pwc-archive/papers-with-abstracts huggingface dataset:
GAIT: Generating Aesthetic Indoor Tours with Deep Reinforcement Learning
Authors: Desai Xie, Ping Hu, Xin Sun, Soren Pirk, Jianming Zhang, Radomir Mech, Arie E. Kaufman
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Deep Reinforcement Learning, Mixed Reality, reinforcement-learning, Reinforcement Learning

    Placing and orienting a camera to compose aesthetically meaningful shots of a scene is not only a key objective in real-world photography and cinematography but also for virtual content creation. The framing of a camera often significantly contributes to the story telling in movies, games, and mixed reality applications. Generating single camera poses or even contiguous trajectories either requires a significant amount of manual labor or requires solving high-dimensional optimization problems, which can be computationally demanding and error-prone. In this paper, we introduce GAIT, a framework for training a Deep Reinforcement Learning (DRL) agent, that learns to automatically control a camera to generate a sequence of aesthetically meaningful views for synthetic 3D indoor scenes. To generate sequences of frames with high aesthetic value, GAIT relies on a neural aesthetics estimator, which is trained on a crowed-sourced dataset. Additionally, we introduce regularization techniques for diversity and smoothness to generate visually interesting trajectories for a 3D environment, and to constrain agent acceleration in the reward function to generate a smooth sequence of camera frames. We validated our method by comparing it to baseline algorithms, based on a perceptual user study, and through ablation studies. Code and visual results are available on the project website: https://desaixie.github.io/gait-rl
Plausible Uncertainties for Human Pose Regression
Authors: Lennart Bramlage, Michelle Karg, Cristóbal Curio
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Autonomous Driving, Pose Estimation, regression, Scene Understanding, Variational Inference

    Human pose estimation (HPE) is integral to scene understanding in numerous safety-critical domains involving human-machine interaction, such as autonomous driving or semi-automated work environments. Avoiding costly mistakes is synonymous with anticipating failure in model predictions, which necessitates meta-judgments on the accuracy of the applied models. Here, we propose a straightforward human pose regression framework to examine the behavior of two established methods for simultaneous aleatoric and epistemic uncertainty estimation: maximum a-posteriori (MAP) estimation with Monte-Carlo variational inference and deep evidential regression (DER). First, we evaluate both approaches on the quality of their predicted variances and whether these truly capture the expected model error. The initial assessment indicates that both methods exhibit the overconfidence issue common in deep probabilistic models. This observation motivates our implementation of an additional recalibration step to extract reliable confidence intervals. We then take a closer look at deep evidential regression, which, to our knowledge, is applied comprehensively for the first time to the HPE problem. Experimental results indicate that DER behaves as expected in challenging and adverse conditions commonly occurring in HPE and that the predicted uncertainties match their purported aleatoric and epistemic sources. Notably, DER achieves smooth uncertainty estimates without the need for a costly sampling step, making it an attractive candidate for uncertainty estimation on resource-limited platforms.
Open Set Video HOI detection from Action-Centric Chain-of-Look Prompting
Authors: Nan Xi, Jingjing Meng, Junsong Yuan
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Human-Object Interaction Detection, Language Modelling, Visual Reasoning

    Human-Object Interaction (HOI) detection is essential for understanding and modeling real-world events. Existing works on HOI detection mainly focus on static images and a closed setting, where all HOI classes are provided in the training set. In comparison, detecting HOIs in videos in open set scenarios is more challenging. First, under open set circumstances, HOI detectors are expected to hold strong generalizability to recognize unseen HOIs not included in the training data. Second, accurately capturing temporal contextual information from videos is difficult, but it is crucial for detecting temporal-related actions such as open, close, pull, push. To this end, we propose ACoLP, a model of Action-centric Chain-of-Look Prompting for open set video HOI detection. ACoLP regards actions as the carrier of semantics in videos, which captures the essential semantic information across frames. To make the model generalizable on unseen classes, inspired by the chain-of-thought prompting in natural language processing, we introduce the chain-of-look prompting scheme that decomposes prompt generation from large-scale vision-language model into a series of intermediate visual reasoning steps. Consequently, our model captures complex visual reasoning processes underlying the HOI events in videos, providing essential guidance for detecting unseen classes. Extensive experiments on two video HOI datasets, VidHOI and CAD120, demonstrate that ACoLP achieves competitive performance compared with the state-of-the-art methods in the conventional closed setting, and outperforms existing methods by a large margin in the open set setting. Our code is avaliable at https://github. com/southnx/ACoLP.
Building3D: A Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds
Authors: Ruisheng Wang, Shangfeng Huang, Hongxin Yang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Autonomous Navigation, Self-Supervised Learning

    Urban modeling from LiDAR point clouds is an important topic in computer vision, computer graphics, photogrammetry and remote sensing. 3D city models have found a wide range of applications in smart cities, autonomous navigation, urban planning and mapping etc. However, existing datasets for 3D modeling mainly focus on common objects such as furniture or cars. Lack of building datasets has become a major obstacle for applying deep learning technology to specific domains such as urban modeling. In this paper, we present a urban-scale dataset consisting of more than 160 thousands buildings along with corresponding point clouds, mesh and wire-frame models, covering 16 cities in Estonia about 998 Km2. We extensively evaluate performance of state-of-the-art algorithms including handcrafted and deep feature based methods. Experimental results indicate that Building3D has challenges of high intra-class variance, data imbalance and large-scale noises. The Building3D is the first and largest urban-scale building modeling benchmark, allowing a comparison of supervised and self-supervised learning methods. We believe that our Building3D will facilitate future research on urban modeling, aerial path planning, mesh simplification, and semantic/part segmentation etc.
Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans
Authors: Zhanghexuan Ji, Dazhou Guo, Puyang Wang, Ke Yan, Le Lu, Minfeng Xu, Qifeng Wang, Jia Ge, Mingchen Gao, Xianghua Ye, Dakai Jin
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Continual Semantic Segmentation, Decoder, Image Segmentation, Knowledge Distillation, Medical Image Segmentation, Neural Architecture Search, Organ Segmentation, Segmentation, Semantic Segmentation

    Deep learning empowers the mainstream medical image segmentation methods. Nevertheless, current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new segmentation classes are incrementally added. In the real clinical environment, it can be preferred that segmentation models could be dynamically extended to segment new organs/tumors without the (re-)access to previous training datasets due to obstacles of patient privacy and data storage. This process can be viewed as a continual semantic segmentation (CSS) problem, being understudied for multi-organ segmentation. In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs. Using the encoder/decoder network structure, we demonstrate that a continually trained then frozen encoder coupled with incrementally-added decoders can extract sufficiently representative image features for new classes to be subsequently and validly segmented, while avoiding the catastrophic forgetting in CSS. To maintain a single network model complexity, each decoder is progressively pruned using neural architecture search and teacher-student based knowledge distillation. Finally, we propose a body-part and anomaly-aware output merging module to combine organ predictions originating from different decoders and incorporate both healthy and pathological organs appearing in different datasets. Trained and validated on 3D CT scans of 2500+ patients from four datasets, our single network can segment a total of 143 whole-body organs with very high accuracy, closely reaching the upper bound performance level by training four separate segmentation models (i.e., one model per dataset/task).
DIME-FM : DIstilling Multimodal and Efficient Foundation Models
Authors: Ximeng Sun, Pengchuan Zhang, Peizhao Zhang, Hardik Shah, Kate Saenko, Xide Xia
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: image-classification, Image Classification

    Large Vision-Language Foundation Models (VLFM), such as CLIP, ALIGN and Florence, are trained on large private datasets of image-caption pairs and achieve superior transferability and robustness on downstream tasks, but they are difficult to use in many practical applications due to their large size, high latency and fixed architectures. Unfortunately, recent works show training a small custom VLFM for resource-limited applications is currently very difficult using public and smaller-scale data. In this paper, we introduce a new distillation mechanism (DIME-FM) that allows us to transfer the knowledge contained in large VLFMs to smaller, customized foundation models using a relatively small amount of inexpensive, unpaired images and sentences. We transfer the knowledge from the pre-trained CLIP-ViT-L/14 model to a ViT-B/32 model, with only 40M public images and 28.4M unpaired public sentences. The resulting model "Distill-ViT-B/32" rivals the CLIP-ViT-B/32 model pre-trained on its private WiT dataset (400M image-text pairs): Distill-ViT-B/32 achieves similar results in terms of zero-shot and linear-probing performance on both ImageNet and the ELEVATER (20 image classification tasks) benchmarks. It also displays comparable robustness when evaluated on five datasets with natural distribution shifts from ImageNet.
HairNeRF: Geometry-Aware Image Synthesis for Hairstyle Transfer
Authors: Seunggyu Chang, Gihoon Kim, Hayeon Kim
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Generation, Neural Rendering

    We propose a novel hairstyle transferred image synthesis method considering the underlying head geometry of two input images. In traditional GAN-based methods, transferring hairstyle from one image to the other often makes the synthesized result awkward due to differences in pose, shape, and size of heads. To resolve this, we utilize neural rendering by registering two input heads in the volumetric space to make a transferred hairstyle fit on the head of a target image. Because of the geometric nature of neural rendering, our method can render view varying images of synthesized results from a single transfer process without causing distortion from which extant hairstyle transfer methods built upon traditional GAN-based generators suffer. We verify that our method surpasses other baselines in view of preserving the identity and hairstyle of two input images when synthesizing a hairstyle transferred image rendered at any point of view.
Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification
Authors: Linhao Qu, Zhiwei Yang, Minghong Duan, Yingfan Ma, Shuo Wang, Manning Wang, Zhijian Song
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: image-classification, Image Classification, Multiple Instance Learning

    Bag-based multiple instance learning (MIL) methods have become the mainstream for Whole Slide Image (WSI) classification. However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications. In this paper, we propose MILBooster, a powerful dual-scale multi-stage MIL framework to address these issues from the perspectives of distribution, correlation, and magnification. Specifically, to address issue (1), we propose a plug-and-play bag filter that effectively increases the positive instance ratio of positive bags. For issue (2), we propose a novel window-based Transformer architecture called PiceBlock to model the correlation between local and global features of pathology images. For issue (3), we propose a dual-branch architecture to process different magnifications and design an information interaction module called Scale Mixer for efficient information interaction between them. We conducted extensive experiments on four clinical WSI classification tasks using three datasets. MILBooster achieved new state-of-the-art performance on all these tasks. Codes will be available.
Neural Reconstruction of Relightable Human Model from Monocular Video
Authors: Wenzhang Sun, Yunlong Che, Han Huang, Yandong Guo
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Creating relightable and animatable human characters from monocular video at a low cost is a critical task for digital human modeling and virtual reality applications. This task is complex due to intricate articulation motion, a wide range of ambient lighting conditions, and pose-dependent clothing deformations. In this paper, we introduce a novel self-supervised framework that takes a monocular video of a moving human as input and generates a 3D neural representation capable of being rendered with novel poses under arbitrary lighting conditions. Our framework decomposes dynamic humans under varying illumination into neural fields in canonical space, taking into account geometry and spatially varying BRDF material properties. Additionally, we introduce pose-driven deformation fields, enabling bidirectional mapping between canonical space and observation. Leveraging the proposed appearance decomposition and deformation fields, our framework learns in a self-supervised manner. Ultimately, based on pose-driven deformation, recovered appearance, and physically-based rendering, the reconstructed human figure becomes relightable and can be explicitly driven by novel poses. We demonstrate significant performance improvements over previous works and provide compelling examples of relighting from monocular videos of moving humans in challenging, uncontrolled capture scenarios.
DCPB: Deformable Convolution Based on the Poincare Ball for Top-view Fisheye Cameras
Authors: Xuan Wei, Zhidan Ran, Xiaobo Lu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    The accuracy of the visual tasks for top-view fisheye cameras is limited by the Euclidean geometry for pose-distorted objects in images. In this paper, we demonstrate the analogy between the fisheye model and the Poincare ball and that learning the shape of convolution kernels in the Poincare Ball can alleviate the spatial distortion problem. In particular, we propose the Deformable Convolution based on the Poincare Ball, named DCPB, which conducts the Graph Convolutional Network (GCN) in the Poincare ball and calculates the geodesic distances to Poincare hyperplanes as the offsets and modulation scalars of the modulated deformable convolution. Besides, we explore an appropriate network structure in the baseline with the DCPB. The DCPB markedly improves the neural network's performance. Experimental results on the public dataset THEODORE show that DCPB obtains a higher accuracy, and its efficiency demonstrates the potential for using temporal information in fisheye videos.
Neural Radiance Field with LiDAR maps
Authors: MingFang Chang, Akash Sharma, Michael Kaess, Simon Lucey
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D geometry, Data Augmentation, NeRF

    We address outdoor Neural Radiance Fields (NeRF) with LiDAR maps. Existing NeRF methods usually require specially collected hypersampled source views and do not perform well with the open source camera-LiDAR datasets - significantly limiting the approach's practical utility. In this paper, we demonstrate an approach that allows for these datasets to be utilized for high quality neural renderings. Our design leverages 1) LiDAR sensors for strong 3D geometry priors that significantly improve the ray sampling locality, and 2) Conditional Adversarial Networks (cGANs) to recover image details since aggregating embeddings from imperfect LiDAR maps causes artifacts in the synthesized images. Our experiments show that while NeRF baselines produce either noisy or blurry results on Argoverse 2, the images synthesized by our system not only outperform baselines in image quality metrics under both clean and noisy conditions, but also obtain closer Detectron2 results to the ground truth images. Furthermore, to show the substantial applicability of our system, we demonstrate that our system can be used in data augmentation for training a pose regression network and multi-season view synthesis. Our dataset and code will be released
ReactioNet: Learning High-Order Facial Behavior from Universal Stimulus-Reaction by Dyadic Relation Reasoning
Authors: Xiaotian Li, Taoyue Wang, Geran Zhao, Xiang Zhang, Xi Kang, Lijun Yin
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Unit Detection, Contrastive Learning, Facial Action Unit Detection, Facial Expression Recognition, Relation, Scene Classification

    Diverse visual stimuli can evoke various human affective states, which are usually manifested in an individual's muscular actions and facial expressions. In lab-controlled emotion datasets, such a critical component (i.e., stimulus) was commonly designed in a limited way, making researchers incapable of generalizing the universal correlation and causation of stimulus-reaction as well as predicting possible emotions from context, timing, and relation. In this paper, we collected a large-scale spontaneous facial behavior database ReactioNet, which contains 1.1 million coupled stimulus-reaction tuples (visual/audio/caption from both stimuli and subjects). We introduce a new facial behavior detection scenario, Dyadic Relation Reasoning (DRR), which aims to detect facial actions by reasoning their relations with stimuli. By aggregating the dyadic information, our method essentially forms a relation prototype Universal Stimulus Reaction (U-SR), which encodes the low-order and high-order relationships between stimulus agents and facial reactions. A framework with both non-graph and graph modules is further developed to evaluate DRR-based facial action unit detection, facial expression recognition, and scene classification. Specifically, to learn "what" arouses a facial reaction, the non-graph module associates and projects the fine-grained stimulus-reaction features into common subspaces using cross-domain contrastive learning. To learn "how" stimulus-reaction are mutually related, the graph module adopts Graph Convolution Network to represent, converge, and infer the dyadic U-SR relation under two relation assumptions (i.e., homophily and heterophily). Extensive experiments demonstrate the effectiveness of the proposed work. The new dataset will be available for the research community.
Adaptive Template Transformer for Mitochondria Segmentation in Electron Microscopy Images
Authors: Yuwen Pan, Naisong Luo, Rui Sun, Meng Meng, Tianzhu Zhang, Zhiwei Xiong, Yongdong Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Mitochondria, as tiny structures within the cell, are of significant importance to study cell functions for biological and clinical analysis. And exploring how to automatically segment mitochondria in electron microscopy (EM) images has attracted increasing attention. However, most of existing methods struggle to adapt to different scales and appearances of the input due to the inherent limitations of the traditional CNN architecture. To mitigate these limitations, we propose a novel adaptive template transformer (ATFormer) for mitochondria segmentation. The proposed ATFormer model enjoys several merits. First, the designed structural template learning module can acquire appearance-adaptive templates of background, foreground and contour to sense the characteristics of different shapes of mitochondria. And we further adopt an optimal transport algorithm to enlarge the discrepancy among diverse templates to fully activate corresponding regions. Second, we introduce a hierarchical attention learning mechanism to absorb multi-level information for templates to be adaptive scale-aware classifiers for dense prediction. Extensive experimental results on three challenging benchmarks including MitoEM, Lucchi and NucMM-Z datasets demonstrate that our ATFormer performs favorably against state-of-the-art mitochondria segmentation methods.
Privacy Preserving Localization via Coordinate Permutations
Authors: Linfei Pan, Johannes L. Schönberger, Viktor Larsson, Marc Pollefeys
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image-Based Localization, Pose Estimation, Privacy Preserving

    Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps. The lifting of points to lines effectively drops one of the two geometric constraints traditionally used with point-to-point correspondences in structure-based localization. This leads to a significant loss of accuracy for the privacy-preserving methods. In this paper, we overcome this limitation by devising a coordinate permutation scheme that allows for recovering the original point positions during pose estimation. The recovered points provide the full 2D geometric constraints and enable us to close the gap between privacy-preserving and traditional methods in terms of accuracy. Another limitation of random line methods is their vulnerability to density based 3D line cloud inversion attacks. Our method not only provides better accuracy than the original random line based approach but also provides stronger privacy guarantees against these recently proposed attacks. Extensive experiments on standard benchmark datasets demonstrate these improvements consistently across both scenarios of protecting the privacy of query images as well as the database map.