pwc-archive/papers-with-abstracts huggingface dataset:
IndustReal: A Dataset for Procedure Step Recognition Handling Execution Errors in Egocentric Videos in an Industrial-Like Setting
Authors: Tim J. Schoonbeek, Tim Houben, Hans Onvlee, Peter H. N. de With, Fons van der Sommen
Date: 2023-10-26 00:00:00
Tasks: Action Recognition, Object Detection, Procedure Step Recognition

Although action recognition for procedural tasks has received notable attention, it has a fundamental flaw in that no measure of success for actions is provided. This limits the applicability of such systems especially within the industrial domain, since the outcome of procedural actions is often significantly more important than the mere execution. To address this limitation, we define the novel task of procedure step recognition (PSR), focusing on recognizing the correct completion and order of procedural steps. Alongside the new task, we also present the multi-modal IndustReal dataset. Unlike currently available datasets, IndustReal contains procedural errors (such as omissions) as well as execution errors. A significant part of these errors are exclusively present in the validation and test sets, making IndustReal suitable to evaluate robustness of algorithms to new, unseen mistakes. Additionally, to encourage reproducibility and allow for scalable approaches trained on synthetic data, the 3D models of all parts are publicly available. Annotations and benchmark performance are provided for action recognition and assembly state detection, as well as the new PSR task. IndustReal, along with the code and model weights, is available at: https://github.com/TimSchoonbeek/IndustReal .
Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Authors: Shuai Yang, Zhifei Chen, Pengguang Chen, Xi Fang, Yixun Liang, Shu Liu, Yingcong Chen
Date: 2023-10-26 00:00:00

Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack precision and semantic granularity required for practical applications. In this paper, we introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich semantic details, distinguishing multiple defect types within a single image. Furthermore, we introduce Defect-Gen, a two-stage diffusion-based generator designed to create high-quality and diverse defective images, even when working with limited datasets. The synthetic images generated by Defect-Gen significantly enhance the efficacy of defect inspection models. Overall, The Defect Spectrum dataset demonstrates its potential in defect inspection research, offering a solid platform for testing and refining advanced models.
Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution
Authors: Zhewei Huang, Ailin Huang, Xiaotao Hu, Chen Hu, Jun Xu, Shuchang Zhou
Date: 2023-10-26 00:00:00
Tasks: Space-time Video Super-resolution, Super-Resolution, Video Frame Interpolation, Video Super-Resolution

The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual quality of videos, by simultaneously performing video frame interpolation (VFI) and video super-resolution (VSR). However, facing the challenge of the additional temporal dimension and scale inconsistency, most existing STVSR methods are complex and inflexible in dynamically modeling different motion amplitudes. In this work, we find that choosing an appropriate processing scale achieves remarkable benefits in flow-based feature propagation. We propose a novel Scale-Adaptive Feature Aggregation (SAFA) network that adaptively selects sub-networks with different processing scales for individual samples. Experiments on four public STVSR benchmarks demonstrate that SAFA achieves state-of-the-art performance. Our SAFA network outperforms recent state-of-the-art methods such as TMNet and VideoINR by an average improvement of over 0.5dB on PSNR, while requiring less than half the number of parameters and only 1/3 computational costs.
BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds
Authors: Corentin Sautier, Gilles Puy, Alexandre Boulch, Renaud Marlet, Vincent Lepetit
Date: 2023-10-26 00:00:00
Tasks: Semantic Segmentation

We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds. We design a contrastive loss between features of Lidar scans captured in the same scene. Several such approaches have been proposed in the literature from PointConstrast, which uses a contrast at the level of points, to the state-of-the-art TARL, which uses a contrast at the level of segments, roughly corresponding to objects. While the former enjoys a great simplicity of implementation, it is surpassed by the latter, which however requires a costly pre-processing. In BEVContrast, we define our contrast at the level of 2D cells in the Bird's Eye View plane. Resulting cell-level representations offer a good trade-off between the point-level representations exploited in PointContrast and segment-level representations exploited in TARL: we retain the simplicity of PointContrast (cell representations are cheap to compute) while surpassing the performance of TARL in downstream semantic segmentation.
Generalizing to Unseen Domains in Diabetic Retinopathy Classification
Authors: Chamuditha Jayanga Galappaththige, Gayal Kuruppu, Muhammad Haris Khan
Date: 2023-10-26 00:00:00
Tasks: Classification, Domain Generalization

Diabetic retinopathy (DR) is caused by long-standing diabetes and is among the fifth leading cause for visual impairments. The process of early diagnosis and treatments could be helpful in curing the disease, however, the detection procedure is rather challenging and mostly tedious. Therefore, automated diabetic retinopathy classification using deep learning techniques has gained interest in the medical imaging community. Akin to several other real-world applications of deep learning, the typical assumption of i.i.d data is also violated in DR classification that relies on deep learning. Therefore, developing DR classification methods robust to unseen distributions is of great value. In this paper, we study the problem of generalizing a model to unseen distributions or domains (a.k.a domain generalization) in DR classification. To this end, we propose a simple and effective domain generalization (DG) approach that achieves self-distillation in vision transformers (ViT) via a novel prediction softening mechanism. This prediction softening is an adaptive convex combination one-hot labels with the model's own knowledge. We perform extensive experiments on challenging open-source DR classification datasets under both multi-source and single-source DG settings with three different ViT backbones to establish the efficacy and applicability of our approach against competing methods. For the first time, we report the performance of several state-of-the-art DG methods on open-source DR classification datasets after conducting thorough experiments. Finally, our method is also capable of delivering improved calibration performance than other methods, showing its suitability for safety-critical applications, including healthcare. We hope that our contributions would investigate more DG research across the medical imaging community.
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
Authors: Jiachen Li, Xiaojin Gong
Date: 2023-10-26 00:00:00
Tasks: Contrastive Learning, Person Re-Identification, Prompt Learning, Unsupervised Person Re-Identification, Unsupervised Vehicle Re-Identification

This work aims to adapt large-scale pre-trained vision-language models, such as contrastive language-image pretraining (CLIP), to enhance the performance of object reidentification (Re-ID) across various supervision settings. Although prompt learning has enabled a recent work named CLIP-ReID to achieve promising performance, the underlying mechanisms and the necessity of prompt learning remain unclear due to the absence of semantic labels in ReID tasks. In this work, we first analyze the role prompt learning in CLIP-ReID and identify its limitations. Based on our investigations, we propose a simple yet effective approach to adapt CLIP for supervised object Re-ID. Our approach directly fine-tunes the image encoder of CLIP using a prototypical contrastive learning (PCL) loss, eliminating the need for prompt learning. Experimental results on both person and vehicle Re-ID datasets demonstrate the competitiveness of our method compared to CLIP-ReID. Furthermore, we extend our PCL-based CLIP fine-tuning approach to unsupervised scenarios, where we achieve state-of-the art performance.
Three-dimensional Bone Image Synthesis with Generative Adversarial Networks
Authors: Christoph Angermann, Johannes Bereiter-Payr, Kerstin Stock, Markus Haltmeier, Gerald Degenhart
Date: 2023-10-26 00:00:00
Tasks: Attribute, Image Generation, Image Morphing

Medical image processing has been highlighted as an area where deep learning-based models have the greatest potential. However, in the medical field in particular, problems of data availability and privacy are hampering research progress and thus rapid implementation in clinical routine. The generation of synthetic data not only ensures privacy, but also allows to \textit{draw} new patients with specific characteristics, enabling the development of data-driven models on a much larger scale. This work demonstrates that three-dimensional generative adversarial networks (GANs) can be efficiently trained to generate high-resolution medical volumes with finely detailed voxel-based architectures. In addition, GAN inversion is successfully implemented for the three-dimensional setting and used for extensive research on model interpretability and applications such as image morphing, attribute editing and style mixing. The results are comprehensively validated on a database of three-dimensional HR-pQCT instances representing the bone micro-architecture of the distal radius.
Weakly-Supervised Surgical Phase Recognition
Authors: Roy Hirsch, Regev Cohen, Mathilde Caron, Tomer Golany, Daniel Freedman, Ehud Rivlin
Date: 2023-10-26 00:00:00
Tasks: Few-Shot Learning, Self-Supervised Learning, Surgical phase recognition

A key element of computer-assisted surgery systems is phase recognition of surgical videos. Existing phase recognition algorithms require frame-wise annotation of a large number of videos, which is time and money consuming. In this work we join concepts of graph segmentation with self-supervised learning to derive a random-walk solution for per-frame phase prediction. Furthermore, we utilize within our method two forms of weak supervision: sparse timestamps or few-shot learning. The proposed algorithm enjoys low complexity and can operate in lowdata regimes. We validate our method by running experiments with the public Cholec80 dataset of laparoscopic cholecystectomy videos, demonstrating promising performance in multiple setups.
Exploring Iterative Refinement with Diffusion Models for Video Grounding
Authors: Xiao Liang, Tao Shi, Yaoyuan Liang, Te Tao, Shao-Lun Huang
Date: 2023-10-26 00:00:00
Tasks: Sentence, Video Grounding

Video grounding aims to localize the target moment in an untrimmed video corresponding to a given sentence query. Existing methods typically select the best prediction from a set of predefined proposals or directly regress the target span in a single-shot manner, resulting in the absence of a systematical prediction refinement process. In this paper, we propose DiffusionVG, a novel framework with diffusion models that formulates video grounding as a conditional generation task, where the target span is generated from Gaussian noise inputs and interatively refined in the reverse diffusion process. During training, DiffusionVG progressively adds noise to the target span with a fixed forward diffusion process and learns to recover the target span in the reverse diffusion process. In inference, DiffusionVG can generate the target span from Gaussian noise inputs by the learned reverse diffusion process conditioned on the video-sentence representations. Without bells and whistles, our DiffusionVG demonstrates superior performance compared to existing well-crafted models on mainstream Charades-STA, ActivityNet Captions and TACoS benchmarks.
Blind Image Super-resolution with Rich Texture-Aware Codebooks
Authors: Rui Qin, Ming Sun, Fangyuan Zhang, Xing Wen, Bin Wang
Date: 2023-10-26 00:00:00
Tasks: Blind Super-Resolution, Diversity, Image Super-Resolution, Super-Resolution

Blind super-resolution (BSR) methods based on high-resolution (HR) reconstruction codebooks have achieved promising results in recent years. However, we find that a codebook based on HR reconstruction may not effectively capture the complex correlations between low-resolution (LR) and HR images. In detail, multiple HR images may produce similar LR versions due to complex blind degradations, causing the HR-dependent only codebooks having limited texture diversity when faced with confusing LR inputs. To alleviate this problem, we propose the Rich Texture-aware Codebook-based Network (RTCNet), which consists of the Degradation-robust Texture Prior Module (DTPM) and the Patch-aware Texture Prior Module (PTPM). DTPM effectively mines the cross-resolution correlation of textures between LR and HR images by exploiting the cross-resolution correspondence of textures. PTPM uses patch-wise semantic pre-training to correct the misperception of texture similarity in the high-level semantic regularization. By taking advantage of this, RTCNet effectively gets rid of the misalignment of confusing textures between HR and LR in the BSR scenarios. Experiments show that RTCNet outperforms state-of-the-art methods on various benchmarks by up to 0.16 ~ 0.46dB.
DecoderTracker: Decoder-Only Method for Multiple-Object Tracking
Authors: Liao Pan, Yang Feng, Wu Di, Liu Bo, Zhang Xingle
Date: 2023-10-26 00:00:00
Tasks: Decoder, Multi-Object Tracking, Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking

Decoder-only models, such as GPT, have demonstrated superior performance in many areas compared to traditional encoder-decoder structure transformer models. Over the years, end-to-end models based on the traditional transformer structure, like MOTR, have achieved remarkable performance in multi-object tracking. However, the significant computational resource consumption of these models leads to less friendly inference speeds and training times. To address these issues, this paper attempts to construct a lightweight Decoder-only model: DecoderTracker for end-to-end multi-object tracking. Specifically, drawing on some real-time detection models, we have developed an image feature extraction network which can efficiently extract features from images to replace the encoder structure. In addition to minor innovations in the network, we analyze the potential reasons for the slow training of MOTR-like models and propose an effective training strategy to mitigate the issue of prolonged training times. On the DanceTrack dataset, without any bells and whistles, DecoderTracker's tracking performance slightly surpasses that of MOTR, with approximately twice the inference speed. Furthermore, DecoderTracker requires significantly less training time compared to MOTR.
Bridging Phylogeny and Taxonomy with Protein-protein Interaction Networks
Authors: Long-Huei Chen, Mohana Prasad Sathya Moorthy, Pratyaksh Sharma
Date: 2023-10-26 00:00:00

The protein-protein interaction (PPI) network provides an overview of the complex biological reactions vital to an organism's metabolism and survival. Even though in the past PPI network were compared across organisms in detail, there has not been large-scale research on how individual PPI networks reflect on the species relationships. In this study we aim to increase our understanding of the tree of life and taxonomy by gleaming information from the PPI networks. We successful created (1) a predictor of network statistics based on known traits of existing species in the phylogeny, and (2) a taxonomic classifier of organism using the known protein network statistics, whether experimentally determined or predicted de novo. With the knowledge of protein interactions at its core, our two models effectively connects two field with widely diverging methodologies - the phylogeny and taxonomy of species.
Low-Dimensional Gradient Helps Out-of-Distribution Detection
Authors: Yingwen Wu, Tao Li, Xinwen Cheng, Jie Yang, Xiaolin Huang
Date: 2023-10-26 00:00:00
Tasks: Dimensionality Reduction, Out-of-Distribution Detection

Detecting out-of-distribution (OOD) samples is essential for ensuring the reliability of deep neural networks (DNNs) in real-world scenarios. While previous research has predominantly investigated the disparity between in-distribution (ID) and OOD data through forward information analysis, the discrepancy in parameter gradients during the backward process of DNNs has received insufficient attention. Existing studies on gradient disparities mainly focus on the utilization of gradient norms, neglecting the wealth of information embedded in gradient directions. To bridge this gap, in this paper, we conduct a comprehensive investigation into leveraging the entirety of gradient information for OOD detection. The primary challenge arises from the high dimensionality of gradients due to the large number of network parameters. To solve this problem, we propose performing linear dimension reduction on the gradient using a designated subspace that comprises principal components. This innovative technique enables us to obtain a low-dimensional representation of the gradient with minimal information loss. Subsequently, by integrating the reduced gradient with various existing detection score functions, our approach demonstrates superior performance across a wide range of detection tasks. For instance, on the ImageNet benchmark with ResNet50 model, our method achieves an average reduction of 11.15$\%$ in the false positive rate at 95$\%$ recall (FPR95) compared to the current state-of-the-art approach. The code would be released.
Learning depth from monocular video sequences
Authors: Zhenwei Luo
Date: 2023-10-26 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

Learning single image depth estimation model from monocular video sequence is a very challenging problem. In this paper, we propose a novel training loss which enables us to include more images for supervision during the training process. We propose a simple yet effective model to account the frame to frame pixel motion. We also design a novel network architecture for single image estimation. When combined, our method produces state of the art results for monocular depth estimation on the KITTI dataset in the self-supervised setting.
Deep Imbalanced Regression via Hierarchical Classification Adjustment
Authors: Haipeng Xiong, Angela Yao
Date: 2023-10-26 00:00:00
Proceeding: CVPR 2024 1
Tasks: Age Estimation, Classification, Crowd Counting, Deep imbalanced regression, Depth Estimation, Quantization, regression

Regression tasks in computer vision, such as age estimation or counting, are often formulated into classification by quantizing the target space into classes. Yet real-world data is often imbalanced -- the majority of training samples lie in a head range of target values, while a minority of samples span a usually larger tail range. By selecting the class quantization, one can adjust imbalanced regression targets into balanced classification outputs, though there are trade-offs in balancing classification accuracy and quantization error. To improve regression performance over the entire range of data, we propose to construct hierarchical classifiers for solving imbalanced regression tasks. The fine-grained classifiers limit the quantization error while being modulated by the coarse predictions to ensure high accuracy. Standard hierarchical classification approaches, however, when applied to the regression problem, fail to ensure that predicted ranges remain consistent across the hierarchy. As such, we propose a range-preserving distillation process that can effectively learn a single classifier from the set of hierarchical classifiers. Our novel hierarchical classification adjustment (HCA) for imbalanced regression shows superior results on three diverse tasks: age estimation, crowd counting and depth estimation. We will release the source code upon acceptance.
Simple Baselines for Projection-based Full-reference and No-reference Point Cloud Quality Assessment
Authors: ZiCheng Zhang, Yingjie Zhou, Wei Sun, Xiongkuo Min, Guangtao Zhai
Date: 2023-10-26 00:00:00
Tasks: Point Cloud Quality Assessment

Point clouds are widely used in 3D content representation and have various applications in multimedia. However, compression and simplification processes inevitably result in the loss of quality-aware information under storage and bandwidth constraints. Therefore, there is an increasing need for effective methods to quantify the degree of distortion in point clouds. In this paper, we propose simple baselines for projection-based point cloud quality assessment (PCQA) to tackle this challenge. We use multi-projections obtained via a common cube-like projection process from the point clouds for both full-reference (FR) and no-reference (NR) PCQA tasks. Quality-aware features are extracted with popular vision backbones. The FR quality representation is computed as the similarity between the feature maps of reference and distorted projections while the NR quality representation is obtained by simply squeezing the feature maps of distorted projections with average pooling The corresponding quality representations are regressed into visual quality scores by fully-connected layers. Taking part in the ICIP 2023 PCVQA Challenge, we succeeded in achieving the top spot in four out of the five competition tracks.
A Classifier Using Global Character Level and Local Sub-unit Level Features for Hindi Online Handwritten Character Recognition
Authors: Anand Sharma, A. G. Ramakrishnan
Date: 2023-10-26 00:00:00

A classifier is developed that defines a joint distribution of global character features, number of sub-units and local sub-unit features to model Hindi online handwritten characters. The classifier uses latent variables to model the structure of sub-units. The classifier uses histograms of points, orientations, and dynamics of orientations (HPOD) features to represent characters at global character level and local sub-unit level and is independent of character stroke order and stroke direction variations. The parameters of the classifier is estimated using maximum likelihood method. Different classifiers and features used in other studies are considered in this study for classification performance comparison with the developed classifier. The classifiers considered are Second Order Statistics (SOS), Sub-space (SS), Fisher Discriminant (FD), Feedforward Neural Network (FFN) and Support Vector Machines (SVM) and the features considered are Spatio Temporal (ST), Discrete Fourier Transform (DFT), Discrete Cosine Transform (SCT), Discrete Wavelet Transform (DWT), Spatial (SP) and Histograms of Oriented Gradients (HOG). Hindi character datasets used for training and testing the developed classifier consist of samples of handwritten characters from 96 different character classes. There are 12832 samples with an average of 133 samples per character class in the training set and 2821 samples with an average of 29 samples per character class in the testing set. The developed classifier has the highest accuracy of 93.5\% on the testing set compared to that of the classifiers trained on different features extracted from the same training set and evaluated on the same testing set considered in this study.