GenAI4ELab/papercli-papers huggingface dataset:
Hand-Transformer: Non-Autoregressive Structured Modeling for 3D Hand Pose Estimation
Authors: Lin Huang; Jianchao Tan; Ji Liu; Junsong Yuan
Venue: ECCV 2020 (main)

Abstract: 3D hand pose estimation is still far from a well-solved problem mainly due to the highly nonlinear dynamics of hand pose and the difficulties of modeling its inherent structural dependencies. To address this issue, we connect this structured output learning problem with the structured modeling framework in sequence transduction field. Standard transduction models like Transformer adopt an autoregressive connection to capture dependencies from previously generated tokens and further correlate this information with the input sequence in order to prioritize the set of relevant input tokens for current token generation. To borrow wisdom from this structured learning framework while avoiding the sequential modeling for hand pose, taking a 3D point set as input, we propose to leverage the Transformer architecture with a novel non-autoregressive structured decoding mechanism. Specifically, instead of using previously generated results, our decoder utilizes a reference hand pose to provide equivalent dependencies among hand joints for each output joint generation. By imposing the reference structural dependencies, we can correlate the information with the input 3D points through a multi-head attention mechanism, aiming to discover informative points from different perspectives, towards each hand joint localization. We demonstrate our model's effectiveness over multiple challenging hand pose datasets, comparing with several state-of-the-art methods."
Boundary-Aware Cascade Networks for Temporal Action Segmentation
Authors: Zhenzhi Wang; Ziteng Gao; Limin Wang; Zhifeng Li; Gangshan Wu
Venue: ECCV 2020 (main)

Abstract: Identifying human action segments in an untrimmed video is still challenging due to boundary ambiguity and over-segmentation issues. To address these problems, we present a new boundary-aware cascade network by introducing two novel components. First, we devise a new cascading paradigm, called Stage Cascade, to enable our model to have adaptive receptive fields and more confident predictions for ambiguous frames. Second, we design a general and principled smoothing operation, termed as local barrier pooling, to aggregate local predictions by leveraging semantic boundary information. Moreover, these two components can be jointly fine-tuned in an end-to-end manner. We perform experiments on three challenging datasets: 50Salads, GTEA and Breakfast dataset, demonstrating that our framework significantly out-performs the current state-of-the-art methods. The code is available at https://github.com/MCG-NJU/BCN."
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation
Authors: Xu Yan; Weibing Zhao; Kun Yuan; Ruimao Zhang; Zhen Li; Shuguang Cui
Venue: ECCV 2020 (main)

Abstract: Recovering realistic textures from a largely down-sampled low resolution (LR) image with complicated patterns is a challenging problem in image super-resolution. This work investigates a novel multi-reference based super-resolution problem by proposing a Content Independent Multi-Reference Super-Resolution (CIMR-SR) model, which is able to adaptively match the visual pattern between references and target image in the low resolution and enhance the feature representation of the target image in the higher resolution. CIMR-SR significantly improves the flexibility of the recently proposed reference-based super-resolution (RefSR), which needs to select the specific high-resolution reference (e.g., content similarity, camera view and relative scale) for each target image. In practice, a universal reference pool (RP) is built up for recovering all LR targets by searching the local matched patterns. By exploiting feature-based patch searching and attentive reference feature aggregation, the proposed CIMR-SR generates realistic images with much better perceptual quality and richer fine-details. Extensive experiments demonstrate the proposed CIMR-SR outperforms state-of-the-art methods in both qualitative and quantitative reconstructions."
Inference Graphs for CNN Interpretation
Authors: Yael Konforti; Alon Shpigler; Boaz Lerner; Aharon Bar-Hillel
Venue: ECCV 2020 (main)

Abstract: Convolutional neural networks (CNNs) have achieved superior accuracy in many visual related tasks. However, the inference process through intermediate layers is opaque, making it difficult to interpret such networks or develop trust in their operation. We propose to model the network hidden layers activity using probabilistic models. The activity patterns in layers of interest are modeled as Gaussian mixture models, and transition probabilities between clusters in consecutive modeled layers are estimated. Based on maximum-likelihood considerations, a subset of the nodes and paths relevant for network prediction are chosen, connected, and visualized as an inference graph. We show that such graphs are useful for understanding the general inference process of a class, as well as explaining decisions the network makes regarding specific images. In addition, the models provide an interesting observation regarding the highly local nature of column activities in top CNN layers."
An End-to-End OCR Text Re-organization Sequence Learning for Rich-text Detail Image Comprehension
Authors: Liangcheng Li; Feiyu Gao; Jiajun Bu; Yongpan Wang; Zhi Yu; Qi Zheng
Venue: ECCV 2020 (main)

Abstract: Nowadays rich description on detail images help users know more about the commodities. With the help of OCR technology, the description text can be detected and recognized as auxiliary information to remove the comprehending barriers among the visual impaired users. However, for lack of proper logical structure among these OCR text blocks, it is challenging to comprehend the detail images accurately. To tackle the above problems, we propose a novel end-to-end OCR text reorganizing model. Specifically, we create a Graph Neural Network with an attention map to encode the text blocks with visual layout features, with which an attention based sequence decoder inspired by the Pointer Network and a Sinkhorn global optimization will reorder the OCR text into a proper sequence. Experimental results illustrate that our model outperforms the other baselines, and the real experiment of the blind users' experience shows that our model improves their comprehension."
Improving Query Efficiency of Black-box Adversarial Attack
Authors: Yang Bai; Yuyuan Zeng; Yong Jiang; Yisen Wang; Shu-Tao Xia; Weiwei Guo
Venue: ECCV 2020 (main)

Abstract: Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting). As plenty of machine learning models have been deployed via online services that only provide query outputs from inaccessible models (e.g., Google Cloud Vision API2), black-box adversarial attacks (inaccessible target model) are of critical security concerns in practice rather than white-box ones. However, existing query-based black-box adversarial attacks often require excessive model queries to maintain a high attack success rate. Therefore, in order to improve query efficiency, we explore the distribution of adversarial examples around benign inputs with the help of image structure information characterized by a Neural Process, and propose a Neural Process based black-box adversarial attack (NP-Attack) in this paper. Extensive experiments show that NP-Attack could greatly decrease the query counts and achieve the highest attack success rate simultaneously under the black-box setting. "
Self-similarity Student for Partial Label Histopathology Image Segmentation
Authors: Hsien-Tzu Cheng; Chun-Fu Yeh; Po-Chen Kuo; Andy Wei; Keng-Chi Liu; Mong-Chi Ko; Kuan-Hua Chao; Yu-Ching Peng; Tyng-Luh Liu
Venue: ECCV 2020 (main)

Abstract: Delineation of cancerous regions in gigapixel whole slide images (WSIs) is a crucial diagnostic procedure in digital pathology. This process is time-consuming because of the large search space in the gigapixel WSIs, causing chances of omission and misinterpretation at indistinct tumor lesions. To tackle this, the development of an automated cancerous region segmentation method is imperative. We frame this issue as a modeling problem with partial label WSIs, where some cancerous regions may be misclassified as benign and vice versa, producing patches with noisy labels. To learn from these patches, we propose Self-similarity Student, combining teacher-student model paradigm with similarity learning. Specifically, for each patch, we first sample its similar and dissimilar patches according to spatial distance. A teacher-student model is then introduced, featuring the exponential moving average on both student model weights and teacher predictions ensemble. While our student model takes patches, teacher model takes all their corresponding similar and dissimilar patches for learning robust representation against noisy label patches. Following this similarity learning, our similarity ensemble merges similar patches’ ensembled predictions as the pseudo-label of a given patch to counteract its noisy label. On the CAMELYON16 dataset, our method substantially outperforms state-of-the-art noise-aware learning methods by 5% and the supervised-trained baseline by 10% in various degrees of noise. Moreover, our method is superior to the baseline on our TVGH TURP dataset with 2% improvement, demonstrating the generalizability to more clinical histopathology segmentation tasks."
BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions
Authors: Arslan Ali; Matteo Testa; Tiziano Bianchi; Enrico Magli
Venue: ECCV 2020 (main)

Abstract: We present BioMetricNet: a novel framework for deep unconstrained face verification which learns a regularized metric to compare facial features. Differently from popular methods such as FaceNet, the proposed approach does not impose any specific metric on facial features; instead, it shapes the decision space by learning a latent representation in which matching and non-matching pairs are mapped onto clearly separated and well-behaved target distributions. In particular, the network jointly learns the best feature representation, and the best metric that follows the target distributions, to be used to discriminate face images. In this paper we present this general framework, first of its kind for facial verification, and tailor it to Gaussian distributions. This choice enables the use of a simple linear decision boundary that can be tuned to achieve the desired trade-off between false alarm and genuine acceptance rate, and leads to a loss function that can be written in closed form. Extensive analysis and experimentation on publicly available datasets such as Labeled Faces in the wild (LFW), Youtube faces (YTF), Celebrities in Frontal-Profile in the Wild (CFP), and challenging datasets like cross-age LFW (CALFW), cross-pose LFW (CPLFW), In-the-wild Age Dataset (AgeDB) show a significant performance improvement and confirms the effectiveness and superiority of BioMetricNet over existing state-of-the-art methods."
A Decoupled Learning Scheme for Real-world Burst Denoising from Raw Images
Authors: Zhetong Liang; Shi Guo; Hong Gu; Huaqi Zhang; Lei Zhang
Venue: ECCV 2020 (main)

Abstract: The recently developed burst denoising approach, which reduces noise by using multiple frames captured in a short time, has demonstrated much better denoising performance than its single-frame counterparts. However, existing learning based burst denoising methods are limited by two factors. On one hand, most of the models are trained on video sequences with synthetic noise. When applied to real-world raw image sequences, visual artifacts often appear due to the different noise statistics. On the other hand, there lacks a real-world burst denoising benchmark of dynamic scenes because the generation of clean ground-truth is very difficult due to the presence of object motions. In this paper, a novel multi-frame CNN model is carefully designed, which decouples the learning of motion from the learning of noise statistics. Consequently, an alternating learning algorithm is developed to learn how to align adjacent frames from a synthetic noisy video dataset, and learn to adapt to the raw noise statistics from real-world noisy datasets of static scenes. Finally, the trained model can be applied to real-world dynamic sequences for burst denoising. Extensive experiments on both synthetic video datasets and real-world dynamic sequences demonstrate the leading burst denoising performance of our proposed method."
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization
Authors: Yunjae Jung; Donghyeon Cho; Sanghyun Woo; In So Kweon
Venue: ECCV 2020 (main)

Abstract: In order to summarize a content video properly, it is important to grasp the sequential structure of video as well as the long-term dependency between frames. The necessity of them is more obvious, especially for unsupervised learning. One possible solution is to utilize a well-known technique in the field of natural language processing for long-term dependency and sequential property: self-attention with relative position embedding (RPE). However, compared to natural language processing, video summarization requires capturing a much longer length of the global context. In this paper, we therefore present a novel input decomposition strategy, which samples the input both globally and locally. This provides an effective temporal window for RPE to operate and improves overall computational efficiency significantly. By combining both Global-and-Local input decomposition and RPE together, we come up with GL-RPE. Our approach allows the network to capture both local and global interdependencies between video frames effectively. Since GL-RPE can be easily integrated into the existing methods, we apply it to two different unsupervised backbones. We provide extensive ablation studies and visual analysis to verify the effectiveness of the proposals. We demonstrate our approach achieves new state-of-the-art performance using the recently proposed rank order-based metrics: Kendall's $ au$ and Spearman's $
ho$. Furthermore, despite our method is unsupervised, we show ours perform on par with the fully-supervised method."
Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
Authors: Jaesung Rim; Haeyun Lee; Jucheol Won; Sunghyun Cho
Venue: ECCV 2020 (main)

Abstract: Numerous learning-based approaches to single image deblurring for camera and object motion blurs have recently been proposed. To generalize such approaches to real-world blurs, large datasets of real blurred images and their ground truth sharp images are essential. However, there are still no such datasets, thus all the existing approaches resort to synthetic ones, which leads to the failure of deblurring real-world images. In this work, we present a large-scale dataset of real-world blurred images and ground truth sharp images for learning and benchmarking single image deblurring methods. To collect our dataset, we build an image acquisition system to simultaneously capture geometrically aligned pairs of blurred and sharp images, and develop a postprocessing method to produce high-quality ground truth images. We analyze the effect of our postprocessing method and the performance of existing deblurring methods. Our analysis shows that our dataset significantly improves deblurring quality for real-world blurred images."
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
Authors: Qing Guo; Xiaofei Xie; Felix Juefei-Xu; Lei Ma; Zhongguo Li; Wanli Xue; Wei Feng; Yang Liu
Venue: ECCV 2020 (main)

Abstract: Adversarial attacks of deep neural networks have been intensively studied on image, audio, natural language, patch, and pixel classification tasks. Nevertheless, as a typical, while important real-world application, the adversarial attacks of online video object tracking that traces an object's moving trajectory instead of its category are rarely explored. In this paper, we identify a new task for the adversarial attack to visual tracking: online generating imperceptible perturbations that mislead trackers along with an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA). To this end, we first propose a spatial-aware basic attack by adapting existing attack methods, \ie, FGSM, BIM, and C\&W, and comprehensively analyze the attacking performance. We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency. To address these challenges, we further propose the spatial-aware online incremental attack (a.k.a. SPARK) that performs spatial-temporal sparse incremental perturbations online and makes the adversarial attack less perceptible. In addition, as an optimization-based method, SPARK quickly converges to very small losses within several iterations by considering historical incremental perturbations, making it much more efficient than basic attacks. The in-depth evaluation of state-of-the-art trackers (i.e., SiamRPN++ with AlexNet, MobileNetv2, and ResNet-50, and SiamDW) on OTB100, VOT2018, UAV123, and LaSOT demonstrates the effectiveness and transferability of SPARK in misleading the trackers under both UA and TA with minor perturbations."
CenterNet Heatmap Propagation for Real-time Video Object Detection
Authors: Zhujun Xu; Emir Hrustic; Damien Vivet
Venue: ECCV 2020 (main)

Abstract: The existing methods for video object detection mainly depend on two-stage image object detectors. The fact that two-stage detectors are generally slow makes it difficult to apply in real-time scenarios. Moreover, adapting directly existing methods to a one-stage detector is inefficient or infeasible. In this work, we introduce a method based on a one-stage detector called CenterNet. We propagate the previous reliable long-term detection in the form of heatmap to boost results of upcoming image. Our method achieves the online real-time performance on ImageNet VID dataset with 76.7% mAP at 37 FPS and the offline performance 78.4% mAP at 34 FPS."
Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection
Authors: Youwei Pang; Lihe Zhang; Xiaoqi Zhao; Huchuan Lu
Venue: ECCV 2020 (main)

Abstract: The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We integrate the features of different modalities through densely connected structures and use their mixed features to generate dynamic filters with receptive fields of different sizes. In the end, we implement a kind of more flexible and efficient multi-scale cross-modal feature processing, i.e. dynamic dilated pyramid module. In order to make the predictions have sharper edges and consistent saliency regions, we design a hybrid enhanced loss function to further optimize the results. This loss function is also validated to be effective in the single-modal RGB SOD task. In terms of six metrics, the proposed method outperforms the existing twelve methods on eight challenging benchmark datasets. A large number of experiments verify the effectiveness of the proposed module and loss function. Our code, model and results are available at \url{https://github.com/lartpang/HDFNet}."
SOLAR: Second-Order Loss and Attention for Image Retrieval
Authors: Tony Ng; Vassileios Balntas; Yurun Tian; Krystian Mikolajczyk
Venue: ECCV 2020 (main)

Abstract: Recent works in deep-learning have shown that second-order information is beneficial in many computer-vision tasks. Second-order information can be enforced both in the spatial context and the abstract feature dimensions. In this work, we explore two second-order components. One is focused on second-order spatial information to increase the performance of image descriptors, both local and global. It is used to re-weight feature maps, and thus emphasise salient image locations that are subsequently used for description. The second component is concerned with a second-order similarity (SOS) loss, that we extend to global descriptors for image retrieval, and is used to enhance the triplet loss with hard-negative mining. We validate our approach on two different tasks and datasets for image retrieval and image matching. The results show that our two second-order components complement each other, bringing significant performance improvements in both tasks and lead to state-of-the-art results across the public benchmarks. Code available at: http://github.com/tonyngjichun/SOLAR"
Fixing Localization Errors to Improve Image Classification
Authors: Guolei Sun; Salman Khan; Wen Li; Hisham Cholakkal; Fahad Shahbaz Khan; Luc Van Gool
Venue: ECCV 2020 (main)

Abstract: Deep neural networks are generally considered black-box models that offer less interpretability for their decision process. To address this limitation, Class Activation Map (CAM) provides an attractive solution that visualizes class-specific discriminative regions in an input image. The remarkable ability of CAMs to locate class discriminating regions has been exploited in weakly-supervised segmentation and localization tasks. In this work, we explore a new direction towards the possible use of CAM in deep network learning process. We note that such visualizations lend insights into the workings of deep CNNs and could be leveraged to introduce additional constraints during the learning stage. Specifically, the CAMs for negative classes (negative CAMs) often have false activations even though those classes are absent from an image. Thereby, we propose a loss function that seeks to minimize peaks within the negative CAMs, called Homogeneous Negative CAM loss. This way, in an effort to fix localization errors, our loss provides an extra supervisory signal that helps the model to better discriminate between similar classes. Our designed loss function is easy to implement and can be readily integrated into existing DNNs. We evaluate it on a number of classification tasks including large-scale recognition, multi-label classification and fine-grained recognition. Our loss provides better performance compared to other loss functions across the studied tasks. Additionally, we show that the proposed loss function provides higher robustness against adversarial attacks and noisy labels. "
PatchPerPix for Instance Segmentation
Authors: Lisa Mais; Peter Hirsch and Dagmar Kainmueller
Venue: ECCV 2020 (main)

Abstract: In this paper we present a novel method for proposal free instance segmentation that can handle sophisticated object shapes that span large parts of an image and form dense object clusters with crossovers. Our method is based on predicting dense local shape descriptors, which we assemble to form instances. All instances are assembled simultaneously in one go. To our knowledge, our method is the first non-iterative method that yields instances that are composed of learnt shape patches. We evaluate our method on a diverse range of data domains, where it defines the new state of the art on four benchmarks, namely the ISBI 2012 EM segmentation benchmark, the BBBC010 C. elegans dataset, and 2d as well as 3d fluorescence microscopy datasets of cell nuclei. We show furthermore that our method also applies to 3d light microscopy data of drosophila neurons, which exhibit extreme cases of complex shape clusters."