pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty-aware Action Decoupling Transformer for Action Anticipation
Authors: Hongji Guo, Nakul Agarwal, Shao-Yuan Lo, Kwonjoon Lee, Qiang Ji
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Action Anticipation

    Human action anticipation aims at predicting what people will do in the future based on past observations. In this paper we introduce Uncertainty-aware Action Decoupling Transformer (UADT) for action anticipation. Unlike existing methods that directly predict action in a verb-noun pair format we decouple the action anticipation task into verb and noun anticipations separately. The objective is to make the two decoupled tasks assist each other and eventually improve the action anticipation task. Specifically we propose a two-stream Transformer-based architecture which is composed of a verb-to-noun model and a noun-to-verb model. The verb-to-noun model leverages the verb information to improve the noun prediction and the other way around. We extend the model in a probabilistic manner and quantify the predictive uncertainty of each decoupled task to select features. In this way the noun prediction leverages the most informative and redundancy-free verb features and verb prediction works similarly. Finally the two streams are combined dynamically based on their uncertainties to make the joint action anticipation. We demonstrate the efficacy of our method by achieving state-of-the-art performance on action anticipation benchmarks including EPIC-KITCHENS EGTEA Gaze+ and 50-Salads.
PostureHMR: Posture Transformation for 3D Human Mesh Recovery
Authors: Yu-Pei Song, Xiao Wu, Zhaoquan Yuan, Jian-Jun Qiao, Qiang Peng
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Decoder, Human Mesh Recovery

    Human Mesh Recovery (HMR) aims to estimate the 3D human body from 2D images which is a challenging task due to inherent ambiguities in translating 2D observations to 3D space. A novel approach called PostureHMR is proposed to leverage a multi-step diffusion-style process which converts this task into a posture transformation from an SMPL T-pose mesh to the target mesh. To inject the learning process of posture transformation with the physical structure of the human body model a kinematics-based forward process is proposed to interpolate the intermediate state with pose and shape decomposition. Moreover a mesh-to-posture (M2P) decoder is designed by combining the input of 3D and 2D mesh constraints estimated from the image to model the posture changes in the reverse process. It mitigates the difficulties of posture change learning directly from RGB pixels. To overcome the limitation of pixel-level misalignment of modeling results with the input image a new trimap-based rendering loss is designed to highlight the areas with poor recognition. Experiments conducted on three widely used datasets demonstrate that the proposed approach outperforms the state-of-the-art methods.
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based Refinement
Authors: Jiuming Liu, Guangming Wang, Weicai Ye, Chaokang Jiang, Jinru Han, Zhe Liu, Guofeng Zhang, Dalong Du, Hesheng Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Diversity, Scene Flow Estimation

    Scene flow estimation which aims to predict per-point 3D displacements of dynamic scenes is a fundamental task in the computer vision field. However previous works commonly suffer from unreliable correlation caused by locally constrained searching ranges and struggle with accumulated inaccuracy arising from the coarse-to-fine structure. To alleviate these problems we propose a novel uncertainty-aware scene flow estimation network (DifFlow3D) with the diffusion probabilistic model. Iterative diffusion-based refinement is designed to enhance the correlation robustness and resilience to challenging cases e.g. dynamics noisy inputs repetitive patterns etc. To restrain the generation diversity three key flow-related features are leveraged as conditions in our diffusion model. Furthermore we also develop an uncertainty estimation module within diffusion to evaluate the reliability of estimated scene flow. Our DifFlow3D achieves state-of-the-art performance with 24.0% and 29.1% EPE3D reduction respectively on FlyingThings3D and KITTI 2015 datasets. Notably our method achieves an unprecedented millimeter-level accuracy (0.0078m in EPE3D) on the KITTI dataset. Additionally our diffusion-based refinement paradigm can be readily integrated as a plug-and-play module into existing scene flow networks significantly increasing their estimation accuracy. Codes are released at https://github.com/IRMVLab/DifFlow3D.
R-Cyclic Diffuser: Reductive and Cyclic Latent Diffusion for 3D Clothed Human Digitalization
Authors: Kennard Yanting Chan, Fayao Liu, Guosheng Lin, Chuan Sheng Foo, Weisi Lin
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Object Reconstruction, Novel View Synthesis, Object Reconstruction

    Recently the authors of Zero-1-to-3 demonstrated that a latent diffusion model pretrained with Internet-scale data can not only address the single-view 3D object reconstruction task but can even attain SOTA results in it. However when applied to the task of single-view 3D clothed human reconstruction Zero-1-to-3 (and related models) are unable to compete with the corresponding SOTA methods in this field despite being trained on clothed human data. In this work we aim to tailor Zero-1-to-3's approach to the single-view 3D clothed human reconstruction task in a much more principled and structured manner. To this end we propose R-Cyclic Diffuser a framework that adapts Zero-1-to-3's novel approach to clothed human data by fusing it with a pixel-aligned implicit model. R-Cyclic Diffuser offers a total of three new contributions. The first and primary contribution is R-Cyclic Diffuser's cyclical conditioning mechanism for novel view synthesis. This mechanism directly addresses the view inconsistency problem faced by Zero-1-to-3 and related models. Secondly we further enhance this mechanism with two key features - Lateral Inversion Constraint and Cyclic Noise Selection. Both features are designed to regularize and restrict the randomness of outputs generated by a latent diffusion model. Thirdly we show how SMPL-X body priors can be incorporated in a latent diffusion model such that novel views of clothed human bodies can be generated much more accurately. Our experiments show that R-Cyclic Diffuser is able to outperform current SOTA methods in single-view 3D clothed human reconstruction both qualitatively and quantitatively. Our code is made publicly available at https://github.com/kcyt/r-cyclic-diffuser.
Traceable Federated Continual Learning
Authors: Qiang Wang, Bingyan Liu, Yawen Li
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Continual Learning

    Federated continual learning (FCL) is a typical mechanism to achieve collaborative model training among clients that own dynamic data. While traditional FCL methods have been proved effective they do not consider the task repeatability and fail to achieve good performance under this practical scenario. In this paper we propose a new paradigm namely Traceable Federated Continual Learning (TFCL) aiming to cope with repetitive tasks by tracing and augmenting them. Following the new paradigm we develop TagFed a framework that enables accurate and effective Tracing augmentation and Federation for TFCL. The key idea is to decompose the whole model into a series of marked sub-models for optimizing each client task before conducting group-wise knowledge aggregation such that the repetitive tasks can be located precisely and federated selectively for improved performance. Extensive experiments on our constructed benchmark demonstrate the effectiveness and efficiency of the proposed framework. We will release our code at: https://github.com/P0werWeirdo/TagFCL.
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning
Authors: Fan Qi, Shuai Li
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Federated Learning, Graph Learning

    In Federated Learning (FL) the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' data vary in modalities. In response to these issues of statistical heterogeneity and modality incompatibility we propose the Adaptive Hyper-graph Aggregation framework a novel solution for Modality-Agnostic Federated Learning. We design a Modular Architecture for Local Model with single modality setting the stage for efficient intra-modality sharing and inter-modality complementarity. An innovative Global Consensus Prototype Enhancer is crafted to assimilate and broadcast global consensus knowledge within the network. At the core of our approach lies the Adaptive Hyper-graph Learning Strategy which effectively tackles the inherent challenges of modality incompatibility and statistical heterogeneity within federated learning environments accomplishing this adaptively even without the server being aware of the clients' modalities. Our approach tested on three multimodal benchmark datasets demonstrated strong performance across diverse data distributions affirming its effectiveness in multimodal federated learning.
Data-Free Quantization via Pseudo-label Filtering
Authors: Chunxiao Fan, Ziqi Wang, Dan Guo, Meng Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Data Free Quantization, Model Compression, Pseudo Label, Pseudo Label Filtering, Quantization

    Quantization for model compression can efficiently reduce the network complexity and storage requirement but the original training data is necessary to remedy the performance loss caused by quantization. The Data-Free Quantization (DFQ) methods have been proposed to handle the absence of original training data with synthetic data. However there are differences between the synthetic and original training data which affects the performance of the quantized network but none of the existing methods considers the differences. In this paper we propose an efficient data-free quantization via pseudo-label filtering which is the first to evaluate the synthetic data before quantization. We design a new metric for evaluating synthetic data using self-entropy which indicates the reliability of synthetic data. The synthetic data can be categorized with the metric into high- and low-reliable datasets for the following training process. Besides the multiple pseudo-labels are designed to label the synthetic data with different reliability which can provide valuable supervision information and avoid misleading training by low-reliable samples. Extensive experiments are implemented on several datasets including CIFAR-10 CIFAR-100 and ImageNet with various models. The experimental results show that our method can perform excellently and outperform existing methods in accuracy.
Bi-level Learning of Task-Specific Decoders for Joint Registration and One-Shot Medical Image Segmentation
Authors: Xin Fan, Xiaolin Wang, Jiaxin Gao, Jia Wang, Zhongxuan Luo, Risheng Liu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Data Augmentation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

    One-shot medical image segmentation (MIS) aims to cope with the expensive time-consuming and inherent human bias annotations. One prevalent method to address one-shot MIS is joint registration and segmentation (JRS) with a shared encoder which mainly explores the voxel-wise correspondence between the labeled data and unlabeled data for better segmentation. However this method omits underlying connections between task-specific decoders for segmentation and registration leading to unstable training. In this paper we propose a novel Bi-level Learning of Task-Specific Decoders for one-shot MIS employing a pretrained fixed shared encoder that is proved to be more quickly adapted to brand-new datasets than existing JRS without fixed shared encoder paradigm. To be more specific we introduce a bi-level optimization training strategy considering registration as a major objective and segmentation as a learnable constraint by leveraging inter-task coupling dependencies. Furthermore we design an appearance conformity constraint strategy that learns the backward transformations generating the fake labeled data used to perform data augmentation instead of the labeled image to avoid performance degradation caused by inconsistent styles between unlabeled data and labeled data in previous methods. Extensive experiments on the brain MRI task across ABIDE ADNI and PPMI datasets demonstrate that the proposed Bi-JROS outperforms state-of-the-art one-shot MIS methods for both segmentation and registration tasks. The code will be available at https://github.com/Coradlut/Bi-JROS.
Towards High-fidelity Artistic Image Vectorization via Texture-Encapsulated Shape Parameterization
Authors: Ye Chen, Bingbing Ni, Jinfan Liu, Xiaoyang Huang, Xuanhong Chen
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Compression, Zero-Shot Learning

    We develop a novel vectorized image representation scheme accommodating both shape/geometry and texture in a decoupled way particularly tailored for reconstruction and editing tasks of artistic/design images such as Emojis and Cliparts. In the heart of this representation is a set of sparsely and unevenly located 2D control points. On one hand these points constitute a collection of parametric/vectorized geometric primitives (e.g. curves and closed shapes) describing the shape characteristics of the target image. On the other hand local texture codes in terms of implicit neural network parameters are spatially distributed into each control point yielding local coordinate-to-RGB mappings within the anchored region of each control point. In the meantime a zero-shot learning algorithm is developed to decompose an arbitrary raster image into the above representation for the sake of high-fidelity image vectorization with convenient editing ability. Extensive experiments on a series of image vectorization and editing tasks well demonstrate the high accuracy offered by our proposed method with a significantly higher image compression ratio over prior art.
Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation
Authors: Huyong Wang, Huisi Wu, Jing Qin
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Semantic Segmentation

    We present a novel semantic segmentation approach for incremental nuclei segmentation from histopathological images which is a very challenging task as we have to incrementally optimize existing models to make them perform well in both old and new classes without using training samples of old classes. Yet it is an indispensable component of computer-aided diagnosis systems. The proposed approach has two key techniques. First we propose a new future-class awareness mechanism by separating some potential regions for future classes from background based on their similarities to both old and new classes in the representation space. With this mechanism we can not only reserve more parameter space for future updates but also enhance the representation capability of learned features. We further propose an innovative compatibility-inspired distillation scheme to make our model take full advantage of the knowledge learned by the old model. We conducted extensive experiments on two famous histopathological datasets and the results demonstrate the proposed approach achieves much better performance than state-of-the-art approaches. The code is available at https://github.com/why19991/InSeg.
EasyDrag: Efficient Point-based Manipulation on Diffusion Models
Authors: Xingzhong Hou, Boxiao Liu, Yi Zhang, Jihao Liu, Yu Liu, Haihang You
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Manipulation

    Generative models are gaining increasing popularity and the demand for precisely generating images is on the rise. However generating an image that perfectly aligns with users' expectations is extremely challenging. The shapes of objects the poses of animals the structures of landscapes and more may not match the user's desires and this applies to real images as well. This is where point-based image editing becomes essential. An excellent image editing method needs to meet the following criteria: user-friendly interaction high performance and good generalization capability. Due to the limitations of StyleGAN DragGAN exhibits limited robustness across diverse scenarios while DragDiffusion lacks user-friendliness due to the necessity of LoRA fine-tuning and masks. In this paper we introduce a novel interactive point-based image editing framework called EasyDrag that leverages pretrained diffusion models to achieve high-quality editing outcomes and user-friendship. Extensive experimentation demonstrates that our approach surpasses DragDiffusion in terms of both image quality and editing precision for point-based image manipulation tasks.
Pixel-Aligned Language Model
Authors: Jiarui Xu, Xingyi Zhou, Shen Yan, Xiuye Gu, Anurag Arnab, Chen Sun, Xiaolong Wang, Cordelia Schmid
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Language Modeling, Language Modelling, model

    Large language models have achieved great success in recent years so as their variants in vision. Existing vision-language models can describe images in natural languages answer visual-related questions or perform complex reasoning about the image. However it is yet unclear how localization tasks such as word grounding or referring localization can be performed using large language models. In this work we aim to develop a vision-language model that can take locations for example a set of points or boxes as either inputs or outputs. When taking locations as inputs the model performs location-conditioned captioning which generates captions for the indicated object or region. When generating locations as outputs our model regresses pixel coordinates for each output word generated by the language model and thus performs dense word grounding. Our model is pre-trained on the Localized Narrative dataset which contains pixel-word-aligned captioning from human attention. We show our model can be applied to various location-aware vision-language tasks including referring localization location-conditioned captioning and dense object captioning archiving state-of-the-art performance on RefCOCO and Visual Genome.
Hierarchical Intra-modal Correlation Learning for Label-free 3D Semantic Segmentation
Authors: Xin Kang, Lei Chu, Jiahao Li, Xuejin Chen, Yan Lu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Semantic Segmentation, Semantic Segmentation

    Recent methods for label-free 3D semantic segmentation aim to assist 3D model training by leveraging the open-world recognition ability of pre-trained vision language models. However these methods usually suffer from inconsistent and noisy pseudo-labels provided by the vision language models. To address this issue we present a hierarchical intra-modal correlation learning framework that captures visual and geometric correlations in 3D scenes at three levels: intra-set intra-scene and inter-scene to help learn more compact 3D representations. We refine pseudo-labels using intra-set correlations within each geometric consistency set and align features of visually and geometrically similar points using intra-scene and inter-scene correlation learning. We also introduce a feedback mechanism to distill the correlation learning capability into the 3D model. Experiments on both indoor and outdoor datasets show the superiority of our method. We achieve a state-of-the-art 36.6% mIoU on the ScanNet dataset and a 23.0% mIoU on the nuScenes dataset with improvements of 7.8% mIoU and 2.2% mIoU compared with previous SOTA. We also provide theoretical analysis and qualitative visualization results to discuss the mechanism and conduct thorough ablation studies to support the effectiveness of our framework.
Towards Automatic Power Battery Detection: New Challenge Benchmark Dataset and Baseline
Authors: Xiaoqi Zhao, Youwei Pang, Zhenyu Chen, Qian Yu, Lihe Zhang, Hanqi Liu, Jiaming Zuo, Huchuan Lu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Crowd Counting, object-detection, Object Detection, Segmentation

    We conduct a comprehensive study on a new task named power battery detection (PBD) which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries. Existing manufacturers usually rely on human eye observation to complete PBD which makes it difficult to balance the accuracy and efficiency of detection. To address this issue and drive more attention into this meaningful task we first elaborately collect a dataset called X-ray PBD which has 1500 diverse X-ray images selected from thousands of power batteries of 5 manufacturers with 7 different visual interference. Then we propose a novel segmentation-based solution for PBD termed multi-dimensional collaborative network (MDCNet). With the help of line and counting predictors the representation of the point segmentation branch can be improved at both semantic and detail aspects. Besides we design an effective distance-adaptive mask generation strategy which can alleviate the visual challenge caused by the inconsistent distribution density of plates to provide MDCNet with stable supervision. Without any bells and whistles our segmentation-based MDCNet consistently outperforms various other corner detection crowd counting and general/tiny object detection-based solutions making it a strong baseline that can help facilitate future research in PBD. Finally we share some potential difficulties and works for future researches. The source code and datasets will be publicly available at \href https://github.com/Xiaoqi-Zhao-DLUT/X-ray-PBD  X-ray PBD .
Edge-Aware 3D Instance Segmentation Network with Intelligent Semantic Prior
Authors: Wonseok Roh, Hwanhee Jung, Giljoo Nam, Jinseop Yeom, Hyunje Park, Sang Ho Yoon, Sangpil Kim
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Instance Segmentation, Instance Segmentation, Language Modeling, Language Modelling, Semantic Segmentation

    While recent 3D instance segmentation approaches show promising results based on transformer architectures they often fail to correctly identify instances with similar appearances. They also ambiguously determine edges leading to multiple misclassifications of adjacent edge points. In this work we introduce a novel framework called EASE to overcome these challenges and improve the perception of complex 3D instances. We first propose a semantic guidance network to leverage rich semantic knowledge from a language model as intelligent priors enhancing the functional understanding of real-world instances beyond relying solely on geometrical information. We explicitly instruct the basic instance queries using text embeddings of each instance to learn deep semantic details. Further we utilize the edge prediction module encouraging the segmentation network to be edge-aware. We extract voxel-wise edge maps from point features and use them as auxiliary information for learning edge cues. In our extensive experiments on large-scale benchmarks ScanNetV2 ScanNet200 S3DIS and STPLS3D our EASE outperforms existing state-of-the-art models demonstrating its superior performance.