pwc-archive/papers-with-abstracts huggingface dataset:
Disentangle then Parse: Night-time Semantic Segmentation with Illumination Disentanglement
Authors: Zhixiang Wei, Lin Chen, Tao Tu, Pengyang Ling, Huaian Chen, Yi Jin
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Disentanglement, Segmentation, Semantic Segmentation

    Most prior semantic segmentation methods have been developed for day-time scenes, while typically underperforming in night-time scenes due to insufficient and complicated lighting conditions. In this work, we tackle this challenge by proposing a novel night-time semantic segmentation paradigm, i.e., disentangle then parse (DTP). DTP explicitly disentangles night-time images into light-invariant reflectance and light-specific illumination components and then recognizes semantics based on their adaptive fusion. Concretely, the proposed DTP comprises two key components: 1) Instead of processing lighting-entangled features as in prior works, our Semantic-Oriented Disentanglement (SOD) framework enables the extraction of reflectance component without being impeded by lighting, allowing the network to consistently recognize the semantics under cover of varying and complicated lighting conditions. 2) Based on the observation that the illumination component can serve as a cue for some semantically confused regions, we further introduce an Illumination-Aware Parser (IAParser) to explicitly learn the correlation between semantics and lighting, and aggregate the illumination features to yield more precise predictions. Extensive experiments on the night-time segmentation task with various settings demonstrate that DTP significantly outperforms state-of-the-art methods. Furthermore, with negligible additional parameters, DTP can be directly used to benefit existing day-time methods for night-time segmentation. Code and dataset are available at https://github.com/w1oves/DTP.git.
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups
Authors: Peixia Li, Pulak Purkait, Thalaiyasingam Ajanthan, Majid Abdolshah, Ravi Garg, Hisham Husain, Chenchen Xu, Stephen Gould, Wanli Ouyang, Anton Van Den Hengel
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Semantic Segmentation, Semi-Supervised Semantic Segmentation

    Semi-supervised semantic segmentation methods use a small amount of clean pixel-level annotations to guide the interpretation of a larger quantity of unlabelled image data. The challenges of providing pixel-accurate annotations at scale mean that the labels are typically noisy, and this contaminates the final results. In this work, we propose an approach that is robust to label noise in the annotated data. The method uses two diverse learning groups with different network architectures to effectively handle both label noise and unlabelled images. Each learning group consists of a teacher network, a student network and a novel filter module. The filter module of each learning group utilizes pixel-level features from the teacher network to detect incorrectly labelled pixels. To reduce confirmation bias, we employ the labels cleaned by the filter module from one learning group to train the other learning group. Experimental results on two different benchmarks and settings demonstrate the superiority of our method over state-of-the-art approaches.
Deep Feature Deblurring Diffusion for Detecting Out-of-Distribution Objects
Authors: Aming Wu, Da Chen, Cheng Deng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Deblurring, object-detection, Object Detection

    To promote the safe application of detectors, a task of unsupervised out-of-distribution object detection (OOD-OD) is recently proposed, whose goal is to detect unseen OOD objects without accessing any auxiliary OOD data. For this task, the challenge mainly lies in how to only leverage the known in-distribution (ID) data to detect OOD objects accurately without affecting the detection of ID objects, which can be framed as the diffusion problem for deep feature synthesis. Accordingly, such challenge could be addressed by the forward and reverse processes in the diffusion model. In this paper, we propose a new approach of Deep Feature Deblurring Diffusion (DFDD), consisting of forward blurring and reverse deblurring processes. Specifically, the forward process gradually performs Gaussian Blur on the extracted features, which is instrumental in retaining sufficient input-relevant information. By this way, the forward process could synthesize virtual OOD features that are close to the classification boundary between ID and OOD objects, which improves the performance of detecting OOD objects. During the reverse process, based on the blurred features, a dedicated deblurring model is designed to continually recover the lost details in the forward process. Both the deblurred features and original features are taken as the input for training, strengthening the discrimination ability. In the experiments, our method is evaluated on OOD-OD, open-set object detection, and incremental object detection. The significant performance gains over baselines demonstrate the superiorities of our method. The source code will be made available at: https://github.com/AmingWu/DFDD-OOD.
XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully- and Semi-Supervised Semantic Segmentation of Biomedical Images
Authors: Yanfeng Zhou, Jiaxing Huang, Chenlong Wang, Le Song, Ge Yang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

    Fully- and semi-supervised semantic segmentation of biomedical images have been advanced with the development of deep neural networks (DNNs). So far, however, DNN models are usually designed to support one of these two learning schemes, unified models that support both fully- and semi-supervised segmentation remain limited. Furthermore, few fully-supervised models focus on the intrinsic low frequency (LF) and high frequency (HF) information of images to improve performance. Perturbations in consistency-based semi-supervised models are often artificially designed. They may introduce negative learning bias that are not beneficial for training. In this study, we propose a wavelet-based LF and HF fusion model XNet, which supports both fully- and semi-supervised semantic segmentation and outperforms state-of-the-art models in both fields. It emphasizes extracting LF and HF information for consistency training to alleviate the learning bias caused by artificial perturbations. Extensive experiments on two 2D and two 3D datasets demonstrate the effectiveness of our model. Code is available at https://github.com/Yanfeng-Zhou/XNet.
Multi-weather Image Restoration via Domain Translation
Authors: Prashant W. Patil, Sunil Gupta, Santu Rana, Svetha Venkatesh, Subrahmanyam Murala
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Restoration, Translation

    Weather degraded conditions such as rain, haze, snow, etc. may degrade the performance of most computer vision systems. Therefore, effective restoration of multi-weather degraded images is an essential prerequisite for successful functioning of such systems. The current multi-weather image restoration approaches utilize a model that is trained on a combined dataset consisting of individual images for rainy, snowy, and hazy weather degradations. These methods may face challenges when dealing with real-world situations where the images may have multiple, more intricate weather conditions. To address this issue, we propose a domain translation-based unified method for multi-weather image restoration. In this approach, the proposed network learns multiple weather degradations simultaneously, making it immune for real-world conditions. Specifically, we first propose an instance-level domain (weather) translation with multi-attentive feature learning approach to get different weather-degraded variants of the same scenario. Next, the original and translated images are used as input to the proposed novel multi-weather restoration network which utilizes a progressive multi-domain deformable alignment (PMDA) with cascaded multi-head attention (CMA). The proposed PMDA facilitates the restoration network to learn weather-invariant clues effectively. Further, PMDA and respective decoder features are merged via proposed CMA module for restoration. Extensive experimental results on synthetic and real-world hazy, rainy, and snowy image databases clearly demonstrate that our model outperforms the state-of-the-art multi-weather image restoration methods. The URL for our code is provided in the supplementary material and will be made public upon acceptance.
Ordinal Label Distribution Learning
Authors: Changsong Wen, Xin Zhang, Xingxu Yao, Jufeng Yang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Age Estimation

    Label distribution learning (LDL) is a recent hot topic, in which ambiguity is modeled via description degrees of the labels. However, in common LDL tasks, e.g., age estimation, labels are in an intrinsic order. The conventional LDL paradigm adopts a per-label manner for optimization, neglecting the internal sequential patterns of labels. Therefore, we propose a new paradigm, termed ordinal label distribution learning (OLDL). We model the sequential patterns of labels from aspects of spatial, semantic, and temporal order relationships. The spatial order depicts the relative position between arbitrary labels. We build cross-label transformation between distributions, which is determined by the spatial margin in labels. Labels naturally yield different semantics, so the semantic order is represented by constructing semantic correlations between arbitrary labels. The temporal order describes that the presence of labels is determined by their order, i.e. five after four. The value of a particular label contains information about previous labels, and we adopt cumulative distribution to construct this relationship. Based on these characteristics of ordinal labels, we propose the learning objectives and evaluation metrics for OLDL, namely CAD, QFD, and CJS. Comprehensive experiments conducted on four tasks demonstrate the superiority of OLDL against other existing LDL methods in both traditional and newly proposed metrics. Our project page can be found at https://downdric23.github.io/.
PPR: Physically Plausible Reconstruction from Monocular Videos
Authors: Gengshan Yang, Shuo Yang, John Z. Zhang, Zachary Manchester, Deva Ramanan
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Given monocular videos, we build 3D models of articulated objects and environments whose 3D configurations satisfy dynamics and contact constraints. At its core, our method leverages differentiable physics simulation to aid visual reconstructions. We couple differentiable physics simulation with differentiable rendering via coordinate descent, which enables end-to-end optimization of, not only 3D reconstructions, but also physical system parameters from videos. We demonstrate the effectiveness of physics-informed reconstruction on monocular videos of quadruped animals and humans. It reduces reconstruction artifacts (e.g., scale ambiguity, unbalanced poses, and foot swapping) that are challenging to address by visual cues alone, and produces better foot contact estimation.
Building Bridge Across the Time: Disruption and Restoration of Murals In the Wild
Authors: Huiyang Shao, Qianqian Xu, Peisong Wen, Peifeng Gao, Zhiyong Yang, Qingming Huang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Diversity, Image Restoration

    In this paper, we focus on the mural-restoration task, which aims to detect damaged regions in the mural and repaint them automatically. Different from traditional image restoration tasks like in/out/blind-painting and image renovation, the corrupted mural suffers from more complicated degradation. However, existing mural-restoration methods and datasets still focus on simple degradation like masking. Such a significant gap prevents mural-restoration from being applied to real scenarios. To fill this gap, in this work, we propose a systematic framework to simulate the physical process for damaged murals and provide a new benchmark dataset for mural-restoration. Limited by the simplification of the data synthesis process, the previous mural-restoration methods suffer from poor performance in our proposed dataset. To handle this problem, we propose the Attention Diffusion Framework (ADF) for this challenging task. Within the framework, a damage attention map module is proposed to estimate the damage extent. Facing the diversity of defects, we propose a series of loss functions to choose repair strategies adaptively. Finally, experimental results support the effectiveness of the proposed framework in terms of both mural synthesis and restoration.
Preparing the Future for Continual Semantic Segmentation
Authors: Zihan Lin, Zilei Wang, Yixin Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Continual Semantic Segmentation, Semantic Segmentation

    In this study, we focus on Continual Semantic Segmentation (CSS) and present a novel approach to tackle the issue of existing methods struggling to learn new classes. The primary challenge of CSS is to learn new knowledge while retaining old knowledge, which is commonly known as the rigidity-plasticity dilemma. Existing approaches strive to address this by carefully balancing the learning of new and old classes during training on new data. Differently, this work aims to avoid this dilemma fundamentally rather than handling the difficulties involved in it. Specifically, we reveal that this dilemma mainly arises from the greater fluctuation of knowledge for new classes because they have never been learned before the current step. Additionally, the data available in incremental steps are usually inadequate, which can impede the model's ability to learn discriminative features for both new and old classes. To address these challenges, we introduce a novel concept of pre-learning for future knowledge. Our approach entails optimizing the feature space and output space for unlabeled data, which thus enables the model to acquire knowledge for future classes. With this approach, updating the model for new classes becomes as smooth as for old classes, effectively avoiding the rigidity-plasticity dilemma. We conducted extensive experiments and the results demonstrate a significant improvement in the learning of new classes compared to previous state-of-the-art methods.
MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training for X-ray Diagnosis
Authors: Chaoyi Wu, Xiaoman Zhang, Ya zhang, Yanfeng Wang, Weidi Xie
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Medical Diagnosis, Triplet

    In this paper, we consider enhancing medical visual-language pre-training (VLP) with domain-specific knowledge, by exploiting the paired image-text reports from the radiological daily practice. In particular, we make the following contributions: First, unlike existing works that directly process the raw reports, we adopt a novel triplet extraction module to extract the medical-related information, avoiding unnecessary complexity from language grammar and enhancing the supervision signals; Second, we propose a novel triplet encoding module with entity translation by querying a knowledge base, to exploit the rich domain knowledge in medical field, and implicitly build relationships between medical entities in the language embedding space; Third, we propose to use a Transformer-based fusion model for spatially aligning the entity description with visual signals at the image patch level, enabling the ability for medical diagnosis; Fourth, we conduct thorough experiments to validate the effectiveness of our architecture, and benchmark on numerous public benchmarks e.g., ChestX-ray14, RSNA Pneumonia, SIIM-ACR Pneumothorax, COVIDx CXR-2, COVID Rural, and EdemaSeverity. In both zero-shot and fine-tuning settings, our model has demonstrated strong performance compared with the former methods on disease classification and grounding.
Computation and Data Efficient Backdoor Attacks
Authors: Yutong Wu, Xingshuo Han, Han Qiu, Tianwei Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Point Cloud Classification, Data Poisoning, Point Cloud Classification, Transfer Learning

    Backdoor attacks against deep learning have been widely studied. Various attack techniques have been proposed for different domains and paradigms, e.g., image, point cloud, natural language processing, transfer learning, etc. These works normally adopt the data poisoning strategy to embed the backdoor. They randomly select samples from the benign training set for poisoning, without considering the distinct contribution of each sample to the backdoor effectiveness, making the attack less optimal. A recent work (IJCAI-22) proposed to use the forgetting score to measure the importance of each poisoned sample and then filter out redundant data for effective backdoor training. However, this method is empirically designed without theoretical proofing. It is also very time-consuming as it needs to go through almost all the training stages for data selection. To address such limitations, we propose a novel confidence-based scoring methodology, which can efficiently measure the contribution of each poisoning sample based on the distance posteriors. We further introduce a greedy search algorithm to find the most informative samples for backdoor injection more promptly. Experimental evaluations on both 2D image and 3D point cloud classification tasks show that our approach can achieve comparable performance or even surpass the forgetting score-based searching method while requiring only several extra epochs' computation of a standard training process.
EMMN: Emotional Motion Memory Network for Audio-driven Emotional Talking Face Generation
Authors: Shuai Tan, Bin Ji, Ye Pan
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Face Generation, Talking Face Generation

    Synthesizing expression is essential to create realistic talking faces. Previous works consider expressions and mouth shapes as a whole and predict them solely from audio inputs. However, the limited information contained in audio, such as phonemes and coarse emotion embedding, may not be suitable as the source of elaborate expressions. Besides, since expressions are tightly coupled to lip motions, generating expression from other sources is tricky and always neglects expression performed on mouth region, leading to inconsistency between them. To tackle the issues, this paper proposes Emotional Motion Memory Net (EMMN) that synthesizes expression overall on the talking face via emotion embedding and lip motion instead of the sole audio. Specifically, we extract emotion embedding from audio and design Motion Reconstruction module to decompose ground truth videos into mouth features and expression features before training, where the latter encode all facial factors about expression. During training, the emotion embedding and mouth features are used as keys, and the corresponding expression features are used as values to create key-value pairs stored in the proposed Motion Memory Net. Hence, once the audio-relevant mouth features and emotion embedding are individually predicted from audio at inference time, we treat them as a query to retrieve the best-matching expression features, performing expression overall on the face and thus avoiding inconsistent results. Extensive experiments demonstrate that our method can generate high-quality talking face videos with accurate lip movements and vivid expressions on unseen subjects.
AREA: Adaptive Reweighting via Effective Area for Long-Tailed Classification
Authors: Xiaohua Chen, Yucan Zhou, Dayan Wu, Chule Yang, Bo Li, QinGhua Hu, Weiping Wang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Large-scale data from real-world usually follow a long-tailed distribution (i.e., a few majority classes occupy plentiful training data, while most minority classes have few samples), making the hyperplanes heavily skewed to the minority classes. Traditionally, reweighting is adopted to make the hyperplanes fairly split the feature space, where the weights are designed according to the number of samples. However, we find that the number of samples in a class can not accurately measure the size of its spanned space, especially for the majority class, where the size of its spanned space is usually larger than the samples' number because of the high diversity. Therefore, weights designed based on the samples' number will still compress the space of minority classes. In this paper, we reconsider reweighting from a totally new perspective of analyzing the spanned space of each class. We argue that, besides statistical numbers, relations between samples are also significant for sufficiently depicting the spanned space. Consequently, we estimate the size of the spanned space for each category, namely effective area, by detailedly analyzing its samples' distribution. By treating samples of a class as identically distributed random variables and analyzing their correlations, a simple and non-parametric formula is derived to estimate the effective area. Then, the weight simply calculated inversely proportional to the effective area of each class is adopted to achieve fairer training. Note that our weights are more flexible as they can be adaptively adjusted along with the optimizing features during training.  Experiments on four long-tailed datasets show that the proposed weights outperform the state-of-the-art reweighting methods. Moreover, our method can also achieve better results on statistically balanced CIFAR-10/100. Code is available at https://github.com/xiaohua-chen/AREA.
Distilling DETR with Visual-Linguistic Knowledge for Open-Vocabulary Object Detection
Authors: Liangqi Li, Jiaxu Miao, Dahu Shi, Wenming Tan, Ye Ren, Yi Yang, ShiLiang Pu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, object-detection, Object Detection, Open-vocabulary object detection, Open Vocabulary Object Detection

    Current methods for open-vocabulary object detection (OVOD) rely on a pre-trained vision-language model (VLM) to acquire the recognition ability. In this paper, we propose a simple yet effective framework to Distill the Knowledge from the VLM to a DETR-like detector, termed DK-DETR. Specifically, we present two ingenious distillation schemes named semantic knowledge distillation (SKD) and relational knowledge distillation (RKD). To utilize the rich knowledge from the VLM systematically, SKD transfers the semantic knowledge explicitly, while RKD exploits implicit relationship information between objects. Furthermore, a distillation branch including a group of auxiliary queries is added to the detector to mitigate the negative effect on base categories. Equipped with SKD and RKD on the distillation branch, DK-DETR improves the detection performance of novel categories significantly and avoids disturbing the detection of base categories. Extensive experiments on LVIS and COCO datasets show that DK-DETR surpasses existing OVOD methods under the setting that the base-category supervision is solely available. The code and models are available at https://github.com/hikvision-research/opera.