pwc-archive/papers-with-abstracts huggingface dataset:
Global Balanced Experts for Federated Long-Tailed Learning
Authors: Yaopei Zeng, Lei Liu, Li Liu, Li Shen, Shaoguo Liu, Baoyuan Wu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Federated Learning, Privacy Preserving

    Federated learning (FL) is a prevalent distributed machine learning approach that enables collaborative training of a global model across multiple devices without sharing local data. However, the presence of long-tailed data can negatively deteriorate the model's performance in real-world FL applications. Moreover, existing re-balance strategies are less effective for the federated long-tailed issue when directly utilizing local label distribution as the class prior at the clients' side. To this end, we propose a novel Global Balanced Multi-Expert (GBME) framework to optimize a balanced global objective, which does not require additional information beyond the standard FL pipeline. In particular, a proxy is derived from the accumulated gradients uploaded by the clients after local training, and is shared by all clients as the class prior for re-balance training. Such a proxy can also guide the client grouping to train a multi-expert model, where the knowledge from different clients can be aggregated via the ensemble of different experts corresponding to different client groups. To further strengthen the privacy-preserving ability, we present a GBME-p algorithm with a theoretical guarantee to prevent privacy leakage from the proxy. Extensive experiments on long-tailed decentralized datasets demonstrate the effectiveness of GBME and GBME-p, both of which show superior performance to state-of-the-art methods.
Foreground-Background Distribution Modeling Transformer for Visual Object Tracking
Authors: Dawei Yang, Jianfeng He, Yinchao Ma, Qianjin Yu, Tianzhu Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Object, Object Tracking, State Estimation, Visual Object Tracking

    Visual object tracking is a fundamental research topic with a broad range of applications. Benefiting from the rapid development of Transformer, pure Transformer trackers have achieved great progress. However, the feature learning of these Transformer-based trackers is easily disturbed by complex backgrounds. To address the above limitations, we propose a novel foreground-background distribution modeling transformer for visual object tracking (F-BDMTrack), including a fore-background agent learning (FBAL) module and a distribution-aware attention (DA2) module in a unified transformer architecture. The proposed F-BDMTrack enjoys several merits. First, the proposed FBAL module can effectively mine fore-background information with designed fore-background agents. Second, the DA2 module can suppress the incorrect interaction between foreground and background by modeling fore-background distribution similarities. Finally, F-BDMTrack can extract discriminative features under ever-changing tracking scenarios for more accurate target state estimation. Extensive experiments show that our F-BDMTrack outperforms previous state-of-the-art trackers on eight tracking benchmarks.
CHORUS : Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images
Authors: Sookwan Han, Hanbyul Joo
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Common Sense Reasoning, Diversity, Human-Object Interaction Detection, Object

    We present a method for teaching machines to understand and model the underlying spatial common sense of diverse human-object interactions in 3D in a self-supervised way. This is a challenging task, as there exist specific manifolds of the interactions that can be considered human-like and natural, but the human pose and the geometry of objects can vary even for similar interactions. Such diversity makes the annotating task of 3D interactions difficult and hard to scale, which limits the potential to reason about that in a supervised way. One way of learning the 3D spatial relationship between humans and objects during interaction is by showing multiple 2D images captured from different viewpoints when humans interact with the same type of objects. The core idea of our method is to leverage a generative model that produces high-quality 2D images from an arbitrary text prompt input as an "unbounded" data generator with effective controllability and view diversity. Despite its imperfection of the image quality over real images, we demonstrate that the synthesized images are sufficient to learn the 3D human-object spatial relations. We present multiple strategies to leverage the synthesized images, including (1) the first method to leverage a generative image model for 3D human-object spatial relation learning; (2) a framework to reason about the 3D spatial relations from inconsistent 2D cues in a self-supervised manner via 3D occupancy reasoning with pose canonicalization; (3) semantic clustering to disambiguate different types of interactions with the same object types; and (4) a novel metric to assess the quality of 3D spatial learning of interaction. Project Page: https://jellyheadandrew.github.io/projects/chorus
Template Inversion Attack against Face Recognition Systems using 3D Face Reconstruction
Authors: Hatef Otroshi Shahreza, Sébastien Marcel
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Face Reconstruction, Face Recognition, Face Reconstruction, Generative Adversarial Network

    Face recognition systems are increasingly being used in different applications. In such systems, some features (also known as embeddings or templates) are extracted from each face image. Then, the extracted templates are stored in the system's database during the enrollment stage and are later used for recognition. In this paper, we focus on template inversion attacks against face recognition systems and introduce a novel method (dubbed GaFaR) to reconstruct 3D face from facial templates. To this end, we use a geometry-aware generator network based on generative neural radiance fields (GNeRF), and learn a mapping from facial templates to the intermediate latent space of the generator network. We train our network with a semi-supervised learning approach using real and synthetic images simultaneously. For the real training data, we use a Generative Adversarial Network (GAN) based framework to learn the distribution of the latent space. For the synthetic training data, where we have the true latent code, we directly train in the latent space of the generator network. In addition, during the inference stage, we also propose optimization on the camera parameters to generate face images to improve the success attack rate (up to 17.14% in our experiments). We evaluate the performance of our method in the whitebox and blackbox attacks against state-of-the-art face recognition models on the LFW and MOBIO datasets. To our knowledge, this paper is the first work on 3D face reconstruction from facial templates. The project page is available at: https://www.idiap.ch/paper/gafar
Chasing Clouds: Differentiable Volumetric Rasterisation of Point Clouds as a Highly Efficient and Accurate Loss for Large-Scale Deformable 3D Registration
Authors: Mattias P. Heinrich, Alexander Bigalke, Christoph Großbröhmer, Lasse Hansen
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Self-Supervised Learning

    Learning-based registration for large-scale 3D point clouds has been shown to improve robustness and accuracy compared to classical methods and can be trained without supervision for locally rigid problems. However, for tasks with highly deformable structures, such as alignment of pulmonary vascular trees for medical diagnostics, previous approaches of self-supervision with regularisation and point distance losses have failed to succeed, leading to the need for complex synthetic augmentation strategies to obtain reliably strong supervision. In this work, we introduce a novel Differentiable Volumetric Rasterisation of point Clouds (DiVRoC) that overcomes those limitations and offers a highly efficient and accurate loss for large-scale deformable 3D registration. DiVRoC drastically reduces the computational complexity for measuring point cloud distances for high-resolution data with over 100k 3D points and can also be employed to extrapolate and regularise sparse motion fields, as loss in a self-training setting and as objective function in instance optimisation. DiVRoC can be successfully embedded into geometric registration networks, including PointPWC-Net and other graph CNNs. Our approach yields new state-of-the-art accuracy on the challenging PVT dataset in three different settings without training with manual ground truth: 1) unsupervised metric-based learning 2) self-supervised learning with pseudo labels generated by self-training and 3) optimisation based alignment without learning. https://github.com/mattiaspaul/ChasingClouds
Dual Learning with Dynamic Knowledge Distillation for Partially Relevant Video Retrieval
Authors: Jianfeng Dong, Minsong Zhang, Zheng Zhang, Xianke Chen, Daizong Liu, Xiaoye Qu, Xun Wang, Baolong Liu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Knowledge Distillation, Language Modelling, Partially Relevant Video Retrieval, Retrieval, Text to Video Retrieval, Video Retrieval

    Almost all previous text-to-video retrieval works assume that videos are pre-trimmed with short durations. However, in practice, videos are generally untrimmed containing much background content. In this work, we investigate the more practical but challenging Partially Relevant Video Retrieval (PRVR) task, which aims to retrieve partially relevant untrimmed videos with the query input. Particularly, we propose to address PRVR from a new perspective, i.e., distilling the generalization knowledge from the large-scale vision-language pre-trained model and transferring it to a task-specific PRVR network. To be specific, we introduce a Dual Learning framework with Dynamic Knowledge Distillation (DL-DKD), which exploits the knowledge of a large vision-language model as the teacher to guide a student model. During the knowledge distillation, an inheritance student branch is devised to absorb the knowledge from the teacher model. Considering that the large model may be of mediocre performance due to the domain gaps, we further develop an exploration student branch to take the benefits of task-specific information. By jointly training the above two branches in a dual-learning way, our model is able to selectively acquire appropriate knowledge from the teacher model while capturing the task-specific property. In addition, a dynamical knowledge distillation strategy is further devised to adjust the effect of each student branch learning during the training. Experiment results demonstrate that our proposed model achieves state-of-the-art performance on ActivityNet and TVR datasets for PRVR.
DeepChange: A Long-Term Person Re-Identification Benchmark with Clothes Change
Authors: Peng Xu, Xiatian Zhu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Person Re-Identification

    Long-term re-id with clothes change is a challenging problem in surveillance AI. Currently, its major bottleneck is that this field is still missing a large realistic benchmark. In this work, we contribute a large, realistic long-term person re-identification benchmark, termed DeepChange. Its unique characteristics are: (1) Realistic and rich personal appearance (e.g., clothes and hair style) and variations: Highly diverse clothes change and styles, with varying reappearing gaps in time from minutes to seasons, different weather conditions (e.g., sunny, cloudy, windy, rainy, snowy, extremely cold) and events (e.g., working, leisure, daily activities). (2) Rich camera setups: Raw videos were recorded by 17 outdoor varying-resolution cameras operating in a real-world surveillance system. (3) The currently largest number of (17) cameras, (1, 121) identities, and (178, 407) bounding boxes, over the longest time span (12 months). We benchmark the representative supervised and unsupervised re-id methods on our dataset. In addition, we investigate multimodal fusion strategies for tackling the clothes change challenge. Extensive experiments show that our fusion models outperform a wide variety of state-of-the-art models on DeepChange. Our dataset and documents are available at https://github.com/PengBoXiangShang/deepchange.
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning
Authors: Yangru Huang, Peixi Peng, Yifan Zhao, Yunpeng Zhai, Haoran Xu, Yonghong Tian
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: reinforcement-learning, Reinforcement Learning (RL)

    Efficient motion and appearance modeling are critical for vision-based Reinforcement Learning (RL). However, existing methods struggle to reconcile motion and appearance information within the state representations learned from a single observation encoder. To address the problem, we present Synergizing Interactive Motion-appearance Understanding (Simoun), a unified framework for vision-based RL. Given consecutive observation frames, Simoun deliberately and interactively learns both motion and appearance features through a dual-path network architecture. The learning process collaborates with a structural interactive module, which explores the latent motion-appearance structures from the two network paths to leverage their complementarity. To promote sample efficiency, we further design a consistency-guided curiosity module to encourage the exploration of under-learned observations. During training, the curiosity module provides intrinsic rewards according to the consistency of environmental temporal dynamics, which are deduced from both motion and appearance network paths. Experiments conducted on the DeepMind control suite and CARLA automatic driving benchmarks demonstrate the effectiveness of Simoun, where it performs favorably against state-of-the-art methods.
Rethinking Fast Fourier Convolution in Image Inpainting
Authors: Tianyi Chu, Jiafu Chen, Jiakai Sun, Shuobin Lian, Zhizhong Wang, Zhiwen Zuo, Lei Zhao, Wei Xing, Dongming Lu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: image-classification, Image Classification, Image Inpainting

    Recently proposed image inpainting method LaMa builds its network upon Fast Fourier Convolution (FFC), which was originally proposed for high-level vision tasks like image classification. FFC empowers the fully convolutional network to have a global receptive field in its early layers. Thanks to the unique character of the FFC module, LaMa has the ability to produce robust repeating texture, which can not be achieved by the previous inpainting methods. However, is the vanilla FFC module suitable for low-level vision tasks like image inpainting? In this paper, we analyze the fundamental flaws of using FFC in image inpainting, which are 1) spectrum shifting, 2) unexpected spatial activation, and 3) limited frequency receptive field. Such flaws make FFC-based inpainting framework difficult in generating complicated texture and performing faithful reconstruction. Based on the above analysis, we propose a novel Unbiased Fast Fourier Convolution (UFFC) module, which modifies the vanilla FFC module with 1) range transform and inverse transform, 2) absolute position embedding, 3) dynamic skip connection, and 4) adaptive clip, to overcome such flaws, achieving better inpainting results. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our method, outperforming the state-of-the-art methods in both texture-capturing ability and expressiveness.
Reconciling Object-Level and Global-Level Objectives for Long-Tail Detection
Authors: Shaoyu Zhang, Chen Chen, Silong Peng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Multi-Task Learning, Object

    Large vocabulary object detectors are often faced with the long-tailed label distributions, seriously degrading their ability to detect rarely seen categories. On one hand, the rare objects are prone to be misclassified as frequent categories. On the other hand, due to the limitation on the total number of detections per image, detectors usually rank all the confidence scores globally and filter out the lower-ranking ones. This may result in missed detection during inference, especially for the rare categories that naturally come with lower scores. Existing methods mainly focus on the former problem and design various classification loss to enhance the object-level classification accuracy, but largely overlook the global-level ranking task. In this paper, we propose a novel framework that Reconciles Object-level and Global-level (ROG) objectives to address both problems. As a multi-task learning framework, ROG simultaneously trains the model with two tasks: classifying each object proposal individually and ranking all the confidence scores globally. Specifically, complementary to the object-level classification loss for model discrimination, we design a generalized average precision (GAP) loss to explicitly optimize the global-level score ranking across different objects. For each category, GAP loss generates balanced gradients to rectify the ranking errors. In experiments, we show that GAP loss is highly versatile to be plugged into various advanced methods and brings considerable benefits.
Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action Recognition
Authors: Yisheng Zhu, Hu Han, Zhengtao Yu, Guangcan Liu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Recognition, Contrastive Learning, Data Augmentation, Self-Supervised Human Action Recognition, Self-supervised Skeleton-based Action Recognition, Skeleton Based Action Recognition

    Visual tempo characterizes the dynamics and the temporal evolution, which helps describe actions. Recent approaches directly perform visual tempo prediction on skeleton sequences, which may suffer from insufficient feature representation issue. In this paper, we observe that relative visual tempo is more in line with human intuition, and thus providing more effective supervision signals. Based on this, we propose a novel Relative Visual Tempo Contrastive Learning framework for skeleton action Representation (RVTCLR). Specifically, we design a Relative Visual Tempo Learning (RVTL) task to explore the motion information in intra-video clips, and an Appearance-Consistency (AC) task to learn appearance information simultaneously, resulting in more representative spatiotemporal features. Furthermore, skeleton sequence data is much sparser than RGB data, making the network learn shortcuts, and overfit to low-level information such as skeleton scales. To learn high-order semantics, we further design a new Distribution-Consistency (DC) branch, containing three components: Skeleton-specific Data Augmentation (SDA), Fine-grained Skeleton Encoding Module (FSEM), and Distribution-aware Diversity (DD) Loss. We term our entire method (RVTCLR with DC) as RVTCLR+. Extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets demonstrate that our RVTCLR+ can achieve competitive results over the state-of-the-art methods. Code is available at https://github.com/Zhuysheng/RVTCLR.
ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion
Authors: Qi Cai, Yingwei Pan, Ting Yao, Chong-Wah Ngo, Tao Mei
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Detection, Depth Estimation, Object, object-detection, Object Detection

    Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth estimation of each pixel, resulting in spatial misalignment between these two multi-modal features. Moreover, such transformation also inevitably leads to projection distortion of camera image features in BEV space. In this paper, we propose a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for 3D object detection. ObjectFusion first learns three kinds of modality-specific feature maps (i.e., voxel, BEV, and image features) from LiDAR point clouds and its BEV projections, camera images. Then a set of 3D object proposals are produced from the BEV features via a heatmap-based proposal generator. Next, the 3D object proposals are reprojected back to voxel, BEV, and image spaces. We leverage voxel and RoI pooling to generate spatially aligned object-centric features for each modality. All the object-centric features of three modalities are further fused at object level, which is finally fed into the detection heads. Extensive experiments on nuScenes dataset demonstrate the superiority of our ObjectFusion, by achieving 69.8% mAP on nuScenes validation set and improving BEVFusion by 1.3%.
Learning Long-Range Information with Dual-Scale Transformers for Indoor Scene Completion
Authors: Ziqi Wang, Fei Luo, Xiaoxiao Long, Wenxiao Zhang, Chunxia Xiao
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Due to the limited resolution of 3D sensors and the inevitable mutual occlusion between objects, 3D scans of real scenes are commonly incomplete.  Previous scene completion methods struggle to capture long-range spatial feature, resulting in unsatisfactory completion results.  To alleviate the problem, we propose a novel Dual-Scale Transformer Network (DST-Net) that efficiently utilizes both long-range and short-range spatial context information to improve the quality of 3D scene completion.  To reduce the heavy computation cost of extracting long-range features via transformers, DST-Net adopts a self-supervised two-stage completion strategy. In the first stage, we split the input scene into blocks, and perform completion on individual blocks. In the second stage, the blocks are merged together as a whole and then further refined to improve completeness. More importantly, we propose a contrastive attention training strategy to encourage the transformers to learn distinguishable features for better scene completion. Experiments on datasets of Matterport3D, ScanNet, and ICL-NUIM demonstrate that our method can generate better completion results, and our method outperforms the state-of-the-art methods quantitatively and qualitatively.