pwc-archive/papers-with-abstracts huggingface dataset:
BiViT: Extremely Compressed Binary Vision Transformers
Authors: Yefei He, Zhenyu Lou, Luoming Zhang, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Binarization, object-detection, Object Detection

    Model binarization can significantly compress model size, reduce energy consumption, and accelerate inference through efficient bit-wise operations. Although binarizing convolutional neural networks have been extensively studied, there is little work on exploring binarization of vision Transformers which underpin most recent breakthroughs in visual recognition. To this end, we propose to solve two fundamental challenges to push the horizon of Binary Vision Transformers (BiViT). First, the traditional binary method does not take the long-tailed distribution of softmax attention into consideration, bringing large binarization errors in the attention module. To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization. Second, to better preserve the information of the pretrained model and restore accuracy, we propose a Cross-layer Binarization scheme that decouples the binarization of self-attention and multi-layer perceptrons (MLPs), and Parameterized Weight Scales which introduce learnable scaling factors for weight binarization. Overall, our method performs favorably against state-of-the-arts by 19.8% on the TinyImageNet dataset. On ImageNet, our BiViT achieves a competitive 75.6% Top-1 accuracy over Swin-S model. Additionally, on COCO object detection, our method achieves an mAP of 40.8 with a Swin-T backbone over Cascade Mask R-CNN framework.
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and Reconstruction
Authors: Pengcheng Lei, Faming Fang, Guixu Zhang, Tieyong Zeng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: MRI Reconstruction, Super-Resolution

    Multi-contrast MRI super-resolution (SR) and reconstruction methods aim to explore complementary information from the reference image to help the reconstruction of the target image. Existing deep learning-based methods usually manually design fusion rules to aggregate the multi-contrast images, fail to model their correlations accurately and lack certain interpretations. Against these issues, we propose a multi-contrast variational network (MC-VarNet) to explicitly model the relationship of multi-contrast images. Our model is constructed based on an intuitive motivation that multi-contrast images have consistent (edges and structures) and inconsistent (contrast) information. We thus build a model to reconstruct the target image and decompose the reference image as a common component and a unique component. In the feature interaction phase, only the common component is transferred to the target image. We solve the variational model and unfold the iterative solutions into a deep network. Hence, the proposed method combines the good interpretability of model-based methods with the powerful representation ability of deep learning-based methods. Experimental results on the multi-contrast MRI reconstruction and SR demonstrate the effectiveness of the proposed model. Especially, since we explicitly model the multi-contrast images, our model is more robust to the reference images with noises and large inconsistent structures. The code is available at https://github.com/lpcccc-cv/MC-VarNet.
Joint Demosaicing and Deghosting of Time-Varying Exposures for Single-Shot HDR Imaging
Authors: Jungwoo Kim, Min H. Kim
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Demosaicking, HDR Reconstruction

    The quad-Bayer patterned image sensor has made significant improvements in spatial resolution over recent years due to advancements in image sensor technology. This has enabled single-shot high-dynamic-range (HDR) imaging using spatially varying multiple exposures. Popular methods for multi-exposure array sensors involve varying the gain of each exposure, but this does not effectively change the photoelectronic energy in each exposure. Consequently, HDR images produced using gain-based exposure variation may suffer from noise and details being saturated. To address this problem, we intend to use time-varying exposures in quad-Bayer patterned sensors. This approach allows long-exposure pixels to receive more photon energy than short- or middle-exposure pixels, resulting in higher-quality HDR images. However, time-varying exposures are not ideal for dynamic scenes and require an additional deghosting method. To tackle this issue, we propose a single-shot HDR demosaicing method that takes time-varying multiple exposures as input and jointly solves both the demosaicing and deghosting problems. Our method uses a feature-extraction module to handle mosaiced multiple exposures and a multiscale transformer module to register spatial displacements of multiple exposures and colors. We also created a dataset of quad-Bayer sensor input with time-varying exposures and trained our network using this dataset. Results demonstrate that our method outperforms baseline HDR reconstruction methods with both synthetic and real datasets. With our method, we can achieve high-quality HDR images in challenging lighting conditions.
A Simple Vision Transformer for Weakly Semi-supervised 3D Object Detection
Authors: Dingyuan Zhang, Dingkang Liang, Zhikang Zou, Jingyu Li, Xiaoqing Ye, Zhe Liu, Xiao Tan, Xiang Bai
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Detection, Object, object-detection, Object Detection

    Advanced 3D object detection methods usually rely on large-scale, elaborately labeled datasets to achieve good performance. However, labeling the bounding boxes for the 3D objects is difficult and expensive. Although semi-supervised (SS3D) and weakly-supervised 3D object detection (WS3D) methods can effectively reduce the annotation cost, they suffer from two limitations: 1) their performance is far inferior to the fully-supervised counterparts; 2) they are difficult to adapt to different detectors or scenes (e.g, indoor or outdoor). In this paper, we study weakly semi-supervised 3D object detection (WSS3D) with point annotations, where the dataset comprises a small number of fully labeled and massive weakly labeled data with a single point annotated for each 3D object. To fully exploit the point annotations, we employ the plain and non-hierarchical vision transformer to form a point-to-box converter, termed ViT-WSS3D. By modeling global interactions between LiDAR points and corresponding weak labels, our ViT-WSS3D can generate high-quality pseudo-bounding boxes, which are then used to train any 3D detectors without exhaustive tuning. Extensive experiments on indoor and outdoor datasets (SUN RGBD and KITTI) show the effectiveness of our method. In particular, when only using 10% fully labeled and the rest as point labeled data, our ViT-WSS3D can enable most detectors to achieve similar performance with the oracle model using 100% fully labeled data.
Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation
Authors: Guangnian Wan, Haitao Du, Xuejing Yuan, Jun Yang, Meiling Chen, Jie Xu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Federated Learning

    Federated learning (FL) is a privacy-enhanced distributed machine learning framework, in which multiple clients collaboratively train a global model by exchanging their model updates without sharing local private data. However, the adversary can use gradient inversion attacks to reveal the clients' privacy from the shared model updates. Previous attacks assume the adversary can infer the local learning rate of each client, while we observe that: (1) using the uniformly distributed random local learning rates does not incur much accuracy loss of the global model, and (2) personalizing local learning rates can mitigate the drift issue which is caused by non-IID (identically and independently distributed) data. Moreover, we theoretically derive a convergence guarantee to FedAvg with uniformly perturbed local learning rates. Therefore, by perturbing the learning rate of each client with random noise, we propose a learning rate perturbation (LRP) defense against gradient inversion attacks. Specifically, for classification tasks, we adapt LPR to ada-LPR by personalizing the expectation of each local learning rate. The experiments show that our defenses can well enhance privacy preservation against existing gradient inversion attacks, and LRP outperforms 5 baseline defenses against a state-of-the-art gradient inversion attack. In addition, our defenses only incur minor accuracy reductions (less than 0.5%) of the global model. So they are effective in real applications.
Adaptive Image Anonymization in the Context of Image Classification with Neural Networks
Authors: Nadiya Shvai, Arcadi Llanza Carmona, Amir Nakib
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: image-classification, Image Classification

    Deep learning based methods have become the de-facto standard for various computer vision tasks. Nevertheless, they have repeatedly shown their vulnerability to various form of input perturbations such as pixels modification, region anonymization, etc. which are closely related to the adversarial attacks. This research particularly addresses the case of image anonymization, which is significantly important to preserve privacy and hence to secure digitized form of personal information from being exposed and potentially misused by different services that have captured it for various purposes. However, applying anonymization causes the classifier to provide different class decisions before and after applying it and therefore reduces the classifier's reliability and usability. In order to achieve a robust solution to this problem we propose a novel anonymization procedure that allows the existing classifiers to become class decision invariant on the anonymized images without any modification requires to apply on the classification models. We conduct numerous experiments on the popular ImageNet benchmark as well as on a large scale industrial toll classification problem's dataset. Obtained results confirm the efficiency and effectiveness of the proposed method as it obtained 0% rate of class decision change for both datasets compared to 15.95% on ImageNet and 0.18% on toll dataset obtained by applying the naive anonymization approaches. Moreover, it has shown a great potential to be applied to similar problems from different domains.
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives
Authors: Tao Zhou, Qi Ye, Wenhan Luo, Kaihao Zhang, Zhiguo Shi, Jiming Chen
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Adversarial Attack, Multi-Object Tracking, Object, Object Tracking

    Multi-object tracking (MOT) aims to build moving trajectories for number-agnostic objects. Modern multi-object trackers commonly follow the tracking-by-detection strategy. Therefore, fooling detectors can be an effective solution but it usually requires attacks in multiple successive frames, resulting in low efficiency. Attacking association processes improves efficiency but may require model-specific design, leading to poor generalization. In this paper, we propose a novel False negative and False positive attack (F&F attack) mechanism: it perturbs the input image to erase original detections and to inject deceptive false alarms around original ones while integrating the association attack implicitly. The mechanism can produce effective identity switches against multi-object trackers by only fooling detectors in a few frames. To demonstrate the flexibility of the mechanism, we deploy it to three multi-object trackers (ByteTrack, SORT, and CenterTrack) which are enabled by two representative detectors (YOLOX and CenterNet). Comprehensive experiments on MOT17 and MOT20 datasets show that our method significantly outperforms existing attackers, revealing the vulnerability of the tracking-by-detection paradigm to detection attacks.
Towards Grand Unified Representation Learning for Unsupervised Visible-Infrared Person Re-Identification
Authors: Bin Yang, Jun Chen, Mang Ye
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Person Re-Identification, Representation Learning

    Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) is an extremely important and challenging task, which can alleviate the issue of expensive cross-modality annotations. Existing works focus on handling the cross-modality discrepancy under unsupervised conditions. However, they ignore the fact that USL-VI-ReID is a cross-modality retrieval task with the hierarchical discrepancy, i.e., camera variation and modality discrepancy, resulting in clustering inconsistencies and ambiguous cross-modality label association. To address these issues, we propose a hierarchical framework to learn grand unified representation (GUR) for USL-VI-ReID. The grand unified representation lies in two aspects: 1) GUR adopts a bottom-up domain learning strategy with a cross-memory association embedding module to explore the information of hierarchical domains, i.e., intra-camera, inter-camera, and inter-modality domains, learning a unified and robust representation against hierarchical discrepancy. 2) To unify the identities of the two modalities, we develop a cross-modality label unification module that constructs a cross-modality affinity matrix as a bridge for propagating labels between two modalities. Then, we utilize the homogeneous structure matrix to smooth the propagated labels, ensuring that the label structure within one modality remains unchanged. Extensive experiments demonstrate that our GUR framework significantly outperforms existing USL-VI-ReID methods, and even surpasses some supervised counterparts.
C2ST: Cross-Modal Contextualized Sequence Transduction for Continuous Sign Language Recognition
Authors: Huaiwen Zhang, Zihang Guo, Yang Yang, Xin Liu, De Hu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Language Modelling, Representation Learning, Sign Language Recognition

    Continuous Sign Language Recognition (CSLR) aims to transcribe the signs of an untrimmed video into written words or glosses. The mainstream framework for CSLR consists of a spatial module for visual representation learning, a temporal module aggregating the local and global temporal information of frame sequence, and the connectionist temporal classification (CTC) loss, which aligns video features with gloss sequence. Unfortunately, the language prior implicit in the gloss sequence is ignored throughout the modeling process. Furthermore, the contextualization of glosses is further ignored in alignment learning, as CTC makes an independence assumption between glosses. In this paper, we propose a Cross-modal Contextualized Sequence Transduction (C2ST) for CSLR, which effectively incorporates the knowledge of gloss sequence into the process of video representation learning and sequence transduction. Specifically, we introduce a cross-modal context learning framework for CSLR, in which the linguistic features of gloss sequences is extracted by a language model, and recurrently integrate with visual features for video modelling. Moreover, we introduce the contextualized sequence transduction loss that incorporates the contextual information of gloss sequences in label prediction, without making any independence assumptions between the glosses. Our method sets the new state of the art on three widely used large-scale sign language recognition datasets: Phoenix-2014, Phoenix-2014-T, and CSL-Daily. On CSL-Daily, our approach achieves an absolute gain of 4.9% WER compared to the best published results.
LaPE: Layer-adaptive Position Embedding for Vision Transformers with Independent Layer Normalization
Authors: Runyi Yu, Zhennan Wang, Yinhuai Wang, Kehan Li, Chang Liu, Haoyi Duan, Xiangyang Ji, Jie Chen
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: image-classification, Image Classification, object-detection, Object Detection, Position, Semantic Segmentation

    Position information is critical for Vision Transformers (VTs) due to the permutation-invariance of self-attention operations. A typical way to introduce position information is adding the absolute Position Embedding (PE) to patch embedding before entering VTs. However, this approach operates the same Layer Normalization (LN) to token embedding and PE, and delivers the same PE to each layer. This results in restricted and monotonic PE across layers, as the shared LN affine parameters are not dedicated to PE, and the PE cannot be adjusted on a per-layer basis. To overcome these limitations, we propose using two independent LNs for token embeddings and PE in each layer, and progressively delivering PE across layers. By implementing this approach, VTs will receive layer-adaptive and hierarchical PE. We name our method as Layer-adaptive Position Embedding, abbreviated as LaPE, which is simple, effective, and robust. Extensive experiments on image classification, object detection, and semantic segmentation demonstrate that LaPE significantly outperforms the default PE method. For example, LaPE improves +1.06% for CCT on CIFAR100, +1.57% for DeiT-Ti on ImageNet-1K, +0.7 box AP and +0.5 mask AP for ViT-Adapter-Ti on COCO, and +1.37 mIoU for tiny Segmenter on ADE20K. This is remarkable considering LaPE only increases negligible parameters, memory, and computational cost.
Discrepant and Multi-Instance Proxies for Unsupervised Person Re-Identification
Authors: Chang Zou, Zeqi Chen, Zhichao Cui, Yuehu Liu, Chi Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Contrastive Learning, Person Re-Identification, Unsupervised Person Re-Identification

    Most recent unsupervised person re-identification methods maintain a cluster uni-proxy for contrastive learning. However, due to the intra-class variance and inter-class similarity, the cluster uni-proxy is prone to be biased and confused with similar classes, resulting in the learned features lacking intra-class compactness and inter-class separation in the embedding space. To completely and accurately represent the information contained in a cluster and learn discriminative features, we propose to maintain discrepant cluster proxies and multi-instance proxies for a cluster. Each cluster proxy focuses on representing a part of the information, and several discrepant proxies collaborate to represent the entire cluster completely. As a complement to the overall representation, multi-instance proxies are used to accurately represent the fine-grained information contained in the instances of the cluster. Based on the proposed discrepant cluster proxies, we construct cluster contrastive loss to use the proxies as hard positive samples to pull instances of a cluster closer and reduce intra-class variance. Meanwhile, instance contrastive loss is constructed by global hard negative sample mining in multi-instance proxies to push away the truly indistinguishable classes and decrease inter-class similarity. Extensive experiments on Market-1501 and MSMT17 demonstrate that the proposed method outperforms state-of-the-art approaches.
MasQCLIP for Open-Vocabulary Universal Image Segmentation
Authors: Xin Xu, Tianyi Xiong, Zheng Ding, Zhuowen Tu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation

    We present a new method for open-vocabulary universal image segmentation, which is capable of performing instance, semantic, and panoptic segmentation under a unified framework. Our approach, called MasQCLIP, seamlessly integrates with a pre-trained CLIP model by utilizing its dense features, thereby circumventing the need for extensive parameter training. MasQCLIP emphasizes two new aspects when building an image segmentation method with a CLIP model: 1) a student-teacher module to deal with masks of the novel (unseen) classes by distilling information from the base (seen) classes; 2) a fine-tuning process to update model parameters for the queries Q within the CLIP model. Thanks to these two simple and intuitive designs, MasQCLIP is able to achieve state-of-the-art performances with a substantial gain over the competing methods by a large margin across all three tasks, including open-vocabulary instance, semantic, and panoptic segmentation. Project page is at https://masqclip.github.io/.
HandR2N2: Iterative 3D Hand Pose Estimation Using a Residual Recurrent Neural Network
Authors: Wencan Cheng, Jong Hwan Ko
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation

    3D hand pose estimation is a critical task in various human-computer interaction applications. Numerous deep learning based estimation models in this domain have been actively explored. However, the existing models follows a non-recurrent scheme and thus require complex architectures or redundant parameters in order to achieve acceptable model capacity. To tackle this limitation, this paper proposes HandR2N2, a compact neural network that iteratively regresses the hand pose using a novel residual recurrent unit. The recurrent design allows recursive exploitation of partial layers to gradually optimize previously estimated joint locations. In addition, we exploit graph reasoning to capture kinematic dependencies between joints for better performance. Experimental results show that the proposed model significantly outperforms the existing methods on three hand pose benchmark datasets in terms of both accuracy and efficiency. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandR2N2.
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection
Authors: Zhihao Gu, Liang Liu, Xu Chen, Ran Yi, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Annan Shu, Guannan Jiang, Lizhuang Ma
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 10
Tasks: Anomaly Detection, Knowledge Distillation, Unsupervised Anomaly Detection

    Knowledge distillation (KD) has been widely explored in unsupervised anomaly detection (AD). The student is assumed to constantly produce representations of typical patterns within trained data, named "normality", and the representation discrepancy between the teacher and student model is identified as anomalies. However, it suffers from the "normality forgetting" issue. Trained on anomaly-free data, the student still well reconstructs anomalous representations for anomalies and is sensitive to fine patterns in normal data, which also appear in training. To mitigate this issue, we introduce a novel Memory-guided Knowledge-Distillation (MemKD) framework that adaptively modulates the normality of student features in detecting anomalies. Specifically, we first propose a normality recall memory (NR Memory) to strengthen the normality of student-generated features by recalling the stored normal information. In this sense, representations will not present anomalies and fine patterns will be well described. Subsequently, we employ a normality embedding learning strategy to promote information learning for the NR Memory. It constructs a normal exemplar set so that the NR Memory can memorize prior knowledge in anomaly-free data and later recall them from the query feature. Consequently, comprehensive experiments demonstrate that the proposed MemKD achieves promising results on five benchmarks, i.e., MVTec AD, VisA, MPDD, MVTec 3D-AD, and Eyecandies.