pwc-archive/papers-with-abstracts huggingface dataset:
Learning Spatial-context-aware Global Visual Feature Representation for Instance Image Retrieval
Authors: Zhongyan Zhang, Lei Wang, Luping Zhou, Piotr Koniusz
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Retrieval, Retrieval

    In instance image retrieval, considering local spatial information within an image has proven effective to boost retrieval performance, as demonstrated by local visual descriptor based geometric verification. Nevertheless, it will be highly valuable to make ordinary global image representations spatial-context-aware because global representation based image retrieval is appealing thanks to its algorithmic simplicity, low memory cost, and being friendly to sophisticated data structures. To this end, we propose a novel feature learning framework for instance image retrieval, which embeds local spatial context information into the learned global feature representations. Specifically, in parallel to the visual feature branch in a CNN backbone, we design a spatial context branch that consists of two modules called online token learning and distance encoding. For each local descriptor learned in CNN, the former module is used to indicate the types of its surrounding descriptors, while their spatial distribution information is captured by the latter module. After that, the visual feature branch and the spatial context branch are fused to produce a single global feature representation per image. As experimentally demonstrated, with the spatial-context-aware characteristic, we can well improve the performance of global representation based image retrieval while maintaining all of its appealing properties. Our code is available at https://github.com/Zy-Zhang/SpCa
Personalized Semantics Excitation for Federated Image Classification
Authors: Haifeng Xia, Kai Li, Zhengming Ding
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Classification, Federated Learning, image-classification, Image Classification, Personalized Federated Learning, Specificity

    Federated learning casts a light on the collaboration of distributed local clients with privacy protected to attain a more generic global model. However, significant distribution shift in input/label space across different clients makes it challenging to well generalize to all clients, which motivates personalized federated learning (PFL). Existing PFL methods typically customize the local model by fine-tuning with limited local supervision and the global model regularizer, which secures local specificity but risks ruining the global discriminative knowledge. In this paper, we propose a novel Personalized Semantics Excitation (PSE) mechanism to breakthrough this limitation by exciting and fusing personalized semantics from the global model during local model customization. Specifically, PSE explores channel-wise gradient differentiation across global and local models to identify important low-level semantics mostly from convolutional layers which are embedded into the client-specific training. In addition, PSE deploys the collaboration of global and local models to enrich high-level feature representations and facilitate the robustness of client classifier through a cross-model attention module. Extensive experiments and analysis on various image classification benchmarks demonstrate the effectiveness and advantage of our method over the state-of-the-art PFL methods.
Size Does Matter: Size-aware Virtual Try-on via Clothing-oriented Transformation Try-on Network
Authors: Chieh-Yun Chen, Yi-Chung Chen, Hong-Han Shuai, Wen-Huang Cheng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Virtual Try-on

    Virtual try-on tasks aim at synthesizing realistic try-on results by trying target clothes on humans. Most previous works relied on the Thin Plate Spline or appearance flows to warp clothes to fit human body shapes. However, both approaches cannot handle complex warping, leading to over distortion or misalignment. Furthermore, there is a critical unaddressed challenge of adjusting clothing sizes for try-on. To tackle these issues, we propose a Clothing-Oriented Transformation Try-On Network (COTTON). COTTON leverages clothing structure with landmarks and segmentation to design a novel landmark-guided transformation for precisely deforming clothes, allowing for size adjustment during try-on. Additionally, to properly remove the clothing region from the human image without losing significant human characteristics, we propose a clothing elimination policy based on both transformed clothes and human segmentation. This method enables users to try on clothes tucked-in or untucked while retaining more human characteristics. Both qualitative and quantitative results show that COTTON outperforms the state-of-the-art high-resolution virtual try-on approaches. All the code is available at https://github.com/cotton6/COTTON-size-does-matter.
Content-Aware Local GAN for Photo-Realistic Super-Resolution
Authors: JoonKyu Park, Sanghyun Son, Kyoung Mu Lee
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Super-Resolution, Super-Resolution

    Recently, GAN has successfully contributed to making single-image super-resolution (SISR) methods produce more realistic images. However, natural images have complex distribution in the real world, and a single classifier in the discriminator may not have enough capacity to classify real and fake samples, making the preceding SR network generate unpleasing noise and artifacts. To solve the problem, we propose a novel content-aware local GAN framework, CAL-GAN, which processes a large and complicated distribution of real-world images by dividing them into smaller subsets based on similar contents. Our mixture of classifiers (MoC) design allocates different super-resolved patches to corresponding expert classifiers. Additionally, we introduce novel routing and orthogonality loss terms so that different classifiers can handle various contents and learn separable features. By feeding similar distributions into the corresponding specialized classifiers, CAL-GAN enhances the representation power of existing super-resolution models, achieving state-of-the-art perceptual performance on standard benchmarks and real-world images without modifying the generator-side architecture.
Counterfactual-based Saliency Map: Towards Visual Contrastive Explanations for Neural Networks
Authors: Xue Wang, Zhibo Wang, Haiqin Weng, Hengchang Guo, Zhifei Zhang, Lu Jin, Tao Wei, Kui Ren
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: counterfactual

    Explaining deep models in a human-understandable way has been explored by many works that mostly explain why an input causes a corresponding prediction (ie., Why P?). However, seldom they could handle those more complex causal questions like "why P rather than Q?" and "why one is P while another is Q?", which would better help humans understand the behavior of deep models. Considering the insufficient study on such complex causal questions, we make the first attempt to explain different causal questions by contrastive explanations in a unified framework, ie., Counterfactual Contrastive Explanation (CCE), which visually and intuitively explains the aforementioned questions via a novel positive-negative saliency-based explanation scheme. More specifically, we propose a content-aware counterfactual perturbing algorithm to stimulate contrastive examples, from which a pair of positive and negative saliency maps could be derived to contrastively explain why P (positive class) rather than Q (negative class). Beyond existing works, our counterfactual perturbation meets the principles of validity, sparsity, and data distribution closeness at the same time. In addition, by slightly adjusting the objective of perturbation, our framework can adapt to different causal questions. Extensive experimental evaluation demonstrates the effectiveness and superior performance of the proposed CCE on different benchmark metrics for interpretability, including Sanity Check, Class Deviation Score and Insertion-Deletion tests. A user study is conducted and the results show that user confidence is increasing significantly when presented with CCE compared to standard saliency map baselines.
Unleashing the Potential of Spiking Neural Networks with Dynamic Confidence
Authors: Chen Li, Edward G Jones, Steve Furber
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Decision Making

    This paper presents a new methodology to alleviate the fundamental trade-off between accuracy and latency in spiking neural networks (SNNs). The approach involves decoding confidence information over time from the SNN outputs and using it to develop a decision-making agent that can dynamically determine when to terminate each inference.   The proposed method, Dynamic Confidence, provides several significant benefits to SNNs. 1. It can effectively optimize latency dynamically at runtime, setting it apart from many existing low-latency SNN algorithms. Our experiments on CIFAR-10 and ImageNet datasets have demonstrated an average 40% speedup across eight different settings after applying Dynamic Confidence. 2. The decision-making agent in Dynamic Confidence is straightforward to construct and highly robust in parameter space, making it extremely easy to implement. 3. The proposed method enables visualizing the potential of any given SNN, which sets a target for current SNNs to approach. For instance, if an SNN can terminate at the most appropriate time point for each input sample, a ResNet-50 SNN can achieve an accuracy as high as 82.47% on ImageNet within just 4.71 time steps on average. Unlocking the potential of SNNs needs a highly-reliable decision-making agent to be constructed and fed with a high-quality estimation of ground truth. In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs.
Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan
Authors: Yongheng Sun, Fan Wang, Jun Shu, Haifeng Wang, Li Wang, Deyu Meng, Chunfeng Lian
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Meta-Learning, Segmentation

    Brain tissue segmentation is essential for neuroscience and clinical studies. However, segmentation on longitudinal data is challenging due to dynamic brain changes across the lifespan. Previous researches mainly focus on self-supervision with regularizations and will lose longitudinal generalization when fine-tuning on a specific age group. In this paper, we propose a dual meta-learning paradigm to learn longitudinally consistent representations and persist when fine-tuning. Specifically, we learn a plug-and-play feature extractor to extract longitudinal-consistent anatomical representations by meta-feature learning and a well-initialized task head for fine-tuning by meta-initialization learning. Besides, two class-aware regularizations are proposed to encourage longitudinal consistency. Experimental results on the iSeg2019 and ADNI datasets demonstrate the effectiveness of our method.
RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature Dictionary
Authors: Mengyao Li, Liquan Shen, Peng Ye, Guorui Feng, Zheyin Wang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Compression, MORPH

    Thriving underwater applications demand efficient extreme compression technology to realize the transmission of underwater images (UWIs) in very narrow underwater bandwidth. However, existing image compression methods achieve inferior performance on UWIs because they do not consider the characteristics of UWIs: (1) Multifarious underwater styles of color shift and distance-dependent clarity, caused by the unique underwater physical imaging; (2) Massive redundancy between different UWIs, caused by the fact that different UWIs contain several common ocean objects, which have plenty of similarities in structures and semantics. To remove redundancy among UWIs, we first construct an exhaustive underwater multi-scale feature dictionary to provide coarse-to-fine reference features for UWI compression. Subsequently, an extreme UWI compression network with reference to the feature dictionary (RFD-ECNet) is creatively proposed, which utilizes feature match and reference feature variant to significantly remove redundancy among UWIs. To align the multifarious underwater styles and improve the accuracy of feature match, an underwater style normalized block (USNB) is proposed, which utilizes underwater physical priors extracted from the underwater physical imaging model to normalize the underwater styles of dictionary features toward the input. Moreover, a reference feature variant module (RFVM) is designed to adaptively morph the reference features, improving the similarity between the reference and input features. Experimental results on four UWI datasets show that our RFD-ECNet is the first work that achieves a significant BD-rate saving of 31% over the most advanced VVC.
Anchor Structure Regularization Induced Multi-view Subspace Clustering via Enhanced Tensor Rank Minimization
Authors: Jintian Ji, Songhe Feng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Clustering, Multi-view Subspace Clustering

    The tensor-based multi-view subspace clustering algorithms have received widespread attention due to the powerful ability to capture high-order correlation across views. Although such algorithms have achieved remarkable success, they still suffer from three main issues: 1) The extremely high computational complexity makes tensor-based methods difficult to handle large-scale data sets. 2) The commonly used Tensor Nuclear Norm (TNN) treats different singular values equally and under-penalizes the noise components, resulting in a sub-optimal representation tensor. 3) The subspace-based methods usually ignore the local geometric structure of the original data. Being aware of these, we propose Anchor Structure Regularitation Induced Multi-view Subspace Clustering via Enhanced Tensor Rank Minimization (ASR-ETR). Specifically, an anchor representation tensor is constructed by using the anchor representation strategy rather than the self-representation strategy to reduce the time complexity, and an Anchor Structure Regularization (ASR) is employed to enhance the local geometric structure in the learned anchor-representation tensor. We further define an Enhanced Tensor Rank (ETR), which is a tighter surrogate of the tensor rank and more effective to drive the noise out. Moreover, an efficient iterative optimization algorithm is designed to solve the ASR-ETR, which enjoys both linear complexity and favorable convergence. Extensive experimental results on various data sets demonstrate the superiority of the proposed algorithm as compared to state-of-the-art methods.
Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models
Authors: Mischa Dombrowski, Hadrien Reynaud, Matthew Baugh, Bernhard Kainz
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Conditional Image Generation, Image Generation, Medical Image Analysis, Object, Segmentation, Semantic Segmentation

    Curating datasets for object segmentation is a difficult task. With the advent of large-scale pre-trained generative models, conditional image generation has been given a significant boost in result quality and ease of use. In this paper, we present a novel method that enables the generation of general foreground-background segmentation models from simple textual descriptions, without requiring segmentation labels. We leverage and explore pre-trained latent diffusion models, to automatically generate weak segmentation masks for concepts and objects. The masks are then used to fine-tune the diffusion model on an inpainting task, which enables fine-grained removal of the object, while at the same time providing a synthetic foreground and background dataset. We demonstrate that using this method beats previous methods in both discriminative and generative performance and closes the gap with fully supervised training while requiring no pixel-wise object labels. We show results on the task of segmenting four different objects (humans, dogs, cars, birds) and a use case scenario in medical image analysis. The code is available at https://github.com/MischaD/fobadiffusion.
MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos with Spherical Buffers and Padded Convolutions
Authors: Mathias Parger, Chengcheng Tang, Thomas Neff, Christopher D. Twigg, Cem Keskin, Robert Wang, Markus Steinberger
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Convolutional neural network inference on video input is computationally expensive and requires high memory bandwidth. Recently, DeltaCNN managed to reduce the cost by only processing pixels with significant updates over the previous frame. However, DeltaCNN relies on static camera input. Moving cameras add new challenges in how to fuse newly unveiled image regions with already processed regions efficiently to minimize the update rate - without increasing memory overhead and without knowing the camera extrinsics of future frames. In this work, we propose MotionDeltaCNN, a sparse CNN inference framework that supports moving cameras. We introduce spherical buffers and padded convolutions to enable seamless fusion of newly unveiled regions and previously processed regions - without increasing memory footprint. Our evaluation shows that we outperform DeltaCNN by up to 90% for moving camera videos.
Multi-Scale Bidirectional Recurrent Network with Hybrid Correlation for Point Cloud Based Scene Flow Estimation
Authors: Wencan Cheng, Jong Hwan Ko
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Scene Flow Estimation

    Scene flow estimation provides the fundamental motion perception of a dynamic scene, which is of practical importance in many computer vision applications. In this paper, we propose a novel multi-scale bidirectional recurrent architecture that iteratively optimizes the coarse-to-fine scene flow estimation. In each resolution scale of estimation, a novel bidirectional gated recurrent unit is proposed to bidirectionally and iteratively augment point features and produce progressively optimized scene flow. The optimization of each iteration is integrated with the hybrid correlation that captures not only local correlation but also semantic correlation for more accurate estimation. Experimental results indicate that our proposed architecture significantly outperforms the existing state-of-the-art approaches on both FlyingThings3D and KITTI benchmarks while maintaining superior time efficiency. Codes and pre-trained models are publicly available at https://github.com/cwc1260/MSBRN.
Boosting Single Image Super-Resolution via Partial Channel Shifting
Authors: XiaoMing Zhang, Tianrui Li, Xiaole Zhao
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Diversity, Image Super-Resolution, Super-Resolution, Video Understanding

    Although deep learning has significantly facilitated the progress of single image super-resolution (SISR) in recent years, it still hits bottlenecks to further improve SR performance with the continuous growth of model scale. Therefore, one of the hotspots in the field is to construct efficient SISR models by elevating the effectiveness of feature representation. In this work, we present a straightforward and generic approach for feature enhancement that can effectively promote the performance of SR models, dubbed partial channel shifting (PCS). Specifically, it is inspired by the temporal shifting in video understanding and displaces part of the channels along the spatial dimensions, thus allowing the effective receptive field to be amplified and the feature diversity to be augmented at almost zero cost. Also, it can be assembled into off-the-shelf models as a plug-and-play component for performance boosting without extra network parameters and computational overhead. However, regulating the features with PCS encounters some issues, like shifting directions and amplitudes, proportions, and patterns of shifted channels, etc. We impose some technical constraints on the issues to simplify the general channel shifting. Extensive and throughout experiments illustrate that the PCS indeed enlarges the effective receptive field, augments the feature diversity for efficiently enhancing SR recovery, and can endow obvious performance gains to existing models.
3DPPE: 3D Point Positional Encoding for Transformer-based Multi-Camera 3D Object Detection
Authors: Changyong Shu, Jiajun Deng, Fisher Yu, Yifan Liu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Detection, Decoder, Monocular 3D Object Detection, object-detection, Object Detection

    Transformer-based methods have swept the benchmarks on 2D and 3D detection on images. Because tokenization before the attention mechanism drops the spatial information, positional encoding becomes critical for those methods. Recent works found that encodings based on samples of the 3D viewing rays can significantly improve the quality of multi-camera 3D object detection. We hypothesize that 3D point locations can provide more information than rays. Therefore, we introduce 3D point positional encoding, 3DPPE, to the 3D detection Transformer decoder. Although 3D measurements are not available at the inference time of monocular 3D object detection, 3DPPE uses predicted depth to approximate the real point positions. Our hybrid-depth module combines direct and categorical depth to estimate the refined depth of each pixel. Despite the approximation, 3DPPE achieves 46.0 mAP and 51.4 NDS on the competitive nuScenes dataset, significantly outperforming encodings based on ray samples. The codes are available at https://github.com/drilistbox/3DPPE.
LoTE-Animal: A Long Time-span Dataset for Endangered Animal Behavior Understanding
Authors: Dan Liu, Jin Hou, Shaoli Huang, Jing Liu, Yuxin He, Bochuan Zheng, Jifeng Ning, Jingdong Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Recognition, Domain Adaptation, Instance Segmentation, object-detection, Object Detection, Pose Estimation, Semantic Segmentation

    Understanding and analyzing animal behavior is increasingly essential to protect endangered animal species. However, the application of advanced computer vision techniques in this regard is minimal, which boils down to lacking large and diverse datasets for training deep models. To break the deadlock, we present LoTE-Animal, a large-scale endangered animal dataset collected over 12 years, to foster the application of deep learning in rare species conservation. The collected data contains vast variations such as ecological seasons, weather conditions, periods, viewpoints, and habitat scenes. So far, we retrieved at least 500K videos and 1.2 million images. Specifically, we selected and annotated 11 endangered animals for behavior understanding, including 10K video sequences for the action recognition task, 28K images for object detection, instance segmentation, and pose estimation tasks. In addition, we gathered 7K web images of the same species as source domain data for the domain adaptation task. We provide evaluation results of representative vision understanding approaches and cross-domain experiments. LoTE-Animal dataset would facilitate the community to research more advanced machine learning models and explore new tasks to aid endangered animal conservation. Our dataset will be released with the paper.