GenAI4ELab/papercli-papers huggingface dataset:
ClothPose: A Real-world Benchmark for Visual Analysis of Garment Pose via An Indirect Recording Solution
Authors: Wenqiang Xu; Wenxin Du; Han Xue; Yutong Li; Ruolin Ye; Yan-Feng Wang; Cewu Lu
Venue: ICCV 2023 (main)

Abstract: Garments are important and pervasive in daily life. However, visual analysis on them for pose estimation is challenging because it requires recovering the complete configurations of garments, which is difficult, if not impossible, to annotate in the real world. In this work, we propose a recording system, GarmentTwin, which can track garment poses in dynamic settings such as manipulation. GarmentTwin first collects garment models and RGB-D manipulation videos from the real world and then replays the manipulation process using physics-based animation. This way, we can obtain deformed garments with poses coarsely aligned with real-world observations. Finally, we adopt an optimization-based approach to fit the pose with real-world observations. We verify the fitting results quantitatively and qualitatively. With GarmentTwin, we construct a large-scale dataset named ClothPose, which consists of 30K RGB-D frames from 2K video clips on 600 garments of 10 categories. We benchmark two tasks on the proposed ClothPose: non-rigid reconstruction and pose estimation. The experiments show that previous baseline methods struggle with highly large non-rigid deformation of manipulated garments. Therefore, we hope that the recording system and the dataset can facilitate research on pose estimation tasks on non-rigid objects. Datasets, models, and codes are made publicly available.
Semantically Structured Image Compression via Irregular Group-Based Decoupling
Authors: Ruoyu Feng; Yixin Gao; Xin Jin; Runsen Feng; Zhibo Chen
Venue: ICCV 2023 (main)

Abstract: Image compression techniques typically focus on compressing rectangular images for human consumption, however, resulting in transmitting redundant content for downstream applications. To overcome this limitation, some previous works propose to semantically structure the bitstream, which can meet specific application requirements by selective transmission and reconstruction. Nevertheless, they divide the input image into multiple rectangular regions according to semantics and ignore avoiding information interaction among them, causing waste of bitrate and distorted reconstruction of region boundaries. In this paper, we propose to decouple an image into multiple groups with irregular shapes based on a customized group mask and compress them independently. Our group mask describes the image at a finer granularity, enabling significant bitrate saving by reducing the transmission of redundant content. Moreover, to ensure the fidelity of selective reconstruction, this paper proposes the concept of group-independent transform that maintain the independence among distinct groups. And we instantiate it by the proposed Group-Independent Swin-Block (GI Swin-Block). Experimental results demonstrate that our framework structures the bitstream with negligible cost, and exhibits superior performance on both visual quality and intelligent task supporting.
PhaseMP: Robust 3D Pose Estimation via Phase-conditioned Human Motion Prior
Authors: Mingyi Shi; Sebastian Starke; Yuting Ye; Taku Komura; Jungdam Won
Venue: ICCV 2023 (main)

Abstract: We present a novel motion prior, called PhaseMP, modeling a probability distribution on pose transitions conditioned by a frequency domain feature extracted from a periodic autoencoder. The phase feature further enforces the pose transitions to be unidirectional (i.e. no backward movement in time), from which more stable and natural motions can be generated. Specifically, our motion prior can be useful for accurately estimating 3D human motions in the presence of challenging input data, including long periods of spatial and temporal occlusion, as well as noisy sensor measurements. Through a comprehensive evaluation, we demonstrate the efficacy of our novel motion prior, showcasing its superiority over existing state-of-the-art methods by a significant margin across various applications, including video-to-motion and motion estimation from sparse sensor data, and etc.
NLOS-NeuS: Non-line-of-sight Neural Implicit Surface
Authors: Yuki Fujimura; Takahiro Kushida; Takuya Funatomi; Yasuhiro Mukaigawa
Venue: ICCV 2023 (main)

Abstract: Non-line-of-sight (NLOS) imaging is conducted to infer invisible scenes from indirect light on visible objects. The neural transient field (NeTF) was proposed for representing scenes as neural radiance fields in NLOS scenes. We propose NLOS neural implicit surface (NLOS-NeuS), which extends the NeTF to neural implicit surfaces with a signed distance function (SDF) for reconstructing three-dimensional surfaces in NLOS scenes. We introduce two constraints as loss functions for correctly learning an SDF to avoid non-zero level-set surfaces. We also introduce a lower bound constraint of an SDF based on the geometry of the first-returning photons. The experimental results indicate that these constraints are essential for learning a correct SDF in NLOS scenes. Compared with previous methods with discretized representation, NLOS-NeuS with the neural continuous representation enables us to reconstruct smooth surfaces while preserving fine details in NLOS scenes. To the best of our knowledge, this is the first study on neural implicit surfaces with volume rendering in NLOS scenes. Project page: https://yfujimura. github.io/nlos-neus/
Unsupervised Object Localization with Representer Point Selection
Authors: Yeonghwan Song; Seokwoo Jang; Dina Katabi; Jeany Son
Venue: ICCV 2023 (main)

Abstract: We propose a novel unsupervised object localization method that allows us to explain the predictions of the model by utilizing self-supervised pre-trained models without additional finetuning. Existing unsupervised and self-supervised object localization methods often utilize class-agnostic activation maps or self-similarity maps of a pre-trained model. Although these maps can offer valuable information for localization, their limited ability to explain how the model makes predictions remains challenging. In this paper, we propose a simple yet effective unsupervised object localization method based on representer point selection, where the predictions of the model can be represented as a linear combination of representer values of training points. By selecting representer points, which are the most important examples for the model predictions, our model can provide insights into how the model predicts the foreground object by providing relevant examples as well as their importance. Our method outperforms the state-of-the-art unsupervised and self-supervised object localization methods on various datasets with significant margins and even outperforms recent weakly supervised and few-shot methods.
SEMPART: Self-supervised Multi-resolution Partitioning of Image Semantics
Authors: Sriram Ravindran; Debraj Basu
Venue: ICCV 2023 (main)

Abstract: Accurately determining salient regions of an image is challenging when labeled data is scarce. DINO-based self-supervised approaches have recently leveraged meaningful image semantics captured by patch-wise features for locating foreground objects. Recent methods have also incorporated intuitive priors and demonstrated value in unsupervised methods for object partitioning. In this paper, we propose SEMPART, which jointly infers coarse and fine bi-partitions over an image's DINO-based semantic graph. Furthermore, SEMPART preserves fine boundary details using graph-driven regularization and successfully distills the coarse mask semantics into the fine mask. Our salient object detection and single object localization findings suggest that SEMPART produces high-quality masks rapidly without additional post-processing and benefits from co-optimizing the coarse and fine branches.
Flatness-Aware Minimization for Domain Generalization
Authors: Xingxuan Zhang; Renzhe Xu; Han Yu; Yancheng Dong; Pengfei Tian; Peng Cui
Venue: ICCV 2023 (main)

Abstract: Domain generalization (DG) seeks to learn robust models that generalize well under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not been explored in depth. Currently, most DG methods follow the widely used benchmark, DomainBed, and utilize Adam as the default optimizer for all datasets. However, we reveal that Adam is not necessarily the optimal choice for the majority of current DG methods and datasets. Based on the perspective of loss landscape flatness, we propose a novel approach, Flatness-Aware Minimization for Domain Generalization (FAD), which can efficiently optimize both zeroth-order and first-order flatness simultaneously for DG. We provide theoretical analyses of the FAD's out-of-distribution (OOD) generalization error and convergence. Our experimental results demonstrate the superiority of FAD on various DG datasets.
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation
Authors: Hansol Kim; Youngjun Kwak; Minyoung Jung; Jinho Shin; Youngsung Kim; Changick Kim
Venue: ICCV 2023 (main)

Abstract: Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems. However, limited round communications, scarce representation, and scalability pose significant challenges to its deployment, hindering its full potential. In this paper, we propose 'ProtoFL', Prototypical Representation Distillation based unsupervised Federated Learning to enhance the representation power of a global model and reduce round communication costs. Additionally, we introduce a local one-class classifier based on normalizing flows to improve performance with limited data. Our study represents the first investigation of using FL to improve one-class classification performance. We conduct extensive experiments on five widely used benchmarks, namely MNIST, CIFAR-10, CIFAR-100, ImageNet-30, and Keystroke-Dynamics, to demonstrate the superior performance of our proposed framework over previous methods in the literature.
Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation
Authors: Yuecong Xu; Jianfei Yang; Yunjiao Zhou; Zhenghua Chen; Min Wu; Xiaoli Li
Venue: ICCV 2023 (main)

Abstract: For video models to be transferred and applied seamlessly across video tasks in varied environments, Video Unsupervised Domain Adaptation (VUDA) has been introduced to improve the robustness and transferability of video models. However, current VUDA methods rely on a vast amount of high-quality unlabeled target data, which may not be available in real-world cases. We thus consider a more realistic Few-Shot Video-based Domain Adaptation (FSVDA) scenario where we adapt video models with only a few target video samples. While a few methods have touched upon Few-Shot Domain Adaptation (FSDA) in images and in FSVDA, they rely primarily on spatial augmentation for target domain expansion with alignment performed statistically at the instance level. However, videos contain more knowledge in terms of rich temporal and semantic information, which should be fully considered while augmenting target domains and performing alignment in FSVDA. We propose a novel SSA2lign to address FSVDA at the snippet level, where the target domain is expanded through a simple snippet-level augmentation followed by the attentive alignment of snippets both semantically and statistically, where semantic alignment of snippets is conducted through multiple perspectives. Empirical results demonstrate state-of-the-art performance of SSA2lign across multiple cross-domain action recognition benchmarks.
Self-Organizing Pathway Expansion for Non-Exemplar Class-Incremental Learning
Authors: Kai Zhu; Kecheng Zheng; Ruili Feng; Deli Zhao; Yang Cao; Zheng-Jun Zha
Venue: ICCV 2023 (main)

Abstract: Non-exemplar class-incremental learning aims to recognize both the old and new classes without access to old class samples. The conflict between old and new class optimization is exacerbated since the shared neural pathways can only be differentiated by the incremental samples. To address this problem, we propose a novel self-organizing pathway expansion scheme. Our scheme consists of a class-specific pathway organization strategy that reduces the coupling of optimization pathway among different classes to enhance the independence of the feature representation, and a pathway-guided feature optimization mechanism to mitigate the update interference between the old and new classes. Extensive experiments on four datasets demonstrate significant performance gains, outperforming the state-of-the-art methods by a margin of 1%, 3%, 2% and 2%, respectively.
Preserving Tumor Volumes for Unsupervised Medical Image Registration
Authors: Qihua Dong; Hao Du; Ying Song; Yan Xu; Jing Liao
Venue: ICCV 2023 (main)

Abstract: Medical image registration is a critical task that estimates the spatial correspondence between pairs of images. However, current traditional and learning-based methods rely on similarity measures to generate a deforming field, which often results in disproportionate volume changes in dissimilar regions, especially in tumor regions. These changes can significantly alter the tumor size and underlying anatomy, which limits the practical use of image registration in clinical diagnosis. To address this issue, we have formulated image registration with tumors as a constraint problem that preserves tumor volumes while maximizing image similarity in other normal regions. Our proposed framework involves a two-stage process. In the first stage, we use similarity-based registration to identify potential tumor regions by their volume change, generating a soft tumor mask accordingly. In the second stage, we propose a volume-preserving registration with a novel adaptive volume-preserving loss that penalizes the change in size adaptively based on the masks calculated from the previous stage. Our approach balances image similarity and volume preservation in different regions, i.e., normal and tumor regions, by using soft tumor masks to adjust the imposition of volume-preserving loss on each one. This ensures that the tumor volume is preserved during the registration process. We have evaluated our framework on various datasets and network architectures, demonstrating that our method successfully preserves the tumor volume while achieving comparable registration results with state-of-the-art methods. Our code is at: https://dddraxxx.github.io/Volume-Preserving-Registration/.
Multi-label Affordance Mapping from Egocentric Vision
Authors: Lorenzo Mur-Labadia; Jose J. Guerrero; Ruben Martinez-Cantin
Venue: ICCV 2023 (main)

Abstract: Accurate affordance detection and segmentation with pixel precision is an important piece in many complex systems based on interactions, such as robots and assitive devices. We present a new approach to affordance perception which enables accurate multi-label segmentation. Our approach can be used to automatically annotate grounded affordances from first person videos of interactions using a 3D map of the environment providing pixel level precision for the affordance location. We use this method to build the largest and most complete dataset on affordances based on the EPIC-Kitchen dataset, EPIC-Aff, which provides automatic, interaction-grounded, multi-label, metric and spatial affordance annotations. Then, we propose a new approach to affordance segmentation based on multi-label detection which enables multiple affordances to co-exists in the same space, for example if they are associated with the same object. We present several strategies of multi-label detection using several segmentation architectures. The experimental results highlights the importance of the multi-label detection. Finally, we show how our metric representation can be exploited for build a map of interaction hotspots in spatial action-centric zones and use that representation to perform a task-oriented navigation.
Towards Real-World Burst Image Super-Resolution: Benchmark and Method
Authors: Pengxu Wei; Yujing Sun; Xingbei Guo; Chang Liu; Guanbin Li; Jie Chen; Xiangyang Ji; Liang Lin
Venue: ICCV 2023 (main)

Abstract: Despite substantial advances, single-image super-resolution (SISR) is always in a dilemma to reconstruct high-quality images with limited information from one input image, especially in realistic scenarios. In this paper, we establish a large-scale real-world burst super-resolution dataset, i.e., RealBSR, to explore the faithful reconstruction of image details from multiple frames. Furthermore, we introduce a Federated Burst Affinity network (FBAnet) to investigate non-trivial pixel-wise displacements among images under real-world image degradation. Specifically, rather than using pixel-wise alignment, our FBAnet employs a simple homography alignment from a structural geometry aspect and a Federated Affinity Fusion (FAF) strategy to aggregate the complementary information among frames. Those fused informative representations are fed to a Transformer-based module of burst representation decoding. Besides, we have conducted extensive experiments on two versions of our datasets, i.e., RealBSR-RAW and RealBSR-RGB. Experimental results demonstrate that our FBAnet outperforms existing state-of-the-art burst SR methods and also achieves visually-pleasant SR image predictions with model details. Our dataset, codes, and models are publicly available at https://github.com/yjsunnn/FBANet.
Unified Adversarial Patch for Cross-Modal Attacks in the Physical World
Authors: Xingxing Wei; Yao Huang; Yitong Sun; Jie Yu
Venue: ICCV 2023 (main)

Abstract: Recently, physical adversarial attacks have been presented to evade DNNs-based object detectors. To ensure the security, many scenarios are simultaneously deployed with visible sensors and infrared sensors, leading to the failures of these single-modal physical attacks. To show the potential risks under such scenes, we propose a unified adversarial patch to perform cross-modal physical attacks, i.e., fooling visible and infrared object detectors at the same time via a single patch. Considering different imaging mechanisms of visible and infrared sensors, our work focuses on modeling the shapes of adversarial patches, which can be captured in different modalities when they change. To this end, we design a novel boundary-limited shape optimization to achieve the compact and smooth shapes, and thus they can be easily implemented in the physical world. In addition, to balance the fooling degree between visible detector and infrared detector during the optimization process, we propose a score-aware iterative evaluation, which can guide the adversarial patch to iteratively reduce the predicted scores of the multi-modal sensors. We finally test our method against the one-stage detector: YOLOv3 and the two-stage detector: Faster RCNN. Results show that our unified patch achieves an Attack Success Rate (ASR) of 73.33% and 69.17%, respectively. More importantly, we verify the effective attacks in the physical world when visible and infrared sensors shoot the objects under various settings like different angles, distances, postures, and scenes.
Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples
Authors: JoonHo Lee; Jae Oh Woo; Hankyu Moon; Kwonho Lee
Venue: ICCV 2023 (main)

Abstract: Deploying deep visual models can lead to performance drops due to the discrepancies between source and target distributions. Several approaches leverage labeled source data to estimate target domain accuracy, but accessing labeled source data is often prohibitively difficult due to data confidentiality or resource limitations on serving devices. Our work proposes a new framework to estimate model accuracy on unlabeled target data without access to source data. We investigate the feasibility of using pseudo-labels for accuracy estimation and evolve this idea into adopting recent advances in source-free domain adaptation algorithms. Our approach measures the disagreement rate between the source hypothesis and the target pseudo-labeling function, adapted from the source hypothesis. We mitigate the impact of erroneous pseudo-labels that may arise due to a high ideal joint hypothesis risk by employing adaptive adversarial perturbation on the input of the target model. Our proposed source-free framework effectively addresses the challenging distribution shift scenarios and outperforms existing methods requiring source data and labels for training.
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training
Authors: Bumsoo Kim; Yeonsik Jo; Jinhyung Kim; Seunghwan Kim
Venue: ICCV 2023 (main)

Abstract: Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web. To enhance data-efficiency, recent efforts have introduced additional supervision terms that involve random-augmented views of the image. However, since the image augmentation process is unaware of its text counterpart, this procedure could cause various degrees of image-text misalignments during training. Prior methods either disregarded this discrepancy or introduced external models to mitigate the impact of misalignments during training. In contrast, we propose a novel metric learning approach that capitalizes on these misalignments as an additional training source, which we term "Misalign, Contrast then Distill (MCD)". Unlike previous methods that treat augmented images and their text counterparts as simple positive pairs, MCD predicts the continuous scales of misalignment caused by the augmentation. Our extensive experimental results show that our proposed MCD achieves state-of-the-art transferability in multiple classification and retrieval downstream datasets.
SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-Time Performance on Mobile Device
Authors: Weiran Gou; Ziyao Yi; Yan Xiang; Shaoqing Li; Zibin Liu; Dehui Kong; Ke Xu
Venue: ICCV 2023 (main)

Abstract: With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved. Firstly, most low-level vision algorithms are task-specific and independent to each other, which makes them difficult to integrate into a single neural network architecture and accelerate simultaneously without task-level time-multiplexing. Secondly, most of these networks feature large amounts of parameters and huge computational costs in terms of multiplication-and-accumulation operations, and thus it is difficult to achieve real-time performance, especially on mobile devices with limited computing power. To tackle with these problems, we propose a novel network, SYENet, with only 6K parameters. The SYENet consists of two asymmetrical branches with simple building blocks and is able to handle multiple low-level vision tasks on mobile devices in a real-time manner. To effectively connect the results by asymmetrical branches, a Quadratic Connection Unit(QCU) is proposed. Furthermore, in order to improve visual quality, a new Regression Focal Loss is proposed to process the image. The proposed method proves its
 superior performance with the best PSNR and visual quality as compared with other networks in real-time applications such as Image Signal Processing(ISP), Low-Light Enhancement(LLE), and Super-Resolution(SR) with 2K60FPS throughput on Qualcomm 8 Gen 1 mobile SoC(System-on-Chip). Particularly, for ISP task, SYENet got the highest score in MAI 2022 Learned Smartphone ISP challenge.