learning, negative flip rate

The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second way is to ensure that correct predictions are not forgotten when the training set is increased in between rounds. The former is measured by the accuracy of the model and the latter is captured in negative flips between rounds. Negative flips are samples that are correctly predicted when trained with the previous/smaller dataset and incorrectly predicted after additional samples are labeled. In this paper, we discuss improving the performance of active learning algorithms both in terms of prediction accuracy and negative flips. The first observation we make in this paper is that negative flips and overall error rates are decoupled and reducing one does not necessarily imply that the other is reduced. Our observation is important as current active learning algorithms do not consider negative flips directly and implicitly assume the opposite. The second observation is that performing targeted active learning on subsets of the unlabeled pool has a significant impact on the behavior of the active learning algorithm and influences both negative flips and prediction accuracy. We then develop ROSE - a plug-in algorithm that utilizes a small labeled validation set to restrict arbitrary active learning acquisition functions to negative flips within the unlabeled pool. We show that integrating a validation set results in a significant performance boost in terms of accuracy, negative flip rate reduction, or both.
Deep BI-RADS Network for Improved Cancer Detection from Mammograms
Authors: Gil Ben-Artzi, Feras Daragma, Shahar Mahpod
Date: 2024-11-16 00:00:00
Tasks: Breast Cancer Detection, Diagnostic

While state-of-the-art models for breast cancer detection leverage multi-view mammograms for enhanced diagnostic accuracy, they often focus solely on visual mammography data. However, radiologists document valuable lesion descriptors that contain additional information that can enhance mammography-based breast cancer screening. A key question is whether deep learning models can benefit from these expert-derived features. To address this question, we introduce a novel multi-modal approach that combines textual BI-RADS lesion descriptors with visual mammogram content. Our method employs iterative attention layers to effectively fuse these different modalities, significantly improving classification performance over image-only models. Experiments on the CBIS-DDSM dataset demonstrate substantial improvements across all metrics, demonstrating the contribution of handcrafted features to end-to-end.
ChannelDropBack: Forward-Consistent Stochastic Regularization for Deep Networks
Authors: Evgeny Hershkovitch Neiterman, Gil Ben-Artzi
Date: 2024-11-16 00:00:00

Incorporating stochasticity into the training process of deep convolutional networks is a widely used technique to reduce overfitting and improve regularization. Existing techniques often require modifying the architecture of the network by adding specialized layers, are effective only to specific network topologies or types of layers - linear or convolutional, and result in a trained model that is different from the deployed one. We present ChannelDropBack, a simple stochastic regularization approach that introduces randomness only into the backward information flow, leaving the forward pass intact. ChannelDropBack randomly selects a subset of channels within the network during the backpropagation step and applies weight updates only to them. As a consequence, it allows for seamless integration into the training process of any model and layers without the need to change its architecture, making it applicable to various network topologies, and the exact same network is deployed during training and inference. Experimental evaluations validate the effectiveness of our approach, demonstrating improved accuracy on popular datasets and models, including ImageNet and ViT. Code is available at \url{https://github.com/neiterman21/ChannelDropBack.git}.
FIAS: Feature Imbalance-Aware Medical Image Segmentation with Dynamic Fusion and Mixing Attention
Authors: Xiwei Liu, Min Xu, Qirong Ho
Date: 2024-11-16 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

With the growing application of transformer in computer vision, hybrid architecture that combine convolutional neural networks (CNNs) and transformers demonstrates competitive ability in medical image segmentation. However, direct fusion of features from CNNs and transformers often leads to feature imbalance and redundant information. To address these issues, we propose a Feaure Imbalance-Aware Segmentation (FIAS) network, which incorporates a dual-path encoder and a novel Mixing Attention (MixAtt) decoder. The dual-branches encoder integrates a DilateFormer for long-range global feature extraction and a Depthwise Multi-Kernel (DMK) convolution for capturing fine-grained local details. A Context-Aware Fusion (CAF) block dynamically balances the contribution of these global and local features, preventing feature imbalance. The MixAtt decoder further enhances segmentation accuracy by combining self-attention and Monte Carlo attention, enabling the model to capture both small details and large-scale dependencies. Experimental results on the Synapse multi-organ and ACDC datasets demonstrate the strong competitiveness of our approach in medical image segmentation tasks.
Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model
Authors: Arnab Kumar Roy, Hemant Kumar Kathania, Adhitiya Sharma
Date: 2024-11-16 00:00:00
Tasks: Data Augmentation, Emotion Recognition, Facial Emotion Recognition

Facial Emotion Recognition (FER) plays a crucial role in computer vision, with significant applications in human-computer interaction, affective computing, and areas such as mental health monitoring and personalized learning environments. However, a major challenge in FER task is the class imbalance commonly found in available datasets, which can hinder both model performance and generalization. In this paper, we tackle the issue of data imbalance by incorporating synthetic data augmentation and leveraging the ResEmoteNet model to enhance the overall performance on facial emotion recognition task. We employed Stable Diffusion 2 and Stable Diffusion 3 Medium models to generate synthetic facial emotion data, augmenting the training sets of the FER2013 and RAF-DB benchmark datasets. Training ResEmoteNet with these augmented datasets resulted in substantial performance improvements, achieving accuracies of 96.47% on FER2013 and 99.23% on RAF-DB. These findings shows an absolute improvement of 16.68% in FER2013, 4.47% in RAF-DB and highlight the efficacy of synthetic data augmentation in strengthening FER models and underscore the potential of advanced generative models in FER research and applications. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet
Large Vision-Language Models for Remote Sensing Visual Question Answering
Authors: Surasakdi Siripong, Apirak Chaiyapan, Thanakorn Phonchai
Date: 2024-11-16 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering, Visual Question Answering

Remote Sensing Visual Question Answering (RSVQA) is a challenging task that involves interpreting complex satellite imagery to answer natural language questions. Traditional approaches often rely on separate visual feature extractors and language processing models, which can be computationally intensive and limited in their ability to handle open-ended questions. In this paper, we propose a novel method that leverages a generative Large Vision-Language Model (LVLM) to streamline the RSVQA process. Our approach consists of a two-step training strategy: domain-adaptive pretraining and prompt-based finetuning. This method enables the LVLM to generate natural language answers by conditioning on both visual and textual inputs, without the need for predefined answer categories. We evaluate our model on the RSVQAxBEN dataset, demonstrating superior performance compared to state-of-the-art baselines. Additionally, a human evaluation study shows that our method produces answers that are more accurate, relevant, and fluent. The results highlight the potential of generative LVLMs in advancing the field of remote sensing analysis.
NeuroNURBS: Learning Efficient Surface Representations for 3D Solids
Authors: Jiajie Fan, Babak Gholami, Thomas Bäck, Hao Wang
Date: 2024-11-16 00:00:00
Tasks: GPU, Representation Learning

Boundary Representation (B-Rep) is the de facto representation of 3D solids in Computer-Aided Design (CAD). B-Rep solids are defined with a set of NURBS (Non-Uniform Rational B-Splines) surfaces forming a closed volume. To represent a surface, current works often employ the UV-grid approximation, i.e., sample points uniformly on the surface. However, the UV-grid method is not efficient in surface representation and sometimes lacks precision and regularity. In this work, we propose NeuroNURBS, a representation learning method to directly encode the parameters of NURBS surfaces. Our evaluation in solid generation and segmentation tasks indicates that the NeuroNURBS performs comparably and, in some cases, superior to UV-grids, but with a significantly improved efficiency: for training the surface autoencoder, GPU consumption is reduced by 86.7%; memory requirement drops by 79.9% for storing 3D solids. Moreover, adapting BrepGen for solid generation with our NeuroNURBS improves the FID from 30.04 to 27.24, and resolves the undulating issue in generated surfaces.
Automatic Discovery and Assessment of Interpretable Systematic Errors in Semantic Segmentation
Authors: Jaisidh Singh, Sonam Singh, Amit Arvind Kale, Harsh K Gandhi
Date: 2024-11-16 00:00:00
Tasks: Autonomous Driving, Segmentation, Semantic Segmentation

This paper presents a novel method for discovering systematic errors in segmentation models. For instance, a systematic error in the segmentation model can be a sufficiently large number of misclassifications from the model as a parking meter for a target class of pedestrians. With the rapid deployment of these models in critical applications such as autonomous driving, it is vital to detect and interpret these systematic errors. However, the key challenge is automatically discovering such failures on unlabelled data and forming interpretable semantic sub-groups for intervention. For this, we leverage multimodal foundation models to retrieve errors and use conceptual linkage along with erroneous nature to study the systematic nature of these errors. We demonstrate that such errors are present in SOTA segmentation models (UperNet ConvNeXt and UperNet Swin) trained on the Berkeley Deep Drive and benchmark the approach qualitatively and quantitatively, showing its effectiveness by discovering coherent systematic errors for these models. Our work opens up the avenue to model analysis and intervention that have so far been underexplored in semantic segmentation.
AnimateAnything: Consistent and Controllable Animation for Video Generation
Authors: Guojun Lei, Chi Wang, Hong Li, Rong Zhang, Yikai Wang, Weiwei Xu
Date: 2024-11-16 00:00:00
Proceeding: CVPR 2025 1
Tasks: Video Generation

We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations. Specifically, we carefully design a multi-scale control feature fusion network to construct a common motion representation for different conditions. It explicitly converts all control information into frame-by-frame optical flows. Then we incorporate the optical flows as motion priors to guide final video generation. In addition, to reduce the flickering issues caused by large-scale motion, we propose a frequency-based stabilization module. It can enhance temporal coherence by ensuring the video's frequency domain consistency. Experiments demonstrate that our method outperforms the state-of-the-art approaches. For more details and videos, please refer to the webpage: https://yu-shaonian.github.io/Animate_Anything/.
Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume
Authors: Langrui Zhou, Ziteng Zhou, Xinyu Huang, Huiru Wang, Xiangyu Zhang, Guang Li
Date: 2024-11-16 00:00:00
Tasks: Denoising, Image Denoising, Medical Image Denoising

In the last few years, with the rapid development of deep learning technologies, supervised methods based on convolutional neural networks have greatly enhanced the performance of medical image denoising. However, these methods require large quantities of noisy-clean image pairs for training, which greatly limits their practicality. Although some researchers have attempted to train denoising networks using only single noisy images, existing self-supervised methods, including blind-spot-based and data-splitting-based methods, heavily rely on the assumption that noise is pixel-wise independent. However, this assumption often does not hold in real-world medical images. Therefore, in the field of medical imaging, there remains a lack of simple and practical denoising methods that can achieve high-quality denoising performance using only single noisy images. In this paper, we propose a novel self-supervised medical image denoising method, Neighboring Slice Noise2Noise (NS-N2N). The proposed method utilizes neighboring slices within a single noisy image volume to construct weighted training data, and then trains the denoising network using a self-supervised scheme with regional consistency loss and inter-slice continuity loss. NS-N2N only requires a single noisy image volume obtained from one medical imaging procedure to achieve high-quality denoising of the image volume itself. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art self-supervised denoising methods in both denoising performance and processing efficiency. Furthermore, since NS-N2N operates solely in the image domain, it is free from device-specific issues such as reconstruction geometry, making it easier to apply in various clinical practices.
ARM: Appearance Reconstruction Model for Relightable 3D Generation
Authors: Xiang Feng, Chang Yu, Zoubin Bi, Yintong Shang, Feng Gao, Hongzhi Wu, Kun Zhou, Chenfanfu Jiang, Yin Yang
Date: 2024-11-16 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Generation, 3D Reconstruction, Image to 3D, model

Recent image-to-3D reconstruction models have greatly advanced geometry generation, but they still struggle to faithfully generate realistic appearance. To address this, we introduce ARM, a novel method that reconstructs high-quality 3D meshes and realistic appearance from sparse-view images. The core of ARM lies in decoupling geometry from appearance, processing appearance within the UV texture space. Unlike previous methods, ARM improves texture quality by explicitly back-projecting measurements onto the texture map and processing them in a UV space module with a global receptive field. To resolve ambiguities between material and illumination in input images, ARM introduces a material prior that encodes semantic appearance information, enhancing the robustness of appearance decomposition. Trained on just 8 H100 GPUs, ARM outperforms existing methods both quantitatively and qualitatively.
FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations
Authors: Hmrishav Bandyopadhyay, Yi-Zhe Song
Date: 2024-11-16 00:00:00
Proceeding: CVPR 2025 1
Tasks: Visual Storytelling

Sketch animations offer a powerful medium for visual storytelling, from simple flip-book doodles to professional studio productions. While traditional animation requires teams of skilled artists to draw key frames and in-between frames, existing automation attempts still demand significant artistic effort through precise motion paths or keyframe specification. We present FlipSketch, a system that brings back the magic of flip-book animation -- just draw your idea and describe how you want it to move! Our approach harnesses motion priors from text-to-video diffusion models, adapting them to generate sketch animations through three key innovations: (i) fine-tuning for sketch-style frame generation, (ii) a reference frame mechanism that preserves visual integrity of input sketch through noise refinement, and (iii) a dual-attention composition that enables fluid motion without losing visual consistency. Unlike constrained vector animations, our raster frames support dynamic sketch transformations, capturing the expressive freedom of traditional animation. The result is an intuitive system that makes sketch animation as simple as doodling and describing, while maintaining the artistic essence of hand-drawn animation.
DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group Robust Early Diagnosis of MCI to AD Conversion
Authors: DongGyu Lee, Juhyeon Park, Taesup Moon
Date: 2024-11-16 00:00:00

While deep learning-based Alzheimer's disease (AD) diagnosis has recently made significant advancements, particularly in predicting the conversion of mild cognitive impairment (MCI) to AD based on MRI images, there remains a critical gap in research regarding the group robustness of the diagnosis. Although numerous studies pointed out that deep learning-based classifiers may exhibit poor performance in certain groups by relying on unimportant attributes, this issue has been largely overlooked in the early diagnosis of MCI to AD conversion. In this paper, we present the first comprehensive investigation of the group robustness in the early diagnosis of MCI to AD conversion using MRI images, focusing on disparities in accuracy between groups, specifically sMCI and pMCI individuals divided by age. Our experiments reveal that standard classifiers consistently underperform for certain groups across different architectures, highlighting the need for more tailored approaches. To address this, we propose a novel method, dubbed DEAL (DEcoupled classifier with Adaptive Linear modulation), comprising two key components: (1) a linear modulation of features from the penultimate layer, incorporating easily obtainable age and cognitive indicative tabular features, and (2) a decoupled classifier that provides more tailored decision boundaries for each group, further improving performance. Through extensive experiments and evaluations across different architectures, we demonstrate the efficacy of DEAL in improving the group robustness of the MCI to AD conversion prediction.