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AHIVE: Anatomy-aware Hierarchical vision Encoding for Interactive Radiology Report Retrieval

AHIVE: Anatomy-aware Hierarchical Vision Encoding for Intera...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yan, Sixing Cheung, William K. Tsang, Ivor W. Chiu, Keith Tong, Terence M. Cheung, Ka Chun Seel, Simon Hong Kong Baptist Univ Hong Kong Peoples R China Agcy Sci Technol & Res CFAR Singapore Singapore Agcy Sci Technol & Res IHPC Singapore Singapore Nanyang Technol Univ SCSE Singapore Singapore Univ Technol Sydney AAII Sydney NSW Australia Queen Elizabeth Hosp Hong Kong Peoples R China Kwong Wah Hosp Hong Kong Peoples R China Tuen Mun Hosp Hong Kong Peoples R China NVIDIA Corp NVIDIA AI Technol Ctr Santa Clara CA USA

ISBN: (纸本)9798350353006

Automatic radiology report generation using deep learning models has been recently explored and found promising. Neural decoders are commonly used for the report generation, where irrelevant and unfaithful contents are unavoidable. The retrieval-based approach alleviates the limitation by identifying reports which are relevant to the input to assist the generation. To achieve clinically accurate report retrieval, we make reference to clinicians' diagnostic steps of examining a radiology image where anatomical and diagnostic details are typically focused, and propose a novel hierarchical visual concept representation called anatomy-aware hierarchical vision encoding (AHIVE). To learn AHIVE, we first derive a methodology to extract hierarchical diagnostic descriptions from radiology reports and develop a CLIP-based framework for the model training. Also, the hierarchical architecture of AHIVE is designed to support interactive report retrieval so that report revision made at one layer can be propagated to the subsequent ones to trigger other necessary revisions. We conduct extensive experiments and show that AHIVE can outperform the SOTA vision-language retrieval methods in terms of clinical accuracy by a large margin. We provide also a case study to illustrate how it enables interactive report retrieval.

关键词： image-to-text retrieval interactive retrieval system radiology imaging radiology report retrieval

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CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention

CAD-SIGNet: CAD Language Inference from Point Clouds using L...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Khan, Mohammad Sadil Dupont, Elona Ali, Sk Aziz Cherenkova, Kseniya Kacem, Anis Aouada, Djamila Univ Luxembourg SnT Luxembourg Luxembourg German Res Ctr Artificial Intelligence Berlin Germany Artec3D Luxembourg Luxembourg

ISBN: (纸本)9798350353013;9798350353006

Reverse engineering in the realm of computer-Aided Design (CAD) has been a longstanding aspiration, though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD- SIGNet, an end-to-end trainable and aeto-regressive architecture to recover the design history of a CAD model represented as a sequence of sketch-and-extrusion from an input point cloud. Our model learns CAD visual-language representations by layer-wise cross-attention between point cloud and CAD language embedding. In particular, a new Sketch instance Guided Attention (SGA) module is proposed in order to reconstruct the fine-grained details of the sketches. Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices. This allows for an interactive reverse engineering scenario by providing designers with multiple next step choices along with the design process. Extensive experiments on publicly available CAD datasets showcase the effectiveness of our approach against existing baseline models in two settings, namely, full design history recovery and conditional autocompletion from point clouds.

关键词： 3D Point Clouds computer-Aided Design (CAD) Reverse Engineering vision Language Models

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Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval

Do You Remember? Dense Video Captioning with Cross-Modal Mem...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kim, Minkuk Kim, Hyeon Bae Moon, Jinyoung Choi, Jinwoo Kim, Seong Tae Kyung Hee Univ Seoul South Korea Elect & Telecommun Res Inst ETRI Daejeon South Korea

ISBN: (纸本)9798350353006

There has been significant attention to the research on dense video captioning, which aims to automatically localize and caption all events within untrimmed video. Several studies introduce methods by designing dense video captioning as a multitasking problem of event localization and event captioning to consider inter-task relations. However, addressing both tasks using only visual input is challenging due to the lack of semantic content. In this study, we address this by proposing a novel framework inspired by the cognitive information processing of humans. Our model utilizes external memory to incorporate prior knowledge. The memory retrieval method is proposed with cross-modal video-to-text matching. To effectively incorporate retrieved text features, the versatile encoder and the decoder with visual and textual cross-attention modules are designed. Comparative experiments have been conducted to show the effectiveness of the proposed method on ActivityNet Captions and YouCook2 datasets. Experimental results show promising performance of our model with-out extensive pretraining from a large video dataset. Our code is available at https://***/ailab-kyunghee/CM2_DVC.

关键词： Dense Video Captioning Launguage Reasoning Retrieval Agumented Generation Video Captioning vision

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The STVchrono Dataset: Towards Continuous Change recognition in Time

The STVchrono Dataset: Towards Continuous Change Recognition...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sun, Yanjun Qiu, Yue Khan, Mariia Matsuzawa, Fumiya Iwata, Kenji Natl Inst Adv Ind Sci & Technol Tokyo Japan Keio Univ Tokyo Japan Edith Cowan Univ Joondalup Australia

ISBN: (纸本)9798350353006

Recognizing continuous changes offers valuable insights into past historical events, supports current trend analysis, and facilitates future planning. This knowledge is crucial for a variety of fields, such as meteorology and agriculture, environmental science, urban planning and construction, tourism, and cultural preservation. Currently available datasets in the field of scene change understanding primarily concentrate on two main tasks: the detection of changed regions within a scene and the linguistic description of the change content. Existing datasets focus on recognizing discrete changes, such as adding or deleting an object from two images, and largely rely on artificially generated images. Consequently, the existing change understanding methods primarily focus on identifying distinct object differences, overlooking the importance of continuous, gradual changes occurring over extended time intervals. To address the above issues, we propose a novel bench-mark dataset, STVchrono, targeting the localization and description of long-term continuous changes in real-world scenes. The dataset consists of 71,900 photographs from Google Street View API taken over an 18-year span across 50 cities all over the world. Our STVchrono dataset is designed to support real-world continuous change recognition and description in both image pairs and extended image sequences, while also enabling the segmentation of changed regions. We conduct experiments to evaluate state-of-the-art methods on continuous change description and segmentation, as well as multimodal Large Language Models for describing changes. Our findings reveal that even the most advanced methods lag human performance, emphasizing the need to adapt them to continuously changing real-world scenarios. We hope that our benchmark dataset will further facilitate the research of temporal change recognition in a dynamic world. The STVchrono dataset is available at STVchrono Dataset.

关键词： Image segmentation

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From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration

From a Bird's Eye View to See: Joint Camera and Subject Regi...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Qian, Zekun Han, Ruize Feng, Wei Wang, Song Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China Univ South Carolina Columbia SC 29208 USA

ISBN: (纸本)9798350353013;9798350353006

We tackle a new problem of multi-view camera and subject registration in the bird's eye view (BEV) without pre-given camera calibration, which promotes the multi-view subject registration problem to a new calibration-free stage. This greatly alleviates the limitation in many practical applications. However, this is a very challenging problem since its only input is several RGB images from different first-person views (FPVs), without the BEV image and the calibration of the FPVs, while the output is a unified plane aggregated from all views with the positions and orientations of both the subjects and cameras in a BEV. For this purpose, we propose an end-to-end framework solving camera and subject registration together by taking advantage of their mutual dependence, whose main idea is as below: i) creating a subject view-transform module (VTM) to project each pedestrian from FPV to a virtual BEV, ii) deriving a multi-view geometry-based spatial alignment module (SAM) to estimate the relative camera pose in a unified BEV, iii) selecting and refining the subject and camera registration results within the unified BEV. We collect a new large-scale synthetic dataset with rich annotations for training and evaluation. Additionally, we also collect a real dataset for cross-domain evaluation. The experimental results show the remarkable effectiveness of our method. The code and proposed datasets are available at BEVSee.

关键词： Camera Registration computer vision Multiview Detection Subject Registration

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HEAL-SWIN: A vision Transformer On The Sphere

HEAL-SWIN: A Vision Transformer On The Sphere

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Carlsson, Oscar Gerken, Jan E. Linander, Hampus Spiess, Heiner Ohlsson, Fredrik Petersson, Christoffer Persson, Daniel Univ Gothenburg Chalmers Univ Technol Dept Math Sci SE-41296 Gothenburg Sweden Tech Univ Berlin Neural Informat Proc Sci Intelligence DE-10623 Berlin Germany Umea Univ Dept Math & Math Stat Umea degrees SE-90187 Umea Sweden Zenseact SE-41756 Gothenburg Sweden

ISBN: (纸本)9798350353013;9798350353006

High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However, using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer, which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution, distortion-free spherical data. In HEAL-SWIN, the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer, enabling the network to process spherical representations with minimal computational overhead. We demonstrate the superior performance of our model on both synthetic and real automotive datasets, as well as a selection of other image datasets, for semantic segmentation, depth regression and classification tasks. Our code is publicly available(1).

关键词： depth estimation fisheye images image classification omni-directional images semantic segmentation spherical grid transformer

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Initialization Noise in Image Gradients and Saliency Maps

Initialization Noise in Image Gradients and Saliency Maps

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Woerl, Ann-Christin Disselhoff, Jan Wand, Michael Johannes Gutenberg Univ Mainz Inst Comp Sci Mainz Germany

ISBN: (纸本)9798350301298

In this paper, we examine gradients of logits of image classification CNNs by input pixel values. We observe that these fluctuate considerably with training randomness, such as the random initialization of the networks. We extend our study to gradients of intermediate layers, obtained via GradCAM, as well as popular network saliency estimators such as DeepLIFT, SHAP, LIME, Integrated Gradients, and SmoothGrad. While empirical noise levels vary, qualitatively different attributions to image features are still possible with all of these, which comes with implications for interpreting such attributions, in particular when seeking data-driven explanations of the phenomenon generating the data. Finally, we demonstrate that the observed artefacts can be removed by marginalization over the initialization distribution by simple stochastic integration.

关键词： Explainable computer vision

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Taming Self-Training for Open-Vocabulary Object Detection

Taming Self-Training for Open-Vocabulary Object Detection

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhao, Shiyu Schulter, Samuel Zhao, Long Zhang, Zhixing Kumar, Vijay B. G. Suh, Yumin Chandraker, Manmohan Metaxas, Dimitris N. Rutgers State Univ New Brunswick NJ 08901 USA NEC Labs Amer Princeton NJ USA Google Res Mountain View CA USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9798350353006

Recent studies have shown promising performance in open-vocabulary object detection (OVD) by utilizing pseudo labels (PLs) from pretrained vision and language models (VLMs). However, teacher-student self-training, a powerful and widely used paradigm to leverage PLs, is rarely explored for OVD. This work identifies two challenges of using self-training in OVD: noisy PLs from VLMs and frequent distribution changes of PLs. To address these challenges, we propose SAS-Det that tames self-training for OVD from two key perspectives. First, we present a split-and-fusion (SAF) head that splits a standard detection into an open-branch and a closed-branch. This design can reduce noisy supervision from pseudo boxes. Moreover, the two branches learn complementary knowledge from different training data, significantly enhancing performance when fused together. Second, in our view, unlike in closed-set tasks, the PL distributions in OVD are solely determined by the teacher model. We introduce a periodic update strategy to decrease the number of updates to the teacher, thereby decreasing the frequency of changes in PL distributions, which stabilizes the training process. Extensive experiments demonstrate SAS-Det is both efficient and effective. SAS-Det outperforms recent models of the same scale by a clear margin and achieves 37.4 AP50 and 29.1 APr on novel categories of the COCO and LVIS benchmarks, respectively. Code is available at https://***/xiaofeng94/SAS-Det.

关键词： open-vocabulary object detection pseudo label self-training vision and language

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SelfOcc: Self-Supervised vision-Based 3D Occupancy Prediction

SelfOcc: Self-Supervised Vision-Based 3D Occupancy Predictio...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huang, Yuanhui Zheng, Wenzhao Zhang, Borui Zhou, Jie Lu, Jiwen Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350353006

3D occupancy prediction is an important task for the robustness of vision-centric autonomous driving, which aims to predict whether each point is occupied in the surrounding 3D space. Existing methods usually require 3D occupancy labels to produce meaningful results. However, it is very laborious to annotate the occupancy status of each voxel. In this paper, we propose SelfOcc to explore a self-supervised way to learn 3D occupancy using only video sequences. We first transform the images into the 3D space (e.g., bird's eye view) to obtain 3D representation of the scene. We directly impose constraints on the 3D representations by treating them as signed distance fields. We can then render 2D images of previous and future frames as self-supervision signals to learn the 3D representations. We propose an MVS-embedded strategy to directly optimize the SDF-induced weights with multiple depth proposals. Our SelfOcc outperforms the previous best method SceneRF by 58.7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes. SelfOcc produces high-quality depth and achieves state-of-the-art results on novel depth synthesis, monocular depth estimation, and surround-view depth estimation on the SemanticKITTI, KITTI-2015, and nuScenes, respectively. Code: https://***/huang-yh/SelfOcc.

关键词： 3D reconstruction autonomous driving NeRF occupancy self-supervised

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Humans as Light Bulbs: 3D Human Reconstruction from Thermal Reflection

Humans as Light Bulbs: 3D Human Reconstruction from Thermal ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Ruoshi Vondrick, Carl Columbia Univ New York NY 10027 USA

ISBN: (纸本)9798350301298

The relatively hot temperature of the human body causes people to turn into long-wave infrared light sources. Since this emitted light has a larger wavelength than visible light, many surfaces in typical scenes act as infrared mirrors with strong specular reflections. We exploit the thermal reflections of a person onto objects in order to locate their position and reconstruct their pose, even if they are not visible to a normal camera. We propose an analysis-by-synthesis framework that jointly models the objects, people, and their thermal reflections, which combines generative models with differentiable rendering of reflections. Quantitative and qualitative experiments show our approach works in highly challenging cases, such as with curved mirrors or when the person is completely unseen by a normal camera.

关键词： vision + graphics

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