pwc-archive/papers-with-abstracts huggingface dataset:
Frequency-aware GAN for Adversarial Manipulation Generation
Authors: Peifei Zhu, Genki Osada, Hirokatsu Kataoka, Tsubasa Takahashi
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Adversarial Attack, Decoder, Image Manipulation

    Image manipulation techniques have drawn growing concerns as manipulated images might cause morality and security problems. Various methods have been proposed to detect manipulations and achieved promising performance. However, these methods might be vulnerable to adversarial attacks. In this work, we design an Adversarial Manipulation Generation (AMG) task to explore the vulnerability of image manipulation detectors. We first propose an optimal loss function and extend existing attacks to generate adversarial examples. We observe that existing spatial attacks cause large degradation in image quality and find the loss of high-frequency detailed components might be its major reason. Inspired by this observation, we propose a novel adversarial attack that incorporates both spatial and frequency features into the GAN architecture to generate adversarial examples. We further design an encoder-decoder architecture with skip connections of high-frequency components to preserve fine details. We evaluated our method on three image manipulation detectors (FCN, ManTra-Net and MVSS-Net) with three benchmark datasets (DEFACTO, CASIAv2 and COVER). Experiments show that our method generates adversarial examples significantly fast (0.01s per image), preserves better image quality (PSNR 30% higher than spatial attacks) and achieves a high attack success rate. We also observe that the examples generated by AMG can fool both classification and segmentation models, which indicates better transferability among different tasks.
Learning from Noisy Pseudo Labels for Semi-Supervised Temporal Action Localization
Authors: Kun Xia, Le Wang, Sanping Zhou, Gang Hua, Wei Tang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Localization, Pseudo Label, Temporal Action Localization

    Semi-Supervised Temporal Action Localization (SS-TAL) aims to improve the generalization ability of action detectors with large-scale unlabeled videos. Albeit the recent advancement, one of the major challenges still remains: noisy pseudo labels hinder efficient learning on abundant unlabeled videos, embodied as location biases and category errors. In this paper, we dive deep into such an important but understudied dilemma. To this end, we propose a unified framework, termed Noisy Pseudo-Label Learning, to handle both location biases and category errors. Specifically, our method is featured with (1) Noisy Label Ranking to rank pseudo labels based on the semantic confidence and boundary reliability, (2) Noisy Label Filtering to address the class-imbalance problem of pseudo labels caused by category errors, (3) Noisy Label Learning to penalize inconsistent boundary predictions to achieve noise-tolerant learning for heavy location biases. As a result, our method could effectively handle the label noise problem and improve the utilization of a large amount of unlabeled videos. Extensive experiments on THUMOS14 and ActivityNet v1.3 demonstrate the effectiveness of our method. The code is available at github.com/kunnxia/NPL.
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models
Authors: Chanyue Wu, Dong Wang, Yunpeng Bai, Hanyu Mao, Ying Li, Qiang Shen
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Denoising, Hyperspectral Image Super-Resolution, Image Super-Resolution, Super-Resolution

    Despite the proven significance of hyperspectral images (HSIs) in performing various computer vision tasks, its potential is adversely affected by the low-resolution (LR) property in the spatial domain, resulting from multiple physical factors. Inspired by recent advancements in deep generative models, we propose an HSI Super-resolution (SR) approach with Conditional Diffusion Models (HSR-Diff) that merges a high-resolution (HR) multispectral image (MSI) with the corresponding LR-HSI. HSR-Diff generates an HR-HSI via repeated refinement, in which the HR-HSI is initialized with pure Gaussian noise and iteratively refined. At each iteration, the noise is removed with a Conditional Denoising Transformer (CDFormer) that is trained on denoising at different noise levels, conditioned on the hierarchical feature maps of HR-MSI and LR-HSI. In addition, a progressive learning strategy is employed to exploit the global information of full-resolution images. Systematic experiments have been conducted on four public datasets, demonstrating that HSR-Diff outperforms state-of-the-art methods.
Explicit Motion Disentangling for Efficient Optical Flow Estimation
Authors: Changxing Deng, Ao Luo, Haibin Huang, Shaodan Ma, Jiangyu Liu, Shuaicheng Liu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Decoder, Motion Estimation, Optical Flow Estimation

    In this paper, we propose a novel framework for optical flow estimation that achieves a good balance between performance and efficiency. Our approach involves disentangling global motion learning from local flow estimation, treating global matching and local refinement as separate stages. We offer two key insights: First, the multi-scale 4D cost-volume based recurrent flow decoder is computationally expensive and unnecessary for handling small displacement. With the separation, we can utilize lightweight methods for both parts and maintain similar performance. Second, a dense and robust global matching is essential for both flow initialization as well as stable and fast convergence for the refinement stage. Towards this end, we introduce EMD-Flow, a framework that explicitly separates global motion estimation from the recurrent refinement stage. We propose two novel modules: Multi-scale Motion Aggregation (MMA) and Confidence-induced Flow Propagation (CFP). These modules leverage cross-scale matching prior and self-contained confidence maps to handle the ambiguities of dense matching in a global manner, generating a dense initial flow. Additionally, a lightweight decoding module is followed to handle small displacements, resulting in an efficient yet robust flow estimation framework. We further conduct comprehensive experiments on standard optical flow benchmarks with the proposed framework, and the experimental results demonstrate its superior balance between performance and runtime. Code is available at https://github.com/gddcx/EMD-Flow.
Learning Versatile 3D Shape Generation with Improved Auto-regressive Models
Authors: Simian Luo, Xuelin Qian, Yanwei Fu, yinda zhang, Ying Tai, Zhenyu Zhang, Chengjie Wang, xiangyang xue
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Shape Generation, Image Generation, Representation Learning

    Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space. While this approach has been extended to the 3D domain for powerful shape generation, it still has two limitations: expensive computations on volumetric grids and ambiguous auto-regressive order along grid dimensions. To overcome these limitations, we propose the Improved Auto-regressive Model (ImAM) for 3D shape generation, which applies discrete representation learning based on a latent vector instead of volumetric grids. Our approach not only reduces computational costs but also preserves essential geometric details by learning the joint distribution in a more tractable order. Moreover, thanks to the simplicity of our model architecture, we can naturally extend it from unconditional to conditional generation by concatenating various conditioning inputs, such as point clouds, categories, images, and texts. Extensive experiments demonstrate that ImAM can synthesize diverse and faithful shapes of multiple categories, achieving state-of-the-art performance.
MHCN: A Hyperbolic Neural Network Model for Multi-view Hierarchical Clustering
Authors: Fangfei Lin, Bing Bai, Yiwen Guo, Hao Chen, Yazhou Ren, Zenglin Xu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Clustering, MULTI-VIEW LEARNING, Representation Learning

    Multi-view hierarchical clustering (MCHC) plays a pivotal role in comprehending the structures within multi-view data, which hinges on the skillful interaction between hierarchical feature learning and comprehensive representation learning across multiple views. However, existing methods often overlook this interplay due to the simple heuristic agglomerative strategies or the decoupling of multi-view representation learning and hierarchical modeling, thus leading to insufficient representation learning. To address these issues, this paper proposes a novel Multi-view Hierarchical Clustering Network (MHCN) model by performing simultaneous multi-view learning and hierarchy modeling. Specifically, to uncover efficient tree-like structures among all views, we derive multiple hyperbolic autoencoders with latent space mapped onto the Poincare ball. Then, the corresponding hyperbolic embeddings are further regularized to achieve the multi-view representation learning principles for both view-common and view-private information, and to ensure hyperbolic uniformity with a well-balanced hierarchy for better interpretability. Extensive experiments on real-world and synthetic multi-view datasets have demonstrated that our method can achieve state-of-the-art hierarchical clustering performance, and empower the clustering results with good interpretability.
Physically-Plausible Illumination Distribution Estimation
Authors: Egor Ershov, Vasily Tesalin, Ivan Ermakov, Michael S. Brown
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    A camera's auto-white-balance (AWB) module operates under the assumption that there is a single dominant illumination in a captured scene. AWB methods estimate an image's dominant illumination and use it as the target "white point" for correction. However, in natural scenes, there are often many light sources present. We performed a user study that revealed that non-dominant illuminations often produce visually pleasing white-balanced images and, in some cases, are even preferred over the dominant illumination. Motivated by this observation, we revisit AWB to predict a  distribution  of plausible illuminations for use in white balance. As part of this effort, we extend the Cube++ illumination estimation dataset to provide ground truth illumination distributions per image. Using this new ground truth data, we describe how to train a lightweight neural network method to predict the scene's illumination distribution. We describe how our idea can be used with existing image formats by embedding the estimated distribution in the RAW image to enable users to generate visually plausible white-balance images.
Video Action Segmentation via Contextually Refined Temporal Keypoints
Authors: Borui Jiang, Yang Jin, Zhentao Tan, Yadong Mu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Segmentation, Graph Matching, Segmentation

    Video action segmentation refers to the task of densely casting each video frame or short segment in an untrimmed video into some pre-specified action categories. Although recent years have witnessed a great promise in the development of action segmentation techniques.A large body of existing methods still rely on frame-wise segmentation, which tends to render fragmentary results (i.e., over-segmentation).To effectively address above issues, we here propose a video action segmentation model that implements the novel idea of Refined Temporal Keypoints (RTK) for overcoming caveats of existing methods.To act effectively, the proposed model initially seeks for high-quality, sparse temporal keypoints by extracting non-local cues from the video, rather than conducting frame-wise classification as in many competing methods.Afterwards, large improvements over the inital temporal keypoints are pin-pointed as contributions by further refining and re-assembling operations. In specific, we develop a graph matching module that aggregates structural information between different temporal keypoints by learning the corresponding relationship of the temporal source graphs and the annotated target graphs. The initial temporal keypoints are refined by the encoded structural information reusing the graph matching module.A few set of prior rules are harnessed for post-processing and re-assembling all temporal keypoints.The remaining temporal keypoiting going through all refinement are used to generate the final action segmentation results.We perform experiments on three popular datasets: 50salads, GTEA and Breakfast, and our methods significantly outperforms the current methods, particularly achieves the state-of-the-art F1@50 scores of 83.4%, 79.5%, and 60.5% on three datasets, respectively.
Learn TAROT with MENTOR: A Meta-Learned Self-Supervised Approach for Trajectory Prediction
Authors: Mozhgan PourKeshavarz, Changhe Chen, Amir Rasouli
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Meta-Learning, Trajectory Prediction

    Predicting diverse yet admissible trajectories that adhere to the map constraints is challenging. Graph-based scene encoders have been proven effective for preserving local structures of maps by defining lane-level connections. However, such encoders do not capture more complex patterns emerging from long-range heterogeneous connections between nonadjacent interacting lanes. To this end, we shed new light on learning common driving patterns by introducing meTA ROad paTh (TAROT) to formulate combinations of various relations between lanes on the road topology. Intuitively, this can be viewed as finding feasible routes. Furthermore, we propose MEta-road NeTwORk (MENTOR) that helps trajectory prediction by providing it with TAROT as navigation tips. More specifically, 1) we define TAROT prediction as a novel self-supervised proxy task to identify the complex heterogeneous structure of the map. 2) For typical driving actions, we establish several TAROTs that result in multiple Heterogeneous Structure Learning (HSL) tasks. These tasks are used in MENTOR, which performs meta-learning by simultaneously predicting trajectories along with proxy tasks, identifying an optimal combination of them, and automatically balancing them to improve the primary task. We show that our model achieves state-of-the-art performance on the Argoverse dataset, especially on diversity and admissibility metrics, achieving up to 20% improvements in challenging scenarios. We further investigate the contribution of proposed modules in ablation studies.
Few-Shot Common Action Localization via Cross-Attentional Fusion of Context and Temporal Dynamics
Authors: JunTae Lee, Mihir Jain, Sungrack Yun
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Localization

    The goal of this paper is to localize action instances in a long untrimmed query video using just meager trimmed support videos representing a common action whose class information is not given. In this task, it is crucial to mine reliable temporal cues representing a common action from handful support videos. In our work, we develop an attention mechanism using cross-correlation. Based on this cross-attention, we first transform the support videos into query video's context to emphasize query-relevant important frames, and suppress less relevant ones. Next, we summarize sub-sequences of support video frames to represent temporal dynamics in coarse temporal granularity, which is then propagated to the fine-grained support video features through the cross-attention. In each case, the cross-attentions are applied to each support video in the individual-to-all strategy to balance heterogeneity and compatibility of the support videos. In contrast, the candidate instances in the query video are lastly attended by the resulting support video features, at once. In addition, we also develop a relational classifier head based on the query and support video representations. We show the effectiveness of our work with the state-of-the-art (SOTA) performance in benchmark datasets (ActivityNet1.3 and THUMOS14), and analyze each component extensively.
Tiny Updater: Towards Efficient Neural Network-Driven Software Updating
Authors: Linfeng Zhang, Kaisheng Ma
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Efficient Neural Network, image-classification, Image Classification, Image-to-Image Translation, Knowledge Distillation, Model Compression, Video Recognition

    Significant advancements have been accomplished with deep neural networks in diverse visual tasks, which have substantially elevated their deployment in edge device software. However, during the update of neural network-based software, users are required to download all the parameters of the neural network anew, which harms the user experience. Motivated by previous progress in model compression, we propose a novel training methodology named Tiny Updater to address this issue. Specifically, by adopting the variant of pruning and knowledge distillation methods, Tiny Updater can update the neural network-based software by only downloading a few parameters (10% 20%) instead of all the parameters in the neural network. Experiments on eleven datasets of three tasks, including image classification, image-to-image translation, and video recognition have demonstrated its effectiveness. Codes have been released in https://github.com/ArchipLab-LinfengZhang/TinyUpdater.
Building Vision Transformers with Hierarchy Aware Feature Aggregation
Authors: Yongjie Chen, Hongmin Liu, Haoran Yin, Bin Fan
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation

    Thanks to the excellent global modeling capability of attention mechanisms, the Vision Transformer has achieved better results than ConvNet in many computer tasks. However, in generating hierarchical feature maps, the Transformer still adopts the ConvNet feature aggregation scheme. This leads to the problem that the semantic information of the grid area of image becomes confused after feature aggregation, making it difficult for attention to accurately model global relationships. To address this, we propose the Hierarchy Aware Feature Aggregation framework (HAFA). HAFA enhances the extraction of local features adaptively in shallow layers where semantic information is weak, while is able to aggregate patches with similar semantics in deep layers. The clear semantic information of the aggregated patches, enables the attention mechanism to more accurately model global information at the semantic level. Extensive experiments show that after using the HAFA framework, significant improvements have been achieved relative to the baseline models in image classification, object detection, and semantic segmentation tasks.
A Latent Space of Stochastic Diffusion Models for Zero-Shot Image Editing and Guidance
Authors: Chen Henry Wu, Fernando de la Torre
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Denoising

    Diffusion models generate images by iterative denoising. Recent work has shown that by making the denoising process deterministic, one can encode real images into latent codes of the same size, which can be used for image editing. This paper explores the possibility of defining a latent space even when the denoising process remains stochastic. Recall that, in stochastic diffusion models, Gaussian noises are added in each denoising step, and we can concatenate all the noises to form a latent code. This results in a latent space of much higher dimensionality than the original image. We demonstrate that this latent space of stochastic diffusion models can be used in the same way as that of deterministic diffusion models in two applications. First, we propose CycleDiffusion, a method for zero-shot and unpaired image editing using stochastic diffusion models, which improves the performance over its deterministic counterpart. Second, we demonstrate unified, plug-and-play guidance in the latent spaces of deterministic and stochastic diffusion models.
Video Object Segmentation-aware Video Frame Interpolation
Authors: Jun-Sang Yoo, Hongjae Lee, Seung-Won Jung
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Object, Pose Estimation, Segmentation, Semantic Segmentation, Video Enhancement, Video Frame Interpolation, Video Object Segmentation, Video Semantic Segmentation, Visual Tracking

    Video frame interpolation (VFI) is a very active research topic due to its broad applicability to many applications, including video enhancement, video encoding, and slow-motion effects. VFI methods have been advanced by improving the overall image quality for challenging sequences containing occlusions, large motion, and dynamic texture. This mainstream research direction neglects that foreground and background regions have different importance in perceptual image quality. Moreover, accurate synthesis of moving objects can be of utmost importance in computer vision applications. In this paper, we propose a video object segmentation (VOS)-aware training framework called VOS-VFI that allows VFI models to interpolate frames with more precise object boundaries. Specifically, we exploit VOS as an auxiliary task to help train VFI models by providing additional loss functions, including segmentation loss and bi-directional consistency loss. From extensive experiments, we demonstrate that VOS-VFI can boost the performance of existing VFI models by rendering clear object boundaries. Moreover, VOS-VFI displays its effectiveness on multiple benchmarks for different applications, including video object segmentation, object pose estimation, and visual tracking.
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective
Authors: Yulin Jin, XiaoYu Zhang, Jian Lou, Xu Ma, Zilong Wang, Xiaofeng Chen
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Adversarial Attack, Adversarial Robustness, Clustering

    Adversarial training (AT) is the most commonly used mechanism to improve the robustness of deep neural networks. Recently, a novel adversarial attack against intermediate layers exploits the extra fragility of adversarially trained networks to output incorrect predictions. The result implies the insufficiency in the searching space of the adversarial perturbation in adversarial training. To straighten out the reason for the effectiveness of the intermediate-layer attack, we interpret the forward propagation as the Clustering Effect, characterizing that the intermediate-layer representations of neural networks for samples i.i.d. to the training set with the same label are similar, and we theoretically prove the existence of Clustering Effect by corresponding Information Bottleneck Theory. We afterward observe that the intermediate-layer attack disobeys the clustering effect of the AT-trained model. Inspired by these significant observations, we propose a regularization method to extend the perturbation searching space during training, named sufficient adversarial training (SAT). We give a proven robustness bound of neural networks through rigorous mathematical proof. The experimental evaluations manifest the superiority of SAT over other state-of-the-art AT mechanisms in defending against adversarial attacks against both output and intermediate layers. Our code and Appendix can be found at https://github.com/clustering-effect/SAT.