pwc-archive/papers-with-abstracts huggingface dataset:
AdvDiffuser: Natural Adversarial Example Synthesis with Diffusion Models
Authors: Xinquan Chen, Xitong Gao, Juanjuan Zhao, Kejiang Ye, Cheng-Zhong Xu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: SSIM

    Previous work on adversarial examples typically involves a fixed norm perturbation budget, which fails to capture the way humans perceive perturbations. Recent work has shifted towards investigating natural unrestricted adversarial examples (UAEs) that breaks l_p perturbation bounds but nonetheless remain semantically plausible. Current methods use GAN or VAE to generate UAEs by perturbing latent codes. However, this leads to loss of high-level information, resulting in low-quality and unnatural UAEs. In light of this, we propose AddDiffuser, a new method for synthesizing natural UAEs using diffusion models. It can generate UAEs from scratch or conditionally based on reference images. To generate natural UAEs, we perturb predicted images to steer their latent code towards the adversarial sample space of a particular classifier. In addition, we propose adversarial inpainting based on class activation mapping to retain the salient regions of the image while perturbing less important areas. Our method achieves impressive results on CIFAR-10, CelebA and ImageNet, and we demonstrate that it can defeat the most robust models on the RobustBench leaderboard with near 100% success rates. Furthermore, The synthesized UAEs are not only more natural but also stronger compared to the current state-of-the-art attacks. Specifically, compared with GA-attack, the UAEs generated with AdvDiffuser exhibit 6xsmaller LPIPS perturbations, 2 ~ 3 xsmaller FID scores and 0.28 higher in SSIM metrics, making them perceptually stealthier. Lastly, it is capable of generating an unlimited number of natural adversarial examples. For more please visit our project page: Link to follow.
SkeleTR: Towards Skeleton-based Action Recognition in the Wild
Authors: Haodong Duan, Mingze Xu, Bing Shuai, Davide Modolo, Zhuowen Tu, Joseph Tighe, Alessandro Bergamo
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Classification, Action Detection, Action Recognition, Activity Recognition, Human Interaction Recognition, Skeleton Based Action Recognition, Transfer Learning

    We present SkeleTR, a new framework for skeleton-based action recognition. In contrast to prior work, which focuses mainly on controlled environments, we target in-the-wild scenarios that typically involve a variable number of people and various forms of interaction between people. SkeleTR works with a two-stage paradigm. It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in the wild. To mitigate the negative impact of inaccurate skeleton associations, SkeleTR takes relative short skeleton sequences as input and increases the number of sequences. As a unified solution, SkeleTR can be directly applied to multiple skeleton-based action tasks, including video-level action classification, instance-level action detection, and group-level activity recognition. It also enables transfer learning and joint training across different action tasks and datasets, which results in performance improvement. When evaluated on various skeleton-based action recognition benchmarks, SkeleTR achieves the state-of-the-art performance.
A Unified Framework for Robustness on Diverse Sampling Errors
Authors: Myeongho Jeon, Myungjoo Kang, Joonseok Lee
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Domain Generalization

    Recent studies have substantiated that machine learning algorithms including convolutional neural networks often suffer from unreliable generalizations when there is a significant gap between the source and target data distributions. To mitigate this issue, a predetermined distribution shift has been addressed independently (e.g., single domain generalization, de-biasing). However, a distribution mismatch cannot be clearly estimated because the target distribution is unknown at training. Therefore, a conservative approach robust on unexpected diverse distributions is more desirable in practice. Our work starts from a motivation to allow adaptive inference once we know the target, since it is accessible only at testing. Instead of assuming and fixing the target distribution at training, our proposed approach allows adjusting the feature space the model refers to at every prediction, i.e., instance-wise adaptive inference. The extensive evaluation demonstrates our method is effective for generalization on diverse distributions.
CauSSL: Causality-inspired Semi-supervised Learning for Medical Image Segmentation
Authors: Juzheng Miao, Cheng Chen, Furui Liu, Hao Wei, Pheng-Ann Heng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation, Semi-Supervised Semantic Segmentation

    Semi-supervised learning (SSL) has recently demonstrated great success in medical image segmentation, significantly enhancing data efficiency with limited annotations. However, despite its empirical benefits, there are still concerns in the literature about the theoretical foundation and explanation of semi-supervised segmentation. To explore this problem, this study first proposes a novel causal diagram to provide a theoretical foundation for the mainstream semi-supervised segmentation methods. Our causal diagram takes two additional intermediate variables into account, which are neglected in previous work. Drawing from this proposed causal diagram, we then introduce a causality-inspired SSL approach on top of co-training frameworks called CauSSL, to improve SSL for medical image segmentation. Specifically, we first point out the importance of algorithmic independence between two networks or branches in SSL, which is often overlooked in the literature. We then propose a novel statistical quantification of the uncomputable algorithmic independence and further enhance the independence via a min-max optimization process. Our method can be flexibly incorporated into different existing SSL methods to improve their performance. Our method has been evaluated on three challenging medical image segmentation tasks using both 2D and 3D network architectures and has shown consistent improvements over state-of-the-art methods. Our code is publicly available at: https://github.com/JuzhengMiao/CauSSL.
Progressive Spatio-Temporal Prototype Matching for Text-Video Retrieval
Authors: Pandeng Li, Chen-Wei Xie, Liming Zhao, Hongtao Xie, Jiannan Ge, Yun Zheng, Deli Zhao, Yongdong Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Diversity, Object, Retrieval, Sentence, Video Retrieval

    The performance of text-video retrieval has been significantly improved by vision-language cross-modal learning schemes.  The typical solution is to directly align the global video-level and sentence-level features during learning, which would ignore the intrinsic video-text relations, i.e., a text description only corresponds to a spatio-temporal part of videos.  Hence, the matching process should consider both fine-grained spatial content and various temporal semantic events. To this end, we propose a text-video learning framework with progressive spatio-temporal prototype matching. Specifically, the vanilla matching process is decomposed into two complementary phases: object-phrase prototype matching and event-sentence prototype matching. In the object-phrase prototype matching phase, a spatial prototype generation mechanism is developed to predict key patches or words, which are sparsely integrated into object or phrase prototypes. Importantly, optimizing the local alignment between object-phrase prototypes helps the model perceive spatial details. In the event-sentence prototype matching phase, we design a temporal prototype generation mechanism to associate intra-frame objects and interact inter-frame temporal relations. Such progressively generated event prototypes can reveal semantic diversity in videos for dynamic matching. Validated by comprehensive experiments, our method consistently outperforms the state-of-the-art methods on four video retrieval benchmarks.
Bring Clipart to Life
Authors: Nanxuan Zhao, Shengqi Dang, Hexun Lin, Yang Shi, Nan Cao
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    The development of face editing has been boosted since the birth of StyleGAN. While previous works have explored different interactive methods, such as sketching and exemplar photos, they have been limited in terms of expressiveness and generality. In this paper, we propose a new interaction method by guiding the editing with abstract clipart, composed of a set of simple semantic parts, allowing users to control across face photos with simple clicks. However, this is a challenging task given the large domain gap between colorful face photos and abstract clipart with limited data. To solve this problem, we introduce a framework called ClipFaceShop built on top of StyleGAN. The key idea is to take advantage of W+ latent code encoded rich and disentangled visual features, and create a new lightweight selective feature adaptor to predict a modifiable path toward the target output photo. Since no pairwise labeled data exists for training, we design a set of losses to provide supervision signals for learning the modifiable path. Experimental results show that ClipFaceShop generates realistic and faithful face photos, sharing the same facial attributes as the reference clipart. We demonstrate that ClipFaceShop supports clipart in diverse styles, even in form of a free-hand sketch.
Domain Specified Optimization for Deployment Authorization
Authors: Haotian Wang, Haoang Chi, Wenjing Yang, Zhipeng Lin, Mingyang Geng, Long Lan, Jing Zhang, DaCheng Tao
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    This paper explores Deployment Authorization (DPA) as a means of restricting the generalization capabilities of vision models on certain domains to protect intellectual property. Nevertheless, the current advancements in DPA are predominantly confined to fully supervised settings. Such settings require the accessibility of annotated images from any unauthorized domain, rendering the DPA approach impractical for real-world applications due to its exorbitant costs. To address this issue, we propose Source-Only Deployment Authorization (SDPA), which assumes that only authorized domains are accessible during training phases, and the model's performance on unauthorized domains must be suppressed in inference stages. Drawing inspiration from distributional robust statistics, we present a lightweight method called Domain-Specified Optimization (DSO) for SDPA that degrades the model's generalization over a divergence ball. DSO comes with theoretical guarantees on the convergence property and its authorization performance. As a complementary of SDPA, we also propose Target-Combined Deployment Authorization (TPDA), where unauthorized domains are partially accessible, and simplify the DSO method to a perturbation operation on the pseudo predictions, referred to as Target-Dependent Domain-Specified Optimization (TDSO). We demonstrate the effectiveness of our proposed DSO and TDSO methods through extensive experiments on six image benchmarks, achieving dominant performance on both SDPA and TDPA settings.
ICD-Face: Intra-class Compactness Distillation for Face Recognition
Authors: Zhipeng Yu, Jiaheng Liu, Haoyu Qin, Yichao Wu, Kun Hu, Jiayi Tian, Ding Liang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Face Recognition, Knowledge Distillation, Model Compression

    Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR). The current FR distillation methods usually utilize the Feature Consistency Distillation (FCD) (e.g., L2 distance) on the learned embeddings extracted by the teacher and student models. However, after using FCD, we observe that the intra-class similarities of the student model are lower than the intra-class similarities of the teacher model a lot. Therefore, we propose an effective FR distillation method called ICD-Face by introducing intra-class compactness distillation into the existing distillation framework. Specifically, in ICD-Face, we first propose to calculate the similarity distributions of the teacher and student models, where the feature banks are introduced to construct sufficient and high-quality positive pairs. Then, we estimate the probability distributions of the teacher and student models and introduce the Similarity Distribution Consistency (SDC) loss to improve the intra-class compactness of the student model. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed ICD-Face for face recognition.
Inverse Compositional Learning for Weakly-supervised Relation Grounding
Authors: Huan Li, Ping Wei, Zeyu Ma, Nanning Zheng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Relation, Video Understanding

    Video relation grounding (VRG) is a significant and challenging problem in the domains of cross-modal learning and video understanding. In this study, we introduce a novel approach called inverse compositional learning (ICL) for weakly-supervised video relation grounding. Our approach represents relations at both the holistic and partial levels, formulating VRG as a joint optimization problem that encompasses reasoning at both levels. For holistic-level reasoning, we propose an inverse attention mechanism and a compositional encoder to generate compositional relevance features. Additionally, we introduce an inverse loss to evaluate and learn the relevance between visual features and relation features. At the partial-level reasoning, we introduce a grounding by classification scheme. By leveraging the learned holistic-level features and partial-level features, we train the entire model in an end-to-end manner. We conduct evaluations on two challenging datasets and demonstrate the substantial superiority of our proposed method over state-of-the-art methods. Extensive ablation studies confirm the effectiveness of our approach.
Sparse Point Guided 3D Lane Detection
Authors: Chengtang Yao, Lidong Yu, Yuwei Wu, Yunde Jia
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Lane Detection, Lane Detection

    3D lane detection usually builds a dense correspondence between the front-view space and the BEV space to estimate lane points in the 3D space. 3D lanes only occupy a small ratio of the dense correspondence, while most correspondence belongs to the redundant background. This sparsity phenomenon bottlenecks valuable computation and raises the computation cost of building a high-resolution correspondence for accurate results. In this paper, we propose a sparse point-guided 3D lane detection, focusing on points related to 3D lanes. Our method runs in a coarse-to-fine manner, including coarse-level lane detection and iterative fine-level sparse point refinements. In coarse-level lane detection, we build a dense but efficient correspondence between the front view and BEV space at a very low resolution to compute coarse lanes. Then in fine-level sparse point refinement, we sample sparse points around coarse lanes to extract local features from the high-resolution front-view feature map. The high-resolution local information brought by sparse points refines 3D lanes in the BEV space hierarchically from low resolution to high resolution. The sparse point guides a more effective information flow and greatly promotes the SOTA result by 3 points on the overall F1-score and 6 points on several hard situations while reducing almost half memory cost and speeding up 2 times.
Neural Characteristic Function Learning for Conditional Image Generation
Authors: Shengxi Li, Jialu Zhang, Yifei Li, Mai Xu, Xin Deng, Li Li
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Conditional Image Generation, Generative Adversarial Network, Image Generation

    The emergence of conditional generative adversarial networks (cGANs) has revolutionised the way we approach and control the generation, by means of adversarially learning joint distributions of data and auxiliary information. Despite the success, cGANs have been consistently put under scrutiny due to their ill-posed discrepancy measure between distributions, leading to mode collapse and instability problems in training. To address this issue, we propose a novel conditional characteristic function generative adversarial network (CCF-GAN) to reduce the discrepancy by the characteristic functions (CFs), which is able to learn accurate distance measure of joint distributions under theoretical soundness. More specifically, the difference between CFs is first proved to be complete and optimisation-friendly, for measuring the discrepancy of two joint distributions. To relieve the problem of curse of dimensionality in calculating CF difference, we propose to employ the neural network, namely neural CF (NCF), to efficiently minimise an upper bound of the difference. Based on the NCF, we establish the CCF-GAN framework to explicitly decompose CFs of joint distributions, which allows for learning the data distribution and auxiliary information with classified importance. The experimental results on synthetic and real-world datasets verify the superior performances of our CCF-GAN, on both the generation quality and stability.
A Simple Recipe to Meta-Learn Forward and Backward Transfer
Authors: Edoardo Cetin, Antonio Carta, Oya Celiktutan
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Continual Learning, Meta-Learning

    Meta-learning holds the potential to provide a general and explicit solution to tackle interference and forgetting in continual learning. However, many popular algorithms introduce expensive and unstable optimization processes with new key hyper-parameters and requirements, hindering their applicability. We propose a new, general, and simple meta-learning algorithm for continual learning (SiM4C) that explicitly optimizes to minimize forgetting and facilitate forward transfer. We show our method is stable, introduces only minimal computational overhead, and can be integrated with any memory-based continual learning algorithm in only a few lines of code. SiM4C meta-learns how to effectively continually learn even on very long task sequences, largely outperforming prior meta-approaches. Naively integrating with existing memory-based algorithms, we also record universal performance benefits and state-of-the-art results across different visual classification benchmarks without introducing new hyper-parameters.
A Retrospect to Multi-prompt Learning across Vision and Language
Authors: Ziliang Chen, Xin Huang, Quanlong Guan, Liang Lin, Weiqi Luo
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Prompt Learning

    The vision community is undergoing the unprecedented progress with the emergence of Vision-Language Pretraining Models (VLMs). Prompt learning plays as the holy grail of accessing VLMs since it enables their fast adaptation to downstream tasks with limited resources. Whereas existing research milling around single-prompt paradigms, rarely investigate the technical potential behind their multi-prompt learning counterparts. This paper aims to provide a principled retrospect for vision-language multi-prompt learning. We extend the recent constant modality gap phenomenon to learnable prompts and then, justify the superiority of vision-language transfer with multi-prompt augmentation, empirically and theoretically. In terms of this observation, we propose an Energy-based Multi-prompt Learning (EMPL) to generate multiple prompt embeddings by drawing instances from an energy-based distribution, which is implicitly defined by VLMs. So our EMPL is not only parameter-efficient but also rigorously lead to the balance between in-domain and out-of-domain open-vocabulary generalization. Comprehensive experiments have been conducted to justify our claims and the excellence of EMPL.
Self-Supervised Object Detection from Egocentric Videos
Authors: Peri Akiva, Jing Huang, Kevin J Liang, Rama Kovvuri, Xingyu Chen, Matt Feiszli, Kristin Dana, Tal Hassner
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Class-agnostic Object Detection, Object, object-detection, Object Detection, Scene Understanding, Video Understanding

    Understanding the visual world from the perspective of humans (egocentric) has been a long-standing challenge in computer vision. Egocentric videos exhibit high scene complexity and irregular motion flows compared to typical video understanding tasks. With the egocentric domain in mind, we address the problem of self-supervised, class-agnostic object detection, which aims to locate all objects in a given view, regardless of category, without any annotations or pre-training weights. Our method, self-supervised object Detection from Egocentric VIdeos (DEVI), generalizes appearance-based methods to learn features that are category-specific and invariant to viewing angles and illumination conditions from highly ambiguous environments in an end-to-end manner. Our approach leverages typical human behavior and its egocentric perception to sample diverse views of the same objects for our multi-view and scale-regression loss functions. With our learned cluster residual module, we are able to effectively describe multi-category patches for better complex scene understanding. DEVI provides a boost in performance on recent egocentric datasets, with performance gains up to 4.11% AP50, 0.11% AR1, 1.32% AR10, and 5.03% AR100, while significantly reducing model complexity. We also demonstrate competitive performance on out-of-domain datasets without additional training or fine-tuning.
Order-Prompted Tag Sequence Generation for Video Tagging
Authors: Zongyang Ma, Ziqi Zhang, Yuxin Chen, Zhongang Qi, Yingmin Luo, Zekun Li, Chunfeng Yuan, Bing Li, XiaoHu Qie, Ying Shan, Weiming Hu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, TAG

    Video Tagging intends to infer multiple tags spanning relevant content for a given video. Typically, video tags are freely defined and uploaded by a variety of users, so they have two characteristics: abundant in quantity and disordered intra-video. It is difficult for the existing multi-label classification and generation methods to adapt directly to this task. This paper proposes a novel generative model, Order-Prompted Tag Sequence Generation (OP-TSG), according to the above characteristics. It regards video tagging as a tag sequence generation problem guided by sample-dependent order prompts. These prompts are semantically aligned with tags and enable to decouple tag generation order, making the model focus on modeling the tag dependencies. Moreover, the word-based generation strategy enables the model to generate novel tags. To verify the effectiveness and generalization of the proposed method, a Chinese video tagging benchmark CREATE-tagging, and an English image tagging benchmark Pexel-tagging are established. Extensive results show that OP-TSG is significantly superior to other methods, especially the results on rare tags improve by 3.3% and 3% over SOTA methods on CREATE-tagging and Pexel-tagging, and novel tags generated on CREATE-tagging exhibit a tag gain of 7.04%.