pwc-archive/papers-with-abstracts huggingface dataset:
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds
Authors: Yu Pei, Xian Zhao, Hao Li, Jingyuan Ma, Jingwei Zhang, ShiLiang Pu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Detection, Decoder, Object, object-detection, Object Detection

    Attributed to the unstructured and sparse nature of point clouds, the transformer shows greater potential in point clouds data processing. However, the recent query-based 3D detectors usually project the features acquired from a sparse backbone into the structured and compact Bird's Eye View(BEV) plane before adopting the transformer, which destroys the sparsity of features, introducing empty tokens and additional resource consumption for the transformer. To this end, in this paper, we propose a novel query-based 3D detector called Clusterformer, our Clusterformer regards each object as a cluster of 3D space which mainly consists of the non-empty voxels belonging to the same object, and leverages the cluster to conduct the transformer decoder to generate the proposals from the sparse voxel features directly. Such cluster-based transformer structure can effectively improve the performance and convergence speed of query-based detectors by making use of the object prior information contained in the clusters. Additionally, we introduce a Query2Key strategy to enhance the key and value features with the object-level information iteratively in our cluster-based transformer structure. Experimental results show that the proposed Clusterformer outperforms the previous query-based detectors with a lower latency and memory usage, which achieves state-of-the-art performance on the Waymo Open Datasets and KITTI Datasets.
Tangent Sampson Error: Fast Approximate Two-view Reprojection Error for Central Camera Models
Authors: Mikhail Terekhov, Viktor Larsson
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    In this paper we introduce the Tangent Sampson error, which is a generalization of the classical Sampson error in two-view geometry that allows for arbitrary central camera models. It only requires local gradients of the distortion map at the original correspondences (allowing for pre-computation) resulting in a negligible increase in computational cost when used in RANSAC or local refinement. The error effectively approximates the true-reprojection error for a large variety of cameras, including extremely wide field-of-view lenses that cannot be undistorted to a single pinhole image. We show experimentally that the new error outperforms competing approaches both when used for model scoring in RANSAC and for non-linear refinement of the relative camera pose.
Self-Supervised Burst Super-Resolution
Authors: Goutam Bhat, Michaël Gharbi, Jiawen Chen, Luc van Gool, Zhihao Xia
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Super-Resolution

    We introduce a self-supervised training strategy for burst super-resolution that only uses noisy low-resolution bursts during training. Our approach eliminates the need to carefully tune synthetic data simulation pipelines, which often do not match real-world image statistics. Compared to weakly-paired training strategies, which require noisy smartphone burst photos of static scenes, paired with a clean reference obtained from a tripod-mounted DSLR camera, our approach is more scalable, and avoids the color mismatch between the smartphone and DSLR. To achieve this, we propose a new self-supervised objective that uses a forward imaging model to recover a high-resolution image from aliased high frequencies in the burst. Our approach does not require any manual tuning of the forward model's parameters; we learn them from data. Furthermore, we show our training strategy is robust to dynamic scene motion in the burst, which enables training burst super-resolution models using in-the-wild data. Extensive experiments on real and synthetic data show that, despite only using noisy bursts during training, models trained with our self-supervised strategy match, and sometimes surpass, the quality of fully-supervised baselines trained with synthetic data or weakly-paired ground-truth. Finally, we show our training strategy is general using four different burst super-resolution architectures.
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation
Authors: Chongjian Ge, Junsong Chen, Enze Xie, Zhongdao Wang, Lanqing Hong, Huchuan Lu, Zhenguo Li, Ping Luo
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Detection, Autonomous Driving, Multi-Task Learning, object-detection, Object Detection, Segmentation

    Perception systems in modern autonomous driving vehicles typically take inputs from complementary multi-modal sensors, e.g., LiDAR and cameras. However, in real-world applications, sensor corruptions and failures lead to inferior performances, thus compromising autonomous safety. In this paper, we propose a robust framework, called MetaBEV, to address extreme real-world environments, involving overall six sensor corruptions and two extreme sensor-missing situations. In MetaBEV, signals from multiple sensors are first processed by modal-specific encoders. Subsequently, a set of dense BEV queries are initialized, termed meta-BEV. These queries are then processed iteratively by a BEV-Evolving decoder, which selectively aggregates deep features from either LiDAR, cameras, or both modalities. The updated BEV representations are further leveraged for multiple 3D prediction tasks. Additionally, we introduce a new \moe   structure to alleviate the performance drop on distinct tasks in multi-task joint learning. Finally, MetaBEV is evaluated on the nuScenes dataset with 3D object detection and BEV map segmentation tasks. Experiments show MetaBEV outperforms prior arts by a large margin on both full and corrupted modalities. For instance, when the LiDAR signal is missing, MetaBEV improves 35.5% detection NDS and 17.7% segmentation mIoU upon the vanilla BEVFusion model; and when the camera signal is absent, MetaBEV still achieves 69.2% NDS and 53.7%mIoU, which is even higher than previous works that perform on full-modalities. Moreover, MetaBEV performs moderately against previous methods in both canonical perception and multi-task learning settings, refreshing state-of-the-art nuScenes BEV map segmentation with 70.4% mIoU.
FedPD: Federated Open Set Recognition with Parameter Disentanglement
Authors: Chen Yang, Meilu Zhu, Yifan Liu, Yixuan Yuan
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Disentanglement, Federated Learning, Open Set Learning

    Existing federated learning (FL) approaches are deployed under the unrealistic closed-set setting, with both training and testing classes belong to the same set, which makes the global model fail to identify the unseen classes as `unknown'. To this end, we aim to study a novel problem of federated open-set recognition (FedOSR), which learns an open-set recognition (OSR) model under federated paradigm such that it classifies seen classes while at the same time detects unknown classes. In this work, we propose a parameter disentanglement guided federated open-set recognition (FedPD) algorithm to address two core challenges of FedOSR: cross-client inter-set interference between learning closed-set and open-set knowledge and cross-client intra-set inconsistency by data heterogeneity. The proposed FedPD framework mainly leverages two modules, i.e., local parameter disentanglement (LPD) and global divide-and-conquer aggregation (GDCA), to first disentangle client OSR model into different subnetworks, then align the corresponding parts cross clients for matched model aggregation. Specifically, on the client side, LPD decouples an OSR model into a closed-set subnetwork and an open-set subnetwork by the task-related importance, thus preventing inter-set interference. On the server side, GDCA first partitions the two subnetworks into specific and shared parts, and subsequently aligns the corresponding parts through optimal transport to eliminate parameter misalignment. Extensive experiments on various datasets demonstrate the superior performance of our proposed method.
WaveIPT: Joint Attention and Flow Alignment in the Wavelet domain for Pose Transfer
Authors: Liyuan Ma, Tingwei Gao, Haitian Jiang, Haibin Shen, Kejie Huang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Pose Transfer

    Human pose transfer aims to generate a new image of the source person in a target pose. Among the existing methods, attention and flow are two of the most popular and effective approaches. Attention excels in preserving the semantic structure of the source image, which is more reflected in the low-frequency domain. Contrastively, flow is better at retaining fine-grained texture details in the high-frequency domain. To leverage the advantages of both attention and flow simultaneously, we propose Wavelet-aware Image-based Pose Transfer (WaveIPT) to fuse the attention and flow in the wavelet domain. To improve the fusion effect and avoid interference from irrelevant information between different frequencies, WaveIPT first applies Intra-scale Local Correlation (ILC) to adaptively fuse attention and flow in the same scale according to their strengths in low and high-frequency domains, and then uses Inter-scale Feature Interaction (IFI) module to explore inter-scale frequency features for effective information transfer across different scales. We further introduce an effective Progressive Flow Regularization to alleviate the challenges of flow estimation under large pose differences. Our experiments on the DeepFashion dataset demonstrate that WaveIPT achieves a new state-of-the-art in terms of FID and LPIPS, with improvements of 4.97% and 3.89%, respectively.
Protoype-based Dataset Comparison
Authors: Nanne van Noord
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Self-Supervised Learning

    Dataset summarisation is a fruitful approach to dataset inspection. However, when applied to a single dataset the discovery of visual concepts is restricted to those most prominent. We argue that a comparative approach can expand upon this paradigm to enable richer forms of dataset inspection that go beyond the most prominent concepts. To enable dataset comparison we present a module that learns concept-level prototypes across datasets. We leverage self-supervised learning to discover these prototypes without supervision, and we demonstrate the benefits of our approach in two case-studies. Our findings show that dataset comparison extends dataset inspection and we hope to encourage more works in this direction. Code and usage instructions available at https://github.com/Nanne/ProtoSim
Geometric Viewpoint Learning with Hyper-Rays and Harmonics Encoding
Authors: Zhixiang Min, Juan Carlos Dibene, Enrique Dunn
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Viewpoint is a fundamental modality that carries the interaction between observers and their environment. This paper proposes the first deep-learning framework for the viewpoint modality. The challenge in formulating learning frameworks for viewpoints resides in a suitable multimodal representation that links across the camera viewing space and 3D environment. Traditional approaches reduce the problem to image analysis instances, making them computationally expensive and not adequately modelling the intrinsic geometry and environmental context of 6DoF viewpoints. We improve these issues in two ways. 1) We propose a generalized viewpoint representation forgoing the analysis of photometric pixels in favor of encoded viewing ray embeddings attained from point cloud learning frameworks. 2) We propose a novel SE(3)-bijective 6D viewing ray, hyper-ray, that addresses the DoF deficiency problem of using 5DoF viewing rays representing 6DoF viewpoints. We demonstrate our approach has both efficiency and accuracy superiority over existing methods in novel real-world environments.
Uncertainty-guided Learning for Improving Image Manipulation Detection
Authors: Kaixiang Ji, Feng Chen, Xin Guo, Yadong Xu, Jian Wang, Jingdong Chen
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Manipulation, Image Manipulation Detection, Misinformation

    Image manipulation detection (IMD) is of vital importance as faking images and spreading misinformation can be malicious and harm our daily life. IMD is the core technique to solve these issues and poses challenges in two main aspects: (1) Data Uncertainty, i.e., the manipulated artifacts are often hard for humans to discern and lead to noisy labels, which may disturb model training; (2) Model Uncertainty, i.e., the same object may hold different categories (tampered or not) due to manipulation operations, which could potentially confuse the model training and result in unreliable outcomes. Previous works mainly focus on solving the model uncertainty issue by designing meticulous features and networks, however, the data uncertainty problem is rarely considered. In this paper, we address both problems by introducing an uncertainty-guided learning framework, which measures data and model uncertainty by a novel Uncertainty Estimation Network (UEN). UEN is trained under dynamic supervision, and outputs estimated uncertainty maps to refine manipulation detection results, which significantly alleviates the learning difficulties. To our knowledge, this is the first work to embed uncertainty modeling into IMD. Extensive experiments on various datasets demonstrate state-of-the-art performance, validating the effectiveness and generalizability of our method.
Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning
Authors: Tiankang Su, Huihui Song, Dong Liu, Bo Liu, Qingshan Liu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Object, Pseudo Label, Segmentation, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

    The existing unsupervised video object segmentation methods depend heavily on the segmentation model trained offline on a labeled training video set, and cannot well generalize to the test videos from a different domain with possible distribution shifts. We propose to perform online fine-tuning on the pre-trained segmentation model to adapt to any ad-hoc videos at the test time. To achieve this, we design an offline semi-supervised adversarial training process, which leverages the unlabeled video frames to improve the model generalizability while aligning the features of the labeled video frames with the features of the unlabeled video frames. With the trained segmentation model, we further conduct an online self-supervised adversarial finetuning, in which a teacher model and a student model are first initialized with the pre-trained segmentation model weights, and the pseudo label produced by the teacher model is used to supervise the student model in an adversarial learning framework. Through online finetuning, the student model is progressively updated according to the emerging patterns in each test video, which significantly reduces the test-time domain gap. We integrate our offline training and online fine-tuning in a unified framework for unsupervised video object segmentation and dub our method Online Adversarial Self-Tuning (OAST). The experiments show that our method out-performs the state-of-the-arts with significant gains on the popular video object segmentation datasets.
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching
Authors: Cheng Fu, Hanxian Huang, Zixuan Jiang, Yun Ni, Lifeng Nai, Gang Wu, Liqun Cheng, Yanqi Zhou, Sheng Li, Andrew Li, Jishen Zhao
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Knowledge Distillation, Neural Architecture Search

    One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence. Previous works designed expansion operators to scale up pretrained models to the target model before training. Yet, model functionality is difficult to preserve when scaling a transformer in all dimensions at once. Moreover, maintaining the pretrained optimizer states for weights is critical for model scaling, whereas the new weights added during expansion lack these states in pretrained models. To address these issues, we propose TripLe, which partially scales a model before training, while growing the rest of the new parameters during training by copying both the warmed-up weights with the optimizer states from existing weights. As such, the new parameters introduced during training will obtain their training states. Furthermore, through serializing the scaling of model width and depth, the functionality of each expansion can be preserved. We evaluate TripLe in both single-trial model scaling and multi-trial neural architecture search (NAS). Due to the fast training convergence of TripLe, the proxy accuracy from TripLe better reveals the model quality compared to from-scratch training in multi-trial NAS. Experiments show that TripLe outperforms both from-scratch training and knowledge distillation (KD) in both training time and task performance. TripLe can also be combined with KD to achieve an even higher task accuracy. For NAS, the model obtained from TripLe outperforms DeiT-B in task accuracy with 69% reduction in parameter size and FLOPs.
Masked Retraining Teacher-Student Framework for Domain Adaptive Object Detection
Authors: Zijing Zhao, Sitong Wei, Qingchao Chen, Dehui Li, Yifan Yang, Yuxin Peng, Yang Liu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Decoder, object-detection, Object Detection, Unsupervised Domain Adaptation

    Domain adaptive Object Detection (DAOD) leverages a labeled domain (source) to learn an object detector generalizing to a novel domain without annotation (target). Recent advances use a teacher-student framework, i.e., a student model is supervised by the pseudo labels from a teacher model. Though great success, they suffer from the limited number of pseudo boxes with incorrect predictions caused by the domain shift, misleading the student model to get sub-optimal results. To mitigate this problem, we propose Masked Retraining Teacher-student framework (MRT) which leverages masked autoencoder and selective retraining mechanism on detection transformer. Specifically, we present a customized design of masked autoencoder branch, masking the multi-scale feature maps of target images and reconstructing features by the encoder of the student model and an auxiliary decoder. This helps the student model capture target domain characteristics and become a more data-efficient learner to gain knowledge from the limited number of pseudo boxes. Furthermore, we adopt selective retraining mechanism, periodically re-initializing certain parts of the student parameters with masked autoencoder refined weights to allow the model to jump out of the local optimum biased to the incorrect pseudo labels. Experimental results on three DAOD benchmarks demonstrate the effectiveness of our method. Code can be found at https://github.com/JeremyZhao1998/MRT-release.
Foreground and Text-lines Aware Document Image Rectification
Authors: Heng Li, XiangPing Wu, Qingcai Chen, Qianjin Xiang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    This paper aims at the distorted document image rectification problem, the objective to eliminate the geometric distortion in the document images and realize document intelligence. Improving the readability of distorted documents is crucial to effectively extract information from deformed images. According to our observations, the foreground and text-line of the original warped image can represent the deformation tendency. However, previous distorted image rectification methods pay little attention to the readability of the warped paper. In this paper, we focus on the foreground and text-line regions of distorted paper and proposes a global and local fusion method to improve the rectification effect of distorted images and enhance the readability of document images. We introduce cross attention to capture the features of the foreground and text-lines in the warped document and effectively fuse them. The proposed method is evaluated quantitatively and qualitatively on the public DocUNet benchmark and DIR300 Dataset, which achieve state-of-the-art performances. Experimental analysis shows the proposed method can well perform overall geometric rectification of distorted images and effectively improve document readability (using the metrics of Character Error Rate and Edit Distance). The code is available at https://github.com/xiaomore/Document-Image-Dewarping.
Trajectory Unified Transformer for Pedestrian Trajectory Prediction
Authors: Liushuai Shi, Le Wang, Sanping Zhou, Gang Hua
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Decoder, Pedestrian Trajectory Prediction, Prediction, Trajectory Prediction

    Pedestrian trajectory prediction is an essentially connecting link to understanding human behavior. Recent works achieve state-of-the-art performance gained from the hand-designed post-processing, e.g., clustering. However, this post-processing suffers from expensive inference time and neglects the probability of the predicted trajectory disturbing downstream safety decisions. In this paper, we present Trajectory Unified TRansformer, called TUTR, which unifies the trajectory prediction components, social interaction and multimodal trajectory prediction, into a transformer encoder-decoder architecture to effectively remove the need for post-processing. Specifically, TUTR parses the relationships across various motion modes by an explicit global prediction and an implicit mode-level transformer encoder. Then, TUTR attends to the social interactions with neighbors by a social-level transformer decoder. Finally, a dual prediction forecasts diverse trajectories and corresponding probabilities in parallel without post-processing. TUTR achieves state-of-the-art accuracy performance and about 10x - 40x inference speed improvements compared with previous well-tuning state-of-the-art methods using post-processing.
Moment Detection in Long Tutorial Videos
Authors: Ioana Croitoru, Simion-Vlad Bogolin, Samuel Albanie, Yang Liu, Zhaowen Wang, Seunghyun Yoon, Franck Dernoncourt, Hailin Jin, Trung Bui
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Tutorial videos play an increasingly important role in professional development and self-directed education. For users to realise the full benefits of this medium, tutorial videos must be efficiently searchable. In this work, we focus on the task of moment detection, in which the goal is to localise the temporal window where a given event occurs within a given tutorial video. Prior work on moment detection has focused primarily on short videos (typically on videos shorter than three minutes). However, many tutorial videos are substantially longer (stretching to hours in duration), presenting significant challenges for existing moment detection approaches. To study this problem, we propose the first dataset of untrimmed, long-form tutorial videos for the task of Moment Detection called the Behance Moment Detection (BMD) dataset. BMD videos have an average duration of over one hour and are characterised by slowly evolving visual content and wide-ranging dialogue. To meet the unique challenges of this dataset, we propose a new framework, LongMoment-DETR, and demonstrate that it outperforms strong baselines. Additionally, we introduce a variation of the dataset that contains YouTube Chapter annotations and show that the features obtained by our framework can be successfully used to boost the performance on the task of chapter detection. Code and data can be found at https://github.com/ioanacroi/longmoment-detr.