pwc-archive/papers-with-abstracts huggingface dataset:
Vision-language models for decoding provider attention during neonatal resuscitation
Authors: Felipe Parodi, Jordan Matelsky, Alejandra Regla-Vargas, Elizabeth Foglia, Charis Lim, Danielle Weinberg, Konrad Kording, Heidi Herrick, Michael Platt
Date: 2024-04-01 00:00:00
Tasks: Decision Making

Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously. Gaze strongly influences decision making; thus, understanding where a provider is looking during neonatal resuscitations could inform provider training, enhance real-time decision support, and improve the design of delivery rooms and neonatal intensive care units (NICUs). Current approaches to quantifying neonatal providers' gaze rely on manual coding or simulations, which limit scalability and utility. Here, we introduce an automated, real-time, deep learning approach capable of decoding provider gaze into semantic classes directly from first-person point-of-view videos recorded during live resuscitations. Combining state-of-the-art, real-time segmentation with vision-language models (CLIP), our low-shot pipeline attains 91\% classification accuracy in identifying gaze targets without training. Upon fine-tuning, the performance of our gaze-guided vision transformer exceeds 98\% accuracy in gaze classification, approaching human-level precision. This system, capable of real-time inference, enables objective quantification of provider attention dynamics during live neonatal resuscitation. Our approach offers a scalable solution that seamlessly integrates with existing infrastructure for data-scarce gaze analysis, thereby offering new opportunities for understanding and refining clinical decision making.
Video Interpolation with Diffusion Models
Authors: Siddhant Jain, Daniel Watson, Eric Tabellion, Aleksander Hołyński, Ben Poole, Janne Kontkanen
Date: 2024-04-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Super-Resolution

We present VIDIM, a generative model for video interpolation, which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data, VIDIM uses cascaded diffusion models to first generate the target video at low resolution, and then generate the high-resolution video conditioned on the low-resolution generated video. We compare VIDIM to previous state-of-the-art methods on video interpolation, and demonstrate how such works fail in most settings where the underlying motion is complex, nonlinear, or ambiguous while VIDIM can easily handle such cases. We additionally demonstrate how classifier-free guidance on the start and end frame and conditioning the super-resolution model on the original high-resolution frames without additional parameters unlocks high-fidelity results. VIDIM is fast to sample from as it jointly denoises all the frames to be generated, requires less than a billion parameters per diffusion model to produce compelling results, and still enjoys scalability and improved quality at larger parameter counts.
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Authors: Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Aflalo, Sayak Paul, Dhruba Ghosh, Tejas Gokhale, Ludwig Schmidt, Hannaneh Hajishirzi, Vasudev Lal, Chitta Baral, Yezhou Yang
Date: 2024-04-01 00:00:00
Tasks: Spatial Reasoning

One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt. In this paper, we offer a comprehensive investigation of this limitation, while also developing datasets and methods that support algorithmic solutions to improve spatial reasoning in T2I models. We find that spatial relationships are under-represented in the image descriptions found in current vision-language datasets. To alleviate this data bottleneck, we create SPRIGHT, the first spatially focused, large-scale dataset, by re-captioning 6 million images from 4 widely used vision datasets and through a 3-fold evaluation and analysis pipeline, show that SPRIGHT improves the proportion of spatial relationships in existing datasets. We show the efficacy of SPRIGHT data by showing that using only $\sim$0.25% of SPRIGHT results in a 22% improvement in generating spatially accurate images while also improving FID and CMMD scores. We also find that training on images containing a larger number of objects leads to substantial improvements in spatial consistency, including state-of-the-art results on T2I-CompBench with a spatial score of 0.2133, by fine-tuning on <500 images. Through a set of controlled experiments and ablations, we document additional findings that could support future work that seeks to understand factors that affect spatial consistency in text-to-image models.
Adaptive Query Prompting for Multi-Domain Landmark Detection
Authors: Qiusen Wei, Guoheng Huang, Xiaochen Yuan, Xuhang Chen, Guo Zhong, Jianwen Huang, Jiajie Huang
Date: 2024-04-01 00:00:00
Tasks: Decoder

Medical landmark detection is crucial in various medical imaging modalities and procedures. Although deep learning-based methods have achieve promising performance, they are mostly designed for specific anatomical regions or tasks. In this work, we propose a universal model for multi-domain landmark detection by leveraging transformer architecture and developing a prompting component, named as Adaptive Query Prompting (AQP). Instead of embedding additional modules in the backbone network, we design a separate module to generate prompts that can be effectively extended to any other transformer network. In our proposed AQP, prompts are learnable parameters maintained in a memory space called prompt pool. The central idea is to keep the backbone frozen and then optimize prompts to instruct the model inference process. Furthermore, we employ a lightweight decoder to decode landmarks from the extracted features, namely Light-MLD. Thanks to the lightweight nature of the decoder and AQP, we can handle multiple datasets by sharing the backbone encoder and then only perform partial parameter tuning without incurring much additional cost. It has the potential to be extended to more landmark detection tasks. We conduct experiments on three widely used X-ray datasets for different medical landmark detection tasks. Our proposed Light-MLD coupled with AQP achieves SOTA performance on many metrics even without the use of elaborate structural designs or complex frameworks.
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
Authors: Fengtao Zhou, Yingxue Xu, Yanfen Cui, Shenyan Zhang, Yun Zhu, Weiyang He, Jiguang Wang, Xin Wang, Ronald Chan, Louis Ho Shing Lau, Chu Han, Dafu Zhang, Zhenhui Li, Hao Chen
Date: 2024-04-01 00:00:00
Tasks: Data Integration, Survival Analysis

Gastric cancer (GC) is a prevalent malignancy worldwide, ranking as the fifth most common cancer with over 1 million new cases and 700 thousand deaths in 2020. Locally advanced gastric cancer (LAGC) accounts for approximately two-thirds of GC diagnoses, and neoadjuvant chemotherapy (NACT) has emerged as the standard treatment for LAGC. However, the effectiveness of NACT varies significantly among patients, with a considerable subset displaying treatment resistance. Ineffective NACT not only leads to adverse effects but also misses the optimal therapeutic window, resulting in lower survival rate. However, existing multimodal learning methods assume the availability of all modalities for each patient, which does not align with the reality of clinical practice. The limited availability of modalities for each patient would cause information loss, adversely affecting predictive accuracy. In this study, we propose an incomplete multimodal data integration framework for GC (iMD4GC) to address the challenges posed by incomplete multimodal data, enabling precise response prediction and survival analysis. Specifically, iMD4GC incorporates unimodal attention layers for each modality to capture intra-modal information. Subsequently, the cross-modal interaction layers explore potential inter-modal interactions and capture complementary information across modalities, thereby enabling information compensation for missing modalities. To evaluate iMD4GC, we collected three multimodal datasets for GC study: GastricRes (698 cases) for response prediction, GastricSur (801 cases) for survival analysis, and TCGA-STAD (400 cases) for survival analysis. The scale of our datasets is significantly larger than previous studies. The iMD4GC achieved impressive performance with an 80.2% AUC on GastricRes, 71.4% C-index on GastricSur, and 66.1% C-index on TCGA-STAD, significantly surpassing other compared methods.
BEM: Balanced and Entropy-based Mix for Long-Tailed Semi-Supervised Learning
Authors: Hongwei Zheng, Linyuan Zhou, Han Li, Jinming Su, Xiaoming Wei, Xiaoming Xu
Date: 2024-04-01 00:00:00
Proceeding: CVPR 2024 1

Data mixing methods play a crucial role in semi-supervised learning (SSL), but their application is unexplored in long-tailed semi-supervised learning (LTSSL). The primary reason is that the in-batch mixing manner fails to address class imbalance. Furthermore, existing LTSSL methods mainly focus on re-balancing data quantity but ignore class-wise uncertainty, which is also vital for class balance. For instance, some classes with sufficient samples might still exhibit high uncertainty due to indistinguishable features. To this end, this paper introduces the Balanced and Entropy-based Mix (BEM), a pioneering mixing approach to re-balance the class distribution of both data quantity and uncertainty. Specifically, we first propose a class balanced mix bank to store data of each class for mixing. This bank samples data based on the estimated quantity distribution, thus re-balancing data quantity. Then, we present an entropy-based learning approach to re-balance class-wise uncertainty, including entropy-based sampling strategy, entropy-based selection module, and entropy-based class balanced loss. Our BEM first leverages data mixing for improving LTSSL, and it can also serve as a complement to the existing re-balancing methods. Experimental results show that BEM significantly enhances various LTSSL frameworks and achieves state-of-the-art performances across multiple benchmarks.
SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Grounding
Authors: Wenrui Li, Xiaopeng Hong, Ruiqin Xiong, Xiaopeng Fan
Date: 2024-04-01 00:00:00
Tasks: Mamba, State Space Models, Video Grounding

Temporal video grounding (TVG) is a critical task in video content understanding, requiring precise alignment between video content and natural language instructions. Despite significant advancements, existing methods face challenges in managing confidence bias towards salient objects and capturing long-term dependencies in video sequences. To address these issues, we introduce SpikeMba: a multi-modal spiking saliency mamba for temporal video grounding. Our approach integrates Spiking Neural Networks (SNNs) with state space models (SSMs) to leverage their unique advantages in handling different aspects of the task. Specifically, we use SNNs to develop a spiking saliency detector that generates the proposal set. The detector emits spike signals when the input signal exceeds a predefined threshold, resulting in a dynamic and binary saliency proposal set. To enhance the model's capability to retain and infer contextual information, we introduce relevant slots which learnable tensors that encode prior knowledge. These slots work with the contextual moment reasoner to maintain a balance between preserving contextual information and exploring semantic relevance dynamically. The SSMs facilitate selective information propagation, addressing the challenge of long-term dependency in video content. By combining SNNs for proposal generation and SSMs for effective contextual reasoning, SpikeMba addresses confidence bias and long-term dependencies, thereby significantly enhancing fine-grained multimodal relationship capture. Our experiments demonstrate the effectiveness of SpikeMba, which consistently outperforms state-of-the-art methods across mainstream benchmarks.
Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussian Splatting
Authors: Jiarui Meng, Haijie Li, Yanmin Wu, Qiankun Gao, Shuzhou Yang, Jian Zhang, Siwei Ma
Date: 2024-04-01 00:00:00
Tasks: 3DGS, 3D Scene Reconstruction, NeRF, Novel View Synthesis

3D Gaussian Splatting (3DGS) has significantly advanced 3D scene reconstruction and novel view synthesis. However, like Neural Radiance Fields (NeRF), 3DGS struggles with accurately modeling physical reflections, particularly in mirrors, leading to incorrect reconstructions and inconsistent reflective properties. To address this challenge, we introduce Mirror-3DGS, a novel framework designed to accurately handle mirror geometries and reflections, thereby generating realistic mirror reflections. By incorporating mirror attributes into 3DGS and leveraging plane mirror imaging principles, Mirror-3DGS simulates a mirrored viewpoint from behind the mirror, enhancing the realism of scene renderings. Extensive evaluations on both synthetic and real-world scenes demonstrate that our method can render novel views with improved fidelity in real-time, surpassing the state-of-the-art Mirror-NeRF, especially in mirror regions.
Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models
Authors: Amir Faghihi, Mohammadreza Fathollahi, Roozbeh Rajabi
Date: 2024-04-01 00:00:00
Tasks: Classification, image-classification, Image Classification, Lesion Classification, Lesion Detection, Skin Lesion Classification, Transfer Learning

Today, skin cancer is considered as one of the most dangerous and common cancers in the world which demands special attention. Skin cancer may be developed in different types; including melanoma, actinic keratosis, basal cell carcinoma, squamous cell carcinoma, and Merkel cell carcinoma. Among them, melanoma is more unpredictable. Melanoma cancer can be diagnosed at early stages increasing the possibility of disease treatment. Automatic classification of skin lesions is a challenging task due to diverse forms and grades of the disease, demanding the requirement of novel methods implementation. Deep convolution neural networks (CNN) have shown an excellent potential for data and image classification. In this article, we inspect skin lesion classification problem using CNN techniques. Remarkably, we present that prominent classification accuracy of lesion detection can be obtained by proper designing and applying of transfer learning framework on pre-trained neural networks, without any requirement for data enlargement procedures i.e. merging VGG16 and VGG19 architectures pre-trained by a generic dataset with modified AlexNet network, and then, fine-tuned by a subject-specific dataset containing dermatology images. The convolution neural network was trained using 2541 images and, in particular, dropout was used to prevent the network from overfitting. Finally, the validity of the model was checked by applying the K-fold cross validation method. The proposed model increased classification accuracy by 3% (from 94.2% to 98.18%) in comparison with other methods.
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs
Authors: Jialou Wang, Manli Zhu, Yulei Li, Honglei Li, Longzhi Yang, Wai Lok Woo
Date: 2024-04-01 00:00:00
Tasks: Common Sense Reasoning, Object, object-detection, Object Detection, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Localization plays a crucial role in enhancing the practicality and precision of VQA systems. By enabling fine-grained identification and interaction with specific parts of an object, it significantly improves the system's ability to provide contextually relevant and spatially accurate responses, crucial for applications in dynamic environments like robotics and augmented reality. However, traditional systems face challenges in accurately mapping objects within images to generate nuanced and spatially aware responses. In this work, we introduce "Detect2Interact", which addresses these challenges by introducing an advanced approach for fine-grained object visual key field detection. First, we use the segment anything model (SAM) to generate detailed spatial maps of objects in images. Next, we use Vision Studio to extract semantic object descriptions. Third, we employ GPT-4's common sense knowledge, bridging the gap between an object's semantics and its spatial map. As a result, Detect2Interact achieves consistent qualitative results on object key field detection across extensive test cases and outperforms the existing VQA system with object detection by providing a more reasonable and finer visual representation.
Structured Initialization for Attention in Vision Transformers
Authors: Jianqiao Zheng, Xueqian Li, Simon Lucey
Date: 2024-04-01 00:00:00
Tasks: Inductive Bias

The training of vision transformer (ViT) networks on small-scale datasets poses a significant challenge. By contrast, convolutional neural networks (CNNs) have an architectural inductive bias enabling them to perform well on such problems. In this paper, we argue that the architectural bias inherent to CNNs can be reinterpreted as an initialization bias within ViT. This insight is significant as it empowers ViTs to perform equally well on small-scale problems while maintaining their flexibility for large-scale applications. Our inspiration for this ``structured'' initialization stems from our empirical observation that random impulse filters can achieve comparable performance to learned filters within CNNs. Our approach achieves state-of-the-art performance for data-efficient ViT learning across numerous benchmarks including CIFAR-10, CIFAR-100, and SVHN.
CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians
Authors: Yang Liu, He Guan, Chuanchen Luo, Lue Fan, Naiyan Wang, Junran Peng, Zhaoxiang Zhang
Date: 2024-04-01 00:00:00
Tasks: 3DGS, 3D Scene Reconstruction, Novel View Synthesis

The advancement of real-time 3D scene reconstruction and novel view synthesis has been significantly propelled by 3D Gaussian Splatting (3DGS). However, effectively training large-scale 3DGS and rendering it in real-time across various scales remains challenging. This paper introduces CityGaussian (CityGS), which employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and rendering. Specifically, the global scene prior and adaptive training data selection enables efficient training and seamless fusion. Based on fused Gaussian primitives, we generate different detail levels through compression, and realize fast rendering across various scales through the proposed block-wise detail levels selection and aggregation strategy. Extensive experimental results on large-scale scenes demonstrate that our approach attains state-of-theart rendering quality, enabling consistent real-time rendering of largescale scenes across vastly different scales. Our project page is available at https://dekuliutesla.github.io/citygs/.
CMT: Cross Modulation Transformer with Hybrid Loss for Pansharpening
Authors: Wen-Jie Shu, Hong-Xia Dou, Rui Wen, Xiao Wu, Liang-Jian Deng
Date: 2024-04-01 00:00:00
Tasks: Pansharpening

Pansharpening aims to enhance remote sensing image (RSI) quality by merging high-resolution panchromatic (PAN) with multispectral (MS) images. However, prior techniques struggled to optimally fuse PAN and MS images for enhanced spatial and spectral information, due to a lack of a systematic framework capable of effectively coordinating their individual strengths. In response, we present the Cross Modulation Transformer (CMT), a pioneering method that modifies the attention mechanism. This approach utilizes a robust modulation technique from signal processing, integrating it into the attention mechanism's calculations. It dynamically tunes the weights of the carrier's value (V) matrix according to the modulator's features, thus resolving historical challenges and achieving a seamless integration of spatial and spectral attributes. Furthermore, considering that RSI exhibits large-scale features and edge details along with local textures, we crafted a hybrid loss function that combines Fourier and wavelet transforms to effectively capture these characteristics, thereby enhancing both spatial and spectral accuracy in pansharpening. Extensive experiments demonstrate our framework's superior performance over existing state-of-the-art methods. The code will be publicly available to encourage further research.
Motion Blur Decomposition with Cross-shutter Guidance
Authors: Xiang Ji, Haiyang Jiang, Yinqiang Zheng
Date: 2024-04-01 00:00:00
Proceeding: CVPR 2024 1

Motion blur is a frequently observed image artifact, especially under insufficient illumination where exposure time has to be prolonged so as to collect more photons for a bright enough image. Rather than simply removing such blurring effects, recent researches have aimed at decomposing a blurry image into multiple sharp images with spatial and temporal coherence. Since motion blur decomposition itself is highly ambiguous, priors from neighbouring frames or human annotation are usually needed for motion disambiguation. In this paper, inspired by the complementary exposure characteristics of a global shutter (GS) camera and a rolling shutter (RS) camera, we propose to utilize the ordered scanline-wise delay in a rolling shutter image to robustify motion decomposition of a single blurry image. To evaluate this novel dual imaging setting, we construct a triaxial system to collect realistic data, as well as a deep network architecture that explicitly addresses temporal and contextual information through reciprocal branches for cross-shutter motion blur decomposition. Experiment results have verified the effectiveness of our proposed algorithm, as well as the validity of our dual imaging setting.
Diffusion based Zero-shot Medical Image-to-Image Translation for Cross Modality Segmentation
Authors: ZiHao Wang, Yingyu Yang, Yuzhou Chen, Tingting Yuan, Maxime Sermesant, Herve Delingette, Ona Wu
Date: 2024-04-01 00:00:00
Tasks: Image Segmentation, Image-to-Image Translation, Segmentation, Semantic Segmentation, Translation, Zero-Shot Learning

Cross-modality image segmentation aims to segment the target modalities using a method designed in the source modality. Deep generative models can translate the target modality images into the source modality, thus enabling cross-modality segmentation. However, a vast body of existing cross-modality image translation methods relies on supervised learning. In this work, we aim to address the challenge of zero-shot learning-based image translation tasks (extreme scenarios in the target modality is unseen in the training phase). To leverage generative learning for zero-shot cross-modality image segmentation, we propose a novel unsupervised image translation method. The framework learns to translate the unseen source image to the target modality for image segmentation by leveraging the inherent statistical consistency between different modalities for diffusion guidance. Our framework captures identical cross-modality features in the statistical domain, offering diffusion guidance without relying on direct mappings between the source and target domains. This advantage allows our method to adapt to changing source domains without the need for retraining, making it highly practical when sufficient labeled source domain data is not available. The proposed framework is validated in zero-shot cross-modality image segmentation tasks through empirical comparisons with influential generative models, including adversarial-based and diffusion-based models.