pwc-archive/papers-with-abstracts huggingface dataset:
Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?
Authors: Christoph Koller, Göran Kauermann, Xiao Xiang Zhu
Date: 2022-05-30 00:00:00
Tasks: Deep Learning, image-classification, Image Classification

Technological and computational advances continuously drive forward the broad field of deep learning. In recent years, the derivation of quantities describing theuncertainty in the prediction - which naturally accompanies the modeling process - has sparked general interest in the deep learning community. Often neglected in the machine learning setting is the human uncertainty that influences numerous labeling processes. As the core of this work, label uncertainty is explicitly embedded into the training process via distributional labels. We demonstrate the effectiveness of our approach on image classification with a remote sensing data set that contains multiple label votes by domain experts for each image: The incorporation of label uncertainty helps the model to generalize better to unseen data and increases model performance. Similar to existing calibration methods, the distributional labels lead to better-calibrated probabilities, which in turn yield more certain and trustworthy predictions.
Pooling Revisited: Your Receptive Field is Suboptimal
Authors: Dong-Hwan Jang, Sanghyeok Chu, Joonhyuk Kim, Bohyung Han
Date: 2022-05-30 00:00:00
Proceeding: CVPR 2022 1
Tasks: image-classification, Image Classification, Semantic Segmentation

The size and shape of the receptive field determine how the network aggregates local information and affect the overall performance of a model considerably. Many components in a neural network, such as kernel sizes and strides for convolution and pooling operations, influence the configuration of a receptive field. However, they still rely on hyperparameters, and the receptive fields of existing models result in suboptimal shapes and sizes. Hence, we propose a simple yet effective Dynamically Optimized Pooling operation, referred to as DynOPool, which optimizes the scale factors of feature maps end-to-end by learning the desirable size and shape of its receptive field in each layer. Any kind of resizing modules in a deep neural network can be replaced by the operations with DynOPool at a minimal cost. Also, DynOPool controls the complexity of a model by introducing an additional loss term that constrains computational cost. Our experiments show that the models equipped with the proposed learnable resizing module outperform the baseline networks on multiple datasets in image classification and semantic segmentation.
VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models
Authors: Wangchunshu Zhou, Yan Zeng, Shizhe Diao, Xinsong Zhang
Date: 2022-05-30 00:00:00
Tasks: Vietnamese Natural Language Understanding, XLM-R

Recent advances in vision-language pre-training (VLP) have demonstrated impressive performance in a range of vision-language (VL) tasks. However, there exist several challenges for measuring the community's progress in building general multi-modal intelligence. First, most of the downstream VL datasets are annotated using raw images that are already seen during pre-training, which may result in an overestimation of current VLP models' generalization ability. Second, recent VLP work mainly focuses on absolute performance but overlooks the efficiency-performance trade-off, which is also an important indicator for measuring progress. To this end, we introduce the Vision-Language Understanding Evaluation (VLUE) benchmark, a multi-task multi-dimension benchmark for evaluating the generalization capabilities and the efficiency-performance trade-off (``Pareto SOTA'') of VLP models. We demonstrate that there is a sizable generalization gap for all VLP models when testing on out-of-distribution test sets annotated on images from a more diverse distribution that spreads across cultures. Moreover, we find that measuring the efficiency-performance trade-off of VLP models leads to complementary insights for several design choices of VLP. We release the VLUE benchmark to promote research on building vision-language models that generalize well to more diverse images and concepts unseen during pre-training, and are practical in terms of efficiency-performance trade-off.
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
Authors: Wenyu Zhang, Li Shen, Wanyue Zhang, Chuan-Sheng Foo
Date: 2022-05-30 00:00:00
Tasks: Domain Adaptation, domain classification, Semantic Segmentation, Test-time Adaptation

Deep networks are prone to performance degradation when there is a domain shift between the source (training) data and target (test) data. Recent test-time adaptation methods update batch normalization layers of pre-trained source models deployed in new target environments with streaming data to mitigate such performance degradation. Although such methods can adapt on-the-fly without first collecting a large target domain dataset, their performance is dependent on streaming conditions such as mini-batch size and class-distribution, which can be unpredictable in practice. In this work, we propose a framework for few-shot domain adaptation to address the practical challenges of data-efficient adaptation. Specifically, we propose a constrained optimization of feature normalization statistics in pre-trained source models supervised by a small support set from the target domain. Our method is easy to implement and improves source model performance with as few as one sample per class for classification tasks. Extensive experiments on 5 cross-domain classification and 4 semantic segmentation datasets show that our method achieves more accurate and reliable performance than test-time adaptation, while not being constrained by streaming conditions.
Few-shot Class-incremental Learning for 3D Point Cloud Objects
Authors: Townim Chowdhury, Ali Cheraghian, Sameera Ramasinghe, Sahar Ahmadi, Morteza Saberi, Shafin Rahman
Date: 2022-05-30 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Few-Shot Class-Incremental Learning, Incremental Learning

Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training. Recent efforts address this problem primarily on 2D images. However, due to the advancement of camera technology, 3D point cloud data has become more available than ever, which warrants considering FSCIL on 3D data. This paper addresses FSCIL in the 3D domain. In addition to well-known issues of catastrophic forgetting of past knowledge and overfitting of few-shot data, 3D FSCIL can bring newer challenges. For example, base classes may contain many synthetic instances in a realistic scenario. In contrast, only a few real-scanned samples (from RGBD sensors) of novel classes are available in incremental steps. Due to the data variation from synthetic to real, FSCIL endures additional challenges, degrading performance in later incremental steps. We attempt to solve this problem using Microshapes (orthogonal basis vectors) by describing any 3D objects using a pre-defined set of rules. It supports incremental training with few-shot examples minimizing synthetic to real data variation. We propose new test protocols for 3D FSCIL using popular synthetic datasets (ModelNet and ShapeNet) and 3D real-scanned datasets (ScanObjectNN and CO3D). By comparing state-of-the-art methods, we establish the effectiveness of our approach in the 3D domain.
GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation
Authors: Rolandos Alexandros Potamias, Alexandros Neofytou, Kyriaki-Margarita Bintsi, Stefanos Zafeiriou
Date: 2022-05-30 00:00:00
Tasks: Graph Neural Network

Geodesic paths and distances are among the most popular intrinsic properties of 3D surfaces. Traditionally, geodesic paths on discrete polygon surfaces were computed using shortest path algorithms, such as Dijkstra. However, such algorithms have two major limitations. They are non-differentiable which limits their direct usage in learnable pipelines and they are considerably time demanding. To address such limitations and alleviate the computational burden, we propose a learnable network to approximate geodesic paths. The proposed method is comprised by three major components: a graph neural network that encodes node positions in a high dimensional space, a path embedding that describes previously visited nodes and a point classifier that selects the next point in the path. The proposed method provides efficient approximations of the shortest paths and geodesic distances estimations. Given that all of the components of our method are fully differentiable, it can be directly plugged into any learnable pipeline as well as customized under any differentiable constraint. We extensively evaluate the proposed method with several qualitative and quantitative experiments.
The Devil is in the Pose: Ambiguity-free 3D Rotation-invariant Learning via Pose-aware Convolution
Authors: Ronghan Chen, Yang Cong
Date: 2022-05-30 00:00:00
Proceeding: CVPR 2022 1

Rotation-invariant (RI) 3D deep learning methods suffer performance degradation as they typically design RI representations as input that lose critical global information comparing to 3D coordinates. Most state-of-the-arts address it by incurring additional blocks or complex global representations in a heavy and ineffective manner. In this paper, we reveal that the global information loss stems from an unexplored pose information loss problem, which can be solved more efficiently and effectively as we only need to restore more lightweight local pose in each layer, and the global information can be hierarchically aggregated in the deep networks without extra efforts. To address this problem, we develop a Pose-aware Rotation Invariant Convolution (i.e., PaRI-Conv), which dynamically adapts its kernels based on the relative poses. To implement it, we propose an Augmented Point Pair Feature (APPF) to fully encode the RI relative pose information, and a factorized dynamic kernel for pose-aware kernel generation, which can further reduce the computational cost and memory burden by decomposing the kernel into a shared basis matrix and a pose-aware diagonal matrix. Extensive experiments on shape classification and part segmentation tasks show that our PaRI-Conv surpasses the state-of-the-art RI methods while being more compact and efficient.
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution
Authors: Long Sun, Jinshan Pan, Jinhui Tang
Date: 2022-05-30 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation. In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design. Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently. Since the contexts of natural images are strongly locally correlated, using large depth-wise convolutions only is insufficient to reconstruct fine details. To overcome this problem while maintaining the efficiency of the proposed module, we introduce Fused-MBConvs into the proposed network to model the local connectivity of different features. Experimental results demonstrate that the proposed ShuffleMixer is about 6x smaller than the state-of-the-art methods in terms of model parameters and FLOPs while achieving competitive performance. In NTIRE 2022, our primary method won the model complexity track of the Efficient Super-Resolution Challenge [23]. The code is available at https://github.com/sunny2109/MobileSR-NTIRE2022.
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks
Authors: Jaonary Rabarisoa, Valentin Belissen, Florian Chabot, Quoc-Cuong Pham
Date: 2022-05-30 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Prediction, Semantic Segmentation

We present a new self-supervised pre-training of Vision Transformers for dense prediction tasks. It is based on a contrastive loss across views that compares pixel-level representations to global image representations. This strategy produces better local features suitable for dense prediction tasks as opposed to contrastive pre-training based on global image representation only. Furthermore, our approach does not suffer from a reduced batch size since the number of negative examples needed in the contrastive loss is in the order of the number of local features. We demonstrate the effectiveness of our pre-training strategy on two dense prediction tasks: semantic segmentation and monocular depth estimation.
GAN-based Medical Image Small Region Forgery Detection via a Two-Stage Cascade Framework
Authors: Jianyi Zhang, Xuanxi Huang, Yaqi Liu, Yuyang Han, Zixiao Xiang
Date: 2022-05-30 00:00:00
Tasks: Generative Adversarial Network

Using generative adversarial network (GAN)\cite{RN90} for data enhancement of medical images is significantly helpful for many computer-aided diagnosis (CAD) tasks. A new attack called CT-GAN has emerged. It can inject or remove lung cancer lesions to CT scans. Because the tampering region may even account for less than 1\% of the original image, even state-of-the-art methods are challenging to detect the traces of such tampering. This paper proposes a cascade framework to detect GAN-based medical image small region forgery like CT-GAN. In the local detection stage, we train the detector network with small sub-images so that interference information in authentic regions will not affect the detector. We use depthwise separable convolution and residual to prevent the detector from over-fitting and enhance the ability to find forged regions through the attention mechanism. The detection results of all sub-images in the same image will be combined into a heatmap. In the global classification stage, using gray level co-occurrence matrix (GLCM) can better extract features of the heatmap. Because the shape and size of the tampered area are uncertain, we train PCA and SVM methods for classification. Our method can classify whether a CT image has been tampered and locate the tampered position. Sufficient experiments show that our method can achieve excellent performance.
Deblurring Photographs of Characters Using Deep Neural Networks
Authors: Thomas Germer, Tobias Uelwer, Stefan Harmeling
Date: 2022-05-30 00:00:00
Tasks: Deblurring

In this paper, we present our approach for the Helsinki Deblur Challenge (HDC2021). The task of this challenge is to deblur images of characters without knowing the point spread function (PSF). The organizers provided a dataset of pairs of sharp and blurred images. Our method consists of three steps: First, we estimate a warping transformation of the images to align the sharp images with the blurred ones. Next, we estimate the PSF using a quasi-Newton method. The estimated PSF allows to generate additional pairs of sharp and blurred images. Finally, we train a deep convolutional neural network to reconstruct the sharp images from the blurred images. Our method is able to successfully reconstruct images from the first 10 stages of the HDC 2021 data. Our code is available at https://github.com/hhu-machine-learning/hdc2021-psfnn.
SMUDLP: Self-Teaching Multi-Frame Unsupervised Endoscopic Depth Estimation with Learnable Patchmatch
Authors: Shuwei Shao, Zhongcai Pei, Weihai Chen, Xingming Wu, Zhong Liu, Zhengguo Li
Date: 2022-05-30 00:00:00
Tasks: Depth Estimation

Unsupervised monocular trained depth estimation models make use of adjacent frames as a supervisory signal during the training phase. However, temporally correlated frames are also available at inference time for many clinical applications, e.g., surgical navigation. The vast majority of monocular systems do not exploit this valuable signal that could be deployed to enhance the depth estimates. Those that do, achieve only limited gains due to the unique challenges in endoscopic scenes, such as low and homogeneous textures and inter-frame brightness fluctuations. In this work, we present SMUDLP, a novel and unsupervised paradigm for multi-frame monocular endoscopic depth estimation. The SMUDLP integrates a learnable patchmatch module to adaptively increase the discriminative ability in low-texture and homogeneous-texture regions, and enforces cross-teaching and self-teaching consistencies to provide efficacious regularizations towards brightness fluctuations. Our detailed experiments on both SCARED and Hamlyn datasets indicate that the SMUDLP exceeds state-of-the-art competitors by a large margin, including those that use single or multiple frames at inference time. The source code and trained models will be publicly available upon the acceptance.
Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification
Authors: Zaiyun Yang
Date: 2022-05-30 00:00:00
Tasks: Few-Shot Image Classification, Few-Shot Learning, image-classification, Image Classification

Transductive methods always outperform inductive methods in few-shot image classification scenarios. However, the existing few-shot methods contain a latent condition: the number of samples in each class is the same, which may be unrealistic. To cope with those cases where the query shots of each class are nonuniform (i.e. nonuniform few-shot learning), we propose a Task-Prior Conditional Variational Auto-Encoder model named TP-VAE, conditioned on support shots and constrained by a task-level prior regularization. Our method obtains high performance in the more challenging nonuniform few-shot scenarios. Moreover, our method outperforms the state-of-the-art in a wide range of standard few-shot image classification scenarios. Among them, the accuracy of 1-shot increased by about 3\%.
CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers
Authors: Jun Li, Shangwei Guo, Shaokun Han
Date: 2022-05-30 00:00:00
Tasks: Point Cloud Completion

Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details. In this paper, we propose a novel point cloud completion network, namely CompleteDT. Specifically, features are learned from point clouds with different resolutions, which is sampled from the incomplete input, and are converted to a series of \textit{spots} based on the geometrical structure. Then, the Dense Relation Augment Module (DRA) based on the transformer is proposed to learn features within \textit{spots} and consider the correlation among these \textit{spots}. The DRA consists of Point Local-Attention Module (PLA) and Point Dense Multi-Scale Attention Module (PDMA), where the PLA captures the local information within the local \textit{spots} by adaptively measuring weights of neighbors and the PDMA exploits the global relationship between these \textit{spots} in a multi-scale densely connected manner. Lastly, the complete shape is predicted from \textit{spots} by the Multi-resolution Point Fusion Module (MPF), which gradually generates complete point clouds from \textit{spots}, and updates \textit{spots} based on these generated point clouds. Experimental results show that, because the DRA based on the transformer can learn the expressive features from the incomplete input and the MPF can fully explore these feature to predict the complete input, our method largely outperforms the state-of-the-art methods.
GMML is All you Need
Authors: Sara Atito, Muhammad Awais, Josef Kittler
Date: 2022-05-30 00:00:00
Tasks: All, Data Augmentation, Self-Learning, Self-Supervised Learning

Vision transformers have generated significant interest in the computer vision community because of their flexibility in exploiting contextual information, whether it is sharply confined local, or long range global. However, they are known to be data hungry. This has motivated the research in self-supervised transformer pretraining, which does not need to decode the semantic information conveyed by labels to link it to the image properties, but rather focuses directly on extracting a concise representation of the image data that reflects the notion of similarity, and is invariant to nuisance factors. The key vehicle for the self-learning process used by the majority of self-learning methods is the generation of multiple views of the training data and the creation of pretext tasks which use these views to define the notion of image similarity, and data integrity. However, this approach lacks the natural propensity to extract contextual information. We propose group masked model learning (GMML), a self-supervised learning (SSL) mechanism for pretraining vision transformers with the ability to extract the contextual information present in all the concepts in an image. GMML achieves this by manipulating randomly groups of connected tokens, ensuingly covering a meaningful part of a semantic concept, and then recovering the hidden semantic information from the visible part of the concept. GMML implicitly introduces a novel data augmentation process. Unlike most of the existing SSL approaches, GMML does not require momentum encoder, nor rely on careful implementation details such as large batches and gradient stopping, which are all artefacts of most of the current self-supervised learning techniques. The source code is publicly available for the community to train on bigger corpora: https://github.com/Sara-Ahmed/GMML.
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions
Authors: Shuxuan Guo, Yinlin Hu, Jose M. Alvarez, Mathieu Salzmann
Date: 2022-05-30 00:00:00
Proceeding: CVPR 2023 1
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Knowledge Distillation, Pose Estimation, Prediction

Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose estimation. In this work, we introduce the first knowledge distillation method driven by the 6D pose estimation task. To this end, we observe that most modern 6D pose estimation frameworks output local predictions, such as sparse 2D keypoints or dense representations, and that the compact student network typically struggles to predict such local quantities precisely. Therefore, instead of imposing prediction-to-prediction supervision from the teacher to the student, we propose to distill the teacher's \emph{distribution} of local predictions into the student network, facilitating its training. Our experiments on several benchmarks show that our distillation method yields state-of-the-art results with different compact student models and for both keypoint-based and dense prediction-based architectures.