pwc-archive/papers-with-abstracts huggingface dataset:
Inter-object Discriminative Graph Modeling for Indoor Scene Recognition
Authors: Chuanxin Song, Hanbo Wu, Xin Ma
Date: 2023-11-10 00:00:00
Tasks: Object, Scene Recognition

Variable scene layouts and coexisting objects across scenes make indoor scene recognition still a challenging task. Leveraging object information within scenes to enhance the distinguishability of feature representations has emerged as a key approach in this domain. Currently, most object-assisted methods use a separate branch to process object information, combining object and scene features heuristically. However, few of them pay attention to interpretably handle the hidden discriminative knowledge within object information. In this paper, we propose to leverage discriminative object knowledge to enhance scene feature representations. Initially, we capture the object-scene discriminative relationships from a probabilistic perspective, which are transformed into an Inter-Object Discriminative Prototype (IODP). Given the abundant prior knowledge from IODP, we subsequently construct a Discriminative Graph Network (DGN), in which pixel-level scene features are defined as nodes and the discriminative relationships between node features are encoded as edges. DGN aims to incorporate inter-object discriminative knowledge into the image representation through graph convolution and mapping operations (GCN). With the proposed IODP and DGN, we obtain state-of-the-art results on several widely used scene datasets, demonstrating the effectiveness of the proposed approach.
Semantic Map Guided Synthesis of Wireless Capsule Endoscopy Images using Diffusion Models
Authors: Haejin Lee, Jeongwoo Ju, Jonghyuck Lee, Yeoun Joo Lee, Heechul Jung
Date: 2023-11-10 00:00:00
Tasks: Diversity, Lesion Detection

Wireless capsule endoscopy (WCE) is a non-invasive method for visualizing the gastrointestinal (GI) tract, crucial for diagnosing GI tract diseases. However, interpreting WCE results can be time-consuming and tiring. Existing studies have employed deep neural networks (DNNs) for automatic GI tract lesion detection, but acquiring sufficient training examples, particularly due to privacy concerns, remains a challenge. Public WCE databases lack diversity and quantity. To address this, we propose a novel approach leveraging generative models, specifically the diffusion model (DM), for generating diverse WCE images. Our model incorporates semantic map resulted from visualization scale (VS) engine, enhancing the controllability and diversity of generated images. We evaluate our approach using visual inspection and visual Turing tests, demonstrating its effectiveness in generating realistic and diverse WCE images.
Central Angle Optimization for 360-degree Holographic 3D Content
Authors: Hakdong Kim, MinSung Yoon, Cheongwon Kim
Date: 2023-11-10 00:00:00

In this study, we propose a method to find an optimal central angle in deep learning-based depth map estimation used to produce realistic holographic content. The acquisition of RGB-depth map images as detailed as possible must be performed to generate holograms of high quality, despite the high computational cost. Therefore, we introduce a novel pipeline designed to analyze various values of central angles between adjacent camera viewpoints equidistant from the origin of an object-centered environment. Then we propose the optimal central angle to generate high-quality holographic content. The proposed pipeline comprises key steps such as comparing estimated depth maps and comparing reconstructed CGHs (Computer-Generated Holograms) from RGB images and estimated depth maps. We experimentally demonstrate and discuss the relationship between the central angle and the quality of digital holographic content.
Automated Heterogeneous Low-Bit Quantization of Multi-Model Deep Learning Inference Pipeline
Authors: Jayeeta Mondal, Swarnava Dey, Arijit Mukherjee
Date: 2023-11-10 00:00:00
Tasks: Ensemble Learning, Multi-Task Learning, Quantization

Multiple Deep Neural Networks (DNNs) integrated into single Deep Learning (DL) inference pipelines e.g. Multi-Task Learning (MTL) or Ensemble Learning (EL), etc., albeit very accurate, pose challenges for edge deployment. In these systems, models vary in their quantization tolerance and resource demands, requiring meticulous tuning for accuracy-latency balance. This paper introduces an automated heterogeneous quantization approach for DL inference pipelines with multiple DNNs.
Domain Generalization by Learning from Privileged Medical Imaging Information
Authors: Steven Korevaar, Ruwan Tennakoon, Ricky O'Brien, Dwarikanath Mahapatra, Alireza Bab-Hadiasha
Date: 2023-11-10 00:00:00
Tasks: Domain Generalization

Learning the ability to generalize knowledge between similar contexts is particularly important in medical imaging as data distributions can shift substantially from one hospital to another, or even from one machine to another. To strengthen generalization, most state-of-the-art techniques inject knowledge of the data distribution shifts by enforcing constraints on learned features or regularizing parameters. We offer an alternative approach: Learning from Privileged Medical Imaging Information (LPMII). We show that using some privileged information such as tumor shape or location leads to stronger domain generalization ability than current state-of-the-art techniques. This paper demonstrates that by using privileged information to predict the severity of intra-layer retinal fluid in optical coherence tomography scans, the classification accuracy of a deep learning model operating on out-of-distribution data improves from $0.911$ to $0.934$. This paper provides a strong starting point for using privileged information in other medical problems requiring generalization.
UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields
Authors: Jing Hu, Qinrui Fan, Shu Hu, Siwei Lyu, Xi Wu, Xin Wang
Date: 2023-11-10 00:00:00
Tasks: Computed Tomography (CT)

In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies. Compared with X-ray images, CT images can provide more information, including multi-planar slices and three-dimensional structures for clinical diagnosis. However, CT imaging requires patients to be exposed to large doses of ionizing radiation for a long time, which may cause irreversible physical harm. In this paper, we propose an Uncertainty-aware MedNeRF (UMedNeRF) network based on generated radiation fields. The network can learn a continuous representation of CT projections from 2D X-ray images by obtaining the internal structure and depth information and using adaptive loss weights to ensure the quality of the generated images. Our model is trained on publicly available knee and chest datasets, and we show the results of CT projection rendering with a single X-ray and compare our method with other methods based on generated radiation fields.
Diffusion Shape Prior for Wrinkle-Accurate Cloth Registration
Authors: Jingfan Guo, Fabian Prada, Donglai Xiang, Javier Romero, Chenglei Wu, Hyun Soo Park, Takaaki Shiratori, Shunsuke Saito
Date: 2023-11-10 00:00:00
Tasks: parameter estimation

Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data. However, previous methods either rely on texture information, which is not always reliable, or achieve only coarse-level alignment. In this work, we present a novel approach to enabling accurate surface registration of texture-less clothes with large deformation. Our key idea is to effectively leverage a shape prior learned from pre-captured clothing using diffusion models. We also propose a multi-stage guidance scheme based on learned functional maps, which stabilizes registration for large-scale deformation even when they vary significantly from training data. Using high-fidelity real captured clothes, our experiments show that the proposed approach based on diffusion models generalizes better than surface registration with VAE or PCA-based priors, outperforming both optimization-based and learning-based non-rigid registration methods for both interpolation and extrapolation tests.
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification
Authors: Fabi Prezja, Leevi Annala, Sampsa Kiiskinen, Suvi Lahtinen, Timo Ojala
Date: 2023-11-10 00:00:00
Tasks: Classification, Neural Architecture Search, Transfer Learning

Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability and is inherently complex to diagnose due to its subtle radiographic markers and individualized progression. One promising classification avenue involves applying deep learning methods; however, these techniques demand extensive, diversified datasets, which pose substantial challenges due to medical data collection restrictions. Existing practices typically resort to smaller datasets and transfer learning. However, this approach often inherits unnecessary pre-learned features that can clutter the classifier's vector space, potentially hampering performance. This study proposes a novel paradigm for improving post-training specialized classifiers by introducing adaptive variance thresholding (AVT) followed by Neural Architecture Search (NAS). This approach led to two key outcomes: an increase in the initial accuracy of the pre-trained KOA models and a 60-fold reduction in the NAS input vector space, thus facilitating faster inference speed and a more efficient hyperparameter search. We also applied this approach to an external model trained for KOA classification. Despite its initial performance, the application of our methodology improved its average accuracy, making it one of the top three KOA classification models.
Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks
Authors: Fabi Prezja, Leevi Annala, Sampsa Kiiskinen, Suvi Lahtinen, Timo Ojala
Date: 2023-11-10 00:00:00
Tasks: Data Augmentation, Diagnostic

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limitations, and the progressive nature of KOA. However, a model capable of projecting genuine radiographs into different OA stages could augment data pools, enhance algorithm training, and offer pre-emptive prognostic insights. In this study, we trained a CycleGAN model to synthesize past and future stages of KOA on any genuine radiograph. The model was validated using a Convolutional Neural Network that was deceived into misclassifying disease stages in transformed images, demonstrating the CycleGAN's ability to effectively transform disease characteristics forward or backward in time. The model was particularly effective in synthesizing future disease states and showed an exceptional ability to retroactively transition late-stage radiographs to earlier stages by eliminating osteophytes and expanding knee joint space, signature characteristics of None or Doubtful KOA. The model's results signify a promising potential for enhancing diagnostic models, data augmentation, and educational and prognostic usage in healthcare. Nevertheless, further refinement, validation, and a broader evaluation process encompassing both CNN-based assessments and expert medical feedback are emphasized for future research and development.
Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter
Authors: Georgios Tziafas, Yucheng Xu, Arushi Goel, Mohammadreza Kasaei, Zhibin Li, Hamidreza Kasaei
Date: 2023-11-09 00:00:00
Tasks: Object, Visual Grounding

Robots operating in human-centric environments require the integration of visual grounding and grasping capabilities to effectively manipulate objects based on user instructions. This work focuses on the task of referring grasp synthesis, which predicts a grasp pose for an object referred through natural language in cluttered scenes. Existing approaches often employ multi-stage pipelines that first segment the referred object and then propose a suitable grasp, and are evaluated in private datasets or simulators that do not capture the complexity of natural indoor scenes. To address these limitations, we develop a challenging benchmark based on cluttered indoor scenes from OCID dataset, for which we generate referring expressions and connect them with 4-DoF grasp poses. Further, we propose a novel end-to-end model (CROG) that leverages the visual grounding capabilities of CLIP to learn grasp synthesis directly from image-text pairs. Our results show that vanilla integration of CLIP with pretrained models transfers poorly in our challenging benchmark, while CROG achieves significant improvements both in terms of grounding and grasping. Extensive robot experiments in both simulation and hardware demonstrate the effectiveness of our approach in challenging interactive object grasping scenarios that include clutter.
PolyMaX: General Dense Prediction with Mask Transformer
Authors: Xuan Yang, Liangzhe Yuan, Kimberly Wilber, Astuti Sharma, Xiuye Gu, Siyuan Qiao, Stephanie Debats, Huisheng Wang, Hartwig Adam, Mikhail Sirotenko, Liang-Chieh Chen
Date: 2023-11-09 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Prediction, Semantic Segmentation, Surface Normal Estimation, Surface Normals Estimation

Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or regression (continuous outputs). This per-pixel prediction paradigm has remained popular due to the prevalence of fully convolutional networks. However, on the recent frontier of segmentation task, the community has been witnessing a shift of paradigm from per-pixel prediction to cluster-prediction with the emergence of transformer architectures, particularly the mask transformers, which directly predicts a label for a mask instead of a pixel. Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction. Motivated by the success of DORN and AdaBins in depth estimation, achieved by discretizing the continuous output space, we propose to generalize the cluster-prediction based method to general dense prediction tasks. This allows us to unify dense prediction tasks with the mask transformer framework. Remarkably, the resulting model PolyMaX demonstrates state-of-the-art performance on three benchmarks of NYUD-v2 dataset. We hope our simple yet effective design can inspire more research on exploiting mask transformers for more dense prediction tasks. Code and model will be made available.
GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot Learning
Authors: Guangyue Xu, Joyce Chai, Parisa Kordjamshidi
Date: 2023-11-09 00:00:00
Tasks: Attribute, Compositional Zero-Shot Learning, Prompt Learning, Zero-Shot Learning

Pre-trained vision-language models (VLMs) have achieved promising success in many fields, especially with prompt learning paradigm. In this work, we propose GIP-COL (Graph-Injected Soft Prompting for COmpositional Learning) to better explore the compositional zero-shot learning (CZSL) ability of VLMs within the prompt-based learning framework. The soft prompt in GIPCOL is structured and consists of the prefix learnable vectors, attribute label and object label. In addition, the attribute and object labels in the soft prompt are designated as nodes in a compositional graph. The compositional graph is constructed based on the compositional structure of the objects and attributes extracted from the training data and consequently feeds the updated concept representation into the soft prompt to capture this compositional structure for a better prompting for CZSL. With the new prompting strategy, GIPCOL achieves state-of-the-art AUC results on all three CZSL benchmarks, including MIT-States, UT-Zappos, and C-GQA datasets in both closed and open settings compared to previous non-CLIP as well as CLIP-based methods. We analyze when and why GIPCOL operates well given the CLIP backbone and its training data limitations, and our findings shed light on designing more effective prompts for CZSL
Whole-body Detection, Recognition and Identification at Altitude and Range
Authors: Siyuan Huang, Ram Prabhakar Kathirvel, Chun Pong Lau, Rama Chellappa
Date: 2023-11-09 00:00:00
Tasks: Body Detection, TAR

In this paper, we address the challenging task of whole-body biometric detection, recognition, and identification at distances of up to 500m and large pitch angles of up to 50 degree. We propose an end-to-end system evaluated on diverse datasets, including the challenging Biometric Recognition and Identification at Range (BRIAR) dataset. Our approach involves pre-training the detector on common image datasets and fine-tuning it on BRIAR's complex videos and images. After detection, we extract body images and employ a feature extractor for recognition. We conduct thorough evaluations under various conditions, such as different ranges and angles in indoor, outdoor, and aerial scenarios. Our method achieves an average F1 score of 98.29% at IoU = 0.7 and demonstrates strong performance in recognition accuracy and true acceptance rate at low false acceptance rates compared to existing models. On a test set of 100 subjects with 444 distractors, our model achieves a rank-20 recognition accuracy of 75.13% and a TAR@1%FAR of 54.09%.
Intelligent Cervical Spine Fracture Detection Using Deep Learning Methods
Authors: Reza Behbahani Nejad, Amir Hossein Komijani, Esmaeil Najafi
Date: 2023-11-09 00:00:00
Tasks: Deep Learning, Fracture detection, image-classification, Image Classification

Cervical spine fractures constitute a critical medical emergency, with the potential for lifelong paralysis or even fatality if left untreated or undetected. Over time, these fractures can deteriorate without intervention. To address the lack of research on the practical application of deep learning techniques for the detection of spine fractures, this study leverages a dataset containing both cervical spine fractures and non-fractured computed tomography images. This paper introduces a two-stage pipeline designed to identify the presence of cervical vertebrae in each image slice and pinpoint the location of fractures. In the first stage, a multi-input network, incorporating image and image metadata, is trained. This network is based on the Global Context Vision Transformer, and its performance is benchmarked against popular deep learning image classification model. In the second stage, a YOLOv8 model is trained to detect fractures within the images, and its effectiveness is compared to YOLOv5. The obtained results indicate that the proposed algorithm significantly reduces the workload of radiologists and enhances the accuracy of fracture detection.
FMViT: A multiple-frequency mixing Vision Transformer
Authors: Wei Tan, Yifeng Geng, Xuansong Xie
Date: 2023-11-09 00:00:00

The transformer model has gained widespread adoption in computer vision tasks in recent times. However, due to the quadratic time and memory complexity of self-attention, which is proportional to the number of input tokens, most existing Vision Transformers (ViTs) encounter challenges in achieving efficient performance in practical industrial deployment scenarios, such as TensorRT and CoreML, where traditional CNNs excel. Although some recent attempts have been made to design CNN-Transformer hybrid architectures to tackle this problem, their overall performance has not met expectations. To tackle these challenges, we propose an efficient hybrid ViT architecture named FMViT. This approach enhances the model's expressive power by blending high-frequency features and low-frequency features with varying frequencies, enabling it to capture both local and global information effectively. Additionally, we introduce deploy-friendly mechanisms such as Convolutional Multigroup Reparameterization (gMLP), Lightweight Multi-head Self-Attention (RLMHSA), and Convolutional Fusion Block (CFB) to further improve the model's performance and reduce computational overhead. Our experiments demonstrate that FMViT surpasses existing CNNs, ViTs, and CNNTransformer hybrid architectures in terms of latency/accuracy trade-offs for various vision tasks. On the TensorRT platform, FMViT outperforms Resnet101 by 2.5% (83.3% vs. 80.8%) in top-1 accuracy on the ImageNet dataset while maintaining similar inference latency. Moreover, FMViT achieves comparable performance with EfficientNet-B5, but with a 43% improvement in inference speed. On CoreML, FMViT outperforms MobileOne by 2.6% in top-1 accuracy on the ImageNet dataset, with inference latency comparable to MobileOne (78.5% vs. 75.9%). Our code can be found at https://github.com/tany0699/FMViT.
Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities
Authors: AJ Piergiovanni, Isaac Noble, Dahun Kim, Michael S. Ryoo, Victor Gomes, Anelia Angelova
Date: 2023-11-09 00:00:00
Proceeding: CVPR 2024 1
Tasks: Action Classification, Audio Classification, Video Question Answering

One of the main challenges of multimodal learning is the need to combine heterogeneous modalities (e.g., video, audio, text). For example, video and audio are obtained at much higher rates than text and are roughly aligned in time. They are often not synchronized with text, which comes as a global context, e.g., a title, or a description. Furthermore, video and audio inputs are of much larger volumes, and grow as the video length increases, which naturally requires more compute dedicated to these modalities and makes modeling of long-range dependencies harder. We here decouple the multimodal modeling, dividing it into separate, focused autoregressive models, processing the inputs according to the characteristics of the modalities. We propose a multimodal model, called Mirasol3B, consisting of an autoregressive component for the time-synchronized modalities (audio and video), and an autoregressive component for the context modalities which are not necessarily aligned in time but are still sequential. To address the long-sequences of the video-audio inputs, we propose to further partition the video and audio sequences in consecutive snippets and autoregressively process their representations. To that end, we propose a Combiner mechanism, which models the audio-video information jointly within a timeframe. The Combiner learns to extract audio and video features from raw spatio-temporal signals, and then learns to fuse these features producing compact but expressive representations per snippet. Our approach achieves the state-of-the-art on well established multimodal benchmarks, outperforming much larger models. It effectively addresses the high computational demand of media inputs by both learning compact representations, controlling the sequence length of the audio-video feature representations, and modeling their dependencies in time.