pwc-archive/papers-with-abstracts huggingface dataset:
MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving
Authors: Kai Chen, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-yan Yeung
Date: 2021-08-27 00:00:00
Proceeding: ICCV 2021 10
Tasks: Autonomous Driving, Image Clustering, Representation Learning, Self-Supervised Learning

Autonomous driving has attracted much attention over the years but turns out to be harder than expected, probably due to the difficulty of labeled data collection for model training. Self-supervised learning (SSL), which leverages unlabeled data only for representation learning, might be a promising way to improve model performance. Existing SSL methods, however, usually rely on the single-centric-object guarantee, which may not be applicable for multi-instance datasets such as street scenes. To alleviate this limitation, we raise two issues to solve: (1) how to define positive samples for cross-view consistency and (2) how to measure similarity in multi-instance circumstances. We first adopt an IoU threshold during random cropping to transfer global-inconsistency to local-consistency. Then, we propose two feature alignment methods to enable 2D feature maps for multi-instance similarity measurement. Additionally, we adopt intra-image clustering with self-attention for further mining intra-image similarity and translation-invariance. Experiments show that, when pre-trained on Waymo dataset, our method called Multi-instance Siamese Network (MultiSiam) remarkably improves generalization ability and achieves state-of-the-art transfer performance on autonomous driving benchmarks, including Cityscapes and BDD100K, while existing SSL counterparts like MoCo, MoCo-v2, and BYOL show significant performance drop. By pre-training on SODA10M, a large-scale autonomous driving dataset, MultiSiam exceeds the ImageNet pre-trained MoCo-v2, demonstrating the potential of domain-specific pre-training. Code will be available at https://github.com/KaiChen1998/MultiSiam.
Rethinking the Misalignment Problem in Dense Object Detection
Authors: Yang Yang, Min Li, Bo Meng, Junxing Ren, Degang Sun, Zihao Huang
Date: 2021-08-27 00:00:00
Tasks: Dense Object Detection, Object, object-detection, Object Detection, regression

Object detection aims to localize and classify the objects in a given image, and these two tasks are sensitive to different object regions. Therefore, some locations predict high-quality bounding boxes but low classification scores, and some locations are quite the opposite. A misalignment exists between the two tasks, and their features are spatially entangled. In order to solve the misalignment problem, we propose a plug-in Spatial-disentangled and Task-aligned operator (SALT). By predicting two task-aware point sets that are located in each task's sensitive regions, SALT can reassign features from those regions and align them to the corresponding anchor point. Therefore, features for the two tasks are spatially aligned and disentangled. To minimize the difference between the two regression stages, we propose a Self-distillation regression (SDR) loss that can transfer knowledge from the refined regression results to the coarse regression results. On the basis of SALT and SDR loss, we propose SALT-Net, which explicitly exploits task-aligned point-set features for accurate detection results. Extensive experiments on the MS-COCO dataset show that our proposed methods can consistently boost different state-of-the-art dense detectors by $\sim$2 AP. Notably, SALT-Net with Res2Net-101-DCN backbone achieves 53.8 AP on the MS-COCO test-dev.
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation
Authors: Wenxuan Zou, Muyi Sun
Date: 2021-08-27 00:00:00
Tasks: Image Segmentation, Knowledge Distillation, Segmentation, Semantic Segmentation, Transfer Learning

In recent years, deep convolutional neural networks have made significant advances in pathology image segmentation. However, pathology image segmentation encounters with a dilemma in which the higher-performance networks generally require more computational resources and storage. This phenomenon limits the employment of high-accuracy networks in real scenes due to the inherent high-resolution of pathological images. To tackle this problem, we propose CoCo DistillNet, a novel Cross-layer Correlation (CoCo) knowledge distillation network for pathological gastric cancer segmentation. Knowledge distillation, a general technique which aims at improving the performance of a compact network through knowledge transfer from a cumbersome network. Concretely, our CoCo DistillNet models the correlations of channel-mixed spatial similarity between different layers and then transfers this knowledge from a pre-trained cumbersome teacher network to a non-trained compact student network. In addition, we also utilize the adversarial learning strategy to further prompt the distilling procedure which is called Adversarial Distillation (AD). Furthermore, to stabilize our training procedure, we make the use of the unsupervised Paraphraser Module (PM) to boost the knowledge paraphrase in the teacher network. As a result, extensive experiments conducted on the Gastric Cancer Segmentation Dataset demonstrate the prominent ability of CoCo DistillNet which achieves state-of-the-art performance.
LassoLayer: Nonlinear Feature Selection by Switching One-to-one Links
Authors: Akihito Sudo, Teng Teck Hou, Masaki Yamaguchi, Yoshinori Tone
Date: 2021-08-27 00:00:00
Tasks: feature selection

Along with the desire to address more complex problems, feature selection methods have gained in importance. Feature selection methods can be classified into wrapper method, filter method, and embedded method. Being a powerful embedded feature selection method, Lasso has attracted the attention of many researchers. However, as a linear approach, the applicability of Lasso has been limited. In this work, we propose LassoLayer that is one-to-one connected and trained by L1 optimization, which work to drop out unnecessary units for prediction. For nonlinear feature selections, we build LassoMLP: the network equipped with LassoLayer as its first layer. Because we can insert LassoLayer in any network structure, it can harness the strength of neural network suitable for tasks where feature selection is needed. We evaluate LassoMLP in feature selection with regression and classification tasks. LassoMLP receives features including considerable numbers of noisy factors that is harmful for overfitting. In the experiments using MNIST dataset, we confirm that LassoMLP outperforms the state-of-the-art method.
A Matching Algorithm based on Image Attribute Transfer and Local Features for Underwater Acoustic and Optical Images
Authors: Xiaoteng Zhou, Changli Yu, Xin Yuan, Citong Luo
Date: 2021-08-27 00:00:00
Tasks: Attribute

In the field of underwater vision research, image matching between the sonar sensors and optical cameras has always been a challenging problem. Due to the difference in the imaging mechanism between them, which are the gray value, texture, contrast, etc. of the acoustic images and the optical images are also variant in local locations, which makes the traditional matching method based on the optical image invalid. Coupled with the difficulties and high costs of underwater data acquisition, it further affects the research process of acousto-optic data fusion technology. In order to maximize the use of underwater sensor data and promote the development of multi-sensor information fusion (MSIF), this study applies the image attribute transfer method based on deep learning approach to solve the problem of acousto-optic image matching, the core of which is to eliminate the imaging differences between them as much as possible. At the same time, the advanced local feature descriptor is introduced to solve the challenging acousto-optic matching problem. Experimental results show that our proposed method could preprocess acousto-optic images effectively and obtain accurate matching results. Additionally, the method is based on the combination of image depth semantic layer, and it could indirectly display the local feature matching relationship between original image pair, which provides a new solution to the underwater multi-sensor image matching problem.
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
Authors: Shulan Ruan, Yong Zhang, Kun Zhang, Yanbo Fan, Fan Tang, Qi Liu, Enhong Chen
Date: 2021-08-27 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Generation, Sentence, Sentence Embedding, Sentence-Embedding

Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency. Previous methods usually generate an initial image with sentence embedding and then refine it with fine-grained word embedding. Despite the significant progress, the 'aspect' information (e.g., red eyes) contained in the text, referring to several words rather than a word that depicts 'a particular part or feature of something', is often ignored, which is highly helpful for synthesizing image details. How to make better utilization of aspect information in text-to-image synthesis still remains an unresolved challenge. To address this problem, in this paper, we propose a Dynamic Aspect-awarE GAN (DAE-GAN) that represents text information comprehensively from multiple granularities, including sentence-level, word-level, and aspect-level. Moreover, inspired by human learning behaviors, we develop a novel Aspect-aware Dynamic Re-drawer (ADR) for image refinement, in which an Attended Global Refinement (AGR) module and an Aspect-aware Local Refinement (ALR) module are alternately employed. AGR utilizes word-level embedding to globally enhance the previously generated image, while ALR dynamically employs aspect-level embedding to refine image details from a local perspective. Finally, a corresponding matching loss function is designed to ensure the text-image semantic consistency at different levels. Extensive experiments on two well-studied and publicly available datasets (i.e., CUB-200 and COCO) demonstrate the superiority and rationality of our method.
Densely-Populated Traffic Detection using YOLOv5 and Non-Maximum Suppression Ensembling
Authors: Raian Rahman, Zadid Bin Azad, Md. Bakhtiar Hasan
Date: 2021-08-27 00:00:00
Tasks: Management, object-detection, Object Detection

Vehicular object detection is the heart of any intelligent traffic system. It is essential for urban traffic management. R-CNN, Fast R-CNN, Faster R-CNN and YOLO were some of the earlier state-of-the-art models. Region based CNN methods have the problem of higher inference time which makes it unrealistic to use the model in real-time. YOLO on the other hand struggles to detect small objects that appear in groups. In this paper, we propose a method that can locate and classify vehicular objects from a given densely crowded image using YOLOv5. The shortcoming of YOLO was solved my ensembling 4 different models. Our proposed model performs well on images taken from both top view and side view of the street in both day and night. The performance of our proposed model was measured on Dhaka AI dataset which contains densely crowded vehicular images. Our experiment shows that our model achieved mAP@0.5 of 0.458 with inference time of 0.75 sec which outperforms other state-of-the-art models on performance. Hence, the model can be implemented in the street for real-time traffic detection which can be used for traffic control and data collection.
Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition
Authors: Tatpong Katanyukul, Pisit Nakjai
Date: 2021-08-27 00:00:00
Tasks: Object, Object Recognition, Open Set Learning

This study investigates an application of a new probabilistic interpretation of a softmax output to Open-Set Recognition (OSR). Softmax is a mechanism wildly used in classification and object recognition. However, a softmax mechanism forces a model to operate under a closed-set paradigm, i.e., to predict an object class out of a set of pre-defined labels. This characteristic contributes to efficacy in classification, but poses a risk of non-sense prediction in object recognition. Object recognition is often operated under a dynamic and diverse condition. A foreign object -- an object of any unprepared class -- can be encountered at any time. OSR is intended to address an issue of identifying a foreign object in object recognition. Based on Bayes theorem and the emphasis of conditioning on the context, softmax inference has been re-interpreted. This re-interpretation has led to a new approach to OSR, called Latent Cognizance (LC). Our investigation employs various scenarios, using Imagenet 2012 dataset as well as fooling and open-set images. The findings support LC hypothesis and show its effectiveness on OSR.
Binocular Mutual Learning for Improving Few-shot Classification
Authors: Ziqi Zhou, Xi Qiu, Jiangtao Xie, Jianan Wu, Chi Zhang
Date: 2021-08-27 00:00:00
Proceeding: ICCV 2021 10
Tasks: Classification, Decision Making, Few-Shot Learning

Most of the few-shot learning methods learn to transfer knowledge from datasets with abundant labeled data (i.e., the base set). From the perspective of class space on base set, existing methods either focus on utilizing all classes under a global view by normal pretraining, or pay more attention to adopt an episodic manner to train meta-tasks within few classes in a local view. However, the interaction of the two views is rarely explored. As the two views capture complementary information, we naturally think of the compatibility of them for achieving further performance gains. Inspired by the mutual learning paradigm and binocular parallax, we propose a unified framework, namely Binocular Mutual Learning (BML), which achieves the compatibility of the global view and the local view through both intra-view and cross-view modeling. Concretely, the global view learns in the whole class space to capture rich inter-class relationships. Meanwhile, the local view learns in the local class space within each episode, focusing on matching positive pairs correctly. In addition, cross-view mutual interaction further promotes the collaborative learning and the implicit exploration of useful knowledge from each other. During meta-test, binocular embeddings are aggregated together to support decision-making, which greatly improve the accuracy of classification. Extensive experiments conducted on multiple benchmarks including cross-domain validation confirm the effectiveness of our method.
FOVEA: Foveated Image Magnification for Autonomous Navigation
Authors: Chittesh Thavamani, Mengtian Li, Nicolas Cebron, Deva Ramanan
Date: 2021-08-27 00:00:00
Proceeding: ICCV 2021 10
Tasks: Autonomous Driving, Autonomous Navigation, GPU, object-detection, Object Detection, Real-Time Object Detection, Small Object Detection

Efficient processing of high-res video streams is safety-critical for many robotics applications such as autonomous driving. To maintain real-time performance, many practical systems downsample the video stream. But this can hurt downstream tasks such as (small) object detection. Instead, we take inspiration from biological vision systems that allocate more foveal "pixels" to salient parts of the scene. We introduce FOVEA, an approach for intelligent downsampling that ensures salient image regions remain "magnified" in the downsampled output. Given a high-res image, FOVEA applies a differentiable resampling layer that outputs a small fixed-size image canvas, which is then processed with a differentiable vision module (e.g., object detection network), whose output is then differentiably backward mapped onto the original image size. The key idea is to resample such that background pixels can make room for salient pixels of interest. In order to ensure the overall pipeline remains efficient, FOVEA makes use of cheap and readily available cues for saliency, including dataset-specific spatial priors or temporal priors computed from object predictions in the recent past. On the autonomous driving datasets Argoverse-HD and BDD100K, our proposed method boosts the detection AP over standard Faster R-CNN, both with and without finetuning. Without any noticeable increase in compute, we improve accuracy on small objects by over 2x without degrading performance on large objects. Finally, FOVEA sets a new record for streaming AP (from 17.8 to 23.0 on a GTX 1080 Ti GPU), a metric designed to capture both accuracy and latency.
Deep Denoising Method for Side Scan Sonar Images without High-quality Reference Data
Authors: Xiaoteng Zhou, Changli Yu, Xin Yuan, Citong Luo
Date: 2021-08-27 00:00:00
Tasks: Denoising

Subsea images measured by the side scan sonars (SSSs) are necessary visual data in the process of deep-sea exploration by using the autonomous underwater vehicles (AUVs). They could vividly reflect the topography of the seabed, but usually accompanied by complex and severe noise. This paper proposes a deep denoising method for SSS images without high-quality reference data, which uses one single noise SSS image to perform self-supervised denoising. Compared with the classical artificially designed filters, the deep denoising method shows obvious advantages. The denoising experiments are performed on the real seabed SSS images, and the results demonstrate that our proposed method could effectively reduce the noise on the SSS image while minimizing the image quality and detail loss.
Detection and Continual Learning of Novel Face Presentation Attacks
Authors: Mohammad Rostami, Leonidas Spinoulas, Mohamed Hussein, Joe Mathai, Wael Abd-Almageed
Date: 2021-08-27 00:00:00
Proceeding: ICCV 2021 10
Tasks: Continual Learning, Face Presentation Attack Detection

Advances in deep learning, combined with availability of large datasets, have led to impressive improvements in face presentation attack detection research. However, state-of-the-art face antispoofing systems are still vulnerable to novel types of attacks that are never seen during training. Moreover, even if such attacks are correctly detected, these systems lack the ability to adapt to newly encountered attacks. The post-training ability of continually detecting new types of attacks and self-adaptation to identify these attack types, after the initial detection phase, is highly appealing. In this paper, we enable a deep neural network to detect anomalies in the observed input data points as potential new types of attacks by suppressing the confidence-level of the network outside the training samples' distribution. We then use experience replay to update the model to incorporate knowledge about new types of attacks without forgetting the past learned attack types. Experimental results are provided to demonstrate the effectiveness of the proposed method on two benchmark datasets as well as a newly introduced dataset which exhibits a large variety of attack types.
Matching Underwater Sonar Images by the Learned Descriptor Based on Style Transfer Method
Authors: Xiaoteng Zhou, Changli Yu, Xin Yuan, Citong Luo
Date: 2021-08-27 00:00:00
Tasks: Style Transfer

This paper proposes a method that combines the style transfer technique and the learned descriptor to enhance the matching performances of underwater sonar images. In the field of underwater vision, sonar is currently the most effective long-distance detection sensor, it has excellent performances in map building and target search tasks. However, the traditional image matching algorithms are all developed based on optical images. In order to solve this contradiction, the style transfer method is used to convert the sonar images into optical styles, and at the same time, the learned descriptor with excellent expressiveness for sonar images matching is introduced. Experiments show that this method significantly enhances the matching quality of sonar images. In addition, it also provides new ideas for the preprocessing of underwater sonar images by using the style transfer approach.
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences
Authors: Alessandro Bruno
Date: 2021-08-26 00:00:00
Tasks: Classification, Content-Based Image Retrieval, image-classification, Image Classification, Image Cropping, Image Retrieval, object-detection, Object Detection, Retrieval

A growing number of commercially available mobile phones come with integrated high-resolution digital cameras. That enables a new class of dedicated applications to image analysis such as mobile visual search, image cropping, object detection, content-based image retrieval, image classification. In this paper, a new mobile application for image content retrieval and classification for mobile device display is proposed to enrich the visual experience of users. The mobile application can extract a certain number of images based on the content of an image with visual saliency methods aiming at detecting the most critical regions in a given image from a perceptual viewpoint. First, the most critical areas from a perceptual perspective are extracted using the local maxima of a 2D saliency function. Next, a salient region is cropped using the bounding box centred on the local maxima of the thresholded Saliency Map of the image. Then, each image crop feds into an Image Classification system based on SVM and SIFT descriptors to detect the class of object present in the image. ImageNet repository was used as the reference for semantic category classification. Android platform was used to implement the mobile application on a client-server architecture. A mobile client sends the photo taken by the camera to the server, which processes the image and returns the results (image contents such as image crops and related target classes) to the mobile client. The application was run on thousands of pictures and showed encouraging results towards a better user visual experience with mobile displays.
Predicting Stable Configurations for Semantic Placement of Novel Objects
Authors: Chris Paxton, Chris Xie, Tucker Hermans, Dieter Fox
Date: 2021-08-26 00:00:00
Tasks: Motion Planning, valid

Human environments contain numerous objects configured in a variety of arrangements. Our goal is to enable robots to repose previously unseen objects according to learned semantic relationships in novel environments. We break this problem down into two parts: (1) finding physically valid locations for the objects and (2) determining if those poses satisfy learned, high-level semantic relationships. We build our models and training from the ground up to be tightly integrated with our proposed planning algorithm for semantic placement of unknown objects. We train our models purely in simulation, with no fine-tuning needed for use in the real world. Our approach enables motion planning for semantic rearrangement of unknown objects in scenes with varying geometry from only RGB-D sensing. Our experiments through a set of simulated ablations demonstrate that using a relational classifier alone is not sufficient for reliable planning. We further demonstrate the ability of our planner to generate and execute diverse manipulation plans through a set of real-world experiments with a variety of objects.