pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty-Aware Deep Multi-View Photometric Stereo
Authors: Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc van Gool
Date: 2022-02-26 00:00:00
Proceeding: CVPR 2022 1
Tasks: Surface Reconstruction

This paper presents a simple and effective solution to the longstanding classical multi-view photometric stereo (MVPS) problem. It is well-known that photometric stereo (PS) is excellent at recovering high-frequency surface details, whereas multi-view stereo (MVS) can help remove the low-frequency distortion due to PS and retain the global geometry of the shape. This paper proposes an approach that can effectively utilize such complementary strengths of PS and MVS. Our key idea is to combine them suitably while considering the per-pixel uncertainty of their estimates. To this end, we estimate per-pixel surface normals and depth using an uncertainty-aware deep-PS network and deep-MVS network, respectively. Uncertainty modeling helps select reliable surface normal and depth estimates at each pixel which then act as a true representative of the dense surface geometry. At each pixel, our approach either selects or discards deep-PS and deep-MVS network prediction depending on the prediction uncertainty measure. For dense, detailed, and precise inference of the object's surface profile, we propose to learn the implicit neural shape representation via a multilayer perceptron (MLP). Our approach encourages the MLP to converge to a natural zero-level set surface using the confident prediction from deep-PS and deep-MVS networks, providing superior dense surface reconstruction. Extensive experiments on the DiLiGenT-MV benchmark dataset show that our method provides high-quality shape recovery with a much lower memory footprint while outperforming almost all of the existing approaches.
A Robust Document Image Watermarking Scheme using Deep Neural Network
Authors: Sulong Ge, Zhihua Xia, Jianwei Fei, Xingming Sun, Jian Weng
Date: 2022-02-26 00:00:00
Tasks: Decoder

Watermarking is an important copyright protection technology which generally embeds the identity information into the carrier imperceptibly. Then the identity can be extracted to prove the copyright from the watermarked carrier even after suffering various attacks. Most of the existing watermarking technologies take the nature images as carriers. Different from the natural images, document images are not so rich in color and texture, and thus have less redundant information to carry watermarks. This paper proposes an end-to-end document image watermarking scheme using the deep neural network. Specifically, an encoder and a decoder are designed to embed and extract the watermark. A noise layer is added to simulate the various attacks that could be encountered in reality, such as the Cropout, Dropout, Gaussian blur, Gaussian noise, Resize, and JPEG Compression. A text-sensitive loss function is designed to limit the embedding modification on characters. An embedding strength adjustment strategy is proposed to improve the quality of watermarked image with little loss of extraction accuracy. Experimental results show that the proposed document image watermarking technology outperforms three state-of-the-arts in terms of the robustness and image quality.
An End-to-End Transformer Model for Crowd Localization
Authors: Dingkang Liang, Wei Xu, Xiang Bai
Date: 2022-02-26 00:00:00
Tasks: Decoder

Crowd localization, predicting head positions, is a more practical and high-level task than simply counting. Existing methods employ pseudo-bounding boxes or pre-designed localization maps, relying on complex post-processing to obtain the head positions. In this paper, we propose an elegant, end-to-end Crowd Localization Transformer named CLTR that solves the task in the regression-based paradigm. The proposed method views the crowd localization as a direct set prediction problem, taking extracted features and trainable embeddings as input of the transformer-decoder. To reduce the ambiguous points and generate more reasonable matching results, we introduce a KMO-based Hungarian matcher, which adopts the nearby context as the auxiliary matching cost. Extensive experiments conducted on five datasets in various data settings show the effectiveness of our method. In particular, the proposed method achieves the best localization performance on the NWPU-Crowd, UCF-QNRF, and ShanghaiTech Part A datasets.
Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance
Authors: Yuki Fujimura, Masaaki Iiyama, Takuya Funatomi, Yasuhiro Mukaigawa
Date: 2022-02-26 00:00:00
Tasks: Depth Estimation

We propose a learning-based depth from focus/defocus (DFF), which takes a focal stack as input for estimating scene depth. Defocus blur is a useful cue for depth estimation. However, the size of the blur depends on not only scene depth but also camera settings such as focus distance, focal length, and f-number. Current learning-based methods without any defocus models cannot estimate a correct depth map if camera settings are different at training and test times. Our method takes a plane sweep volume as input for the constraint between scene depth, defocus images, and camera settings, and this intermediate representation enables depth estimation with different camera settings at training and test times. This camera-setting invariance can enhance the applicability of learning-based DFF methods. The experimental results also indicate that our method is robust against a synthetic-to-real domain gap, and exhibits state-of-the-art performance.
Optical flow-based branch segmentation for complex orchard environments
Authors: Alexander You, Cindy Grimm, Joseph R. Davidson
Date: 2022-02-26 00:00:00
Tasks: Foreground Segmentation, Optical Flow Estimation

Machine vision is a critical subsystem for enabling robots to be able to perform a variety of tasks in orchard environments. However, orchards are highly visually complex environments, and computer vision algorithms operating in them must be able to contend with variable lighting conditions and background noise. Past work on enabling deep learning algorithms to operate in these environments has typically required large amounts of hand-labeled data to train a deep neural network or physically controlling the conditions under which the environment is perceived. In this paper, we train a neural network system in simulation only using simulated RGB data and optical flow. This resulting neural network is able to perform foreground segmentation of branches in a busy orchard environment without additional real-world training or using any special setup or equipment beyond a standard camera. Our results show that our system is highly accurate and, when compared to a network using manually labeled RGBD data, achieves significantly more consistent and robust performance across environments that differ from the training set.
Generalized Label Shift Correction via Minimum Uncertainty Principle: Theory and Algorithm
Authors: You-Wei Luo, Chuan-Xian Ren
Date: 2022-02-26 00:00:00
Tasks: Learning Theory

As a fundamental problem in machine learning, dataset shift induces a paradigm to learn and transfer knowledge under changing environment. Previous methods assume the changes are induced by covariate, which is less practical for complex real-world data. We consider the Generalized Label Shift (GLS), which provides an interpretable insight into the learning and transfer of desirable knowledge. Current GLS methods: 1) are not well-connected with the statistical learning theory; 2) usually assume the shifting conditional distributions will be matched with an implicit transformation, but its explicit modeling is unexplored. In this paper, we propose a conditional adaptation framework to deal with these challenges. From the perspective of learning theory, we prove that the generalization error of conditional adaptation is lower than previous covariate adaptation. Following the theoretical results, we propose the minimum uncertainty principle to learn conditional invariant transformation via discrepancy optimization. Specifically, we propose the \textit{conditional metric operator} on Hilbert space to characterize the distinctness of conditional distributions. For finite observations, we prove that the empirical estimation is always well-defined and will converge to underlying truth as sample size increases. The results of extensive experiments demonstrate that the proposed model achieves competitive performance under different GLS scenarios.
Building a visual semantics aware object hierarchy
Authors: Xiaolei Diao
Date: 2022-02-26 00:00:00
Tasks: Object, Object Recognition, World Knowledge

The semantic gap is defined as the difference between the linguistic representations of the same concept, which usually leads to misunderstanding between individuals with different knowledge backgrounds. Since linguistically annotated images are extensively used for training machine learning models, semantic gap problem (SGP) also results in inevitable bias on image annotations and further leads to poor performance on current computer vision tasks. To address this problem, we propose a novel unsupervised method to build visual semantics aware object hierarchy, aiming to get a classification model by learning from pure-visual information and to dissipate the bias of linguistic representations caused by SGP. Our intuition in this paper comes from real-world knowledge representation where concepts are hierarchically organized, and each concept can be described by a set of features rather than a linguistic annotation, namely visual semantic. The evaluation consists of two parts, firstly we apply the constructed hierarchy on the object recognition task and then we compare our visual hierarchy and existing lexical hierarchies to show the validity of our method. The preliminary results reveal the efficiency and potential of our proposed method.
HCIL: Hierarchical Class Incremental Learning for Longline Fishing Visual Monitoring
Authors: Jie Mei, Suzanne Romain, Craig Rose, Kelsey Magrane, Jenq-Neng Hwang
Date: 2022-02-25 00:00:00
Tasks: Classification, class-incremental learning, Class Incremental Learning, Incremental Learning

The goal of electronic monitoring of longline fishing is to visually monitor the fish catching activities on fishing vessels based on cameras, either for regulatory compliance or catch counting. The previous hierarchical classification method demonstrates efficient fish species identification of catches from longline fishing, where fishes are under severe deformation and self-occlusion during the catching process. Although the hierarchical classification mitigates the laborious efforts of human reviews by providing confidence scores in different hierarchical levels, its performance drops dramatically under the class incremental learning (CIL) scenario. A CIL system should be able to learn about more and more classes over time from a stream of data, i.e., only the training data for a small number of classes have to be present at the beginning and new classes can be added progressively. In this work, we introduce a Hierarchical Class Incremental Learning (HCIL) model, which significantly improves the state-of-the-art hierarchical classification methods under the CIL scenario.
Multi-view Gradient Consistency for SVBRDF Estimation of Complex Scenes under Natural Illumination
Authors: Alen Joy, Charalambos Poullis
Date: 2022-02-25 00:00:00
Tasks: Disentanglement, Image Reconstruction, SVBRDF Estimation

This paper presents a process for estimating the spatially varying surface reflectance of complex scenes observed under natural illumination. In contrast to previous methods, our process is not limited to scenes viewed under controlled lighting conditions but can handle complex indoor and outdoor scenes viewed under arbitrary illumination conditions. An end-to-end process uses a model of the scene's geometry and several images capturing the scene's surfaces from arbitrary viewpoints and under various natural illumination conditions. We develop a differentiable path tracer that leverages least-square conformal mapping for handling multiple disjoint objects appearing in the scene. We follow a two-step optimization process and introduce a multi-view gradient consistency loss which results in up to 30-50% improvement in the image reconstruction loss and can further achieve better disentanglement of the diffuse and specular BRDFs compared to other state-of-the-art. We demonstrate the process in real-world indoor and outdoor scenes from images in the wild and show that we can produce realistic renders consistent with actual images using the estimated reflectance properties. Experiments show that our technique produces realistic results for arbitrary outdoor scenes with complex geometry. The source code is publicly available at: https://gitlab.com/alen.joy/multi-view-gradient-consistency-for-svbrdf-estimation-of-complex-scenes-under-natural-illumination
Weakly Supervised Instance Segmentation using Motion Information via Optical Flow
Authors: Jun Ikeda, Junichiro Mori
Date: 2022-02-25 00:00:00
Tasks: Instance Segmentation, Optical Flow Estimation, Segmentation, Semantic Segmentation, Weakly-supervised instance segmentation

Weakly supervised instance segmentation has gained popularity because it reduces high annotation cost of pixel-level masks required for model training. Recent approaches for weakly supervised instance segmentation detect and segment objects using appearance information obtained from a static image. However, it poses the challenge of identifying objects with a non-discriminatory appearance. In this study, we address this problem by using motion information from image sequences. We propose a two-stream encoder that leverages appearance and motion features extracted from images and optical flows. Additionally, we propose a novel pairwise loss that considers both appearance and motion information to supervise segmentation. We conducted extensive evaluations on the YouTube-VIS 2019 benchmark dataset. Our results demonstrate that the proposed method improves the Average Precision of the state-of-the-art method by 3.1.
A Brief Survey on Adaptive Video Streaming Quality Assessment
Authors: Wei Zhou, Xiongkuo Min, Hong Li, Qiuping Jiang
Date: 2022-02-25 00:00:00
Tasks: BIG-bench Machine Learning, Management, Survey

Quality of experience (QoE) assessment for adaptive video streaming plays a significant role in advanced network management systems. It is especially challenging in case of dynamic adaptive streaming schemes over HTTP (DASH) which has increasingly complex characteristics including additional playback issues. In this paper, we provide a brief overview of adaptive video streaming quality assessment. Upon our review of related works, we analyze and compare different variations of objective QoE assessment models with or without using machine learning techniques for adaptive video streaming. Through the performance analysis, we observe that hybrid models perform better than both quality-of-service (QoS) driven QoE approaches and signal fidelity measurement. Moreover, the machine learning-based model slightly outperforms the model without using machine learning for the same setting. In addition, we find that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems. Therefore, based on the success of deep learned feature representations for traditional video quality prediction, we also apply the off-the-shelf deep convolutional neural network (DCNN) to evaluate the perceptual quality of streaming videos, where the spatio-temporal properties of streaming videos are taken into consideration. Experiments demonstrate its superiority, which sheds light on the future development of specifically designed deep learning frameworks for adaptive video streaming quality assessment. We believe this survey can serve as a guideline for QoE assessment of adaptive video streaming.
Extracting Effective Subnetworks with Gumbel-Softmax
Authors: Robin Dupont, Mohammed Amine Alaoui, Hichem Sahbi, Alice Lebois
Date: 2022-02-25 00:00:00

Large and performant neural networks are often overparameterized and can be drastically reduced in size and complexity thanks to pruning. Pruning is a group of methods, which seeks to remove redundant or unnecessary weights or groups of weights in a network. These techniques allow the creation of lightweight networks, which are particularly critical in embedded or mobile applications. In this paper, we devise an alternative pruning method that allows extracting effective subnetworks from larger untrained ones. Our method is stochastic and extracts subnetworks by exploring different topologies which are sampled using Gumbel Softmax. The latter is also used to train probability distributions which measure the relevance of weights in the sampled topologies. The resulting subnetworks are further enhanced using a highly efficient rescaling mechanism that reduces training time and improves performance. Extensive experiments conducted on CIFAR show the outperformance of our subnetwork extraction method against the related work.
FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment
Authors: Yuval Nirkin, Yosi Keller, Tal Hassner
Date: 2022-02-25 00:00:00
Tasks: Face Reenactment, Face Swapping, Facial Inpainting

We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, we offer a subject agnostic swapping scheme that can be applied to pairs of faces without requiring training on those faces. We derive a novel iterative deep learning--based approach for face reenactment which adjusts significant pose and expression variations that can be applied to a single image or a video sequence. For video sequences, we introduce a continuous interpolation of the face views based on reenactment, Delaunay Triangulation, and barycentric coordinates. Occluded face regions are handled by a face completion network. Finally, we use a face blending network for seamless blending of the two faces while preserving the target skin color and lighting conditions. This network uses a novel Poisson blending loss combining Poisson optimization with a perceptual loss. We compare our approach to existing state-of-the-art systems and show our results to be both qualitatively and quantitatively superior. This work describes extensions of the FSGAN method, proposed in an earlier conference version of our work, as well as additional experiments and results.
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers
Authors: Matthieu Terris, Arwa Dabbech, Chao Tang, Yves Wiaux
Date: 2022-02-25 00:00:00
Tasks: Denoising, Image Reconstruction, Radio Interferometry

We introduce a new class of iterative image reconstruction algorithms for radio interferometry, at the interface of convex optimization and deep learning, inspired by plug-and-play methods. The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal regularization operator of an optimization algorithm. The proposed AIRI (``AI for Regularization in radio-interferometric Imaging'') framework, for imaging complex intensity structure with diffuse and faint emission from visibility data, inherits the robustness and interpretability of optimization, and the learning power and speed of networks. Our approach relies on three steps. Firstly, we design a low dynamic range training database from optical intensity images. Secondly, we train a DNN denoiser at a noise level inferred from the signal-to-noise ratio of the data. We use training losses enhanced with a nonexpansiveness term ensuring algorithm convergence, and including on-the-fly database dynamic range enhancement via exponentiation. Thirdly, we plug the learned denoiser into the forward-backward optimization algorithm, resulting in a simple iterative structure alternating a denoising step with a gradient-descent data-fidelity step. We have validated AIRI against CLEAN, optimization algorithms of the SARA family, and a DNN trained to reconstruct the image directly from visibility data. Simulation results show that AIRI is competitive in imaging quality with SARA and its unconstrained forward-backward-based version uSARA, while providing significant acceleration. CLEAN remains faster but offers lower quality. The end-to-end DNN offers further acceleration, but with far lower quality than AIRI.
Model Attribution of Face-swap Deepfake Videos
Authors: Shan Jia, Xin Li, Siwei Lyu
Date: 2022-02-25 00:00:00
Tasks: Attribute, Decoder, Face Swapping, model

AI-created face-swap videos, commonly known as Deepfakes, have attracted wide attention as powerful impersonation attacks. Existing research on Deepfakes mostly focuses on binary detection to distinguish between real and fake videos. However, it is also important to determine the specific generation model for a fake video, which can help attribute it to the source for forensic investigation. In this paper, we fill this gap by studying the model attribution problem of Deepfake videos. We first introduce a new dataset with DeepFakes from Different Models (DFDM) based on several Autoencoder models. Specifically, five generation models with variations in encoder, decoder, intermediate layer, input resolution, and compression ratio have been used to generate a total of 6,450 Deepfake videos based on the same input. Then we take Deepfakes model attribution as a multiclass classification task and propose a spatial and temporal attention based method to explore the differences among Deepfakes in the new dataset. Experimental evaluation shows that most existing Deepfakes detection methods failed in Deepfakes model attribution, while the proposed method achieved over 70% accuracy on the high-quality DFDM dataset.
OptGAN: Optimizing and Interpreting the Latent Space of the Conditional Text-to-Image GANs
Authors: Zhenxing Zhang, Lambert Schomaker
Date: 2022-02-25 00:00:00
Tasks: Data Augmentation, Image Generation, Text to Image Generation, Text-to-Image Generation

Text-to-image generation intends to automatically produce a photo-realistic image, conditioned on a textual description. It can be potentially employed in the field of art creation, data augmentation, photo-editing, etc. Although many efforts have been dedicated to this task, it remains particularly challenging to generate believable, natural scenes. To facilitate the real-world applications of text-to-image synthesis, we focus on studying the following three issues: 1) How to ensure that generated samples are believable, realistic or natural? 2) How to exploit the latent space of the generator to edit a synthesized image? 3) How to improve the explainability of a text-to-image generation framework? In this work, we constructed two novel data sets (i.e., the Good & Bad bird and face data sets) consisting of successful as well as unsuccessful generated samples, according to strict criteria. To effectively and efficiently acquire high-quality images by increasing the probability of generating Good latent codes, we use a dedicated Good/Bad classifier for generated images. It is based on a pre-trained front end and fine-tuned on the basis of the proposed Good & Bad data set. After that, we present a novel algorithm which identifies semantically-understandable directions in the latent space of a conditional text-to-image GAN architecture by performing independent component analysis on the pre-trained weight values of the generator. Furthermore, we develop a background-flattening loss (BFL), to improve the background appearance in the edited image. Subsequently, we introduce linear interpolation analysis between pairs of keywords. This is extended into a similar triangular `linguistic' interpolation in order to take a deep look into what a text-to-image synthesis model has learned within the linguistic embeddings. Our data set is available at https://zenodo.org/record/6283798#.YhkN_ujMI2w.