pwc-archive/papers-with-abstracts huggingface dataset:
Random Sub-Samples Generation for Self-Supervised Real Image Denoising
Authors: Yizhong Pan, Xiao Liu, Xiangyu Liao, Yuanzhouhan Cao, Chao Ren
Date: 2023-07-31 00:00:00
Proceeding: ICCV 2023 1
Tasks: Denoising, Image Denoising

With sufficient paired training samples, the supervised deep learning methods have attracted much attention in image denoising because of their superior performance. However, it is still very challenging to widely utilize the supervised methods in real cases due to the lack of paired noisy-clean images. Meanwhile, most self-supervised denoising methods are ineffective as well when applied to the real-world denoising tasks because of their strict assumptions in applications. For example, as a typical method for self-supervised denoising, the original blind spot network (BSN) assumes that the noise is pixel-wise independent, which is much different from the real cases. To solve this problem, we propose a novel self-supervised real image denoising framework named Sampling Difference As Perturbation (SDAP) based on Random Sub-samples Generation (RSG) with a cyclic sample difference loss. Specifically, we dig deeper into the properties of BSN to make it more suitable for real noise. Surprisingly, we find that adding an appropriate perturbation to the training images can effectively improve the performance of BSN. Further, we propose that the sampling difference can be considered as perturbation to achieve better results. Finally we propose a new BSN framework in combination with our RSG strategy. The results show that it significantly outperforms other state-of-the-art self-supervised denoising methods on real-world datasets. The code is available at https://github.com/p1y2z3/SDAP.
Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment
Authors: Kun Yuan, Zishang Kong, Chuanchuan Zheng, Ming Sun, Xing Wen
Date: 2023-07-31 00:00:00
Tasks: Action Recognition, Blocking, Video Quality Assessment, Visual Question Answering (VQA)

Video Quality Assessment (VQA), which aims to predict the perceptual quality of a video, has attracted raising attention with the rapid development of streaming media technology, such as Facebook, TikTok, Kwai, and so on. Compared with other sequence-based visual tasks (\textit{e.g.,} action recognition), VQA faces two under-estimated challenges unresolved in User Generated Content (UGC) videos. \textit{First}, it is not rare that several frames containing serious distortions (\textit{e.g.,}blocking, blurriness), can determine the perceptual quality of the whole video, while other sequence-based tasks require more frames of equal importance for representations. \textit{Second}, the perceptual quality of a video exhibits a multi-distortion distribution, due to the differences in the duration and probability of occurrence for various distortions. In order to solve the above challenges, we propose \textit{Visual Quality Transformer (VQT)} to extract quality-related sparse features more efficiently. Methodologically, a Sparse Temporal Attention (STA) is proposed to sample keyframes by analyzing the temporal correlation between frames, which reduces the computational complexity from $O(T^2)$ to $O(T \log T)$. Structurally, a Multi-Pathway Temporal Network (MPTN) utilizes multiple STA modules with different degrees of sparsity in parallel, capturing co-existing distortions in a video. Experimentally, VQT demonstrates superior performance than many \textit{state-of-the-art} methods in three public no-reference VQA datasets. Furthermore, VQT shows better performance in four full-reference VQA datasets against widely-adopted industrial algorithms (\textit{i.e.,} VMAF and AVQT).
DPMix: Mixture of Depth and Point Cloud Video Experts for 4D Action Segmentation
Authors: Yue Zhang, Hehe Fan, Yi Yang, Mohan Kankanhalli
Date: 2023-07-31 00:00:00
Tasks: Action Segmentation, Human-Object Interaction Detection, Segmentation, Video Understanding

In this technical report, we present our findings from the research conducted on the Human-Object Interaction 4D (HOI4D) dataset for egocentric action segmentation task. As a relatively novel research area, point cloud video methods might not be good at temporal modeling, especially for long point cloud videos (\eg, 150 frames). In contrast, traditional video understanding methods have been well developed. Their effectiveness on temporal modeling has been widely verified on many large scale video datasets. Therefore, we convert point cloud videos into depth videos and employ traditional video modeling methods to improve 4D action segmentation. By ensembling depth and point cloud video methods, the accuracy is significantly improved. The proposed method, named Mixture of Depth and Point cloud video experts (DPMix), achieved the first place in the 4D Action Segmentation Track of the HOI4D Challenge 2023.
From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement
Authors: Wanyu Wu, Wei Wang, Zheng Wang, Kui Jiang, Xin Xu
Date: 2023-07-31 00:00:00
Tasks: Flare Removal, Image Enhancement, Low-Light Image Enhancement, Zero-Shot Learning

Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images. However, these methods have limited exploration in another major visibility damage, the glow effects in real night scenes. Glow effects are inevitable in the presence of artificial light sources and cause further diffused blurring when directly enhanced. To settle this issue, we innovatively consider the glow suppression task as learning physical glow generation via multiple scattering estimation according to the Atmospheric Point Spread Function (APSF). In response to the challenges posed by uneven glow intensity and varying source shapes, an APSF-based Nighttime Imaging Model with Near-field Light Sources (NIM-NLS) is specifically derived to design a scalable Light-aware Blind Deconvolution Network (LBDN). The glow-suppressed result is then brightened via a Retinex-based Enhancement Module (REM). Remarkably, the proposed glow suppression method is based on zero-shot learning and does not rely on any paired or unpaired training data. Empirical evaluations demonstrate the effectiveness of the proposed method in both glow suppression and low-light enhancement tasks.
Lightweight Super-Resolution Head for Human Pose Estimation
Authors: Haonan Wang, Jie Liu, Jie Tang, Gangshan Wu
Date: 2023-07-31 00:00:00
Tasks: Pose Estimation, Quantization, Super-Resolution

Heatmap-based methods have become the mainstream method for pose estimation due to their superior performance. However, heatmap-based approaches suffer from significant quantization errors with downscale heatmaps, which result in limited performance and the detrimental effects of intermediate supervision. Previous heatmap-based methods relied heavily on additional post-processing to mitigate quantization errors. Some heatmap-based approaches improve the resolution of feature maps by using multiple costly upsampling layers to improve localization precision. To solve the above issues, we creatively view the backbone network as a degradation process and thus reformulate the heatmap prediction as a Super-Resolution (SR) task. We first propose the SR head, which predicts heatmaps with a spatial resolution higher than the input feature maps (or even consistent with the input image) by super-resolution, to effectively reduce the quantization error and the dependence on further post-processing. Besides, we propose SRPose to gradually recover the HR heatmaps from LR heatmaps and degraded features in a coarse-to-fine manner. To reduce the training difficulty of HR heatmaps, SRPose applies SR heads to supervise the intermediate features in each stage. In addition, the SR head is a lightweight and generic head that applies to top-down and bottom-up methods. Extensive experiments on the COCO, MPII, and CrowdPose datasets show that SRPose outperforms the corresponding heatmap-based approaches. The code and models are available at https://github.com/haonanwang0522/SRPose.
High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection
Authors: Lin Huang, Weisheng Li, Yujuan Tan, Linlin Shen, Jing Yu
Date: 2023-07-31 00:00:00
Tasks: Defect Detection, object-detection, Object Detection

In this study, the structural problems of the YOLOv5 model were analyzed emphatically. Based on the characteristics of fine defects in artificial leather, four innovative structures, namely DFP, IFF, AMP, and EOS, were designed. These advancements led to the proposal of a high-performance artificial leather fine defect detection model named YOLOD. YOLOD demonstrated outstanding performance on the artificial leather defect dataset, achieving an impressive increase of 11.7% - 13.5% in AP_50 compared to YOLOv5, along with a significant reduction of 5.2% - 7.2% in the error detection rate. Moreover, YOLOD also exhibited remarkable performance on the general MS-COCO dataset, with an increase of 0.4% - 2.6% in AP compared to YOLOv5, and a rise of 2.5% - 4.1% in AP_S compared to YOLOv5. These results demonstrate the superiority of YOLOD in both artificial leather defect detection and general object detection tasks, making it a highly efficient and effective model for real-world applications.
Multi-Spectral Image Stitching via Spatial Graph Reasoning
Authors: Zhiying Jiang, Zengxi Zhang, JinYuan Liu, Xin Fan, Risheng Liu
Date: 2023-07-31 00:00:00
Tasks: Image Stitching

Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view (FOV) scene. The primary challenge of this task is to explore the relations between multi-spectral images for aligning and integrating multi-view scenes. Capitalizing on the strengths of Graph Convolutional Networks (GCNs) in modeling feature relationships, we propose a spatial graph reasoning based multi-spectral image stitching method that effectively distills the deformation and integration of multi-spectral images across different viewpoints. To accomplish this, we embed multi-scale complementary features from the same view position into a set of nodes. The correspondence across different views is learned through powerful dense feature embeddings, where both inter- and intra-correlations are developed to exploit cross-view matching and enhance inner feature disparity. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features, generating informative and reliable wide FOV scenes. Moreover, we release a challenging dataset named ChaMS, comprising both real-world and synthetic sets with significant parallax, providing a new option for comprehensive evaluation. Extensive experiments demonstrate that our method surpasses the state-of-the-arts.
UniVTG: Towards Unified Video-Language Temporal Grounding
Authors: Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen, Shraman Pramanick, Difei Gao, Alex Jinpeng Wang, Rui Yan, Mike Zheng Shou
Date: 2023-07-31 00:00:00
Proceeding: ICCV 2023 1
Tasks: Highlight Detection, Moment Retrieval, Natural Language Moment Retrieval, Retrieval, Video Summarization

Video Temporal Grounding (VTG), which aims to ground target clips from videos (such as consecutive intervals or disjoint shots) according to custom language queries (e.g., sentences or words), is key for video browsing on social media. Most methods in this direction develop taskspecific models that are trained with type-specific labels, such as moment retrieval (time interval) and highlight detection (worthiness curve), which limits their abilities to generalize to various VTG tasks and labels. In this paper, we propose to Unify the diverse VTG labels and tasks, dubbed UniVTG, along three directions: Firstly, we revisit a wide range of VTG labels and tasks and define a unified formulation. Based on this, we develop data annotation schemes to create scalable pseudo supervision. Secondly, we develop an effective and flexible grounding model capable of addressing each task and making full use of each label. Lastly, thanks to the unified framework, we are able to unlock temporal grounding pretraining from large-scale diverse labels and develop stronger grounding abilities e.g., zero-shot grounding. Extensive experiments on three tasks (moment retrieval, highlight detection and video summarization) across seven datasets (QVHighlights, Charades-STA, TACoS, Ego4D, YouTube Highlights, TVSum, and QFVS) demonstrate the effectiveness and flexibility of our proposed framework. The codes are available at https://github.com/showlab/UniVTG.
Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging
Authors: M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun, Peter H. N. de With, Fons van der Sommen
Date: 2023-07-31 00:00:00
Tasks: Image Segmentation, Informativeness, Medical Image Segmentation, Segmentation, Semantic Segmentation, Uncertainty Quantification

Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonly referred to as aleatoric uncertainty. In image segmentation, latent density models can be utilized to address this problem. The most popular approach is the Probabilistic U-Net (PU-Net), which uses latent Normal densities to optimize the conditional data log-likelihood Evidence Lower Bound. In this work, we demonstrate that the PU-Net latent space is severely sparse and heavily under-utilized. To address this, we introduce mutual information maximization and entropy-regularized Sinkhorn Divergence in the latent space to promote homogeneity across all latent dimensions, effectively improving gradient-descent updates and latent space informativeness. Our results show that by applying this on public datasets of various clinical segmentation problems, our proposed methodology receives up to 11% performance gains compared against preceding latent variable models for probabilistic segmentation on the Hungarian-Matched Intersection over Union. The results indicate that encouraging a homogeneous latent space significantly improves latent density modeling for medical image segmentation.
DiffPose: SpatioTemporal Diffusion Model for Video-Based Human Pose Estimation
Authors: Runyang Feng, Yixing Gao, Tze Ho Elden Tse, Xueqing Ma, Hyung Jin Chang
Date: 2023-07-31 00:00:00
Proceeding: ICCV 2023 1
Tasks: Denoising, Image Generation, Image Segmentation, object-detection, Object Detection, Pose Estimation, Semantic Segmentation

Denoising diffusion probabilistic models that were initially proposed for realistic image generation have recently shown success in various perception tasks (e.g., object detection and image segmentation) and are increasingly gaining attention in computer vision. However, extending such models to multi-frame human pose estimation is non-trivial due to the presence of the additional temporal dimension in videos. More importantly, learning representations that focus on keypoint regions is crucial for accurate localization of human joints. Nevertheless, the adaptation of the diffusion-based methods remains unclear on how to achieve such objective. In this paper, we present DiffPose, a novel diffusion architecture that formulates video-based human pose estimation as a conditional heatmap generation problem. First, to better leverage temporal information, we propose SpatioTemporal Representation Learner which aggregates visual evidences across frames and uses the resulting features in each denoising step as a condition. In addition, we present a mechanism called Lookup-based MultiScale Feature Interaction that determines the correlations between local joints and global contexts across multiple scales. This mechanism generates delicate representations that focus on keypoint regions. Altogether, by extending diffusion models, we show two unique characteristics from DiffPose on pose estimation task: (i) the ability to combine multiple sets of pose estimates to improve prediction accuracy, particularly for challenging joints, and (ii) the ability to adjust the number of iterative steps for feature refinement without retraining the model. DiffPose sets new state-of-the-art results on three benchmarks: PoseTrack2017, PoseTrack2018, and PoseTrack21.
Guiding Image Captioning Models Toward More Specific Captions
Authors: Simon Kornblith, Lala Li, ZiRui Wang, Thao Nguyen
Date: 2023-07-31 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Captioning, Image Retrieval

Image captioning is conventionally formulated as the task of generating captions for images that match the distribution of reference image-caption pairs. However, reference captions in standard captioning datasets are short and may not uniquely identify the images they describe. These problems are further exacerbated when models are trained directly on image-alt text pairs collected from the internet. In this work, we show that it is possible to generate more specific captions with minimal changes to the training process. We implement classifier-free guidance for an autoregressive captioning model by fine-tuning it to estimate both conditional and unconditional distributions over captions. The guidance scale applied at decoding controls a trade-off between maximizing $p(\mathrm{caption}|\mathrm{image})$ and $p(\mathrm{image}|\mathrm{caption})$. Compared to standard greedy decoding, decoding with a guidance scale of 2 substantially improves reference-free metrics such as CLIPScore (0.808 vs. 0.775) and caption$\to$image retrieval performance in the CLIP embedding space (recall@1 44.6% vs. 26.5%), but worsens standard reference-based captioning metrics (e.g., CIDEr 78.6 vs 126.1). We further explore the use of language models to guide the decoding process, obtaining small improvements over the Pareto frontier of reference-free vs. reference-based captioning metrics that arises from classifier-free guidance, and substantially improving the quality of captions generated from a model trained only on minimally curated web data.
Conditioning Generative Latent Optimization for Sparse-View CT Image Reconstruction
Authors: Thomas Braure, Delphine Lazaro, David Hateau, Vincent Brandon, Kévin Ginsburger
Date: 2023-07-31 00:00:00
Tasks: Computed Tomography (CT), Decoder, Image Reconstruction

Computed Tomography (CT) is a prominent example of Imaging Inverse Problem highlighting the unrivaled performances of data-driven methods in degraded measurements setups like sparse X-ray projections. Although a significant proportion of deep learning approaches benefit from large supervised datasets, they cannot generalize to new experimental setups. In contrast, fully unsupervised techniques, most notably using score-based generative models, have recently demonstrated similar or better performances compared to supervised approaches while being flexible at test time. However, their use cases are limited as they need considerable amounts of training data to have good generalization properties. Another unsupervised approach taking advantage of the implicit natural bias of deep convolutional networks, Deep Image Prior, has recently been adapted to solve sparse CT by reparameterizing the reconstruction problem. Although this methodology does not require any training dataset, it enforces a weaker prior on the reconstructions when compared to data-driven methods. To fill the gap between these two strategies, we propose an unsupervised conditional approach to the Generative Latent Optimization framework (cGLO). Similarly to DIP, without any training dataset, cGLO benefits from the structural bias of a decoder network. However, the prior is further reinforced as the effect of a likelihood objective shared between multiple slices being reconstructed simultaneously through the same decoder network. In addition, the parameters of the decoder may be initialized on an unsupervised, and eventually very small, training dataset to enhance the reconstruction. The resulting approach is tested on full-dose sparse-view CT using multiple training dataset sizes and varying numbers of viewing angles.
Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training
Authors: Negin Ghamsarian, Javier Gamazo Tejero, Pablo Márquez Neila, Sebastian Wolf, Martin Zinkernagel, Klaus Schoeffmann, Raphael Sznitman
Date: 2023-07-31 00:00:00
Tasks: Domain Adaptation, Image Segmentation, Medical Image Segmentation, Semantic Segmentation, Semi-supervised Domain Adaptation

Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In this regard, self-training techniques based on pseudo-labeling have been shown to be highly effective for semi-supervised domain adaptation. However, the unreliability of pseudo labels can hinder the capability of self-training techniques to induce abstract representation from the unlabeled target dataset, especially in the case of large distribution gaps. Since the neural network performance should be invariant to image transformations, we look to this fact to identify uncertain pseudo labels. Indeed, we argue that transformation invariant detections can provide more reasonable approximations of ground truth. Accordingly, we propose a semi-supervised learning strategy for domain adaptation termed transformation-invariant self-training (TI-ST). The proposed method assesses pixel-wise pseudo-labels' reliability and filters out unreliable detections during self-training. We perform comprehensive evaluations for domain adaptation using three different modalities of medical images, two different network architectures, and several alternative state-of-the-art domain adaptation methods. Experimental results confirm the superiority of our proposed method in mitigating the lack of target domain annotation and boosting segmentation performance in the target domain.
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
Authors: Rabab Abdelfattah, Qing Guo, Xiaoguang Li, XiaoFeng Wang, Song Wang
Date: 2023-07-31 00:00:00
Proceeding: ICCV 2023 1
Tasks: Classification, image-classification, Image Classification, Multi-Label Image Classification, text similarity, Weakly Supervised Classification

This paper presents a CLIP-based unsupervised learning method for annotation-free multi-label image classification, including three stages: initialization, training, and inference. At the initialization stage, we take full advantage of the powerful CLIP model and propose a novel approach to extend CLIP for multi-label predictions based on global-local image-text similarity aggregation. To be more specific, we split each image into snippets and leverage CLIP to generate the similarity vector for the whole image (global) as well as each snippet (local). Then a similarity aggregator is introduced to leverage the global and local similarity vectors. Using the aggregated similarity scores as the initial pseudo labels at the training stage, we propose an optimization framework to train the parameters of the classification network and refine pseudo labels for unobserved labels. During inference, only the classification network is used to predict the labels of the input image. Extensive experiments show that our method outperforms state-of-the-art unsupervised methods on MS-COCO, PASCAL VOC 2007, PASCAL VOC 2012, and NUS datasets and even achieves comparable results to weakly supervised classification methods.