pwc-archive/papers-with-abstracts huggingface dataset:
DF^2AM: Dual-level Feature Fusion and Affinity Modeling for RGB-Infrared Cross-modality Person Re-identification
Authors: Junhui Yin, Zhanyu Ma, Jiyang Xie, Shibo Nie, Kongming Liang, Jun Guo
Date: 2021-04-01 00:00:00
Tasks: Cross-Modality Person Re-identification, Person Re-Identification

RGB-infrared person re-identification is a challenging task due to the intra-class variations and cross-modality discrepancy. Existing works mainly focus on learning modality-shared global representations by aligning image styles or feature distributions across modalities, while local feature from body part and relationships between person images are largely neglected. In this paper, we propose a Dual-level (i.e., local and global) Feature Fusion (DF^2) module by learning attention for discriminative feature from local to global manner. In particular, the attention for a local feature is determined locally, i.e., applying a learned transformation function on itself. Meanwhile, to further mining the relationships between global features from person images, we propose an Affinities Modeling (AM) module to obtain the optimal intra- and inter-modality image matching. Specifically, AM employes intra-class compactness and inter-class separability in the sample similarities as supervised information to model the affinities between intra- and inter-modality samples. Experimental results show that our proposed method outperforms state-of-the-arts by large margins on two widely used cross-modality re-ID datasets SYSU-MM01 and RegDB, respectively.
Embedded Self-Distillation in Compact Multi-Branch Ensemble Network for Remote Sensing Scene Classification
Authors: Qi Zhao, Yujing Ma, Shuchang Lyu, Lijiang Chen
Date: 2021-04-01 00:00:00
Tasks: General Classification, Scene Classification

Remote sensing (RS) image scene classification task faces many challenges due to the interference from different characteristics of different geographical elements. To solve this problem, we propose a multi-branch ensemble network to enhance the feature representation ability by fusing features in final output logits and intermediate feature maps. However, simply adding branches will increase the complexity of models and decline the inference efficiency. On this issue, we embed self-distillation (SD) method to transfer knowledge from ensemble network to main-branch in it. Through optimizing with SD, main-branch will have close performance as ensemble network. During inference, we can cut other branches to simplify the whole model. In this paper, we first design compact multi-branch ensemble network, which can be trained in an end-to-end manner. Then, we insert SD method on output logits and feature maps. Compared to previous methods, our proposed architecture (ESD-MBENet) performs strongly on classification accuracy with compact design. Extensive experiments are applied on three benchmark RS datasets AID, NWPU-RESISC45 and UC-Merced with three classic baseline models, VGG16, ResNet50 and DenseNet121. Results prove that our proposed ESD-MBENet can achieve better accuracy than previous state-of-the-art (SOTA) complex models. Moreover, abundant visualization analysis make our method more convincing and interpretable.
Training Multi-bit Quantized and Binarized Networks with A Learnable Symmetric Quantizer
Authors: Phuoc Pham, Jacob Abraham, Jaeyong Chung
Date: 2021-04-01 00:00:00
Tasks: Binarization, Quantization

Quantizing weights and activations of deep neural networks is essential for deploying them in resource-constrained devices, or cloud platforms for at-scale services. While binarization is a special case of quantization, this extreme case often leads to several training difficulties, and necessitates specialized models and training methods. As a result, recent quantization methods do not provide binarization, thus losing the most resource-efficient option, and quantized and binarized networks have been distinct research areas. We examine binarization difficulties in a quantization framework and find that all we need to enable the binary training are a symmetric quantizer, good initialization, and careful hyperparameter selection. These techniques also lead to substantial improvements in multi-bit quantization. We demonstrate our unified quantization framework, denoted as UniQ, on the ImageNet dataset with various architectures such as ResNet-18,-34 and MobileNetV2. For multi-bit quantization, UniQ outperforms existing methods to achieve the state-of-the-art accuracy. In binarization, the achieved accuracy is comparable to existing state-of-the-art methods even without modifying the original architectures.
Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric Segmentation
Authors: Andrew Price, Kun Huang, Dmitry Berenson
Date: 2021-04-01 00:00:00
Tasks: Scene Segmentation, Segmentation

Despite rapid progress in scene segmentation in recent years, 3D segmentation methods are still limited when there is severe occlusion. The key challenge is estimating the segment boundaries of (partially) occluded objects, which are inherently ambiguous when considering only a single frame. In this work, we propose Multihypothesis Segmentation Tracking (MST), a novel method for volumetric segmentation in changing scenes, which allows scene ambiguity to be tracked and our estimates to be adjusted over time as we interact with the scene. Two main innovations allow us to tackle this difficult problem: 1) A novel way to sample possible segmentations from a segmentation tree; and 2) A novel approach to fusing tracking results with multiple segmentation estimates. These methods allow MST to track the segmentation state over time and incorporate new information, such as new objects being revealed. We evaluate our method on several cluttered tabletop environments in simulation and reality. Our results show that MST outperforms baselines in all tested scenes.
Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning
Authors: Junhui Yin, Jiayan Qiu, Siqing Zhang, Jiyang Xie, Zhanyu Ma, Jun Guo
Date: 2021-04-01 00:00:00
Tasks: Clustering, Person Re-Identification, Unsupervised Person Re-Identification

Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models. However, existing methods simply utilize pseudo labels from clustering for supervision and thus have not yet fully explored the semantic information in data itself, which limits representation capabilities of learned models. To address this problem, we design a pretext task for unsupervised re-ID by learning visual consistency from still images and temporal consistency during training process, such that the clustering network can separate the images into semantic clusters automatically. Specifically, the pretext task learns semantically meaningful representations by maximizing the agreement between two encoded views of the same image via a consistency loss in latent space. Meanwhile, we optimize the model by grouping the two encoded views into same cluster, thus enhancing the visual consistency between views. Experiments on Market-1501, DukeMTMC-reID and MSMT17 datasets demonstrate that our proposed approach outperforms the state-of-the-art methods by large margins.
Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images
Authors: Xiaohang Fu, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
Date: 2021-04-01 00:00:00
Tasks: General Classification, Melanoma Diagnosis

The identification of melanoma involves an integrated analysis of skin lesion images acquired using the clinical and dermoscopy modalities. Dermoscopic images provide a detailed view of the subsurface visual structures that supplement the macroscopic clinical images. Melanoma diagnosis is commonly based on the 7-point visual category checklist (7PC). The 7PC contains intrinsic relationships between categories that can aid classification, such as shared features, correlations, and the contributions of categories towards diagnosis. Manual classification is subjective and prone to intra- and interobserver variability. This presents an opportunity for automated methods to improve diagnosis. Current state-of-the-art methods focus on a single image modality and ignore information from the other, or do not fully leverage the complementary information from both modalities. Further, there is not a method to exploit the intercategory relationships in the 7PC. In this study, we address these issues by proposing a graph-based intercategory and intermodality network (GIIN) with two modules. A graph-based relational module (GRM) leverages intercategorical relations, intermodal relations, and prioritises the visual structure details from dermoscopy by encoding category representations in a graph network. The category embedding learning module (CELM) captures representations that are specialised for each category and support the GRM. We show that our modules are effective at enhancing classification performance using a public dataset of dermoscopy-clinical images, and show that our method outperforms the state-of-the-art at classifying the 7PC categories and diagnosis.
TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking
Authors: Peng Chu, Jiang Wang, Quanzeng You, Haibin Ling, Zicheng Liu
Date: 2021-04-01 00:00:00
Tasks: Decoder, Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking, Online Multi-Object Tracking

Tracking multiple objects in videos relies on modeling the spatial-temporal interactions of the objects. In this paper, we propose a solution named TransMOT, which leverages powerful graph transformers to efficiently model the spatial and temporal interactions among the objects. TransMOT effectively models the interactions of a large number of objects by arranging the trajectories of the tracked objects as a set of sparse weighted graphs, and constructing a spatial graph transformer encoder layer, a temporal transformer encoder layer, and a spatial graph transformer decoder layer based on the graphs. TransMOT is not only more computationally efficient than the traditional Transformer, but it also achieves better tracking accuracy. To further improve the tracking speed and accuracy, we propose a cascade association framework to handle low-score detections and long-term occlusions that require large computational resources to model in TransMOT. The proposed method is evaluated on multiple benchmark datasets including MOT15, MOT16, MOT17, and MOT20, and it achieves state-of-the-art performance on all the datasets.
An Energy-Efficient Quad-Camera Visual System for Autonomous Machines on FPGA Platform
Authors: Zishen Wan, Yuyang Zhang, Arijit Raychowdhury, Bo Yu, Yanjun Zhang, Shaoshan Liu
Date: 2021-04-01 00:00:00

In our past few years' of commercial deployment experiences, we identify localization as a critical task in autonomous machine applications, and a great acceleration target. In this paper, based on the observation that the visual frontend is a major performance and energy consumption bottleneck, we present our design and implementation of an energy-efficient hardware architecture for ORB (Oriented-Fast and Rotated- BRIEF) based localization system on FPGAs. To support our multi-sensor autonomous machine localization system, we present hardware synchronization, frame-multiplexing, and parallelization techniques, which are integrated in our design. Compared to Nvidia TX1 and Intel i7, our FPGA-based implementation achieves 5.6x and 3.4x speedup, as well as 3.0x and 34.6x power reduction, respectively.
Less is More: Accelerating Faster Neural Networks Straight from JPEG
Authors: Samuel Felipe dos Santos, Jurandy Almeida
Date: 2021-04-01 00:00:00

Most image data available are often stored in a compressed format, from which JPEG is the most widespread. To feed this data on a convolutional neural network (CNN), a preliminary decoding process is required to obtain RGB pixels, demanding a high computational load and memory usage. For this reason, the design of CNNs for processing JPEG compressed data has gained attention in recent years. In most existing works, typical CNN architectures are adapted to facilitate the learning with the DCT coefficients rather than RGB pixels. Although they are effective, their architectural changes either raise the computational costs or neglect relevant information from DCT inputs. In this paper, we examine different ways of speeding up CNNs designed for DCT inputs, exploiting learning strategies to reduce the computational complexity by taking full advantage of DCT inputs. Our experiments were conducted on the ImageNet dataset. Results show that learning how to combine all DCT inputs in a data-driven fashion is better than discarding them by hand, and its combination with a reduction of layers has proven to be effective for reducing the computational costs while retaining accuracy.
Selective Feature Compression for Efficient Activity Recognition Inference
Authors: Chunhui Liu, Xinyu Li, Hao Chen, Davide Modolo, Joseph Tighe
Date: 2021-04-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Recognition, Activity Recognition, Feature Compression

Most action recognition solutions rely on dense sampling to precisely cover the informative temporal clip. Extensively searching temporal region is expensive for a real-world application. In this work, we focus on improving the inference efficiency of current action recognition backbones on trimmed videos, and illustrate that one action model can also cover then informative region by dropping non-informative features. We present Selective Feature Compression (SFC), an action recognition inference strategy that greatly increase model inference efficiency without any accuracy compromise. Differently from previous works that compress kernel sizes and decrease the channel dimension, we propose to compress feature flow at spatio-temporal dimension without changing any backbone parameters. Our experiments on Kinetics-400, UCF101 and ActivityNet show that SFC is able to reduce inference speed by 6-7x and memory usage by 5-6x compared with the commonly used 30 crops dense sampling procedure, while also slightly improving Top1 Accuracy. We thoroughly quantitatively and qualitatively evaluate SFC and all its components and show how does SFC learn to attend to important video regions and to drop temporal features that are uninformative for the task of action recognition.
Visual Attention in Imaginative Agents
Authors: Samrudhdhi B. Rangrej, James J. Clark
Date: 2021-04-01 00:00:00

We present a recurrent agent who perceives surroundings through a series of discrete fixations. At each timestep, the agent imagines a variety of plausible scenes consistent with the fixation history. The next fixation is planned using uncertainty in the content of the imagined scenes. As time progresses, the agent becomes more certain about the content of the surrounding, and the variety in the imagined scenes reduces. The agent is built using a variational autoencoder and normalizing flows, and trained in an unsupervised manner on a proxy task of scene-reconstruction. The latent representations of the imagined scenes are found to be useful for performing pixel-level and scene-level tasks by higher-order modules. The agent is tested on various 2D and 3D datasets.
Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles
Authors: Muhyun Back, Jinkyu Lee, Kyuho Bae, Sung Soo Hwang, Il Yong Chun
Date: 2021-04-01 00:00:00
Tasks: 2D Object Detection, Autonomous Driving, Depth Estimation, object-detection, Object Detection

In advanced driver assistant systems and autonomous driving, it is crucial to estimate distances between an ego vehicle and target vehicles. Existing inter-vehicle distance estimation methods assume that the ego and target vehicles drive on a same ground plane. In practical driving environments, however, they may drive on different ground planes. This paper proposes an inter-vehicle distance estimation framework that can consider slope changes of a road forward, by estimating road gradients of \emph{both} ego vehicle and target vehicles and using a 2D object detection deep net. Numerical experiments demonstrate that the proposed method significantly improves the distance estimation accuracy and time complexity, compared to deep learning-based depth estimation methods.
Scalable Visual Attribute Extraction through Hidden Layers of a Residual ConvNet
Authors: Andres Baloian, Nils Murrugarra-Llerena, Jose M. Saavedra
Date: 2021-03-31 00:00:00
Tasks: Attribute, Attribute Extraction, Image Retrieval, Retrieval

Visual attributes play an essential role in real applications based on image retrieval. For instance, the extraction of attributes from images allows an eCommerce search engine to produce retrieval results with higher precision. The traditional manner to build an attribute extractor is by training a convnet-based classifier with a fixed number of classes. However, this approach does not scale for real applications where the number of attributes changes frequently. Therefore in this work, we propose an approach for extracting visual attributes from images, leveraging the learned capability of the hidden layers of a general convolutional network to discriminate among different visual features. We run experiments with a resnet-50 trained on Imagenet, on which we evaluate the output of its different blocks to discriminate between colors and textures. Our results show that the second block of the resnet is appropriate for discriminating colors, while the fourth block can be used for textures. In both cases, the achieved accuracy of attribute classification is superior to 93%. We also show that the proposed embeddings form local structures in the underlying feature space, which makes it possible to apply reduction techniques like UMAP, maintaining high accuracy and widely reducing the size of the feature space.
Full Surround Monodepth from Multiple Cameras
Authors: Vitor Guizilini, Igor Vasiljevic, Rares Ambrus, Greg Shakhnarovich, Adrien Gaidon
Date: 2021-03-31 00:00:00
Tasks: Autonomous Driving, Motion Estimation

Self-supervised monocular depth and ego-motion estimation is a promising approach to replace or supplement expensive depth sensors such as LiDAR for robotics applications like autonomous driving. However, most research in this area focuses on a single monocular camera or stereo pairs that cover only a fraction of the scene around the vehicle. In this work, we extend monocular self-supervised depth and ego-motion estimation to large-baseline multi-camera rigs. Using generalized spatio-temporal contexts, pose consistency constraints, and carefully designed photometric loss masking, we learn a single network generating dense, consistent, and scale-aware point clouds that cover the same full surround 360 degree field of view as a typical LiDAR scanner. We also propose a new scale-consistent evaluation metric more suitable to multi-camera settings. Experiments on two challenging benchmarks illustrate the benefits of our approach over strong baselines.
Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images
Authors: Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, Marleen de Bruijne
Date: 2021-03-31 00:00:00
Tasks: Medical Image Analysis, Semantic Segmentation

Adversarial attacks consist in maliciously changing the input data to mislead the predictions of automated decision systems and are potentially a serious threat for automated medical image analysis. Previous studies have shown that it is possible to adversarially manipulate automated segmentations produced by neural networks in a targeted manner in the white-box attack setting. In this article, we studied the effectiveness of adversarial attacks in targeted modification of segmentations of anatomical structures in chest X-rays. Firstly, we experimented with using anatomically implausible shapes as targets for adversarial manipulation. We showed that, by adding almost imperceptible noise to the image, we can reliably force state-of-the-art neural networks to segment the heart as a heart symbol instead of its real anatomical shape. Moreover, such heart-shaping attack did not appear to require higher adversarial noise level than an untargeted attack based the same attack method. Secondly, we attempted to explore the limits of adversarial manipulation of segmentations. For that, we assessed the effectiveness of shrinking and enlarging segmentation contours for the three anatomical structures. We observed that adversarially extending segmentations of structures into regions with intensity and texture uncharacteristic for them presented a challenge to our attacks, as well as, in some cases, changing segmentations in ways that conflict with class adjacency priors learned by the target network. Additionally, we evaluated performances of the untargeted attacks and targeted heart attacks in the black-box attack scenario, using a surrogate network trained on a different subset of images. In both cases, the attacks were substantially less effective. We believe these findings bring novel insights into the current capabilities and limits of adversarial attacks for semantic segmentation.
Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks
Authors: Kajetan Grodecki, Aditya Killekar, Andrew Lin, Sebastien Cadet, Priscilla McElhinney, Aryabod Razipour, Cato Chan, Barry D. Pressman, Peter Julien, Judit Simon, Pal Maurovich-Horvat, Nicola Gaibazzi, Udit Thakur, Elisabetta Mancini, Cecilia Agalbato, Jiro Munechika, Hidenari Matsumoto, Roberto Menè, Gianfranco Parati, Franco Cernigliaro, Nitesh Nerlekar, Camilla Torlasco, Gianluca Pontone, Damini Dey, Piotr J. Slomka
Date: 2021-03-31 00:00:00
Tasks: Computed Tomography (CT)

Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for rapid quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed 5 times with separate hold-out sets using 5-fold cross-validation to segment ground-glass opacity and high opacity (including consolidation and pleural effusion). The performance of the method was evaluated on CT data sets from 197 patients with positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score coefficient of 0.876 $\pm$ 0.005; excellent correlations of 0.978 and 0.981 for ground-glass opacity and high opacity volumes. In the external validation set of 67 patients, there was dice score coefficient of 0.767 $\pm$ 0.009 as well as excellent correlations of 0.989 and 0.996 for ground-glass opacity and high opacity volumes. Computations for a CT scan comprising 120 slices were performed under 2 seconds on a personal computer equipped with NVIDIA Titan RTX graphics processing unit. Therefore, our deep learning-based method allows rapid fully-automated quantitative measurement of pneumonia burden from CT and may generate results with an accuracy similar to the expert readers.