paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v35i3.16278 | Dynamic to Static Lidar Scan Reconstruction Using Adversarially Trained Auto Encoder | https://ojs.aaai.org/index.php/AAAI/article/view/16278 | https://ojs.aaai.org/index.php/AAAI/article/download/16278/16085 | [
"Prashant Kumar",
"Sabyasachi Sahoo",
"Vanshil Shah",
"Vineetha Kondameedi",
"Abhinav Jain",
"Akshaj Verma",
"Chiranjib Bhattacharyya",
"Vinay Vishwanath"
] | Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our knowledge no work has... | main | Computer Vision | 10.1609/aaai.v35i3.16278 | 35 | 3 | 1836-1844 | official | 2105.12774 | title_judge |
10.1609/aaai.v35i3.16279 | Regularizing Attention Networks for Anomaly Detection in Visual Question Answering | https://ojs.aaai.org/index.php/AAAI/article/view/16279 | https://ojs.aaai.org/index.php/AAAI/article/download/16279/16086 | [
"Doyup Lee",
"Yeongjae Cheon",
"Wook-Shin Han"
] | For stability and reliability of real-world applications, the robustness of DNNs in unimodal tasks has been evaluated. However, few studies consider abnormal situations that a visual question answering (VQA) model might encounter at test time after deployment in the real-world. In this study, we evaluate the robustness... | main | Computer Vision | 10.1609/aaai.v35i3.16279 | 35 | 3 | 1845-1853 | official | 2009.10054 | title_snapshot |
10.1609/aaai.v35i3.16280 | Weakly-supervised Temporal Action Localization by Uncertainty Modeling | https://ojs.aaai.org/index.php/AAAI/article/view/16280 | https://ojs.aaai.org/index.php/AAAI/article/download/16280/16087 | [
"Pilhyeon Lee",
"Jinglu Wang",
"Yan Lu",
"Hyeran Byun"
] | Weakly-supervised temporal action localization aims to learn detecting temporal intervals of action classes with only video-level labels. To this end, it is crucial to separate frames of action classes from the background frames (i.e., frames not belonging to any action classes). In this paper, we present a new perspec... | main | Computer Vision | 10.1609/aaai.v35i3.16280 | 35 | 3 | 1854-1862 | official | 2006.07006 | title_snapshot |
10.1609/aaai.v35i3.16281 | Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency | https://ojs.aaai.org/index.php/AAAI/article/view/16281 | https://ojs.aaai.org/index.php/AAAI/article/download/16281/16088 | [
"Seokju Lee",
"Sunghoon Im",
"Stephen Lin",
"In So Kweon"
] | We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion, and depth in a monocular camera setup without supervision. Our technical contributions are three-fold. First, we highlight the fundamental difference between inverse and forward projection whil... | main | Computer Vision | 10.1609/aaai.v35i3.16281 | 35 | 3 | 1863-1872 | official | 2102.02629 | title_snapshot |
10.1609/aaai.v35i3.16282 | Patch-Wise Attention Network for Monocular Depth Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16282 | https://ojs.aaai.org/index.php/AAAI/article/download/16282/16089 | [
"Sihaeng Lee",
"Janghyeon Lee",
"Byungju Kim",
"Eojindl Yi",
"Junmo Kim"
] | In computer vision, monocular depth estimation is the problem of obtaining a high-quality depth map from a two-dimensional image. This map provides information on three-dimensional scene geometry, which is necessary for various applications in academia and industry, such as robotics and autonomous driving. Recent studi... | main | Computer Vision | 10.1609/aaai.v35i3.16282 | 35 | 3 | 1873-1881 | official | null | null |
10.1609/aaai.v35i4.16463 | Context-Guided Adaptive Network for Efficient Human Pose Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16463 | https://ojs.aaai.org/index.php/AAAI/article/download/16463/16270 | [
"Lei Zhao",
"Jun Wen",
"Pengfei Wang",
"Nenggan Zheng"
] | Although recent work has achieved great progress in human pose estimation (HPE), most methods show limitations in either inference speed or accuracy. In this paper, we propose a fast and accurate end-to-end HPE method, which is specifically designed to overcome the commonly encountered jitter box, defective box and amb... | main | Computer Vision | 10.1609/aaai.v35i4.16463 | 35 | 4 | 3492-3499 | official | null | null |
10.1609/aaai.v35i4.16464 | ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16464 | https://ojs.aaai.org/index.php/AAAI/article/download/16464/16271 | [
"Sicheng Zhao",
"Yezhen Wang",
"Bo Li",
"Bichen Wu",
"Yang Gao",
"Pengfei Xu",
"Trevor Darrell",
"Kurt Keutzer"
] | Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations, which are time-consuming and expensive to obtain. Instead, simulation-to-real domain adaptatio... | main | Computer Vision | 10.1609/aaai.v35i4.16464 | 35 | 4 | 3500-3509 | official | 2009.03456 | title_snapshot |
10.1609/aaai.v35i4.16465 | Robust Lightweight Facial Expression Recognition Network with Label Distribution Training | https://ojs.aaai.org/index.php/AAAI/article/view/16465 | https://ojs.aaai.org/index.php/AAAI/article/download/16465/16272 | [
"Zengqun Zhao",
"Qingshan Liu",
"Feng Zhou"
] | This paper presents an efficiently robust facial expression recognition (FER) network, named EfficientFace, which holds much fewer parameters but more robust to the FER in the wild. Firstly, to improve the robustness of the lightweight network, a local-feature extractor and a channel-spatial modulator are designed, in ... | main | Computer Vision | 10.1609/aaai.v35i4.16465 | 35 | 4 | 3510-3519 | official | null | null |
10.1609/aaai.v35i4.16466 | Joint Color-irrelevant Consistency Learning and Identity-aware Modality Adaptation for Visible-infrared Cross Modality Person Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/16466 | https://ojs.aaai.org/index.php/AAAI/article/download/16466/16273 | [
"Zhiwei Zhao",
"Bin Liu",
"Qi Chu",
"Yan Lu",
"Nenghai Yu"
] | Visible-infrared cross modality person re-identification (VI-ReID) is a core but challenging technology in the 24-hours intelligent surveillance system. How to eliminate the large modality gap lies in the heart of VI-ReID. Conventional methods mainly focus on directly aligning the heterogeneous modalities into the same... | main | Computer Vision | 10.1609/aaai.v35i4.16466 | 35 | 4 | 3520-3528 | official | null | null |
10.1609/aaai.v35i4.16467 | Robust Multi-Modality Person Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/16467 | https://ojs.aaai.org/index.php/AAAI/article/download/16467/16274 | [
"Aihua Zheng",
"Zi Wang",
"Zihan Chen",
"Chenglong Li",
"Jin Tang"
] | To avoid the illumination limitation in visible person re-identification (Re-ID) and the heterogeneous issue in cross-modality Re-ID, we propose to utilize complementary advantages of multiple modalities including visible (RGB), near infrared (NI) and thermal infrared (TI) ones for robust person Re-ID. A novel progress... | main | Computer Vision | 10.1609/aaai.v35i4.16467 | 35 | 4 | 3529-3537 | official | null | null |
10.1609/aaai.v35i4.16468 | Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification | https://ojs.aaai.org/index.php/AAAI/article/view/16468 | https://ojs.aaai.org/index.php/AAAI/article/download/16468/16275 | [
"Kecheng Zheng",
"Cuiling Lan",
"Wenjun Zeng",
"Zhizheng Zhang",
"Zheng-Jun Zha"
] | Many unsupervised domain adaptive (UDA) person ReID approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of domain gap, the pseudo-labels are not always reliable and there are noisy/incorrect labels. This would mislead the feature representation learning and deteriorate... | main | Computer Vision | 10.1609/aaai.v35i4.16468 | 35 | 4 | 3538-3546 | official | 2012.08733 | title_snapshot |
10.1609/aaai.v35i4.16469 | RESA: Recurrent Feature-Shift Aggregator for Lane Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16469 | https://ojs.aaai.org/index.php/AAAI/article/download/16469/16276 | [
"Tu Zheng",
"Hao Fang",
"Yi Zhang",
"Wenjian Tang",
"Zheng Yang",
"Haifeng Liu",
"Deng Cai"
] | Lane detection is one of the most important tasks in self-driving. Due to various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals inherent in lane annotations, lane detection task is still challenging. Thus, it is difficult for the ordinary convolutional neural netwo... | main | Computer Vision | 10.1609/aaai.v35i4.16469 | 35 | 4 | 3547-3554 | official | 2008.13719 | title_snapshot |
10.1609/aaai.v35i4.16470 | CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud | https://ojs.aaai.org/index.php/AAAI/article/view/16470 | https://ojs.aaai.org/index.php/AAAI/article/download/16470/16277 | [
"Wu Zheng",
"Weiliang Tang",
"Sijin Chen",
"Li Jiang",
"Chi-Wing Fu"
] | Existing single-stage detectors for locating objects in point clouds often treat object localization and category classification as separate tasks, so the localization accuracy and classification confidence may not well align. To address this issue, we present a new single-stage detector named the Confident IoU-Aware S... | main | Computer Vision | 10.1609/aaai.v35i4.16470 | 35 | 4 | 3555-3562 | official | 2012.03015 | title_snapshot |
10.1609/aaai.v35i4.16471 | Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16471 | https://ojs.aaai.org/index.php/AAAI/article/download/16471/16278 | [
"Benjia Zhou",
"Yunan Li",
"Jun Wan"
] | Human gesture recognition has drawn much attention in the area of computer vision. However, the performance of gesture recognition is always influenced by some gesture-irrelevant factors like the background and the clothes of performers. Therefore, focusing on the regions of hand/arm is important to the gesture recogni... | main | Computer Vision | 10.1609/aaai.v35i4.16471 | 35 | 4 | 3563-3571 | official | 2102.05348 | title_snapshot |
10.1609/aaai.v35i4.16472 | Deep Semantic Dictionary Learning for Multi-label Image Classification | https://ojs.aaai.org/index.php/AAAI/article/view/16472 | https://ojs.aaai.org/index.php/AAAI/article/download/16472/16279 | [
"Fengtao Zhou",
"Sheng Huang",
"Yun Xing"
] | Compared with single-label image classification, multi-label image classification is more practical and challenging. Some recent studies attempted to leverage the semantic information of categories for improving multi-label image classification performance. However, these semantic-based methods only take semantic infor... | main | Computer Vision | 10.1609/aaai.v35i4.16472 | 35 | 4 | 3572-3580 | official | 2012.12509 | title_snapshot |
10.1609/aaai.v35i4.16473 | Model Uncertainty Guides Visual Object Tracking | https://ojs.aaai.org/index.php/AAAI/article/view/16473 | https://ojs.aaai.org/index.php/AAAI/article/download/16473/16280 | [
"Lijun Zhou",
"Antoine Ledent",
"Qintao Hu",
"Ting Liu",
"Jianlin Zhang",
"Marius Kloft"
] | Model object trackers largely rely on the online learning of a discriminative classifier from potentially diverse sample frames. However, noisy or insufficient amounts of samples can deteriorate the classifiers' performance and cause tracking drift. Furthermore, alterations such as occlusion and blurring can cause the ... | main | Computer Vision | 10.1609/aaai.v35i4.16473 | 35 | 4 | 3581-3589 | official | null | null |
10.1609/aaai.v35i4.16474 | Optimizing Information Theory Based Bitwise Bottlenecks for Efficient Mixed-Precision Activation Quantization | https://ojs.aaai.org/index.php/AAAI/article/view/16474 | https://ojs.aaai.org/index.php/AAAI/article/download/16474/16281 | [
"Xichuan Zhou",
"Kui Liu",
"Cong Shi",
"Haijun Liu",
"Ji Liu"
] | Recent researches on information theory shed new light on the continuous attempts to open the black box of neural signal encoding. Inspired by the problem of lossy signal compression for wireless communication, this paper presents a Bitwise Bottleneck approach for quantizing and encoding neural network activations. Bas... | main | Computer Vision | 10.1609/aaai.v35i4.16474 | 35 | 4 | 3590-3598 | official | 2006.05210 | title_judge |
10.1609/aaai.v35i4.16475 | Inferring Camouflaged Objects by Texture-Aware Interactive Guidance Network | https://ojs.aaai.org/index.php/AAAI/article/view/16475 | https://ojs.aaai.org/index.php/AAAI/article/download/16475/16282 | [
"Jinchao Zhu",
"Xiaoyu Zhang",
"Shuo Zhang",
"Junnan Liu"
] | Camouflaged objects, similar to the background, show indefinable boundaries and deceptive textures, which increases the difficulty of detection task and makes the model rely on features with more information. Herein, we design a texture label to facilitate our network for accurate camouflaged object segmentation. Motiv... | main | Computer Vision | 10.1609/aaai.v35i4.16475 | 35 | 4 | 3599-3607 | official | null | null |
10.1609/aaai.v35i4.16476 | Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps | https://ojs.aaai.org/index.php/AAAI/article/view/16476 | https://ojs.aaai.org/index.php/AAAI/article/download/16476/16283 | [
"Qi Zhu",
"Chenyu Gao",
"Peng Wang",
"Qi Wu"
] | Texts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices. Two tasks -- text-based visual question answering and text-based image captioning, with a text extension from existing vision-language appli... | main | Computer Vision | 10.1609/aaai.v35i4.16476 | 35 | 4 | 3608-3615 | official | 2012.05153 | title_snapshot |
10.1609/aaai.v35i4.16477 | Fooling Thermal Infrared Pedestrian Detectors in Real World Using Small Bulbs | https://ojs.aaai.org/index.php/AAAI/article/view/16477 | https://ojs.aaai.org/index.php/AAAI/article/download/16477/16284 | [
"Xiaopei Zhu",
"Xiao Li",
"Jianmin Li",
"Zheyao Wang",
"Xiaolin Hu"
] | Thermal infrared detection systems play an important role in many areas such as night security, autonomous driving, and body temperature detection. They have the unique advantages of passive imaging, temperature sensitivity and penetration. But the security of these systems themselves has not been fully explored, which... | main | Computer Vision | 10.1609/aaai.v35i4.16477 | 35 | 4 | 3616-3624 | official | 2101.08154 | title_snapshot |
10.1609/aaai.v35i4.16478 | ASHF-Net: Adaptive Sampling and Hierarchical Folding Network for Robust Point Cloud Completion | https://ojs.aaai.org/index.php/AAAI/article/view/16478 | https://ojs.aaai.org/index.php/AAAI/article/download/16478/16285 | [
"Daoming Zong",
"Shiliang Sun",
"Jing Zhao"
] | Estimating the complete 3D point cloud from an incomplete one lies at the core of many vision and robotics applications. Existing methods typically predict the complete point cloud based on the global shape representation extracted from the incomplete input. Although they could predict the overall shape of 3D objects, ... | main | Computer Vision | 10.1609/aaai.v35i4.16478 | 35 | 4 | 3625-3632 | official | null | null |
10.1609/aaai.v35i4.16443 | Visual Tracking via Hierarchical Deep Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16443 | https://ojs.aaai.org/index.php/AAAI/article/download/16443/16250 | [
"Dawei Zhang",
"Zhonglong Zheng",
"Riheng Jia",
"Minglu Li"
] | Visual tracking has achieved great progress due to numerous different algorithms. However, deep trackers based on classification or Siamese network still have their specific limitations. In this work, we show how to teach machines to track a generic object in videos like humans, who can use a few search steps to perfor... | main | Computer Vision | 10.1609/aaai.v35i4.16443 | 35 | 4 | 3315-3323 | official | null | null |
10.1609/aaai.v35i4.16444 | One for More: Selecting Generalizable Samples for Generalizable ReID Model | https://ojs.aaai.org/index.php/AAAI/article/view/16444 | https://ojs.aaai.org/index.php/AAAI/article/download/16444/16251 | [
"Enwei Zhang",
"Xinyang Jiang",
"Hao Cheng",
"Ancong Wu",
"Fufu Yu",
"Ke Li",
"Xiaowei Guo",
"Feng Zheng",
"Weishi Zheng",
"Xing Sun"
] | Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch. It will inevitably cause the model to over-fit the data in the dominant position (e.g., head data i... | main | Computer Vision | 10.1609/aaai.v35i4.16444 | 35 | 4 | 3324-3332 | official | 2012.05475 | title_snapshot |
10.1609/aaai.v35i4.16445 | Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16445 | https://ojs.aaai.org/index.php/AAAI/article/download/16445/16252 | [
"Gengwei Zhang",
"Yiming Gao",
"Hang Xu",
"Hao Zhang",
"Zhenguo Li",
"Xiaodan Liang"
] | Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention. While most existing methods focus on designing novel architectures, we steer toward a different perspective: performing automated multi-loss adaptation (named Ada-Segment) on the fly to flexib... | main | Computer Vision | 10.1609/aaai.v35i4.16445 | 35 | 4 | 3333-3341 | official | 2012.03603 | title_snapshot |
10.1609/aaai.v35i4.16446 | SIMPLE: SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16446 | https://ojs.aaai.org/index.php/AAAI/article/download/16446/16253 | [
"Jiabin Zhang",
"Zheng Zhu",
"Jiwen Lu",
"Junjie Huang",
"Guan Huang",
"Jie Zhou"
] | The practical application requests both accuracy and efficiency on multi-person pose estimation algorithms. But the high accuracy and fast inference speed are dominated by top-down methods and bottom-up methods respectively. To make a better trade-off between accuracy and efficiency, we propose a novel multi-person pos... | main | Computer Vision | 10.1609/aaai.v35i4.16446 | 35 | 4 | 3342-3350 | official | 2104.02486 | title_snapshot |
10.1609/aaai.v35i4.16447 | Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16447 | https://ojs.aaai.org/index.php/AAAI/article/download/16447/16254 | [
"Jingran Zhang",
"Xing Xu",
"Fumin Shen",
"Huimin Lu",
"Xin Liu",
"Heng Tao Shen"
] | The recent success of audio-visual representations learning can be largely attributed to their pervasive concurrency property, which can be used as a self-supervision signal and extract correlation information. While most recent works focus on capturing the shared associations between the audio and visual modalities, t... | main | Computer Vision | 10.1609/aaai.v35i4.16447 | 35 | 4 | 3351-3359 | official | null | null |
10.1609/aaai.v35i4.16448 | Unsupervised Domain Adaptation for Person Re-identification via Heterogeneous Graph Alignment | https://ojs.aaai.org/index.php/AAAI/article/view/16448 | https://ojs.aaai.org/index.php/AAAI/article/download/16448/16255 | [
"Minying Zhang",
"Kai Liu",
"Yidong Li",
"Shihui Guo",
"Hongtao Duan",
"Yimin Long",
"Yi Jin"
] | Unsupervised person re-identification (re-ID) is becoming increasingly popular due to its power in real-world systems such as public security and intelligent transportation systems. However, the person re-ID task is challenged by the problems of data distribution discrepancy across cameras and lack of label information... | main | Computer Vision | 10.1609/aaai.v35i4.16448 | 35 | 4 | 3360-3368 | official | null | null |
10.1609/aaai.v35i4.16449 | Proactive Privacy-preserving Learning for Retrieval | https://ojs.aaai.org/index.php/AAAI/article/view/16449 | https://ojs.aaai.org/index.php/AAAI/article/download/16449/16256 | [
"Peng-Fei Zhang",
"Zi Huang",
"Xin-Shun Xu"
] | Deep Neural Networks (DNNs) have recently achieved remarkable performance in image retrieval, yet posing great threats to data privacy. On the one hand, one may misuse a deployed DNNs based system to look up data without consent. On the other hand, organizations or individuals would legally or illegally collect data to... | main | Computer Vision | 10.1609/aaai.v35i4.16449 | 35 | 4 | 3369-3376 | official | null | null |
10.1609/aaai.v35i4.16450 | A Novel Visual Interpretability for Deep Neural Networks by Optimizing Activation Maps with Perturbation | https://ojs.aaai.org/index.php/AAAI/article/view/16450 | https://ojs.aaai.org/index.php/AAAI/article/download/16450/16257 | [
"Qinglong Zhang",
"Lu Rao",
"Yubin Yang"
] | Interpretability has been regarded as an essential component for deploying deep neural networks, in which the saliency-based method is one of the most prevailing interpretable approaches since it can generate individually intuitive heatmaps that highlight parts of the input image that are most important to the decision... | main | Computer Vision | 10.1609/aaai.v35i4.16450 | 35 | 4 | 3377-3384 | official | null | null |
10.1609/aaai.v35i4.16451 | Point Cloud Semantic Scene Completion from RGB-D Images | https://ojs.aaai.org/index.php/AAAI/article/view/16451 | https://ojs.aaai.org/index.php/AAAI/article/download/16451/16258 | [
"Shoulong Zhang",
"Shuai Li",
"Aimin Hao",
"Hong Qin"
] | In this paper, we devise a novel semantic completion network, called point cloud semantic scene completion network (PCSSC-Net), for indoor scenes solely based on point clouds. Existing point cloud completion networks still suffer from their inability of fully recovering complex structures and contents from global geome... | main | Computer Vision | 10.1609/aaai.v35i4.16451 | 35 | 4 | 3385-3393 | official | null | null |
10.1609/aaai.v35i4.16452 | Consensus Graph Representation Learning for Better Grounded Image Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/16452 | https://ojs.aaai.org/index.php/AAAI/article/download/16452/16259 | [
"Wenqiao Zhang",
"Haochen Shi",
"Siliang Tang",
"Jun Xiao",
"Qiang Yu",
"Yueting Zhuang"
] | The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words. The most common way is to encourage the capt... | main | Computer Vision | 10.1609/aaai.v35i4.16452 | 35 | 4 | 3394-3402 | official | 2112.00974 | title_snapshot |
10.1609/aaai.v35i4.16453 | BoW Pooling: A Plug-and-Play Unit for Feature Aggregation of Point Clouds | https://ojs.aaai.org/index.php/AAAI/article/view/16453 | https://ojs.aaai.org/index.php/AAAI/article/download/16453/16260 | [
"Xiang Zhang",
"Xiao Sun",
"Zhouhui Lian"
] | Point cloud provides a compact and flexible representation for 3D shapes and recently attracts more and more attention due to the increasing demands in practical applications. The major challenge of handling such irregular data is how to achieve the permutation invariance of points in the input. Most of existing method... | main | Computer Vision | 10.1609/aaai.v35i4.16453 | 35 | 4 | 3403-3411 | official | null | null |
10.1609/aaai.v35i4.16454 | Diverse Knowledge Distillation for End-to-End Person Search | https://ojs.aaai.org/index.php/AAAI/article/view/16454 | https://ojs.aaai.org/index.php/AAAI/article/download/16454/16261 | [
"Xinyu Zhang",
"Xinlong Wang",
"Jia-Wang Bian",
"Chunhua Shen",
"Mingyu You"
] | Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. The former views person search as two independent tasks and achieves dominant results using separately trained person detection and re-iden... | main | Computer Vision | 10.1609/aaai.v35i4.16454 | 35 | 4 | 3412-3420 | official | 2012.11187 | title_snapshot |
10.1609/aaai.v35i4.16455 | Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud | https://ojs.aaai.org/index.php/AAAI/article/view/16455 | https://ojs.aaai.org/index.php/AAAI/article/download/16455/16262 | [
"Yachao Zhang",
"Zonghao Li",
"Yuan Xie",
"Yanyun Qu",
"Cuihua Li",
"Tao Mei"
] | Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotation. Intuitively, weakly supervised training is a direct solution to reduce the labeling costs. However, for weakly supervised large-scale point cloud semantic segmentation, too few ann... | main | Computer Vision | 10.1609/aaai.v35i4.16455 | 35 | 4 | 3421-3429 | official | 2212.04744 | title_snapshot |
10.1609/aaai.v35i4.16456 | PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16456 | https://ojs.aaai.org/index.php/AAAI/article/download/16456/16263 | [
"Yanan Zhang",
"Di Huang",
"Yunhong Wang"
] | LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds caused by distant and occluded objects. In this paper, we propose a novel two-stage framework, namely PC-RGNN, which deals with these challenges by two specific solutions. On th... | main | Computer Vision | 10.1609/aaai.v35i4.16456 | 35 | 4 | 3430-3437 | official | 2012.10412 | title_snapshot |
10.1609/aaai.v35i4.16457 | Efficient License Plate Recognition via Holistic Position Attention | https://ojs.aaai.org/index.php/AAAI/article/view/16457 | https://ojs.aaai.org/index.php/AAAI/article/download/16457/16264 | [
"Yesheng Zhang",
"Zilei Wang",
"Jiafan Zhuang"
] | License plate recognition (LPR) is a fundamental component of various intelligent transportation systems, and is always expected to be accurate and efficient enough in real-world applications. Nowadays, recognition of single character has been sophisticated benefiting from the power of deep learning, and extracting pos... | main | Computer Vision | 10.1609/aaai.v35i4.16457 | 35 | 4 | 3438-3446 | official | null | null |
10.1609/aaai.v35i4.16458 | Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16458 | https://ojs.aaai.org/index.php/AAAI/article/download/16458/16265 | [
"Yongshun Zhang",
"Xiu-Shen Wei",
"Boyan Zhou",
"Jianxin Wu"
] | In recent years, visual recognition on challenging long-tailed distributions, where classes often exhibit extremely imbalanced frequencies, has made great progress mostly based on various complex paradigms (e.g., meta learning). Apart from these complex methods, simple refinements on training procedures also make contr... | main | Computer Vision | 10.1609/aaai.v35i4.16458 | 35 | 4 | 3447-3455 | official | null | null |
10.1609/aaai.v35i4.16459 | Depth Privileged Object Detection in Indoor Scenes via Deformation Hallucination | https://ojs.aaai.org/index.php/AAAI/article/view/16459 | https://ojs.aaai.org/index.php/AAAI/article/download/16459/16266 | [
"Zhijie Zhang",
"Yan Liu",
"Junjie Chen",
"Li Niu",
"Liqing Zhang"
] | RGB-D object detection has achieved significant advance, because depth provides complementary geometric information to RGB images. Considering depth images are unavailable in some scenarios, we focus on depth privileged object detection in indoor scenes, where the depth images are only available in the training phase. ... | main | Computer Vision | 10.1609/aaai.v35i4.16459 | 35 | 4 | 3456-3464 | official | null | null |
10.1609/aaai.v35i4.16460 | Learning Flexibly Distributional Representation for Low-quality 3D Face Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16460 | https://ojs.aaai.org/index.php/AAAI/article/download/16460/16267 | [
"Zihui Zhang",
"Cuican Yu",
"Shuang Xu",
"Huibin Li"
] | Due to the superiority of using geometric information, 3D Face Recognition (FR) has achieved great successes. Existing methods focus on high-quality 3D FR which is unpractical in real scenarios. Low-quality 3D FR is a more realistic scenario but the low-quality data are born with heavy noises. Therefore, exploring nois... | main | Computer Vision | 10.1609/aaai.v35i4.16460 | 35 | 4 | 3465-3473 | official | null | null |
10.1609/aaai.v35i4.16461 | IA-GM: A Deep Bidirectional Learning Method for Graph Matching | https://ojs.aaai.org/index.php/AAAI/article/view/16461 | https://ojs.aaai.org/index.php/AAAI/article/download/16461/16268 | [
"Kaixuan Zhao",
"Shikui Tu",
"Lei Xu"
] | Existing deep learning methods for graph matching(GM) problems usually considered affinity learningto assist combinatorial optimization in a feedforward pipeline, and parameter learning is executed by back-propagating the gradients of the matching loss. Such a pipeline pays little attention to the possible complementar... | main | Computer Vision | 10.1609/aaai.v35i4.16461 | 35 | 4 | 3474-3482 | official | null | null |
10.1609/aaai.v35i4.16462 | Distribution Adaptive INT8 Quantization for Training CNNs | https://ojs.aaai.org/index.php/AAAI/article/view/16462 | https://ojs.aaai.org/index.php/AAAI/article/download/16462/16269 | [
"Kang Zhao",
"Sida Huang",
"Pan Pan",
"Yinghan Li",
"Yingya Zhang",
"Zhenyu Gu",
"Yinghui Xu"
] | Researches have demonstrated that low bit-width (e.g., INT8) quantization can be employed to accelerate the inference process. It makes the gradient quantization very promising since the backward propagation requires approximately twice more computation than forward one. Due to the variability and uncertainty of gradie... | main | Computer Vision | 10.1609/aaai.v35i4.16462 | 35 | 4 | 3483-3491 | official | 2102.04782 | title_snapshot |
10.1609/aaai.v35i4.16423 | Object Relation Attention for Image Paragraph Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/16423 | https://ojs.aaai.org/index.php/AAAI/article/download/16423/16230 | [
"Li-Chuan Yang",
"Chih-Yuan Yang",
"Jane Yung-jen Hsu"
] | Image paragraph captioning aims to automatically generate a paragraph from a given image. It is an extension of image captioning in terms of generating multiple sentences instead of a single one, and it is more challenging because paragraphs are longer, more informative, and more linguistically complicated. Because a p... | main | Computer Vision | 10.1609/aaai.v35i4.16423 | 35 | 4 | 3136-3144 | official | null | null |
10.1609/aaai.v35i4.16424 | Adversarial Robustness through Disentangled Representations | https://ojs.aaai.org/index.php/AAAI/article/view/16424 | https://ojs.aaai.org/index.php/AAAI/article/download/16424/16231 | [
"Shuo Yang",
"Tianyu Guo",
"Yunhe Wang",
"Chang Xu"
] | Despite the remarkable empirical performance of deep learning models, their vulnerability to adversarial examples has been revealed in many studies. They are prone to make a susceptible prediction to the input with imperceptible adversarial perturbation. Although recent works have remarkably improved the model's robust... | main | Computer Vision | 10.1609/aaai.v35i4.16424 | 35 | 4 | 3145-3153 | official | null | null |
10.1609/aaai.v35i4.16425 | CPCGAN: A Controllable 3D Point Cloud Generative Adversarial Network with Semantic Label Generating | https://ojs.aaai.org/index.php/AAAI/article/view/16425 | https://ojs.aaai.org/index.php/AAAI/article/download/16425/16232 | [
"Ximing Yang",
"Yuan Wu",
"Kaiyi Zhang",
"Cheng Jin"
] | Generative Adversarial Networks (GAN) are good at generating variant samples of complex data distributions. Generating a sample with certain properties is one of the major tasks in the real-world application of GANs. In this paper, we propose a novel generative adversarial network to generate 3D point clouds from rando... | main | Computer Vision | 10.1609/aaai.v35i4.16425 | 35 | 4 | 3154-3162 | official | null | null |
10.1609/aaai.v35i4.16426 | R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object | https://ojs.aaai.org/index.php/AAAI/article/view/16426 | https://ojs.aaai.org/index.php/AAAI/article/download/16426/16233 | [
"Xue Yang",
"Junchi Yan",
"Ziming Feng",
"Tao He"
] | Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progress has been made, for practical settings, there still exist challenges for rotating objects with large aspect ratio, dense distribution and ... | main | Computer Vision | 10.1609/aaai.v35i4.16426 | 35 | 4 | 3163-3171 | official | 1908.05612 | title_snapshot |
10.1609/aaai.v35i4.16427 | One-shot Face Reenactment Using Appearance Adaptive Normalization | https://ojs.aaai.org/index.php/AAAI/article/view/16427 | https://ojs.aaai.org/index.php/AAAI/article/download/16427/16234 | [
"Guangming Yao",
"Yi Yuan",
"Tianjia Shao",
"Shuang Li",
"Shanqi Liu",
"Yong Liu",
"Mengmeng Wang",
"Kun Zhou"
] | The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance. The core of our network is a novel mechanism called appearance adaptive normalization, wh... | main | Computer Vision | 10.1609/aaai.v35i4.16427 | 35 | 4 | 3172-3180 | official | 2102.03984 | title_snapshot |
10.1609/aaai.v35i4.16428 | A Case Study of the Shortcut Effects in Visual Commonsense Reasoning | https://ojs.aaai.org/index.php/AAAI/article/view/16428 | https://ojs.aaai.org/index.php/AAAI/article/download/16428/16235 | [
"Keren Ye",
"Adriana Kovashka"
] | Visual reasoning and question-answering have gathered attention in recent years. Many datasets and evaluation protocols have been proposed; some have been shown to contain bias that allows models to ``cheat'' without performing true, generalizable reasoning. A well-known bias is dependence on language priors (frequency... | main | Computer Vision | 10.1609/aaai.v35i4.16428 | 35 | 4 | 3181-3189 | official | null | null |
10.1609/aaai.v35i4.16429 | Instance Mining with Class Feature Banks for Weakly Supervised Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16429 | https://ojs.aaai.org/index.php/AAAI/article/download/16429/16236 | [
"Yufei Yin",
"Jiajun Deng",
"Wengang Zhou",
"Houqiang Li"
] | Recent progress on weakly supervised object detection (WSOD) is characterized by formulating WSOD as a Multiple Instance Learning (MIL) problem and taking online refinement with the selected region proposals from MIL. However, MIL inclines to select the most discriminative part rather than the entire instance as the to... | main | Computer Vision | 10.1609/aaai.v35i4.16429 | 35 | 4 | 3190-3198 | official | null | null |
10.1609/aaai.v35i4.16430 | Multimodal Fusion via Teacher-Student Network for Indoor Action Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16430 | https://ojs.aaai.org/index.php/AAAI/article/download/16430/16237 | [
"Bruce X.B. Yu",
"Yan Liu",
"Keith C.C. Chan"
] | Indoor action recognition plays an important role in modern society, such as intelligent healthcare in large mobile cabin hospitals. With the wide usage of depth sensors like Kinect, multimodal information including skeleton and RGB modalities brings a promising way to improve the performance. However, existing methods... | main | Computer Vision | 10.1609/aaai.v35i4.16430 | 35 | 4 | 3199-3207 | official | null | null |
10.1609/aaai.v35i4.16431 | ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/16431 | https://ojs.aaai.org/index.php/AAAI/article/download/16431/16238 | [
"Fei Yu",
"Jiji Tang",
"Weichong Yin",
"Yu Sun",
"Hao Tian",
"Hua Wu",
"Haifeng Wang"
] | We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL tries to build the detailed semantic connections (objects, attributes of objects and relationships between objects) across vision and langu... | main | Computer Vision | 10.1609/aaai.v35i4.16431 | 35 | 4 | 3208-3216 | official | 2006.16934 | title_judge |
10.1609/aaai.v35i4.16432 | High-Resolution Deep Image Matting | https://ojs.aaai.org/index.php/AAAI/article/view/16432 | https://ojs.aaai.org/index.php/AAAI/article/download/16432/16239 | [
"Haichao Yu",
"Ning Xu",
"Zilong Huang",
"Yuqian Zhou",
"Humphrey Shi"
] | Image matting is a key technique for image and video editing and composition. Conventionally, deep learning approaches take the whole input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such approaches set state-of-the-arts in image matting; however, they may fail in real-... | main | Computer Vision | 10.1609/aaai.v35i4.16432 | 35 | 4 | 3217-3224 | official | 2009.06613 | title_snapshot |
10.1609/aaai.v35i4.16433 | CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16433 | https://ojs.aaai.org/index.php/AAAI/article/download/16433/16240 | [
"Qihang Yu",
"Yingwei Li",
"Jieru Mei",
"Yuyin Zhou",
"Alan Yuille"
] | 3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition. However, 3D networks can easily lead to over-parameterization which incurs expensive computation cost. In this paper, we propose Channel-wise Automatic KErnel Shrinking (CAK... | main | Computer Vision | 10.1609/aaai.v35i4.16433 | 35 | 4 | 3225-3233 | official | 2003.12798 | title_snapshot |
10.1609/aaai.v35i4.16434 | Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence | https://ojs.aaai.org/index.php/AAAI/article/view/16434 | https://ojs.aaai.org/index.php/AAAI/article/download/16434/16241 | [
"Siyue Yu",
"Bingfeng Zhang",
"Jimin Xiao",
"Eng Gee Lim"
] | Sparse labels have been attracting much attention in recent years. However, the performance gap between weakly supervised and fully supervised salient object detection methods is huge, and most previous weakly supervised works adopt complex training methods with many bells and whistles. In this work, we propose a one-r... | main | Computer Vision | 10.1609/aaai.v35i4.16434 | 35 | 4 | 3234-3242 | official | 2012.04404 | title_snapshot |
10.1609/aaai.v35i4.16435 | Fast and Compact Bilinear Pooling by Shifted Random Maclaurin | https://ojs.aaai.org/index.php/AAAI/article/view/16435 | https://ojs.aaai.org/index.php/AAAI/article/download/16435/16242 | [
"Tan Yu",
"Xiaoyun Li",
"Ping Li"
] | Bilinear pooling has achieved an excellent performance in many computer vision tasks such as fine-grained classification, scene recognition and texture recognition. However, the high-dimension features from bilinear pooling can sometimes be inefficient and prone to over-fitting. Random Maclaurin (RM) is a widely used G... | main | Computer Vision | 10.1609/aaai.v35i4.16435 | 35 | 4 | 3243-3251 | official | null | null |
10.1609/aaai.v35i4.16436 | Simple and Effective Stochastic Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16436 | https://ojs.aaai.org/index.php/AAAI/article/download/16436/16243 | [
"Tianyuan Yu",
"Yongxin Yang",
"Da Li",
"Timothy Hospedales",
"Tao Xiang"
] | Stochastic neural networks (SNNs) are currently topical, with several paradigms being actively investigated including dropout, Bayesian neural networks, variational information bottleneck (VIB) and noise regularized learning. These neural network variants impact several major considerations, including generalization, n... | main | Computer Vision | 10.1609/aaai.v35i4.16436 | 35 | 4 | 3252-3260 | official | null | null |
10.1609/aaai.v35i4.16437 | Learning Visual Context for Group Activity Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16437 | https://ojs.aaai.org/index.php/AAAI/article/download/16437/16244 | [
"Hangjie Yuan",
"Dong Ni"
] | Group activity recognition aims to recognize an overall activity in a multi-person scene. Previous methods strive to reason on individual features. However, they under-explore the person-specific contextual information, which is significant and informative in computer vision tasks. In this paper, we propose a new reaso... | main | Computer Vision | 10.1609/aaai.v35i4.16437 | 35 | 4 | 3261-3269 | official | null | null |
10.1609/aaai.v35i4.16438 | StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding | https://ojs.aaai.org/index.php/AAAI/article/view/16438 | https://ojs.aaai.org/index.php/AAAI/article/download/16438/16245 | [
"Jinshan Zeng",
"Qi Chen",
"Yunxin Liu",
"Mingwen Wang",
"Yuan Yao"
] | The generation of stylish Chinese fonts is an important problem involved in many applications. Most of existing generation methods are based on the deep generative models, particularly, the generative adversarial networks (GAN) based models. However, these deep generative models may suffer from the mode collapse issue,... | main | Computer Vision | 10.1609/aaai.v35i4.16438 | 35 | 4 | 3270-3277 | official | 2012.08687 | title_snapshot |
10.1609/aaai.v35i4.16439 | Demodalizing Face Recognition with Synthetic Samples | https://ojs.aaai.org/index.php/AAAI/article/view/16439 | https://ojs.aaai.org/index.php/AAAI/article/download/16439/16246 | [
"Zhonghua Zhai",
"Pengju Yang",
"Xiaofeng Zhang",
"Maji Huang",
"Haijing Cheng",
"Xuejun Yan",
"Chunmao Wang",
"Shiliang Pu"
] | Using data generated by generative adversarial networks or three-dimensional (3D) technology for face recognition training is a theoretically reasonable solution to the problems of unbalanced data distributions and data scarcity. However, due to the modal difference between synthetic data and real data, the direct use ... | main | Computer Vision | 10.1609/aaai.v35i4.16439 | 35 | 4 | 3278-3286 | official | null | null |
10.1609/aaai.v35i4.16440 | EMLight: Lighting Estimation via Spherical Distribution Approximation | https://ojs.aaai.org/index.php/AAAI/article/view/16440 | https://ojs.aaai.org/index.php/AAAI/article/download/16440/16247 | [
"Fangneng Zhan",
"Changgong Zhang",
"Yingchen Yu",
"Yuan Chang",
"Shijian Lu",
"Feiying Ma",
"Xuansong Xie"
] | Illumination estimation from a single image is critical in 3D rendering and it has been investigated extensively in the computer vision and computer graphic research community. On the other hand, existing works estimate illumination by either regressing light parameters or generating illumination maps that are often ha... | main | Computer Vision | 10.1609/aaai.v35i4.16440 | 35 | 4 | 3287-3295 | official | 2012.11116 | title_snapshot |
10.1609/aaai.v35i4.16441 | Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards a Fourier Perspective | https://ojs.aaai.org/index.php/AAAI/article/view/16441 | https://ojs.aaai.org/index.php/AAAI/article/download/16441/16248 | [
"Chaoning Zhang",
"Philipp Benz",
"Adil Karjauv",
"In So Kweon"
] | The booming interest in adversarial attacks stems from a misalignment between human vision and a deep neural network (DNN), \ie~a human imperceptible perturbation fools the DNN. Moreover, a single perturbation, often called universal adversarial perturbation (UAP), can be generated to fool the DNN for most images. A si... | main | Computer Vision | 10.1609/aaai.v35i4.16441 | 35 | 4 | 3296-3304 | official | 2102.06479 | title_snapshot |
10.1609/aaai.v35i4.16442 | SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16442 | https://ojs.aaai.org/index.php/AAAI/article/download/16442/16249 | [
"Chengwei Zhang",
"Yunlu Xu",
"Zhanzhan Cheng",
"Shiliang Pu",
"Yi Niu",
"Fei Wu",
"Futai Zou"
] | Arbitrary text appearance poses a great challenge in scene text recognition tasks. Existing works mostly handle with the problem in consideration of the shape distortion, including perspective distortions, line curvature or other style variations. Rectification (i.e., spatial transformers) as the preprocessing stage is... | main | Computer Vision | 10.1609/aaai.v35i4.16442 | 35 | 4 | 3305-3314 | official | 2005.13117 | title_snapshot |
10.1609/aaai.v35i4.16403 | Binaural Audio-Visual Localization | https://ojs.aaai.org/index.php/AAAI/article/view/16403 | https://ojs.aaai.org/index.php/AAAI/article/download/16403/16210 | [
"Xinyi Wu",
"Zhenyao Wu",
"Lili Ju",
"Song Wang"
] | Localizing sound sources in a visual scene has many important applications and quite a few traditional or learning-based methods have been proposed for this task. Humans have the ability to roughly localize sound sources within or beyond the range of the vision using their binaural system. However most existing methods... | main | Computer Vision | 10.1609/aaai.v35i4.16403 | 35 | 4 | 2961-2968 | official | null | null |
10.1609/aaai.v35i4.16404 | Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions | https://ojs.aaai.org/index.php/AAAI/article/view/16404 | https://ojs.aaai.org/index.php/AAAI/article/download/16404/16211 | [
"Yuhang Wu",
"Sunpreet S Arora",
"Yanhong Wu",
"Hao Yang"
] | Adversarial examples are input examples that are specifically crafted to deceive machine learning classifiers. State-of-the-art adversarial example detection methods characterize an input example as adversarial either by quantifying the magnitude of feature variations under multiple perturbations or by measuring its di... | main | Computer Vision | 10.1609/aaai.v35i4.16404 | 35 | 4 | 2969-2977 | official | 2012.15386 | title_snapshot |
10.1609/aaai.v35i4.16405 | Shape-Pose Ambiguity in Learning 3D Reconstruction from Images | https://ojs.aaai.org/index.php/AAAI/article/view/16405 | https://ojs.aaai.org/index.php/AAAI/article/download/16405/16212 | [
"Yunjie Wu",
"Zhengxing Sun",
"Youcheng Song",
"Yunhan Sun",
"YiJie Zhong",
"Jinlong Shi"
] | Learning single-image 3D reconstruction with only 2D images supervision is a promising research topic. The main challenge in image-supervised 3D reconstruction is the shape-pose ambiguity, which means a 2D supervision can be explained by an erroneous 3D shape from an erroneous pose. It will introduce high uncertainty a... | main | Computer Vision | 10.1609/aaai.v35i4.16405 | 35 | 4 | 2978-2985 | official | null | null |
10.1609/aaai.v35i4.16406 | Boundary Proposal Network for Two-stage Natural Language Video Localization | https://ojs.aaai.org/index.php/AAAI/article/view/16406 | https://ojs.aaai.org/index.php/AAAI/article/download/16406/16213 | [
"Shaoning Xiao",
"Long Chen",
"Songyang Zhang",
"Wei Ji",
"Jian Shao",
"Lu Ye",
"Jun Xiao"
] | We aim to address the problem of Natural Language Video Localization (NLVL) — localizing the video segment corresponding to a natural language description in a long and untrimmed video. State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based app... | main | Computer Vision | 10.1609/aaai.v35i4.16406 | 35 | 4 | 2986-2994 | official | 2103.08109 | title_snapshot |
10.1609/aaai.v35i4.16407 | Amodal Segmentation Based on Visible Region Segmentation and Shape Prior | https://ojs.aaai.org/index.php/AAAI/article/view/16407 | https://ojs.aaai.org/index.php/AAAI/article/download/16407/16214 | [
"Yuting Xiao",
"Yanyu Xu",
"Ziming Zhong",
"Weixin Luo",
"Jiawei Li",
"Shenghua Gao"
] | Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to the whole image. This is against the human's amodal perception, where human uses the visible part and the shape prior knowledge of the target to infer the occluded region. To mimic the behavior of ... | main | Computer Vision | 10.1609/aaai.v35i4.16407 | 35 | 4 | 2995-3003 | official | 2012.05598 | title_snapshot |
10.1609/aaai.v35i4.16408 | Locate Globally, Segment Locally: A Progressive Architecture With Knowledge Review Network for Salient Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16408 | https://ojs.aaai.org/index.php/AAAI/article/download/16408/16215 | [
"Binwei Xu",
"Haoran Liang",
"Ronghua Liang",
"Peng Chen"
] | Salient object location and segmentation are two different tasks in salient object detection (SOD). The former aims to globally find the most attractive objects in an image, whereas the latter can be achieved only using local regions that contain salient objects. However, previous methods mainly accomplish the two task... | main | Computer Vision | 10.1609/aaai.v35i4.16408 | 35 | 4 | 3004-3012 | official | null | null |
10.1609/aaai.v35i4.16409 | Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16409 | https://ojs.aaai.org/index.php/AAAI/article/download/16409/16216 | [
"Chenxin Xu",
"Siheng Chen",
"Maosen Li",
"Ya Zhang"
] | We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handl... | main | Computer Vision | 10.1609/aaai.v35i4.16409 | 35 | 4 | 3013-3021 | official | 2012.09398 | title_snapshot |
10.1609/aaai.v35i4.16410 | Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning | https://ojs.aaai.org/index.php/AAAI/article/view/16410 | https://ojs.aaai.org/index.php/AAAI/article/download/16410/16217 | [
"Chunpu Xu",
"Min Yang",
"Chengming Li",
"Ying Shen",
"Xiang Ao",
"Ruifeng Xu"
] | Visual storytelling is a task of creating a short story based on photo streams. Different from visual captions, stories contain not only factual descriptions, but also imaginary concepts that do not appear in the images. In this paper, we propose a novel imagine-reason-write generation framework (IRW) for visual storyt... | main | Computer Vision | 10.1609/aaai.v35i4.16410 | 35 | 4 | 3022-3029 | official | null | null |
10.1609/aaai.v35i4.16411 | Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16411 | https://ojs.aaai.org/index.php/AAAI/article/download/16411/16218 | [
"Hongbin Xu",
"Zhipeng Zhou",
"Yu Qiao",
"Wenxiong Kang",
"Qiuxia Wu"
] | Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS). However, existing methods rely on the assumption that the corresponding points among different views share the same color, which may not always be true in practice. This may lead to unrel... | main | Computer Vision | 10.1609/aaai.v35i4.16411 | 35 | 4 | 3030-3038 | official | 2104.05374 | title_snapshot |
10.1609/aaai.v35i4.16412 | Efficient Deep Image Denoising via Class Specific Convolution | https://ojs.aaai.org/index.php/AAAI/article/view/16412 | https://ojs.aaai.org/index.php/AAAI/article/download/16412/16219 | [
"Lu Xu",
"Jiawei Zhang",
"Xuanye Cheng",
"Feng Zhang",
"Xing Wei",
"Jimmy Ren"
] | Deep neural networks have been widely used in image denoising during the past few years. Even though they achieve great success on this problem, they are computationally inefficient which makes them inappropriate to be implemented in mobile devices. In this paper, we propose an efficient deep neural network for image d... | main | Computer Vision | 10.1609/aaai.v35i4.16412 | 35 | 4 | 3039-3046 | official | 2103.01624 | title_snapshot |
10.1609/aaai.v35i4.16413 | Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud | https://ojs.aaai.org/index.php/AAAI/article/view/16413 | https://ojs.aaai.org/index.php/AAAI/article/download/16413/16220 | [
"Mingye Xu",
"Zhipeng Zhou",
"Junhao Zhang",
"Yu Qiao"
] | This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds. The indistinguishable points consist of those located in complex boundary, points with similar local textures but different categories, and points in isolate small hard areas, whic... | main | Computer Vision | 10.1609/aaai.v35i4.16413 | 35 | 4 | 3047-3055 | official | 2103.10339 | title_snapshot |
10.1609/aaai.v35i4.16414 | Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud | https://ojs.aaai.org/index.php/AAAI/article/view/16414 | https://ojs.aaai.org/index.php/AAAI/article/download/16414/16221 | [
"Mutian Xu",
"Junhao Zhang",
"Zhipeng Zhou",
"Mingye Xu",
"Xiaojuan Qi",
"Yu Qiao"
] | In 2D image processing, some attempts decompose images into high and low frequency components for describing edge and smooth parts respectively. Similarly, the contour and flat area of 3D objects, such as the boundary and seat area of a chair, describe different but also complementary geometries. However, such investig... | main | Computer Vision | 10.1609/aaai.v35i4.16414 | 35 | 4 | 3056-3064 | official | 2012.10921 | title_snapshot |
10.1609/aaai.v35i4.16415 | Searching for Alignment in Face Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16415 | https://ojs.aaai.org/index.php/AAAI/article/download/16415/16222 | [
"Xiaqing Xu",
"Qiang Meng",
"Yunxiao Qin",
"Jianzhu Guo",
"Chenxu Zhao",
"Feng Zhou",
"Zhen Lei"
] | A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the face image using a pre-defined template, extracting representations and comparing. Among them, face detection, landmark detection and repre... | main | Computer Vision | 10.1609/aaai.v35i4.16415 | 35 | 4 | 3065-3073 | official | 2102.05447 | title_snapshot |
10.1609/aaai.v35i4.16416 | GIF Thumbnails: Attract More Clicks to Your Videos | https://ojs.aaai.org/index.php/AAAI/article/view/16416 | https://ojs.aaai.org/index.php/AAAI/article/download/16416/16223 | [
"Yi Xu",
"Fan Bai",
"Yingxuan Shi",
"Qiuyu Chen",
"Longwen Gao",
"Kai Tian",
"Shuigeng Zhou",
"Huyang Sun"
] | With the rapid increase of mobile devices and online media, more and more people prefer posting/viewing videos online. Generally, these videos are presented on video streaming sites with image thumbnails and text titles. While facing huge amounts of videos, a viewer clicks through a certain video with high probability ... | main | Computer Vision | 10.1609/aaai.v35i4.16416 | 35 | 4 | 3074-3082 | official | null | null |
10.1609/aaai.v35i4.16417 | FaceController: Controllable Attribute Editing for Face in the Wild | https://ojs.aaai.org/index.php/AAAI/article/view/16417 | https://ojs.aaai.org/index.php/AAAI/article/download/16417/16224 | [
"Zhiliang Xu",
"Xiyu Yu",
"Zhibin Hong",
"Zhen Zhu",
"Junyu Han",
"Jingtuo Liu",
"Errui Ding",
"Xiang Bai"
] | Face attribute editing aims to generate faces with one or multiple desired face attributes manipulated while other details are preserved. Unlike prior works such as GAN inversion which has an expensive reverse mapping process, we propose a simple feed-forward network to generate high-fidelity manipulated faces. By simp... | main | Computer Vision | 10.1609/aaai.v35i4.16417 | 35 | 4 | 3083-3091 | official | 2102.11464 | title_snapshot |
10.1609/aaai.v35i4.16418 | AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses | https://ojs.aaai.org/index.php/AAAI/article/view/16418 | https://ojs.aaai.org/index.php/AAAI/article/download/16418/16225 | [
"Zixuan Xu",
"Banghuai Li",
"Ye Yuan",
"Miao Geng"
] | Facial landmark localization aims to detect the predefined points of human faces, and the topic has been rapidly improved with the recent development of neural network based methods. However, it remains a challenging task when dealing with faces in unconstrained scenarios, especially with large pose variations. In this... | main | Computer Vision | 10.1609/aaai.v35i4.16418 | 35 | 4 | 3092-3100 | official | 2007.03221 | title_snapshot |
10.1609/aaai.v35i4.16419 | Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion | https://ojs.aaai.org/index.php/AAAI/article/view/16419 | https://ojs.aaai.org/index.php/AAAI/article/download/16419/16226 | [
"Xu Yan",
"Jiantao Gao",
"Jie Li",
"Ruimao Zhang",
"Zhen Li",
"Rui Huang",
"Shuguang Cui"
] | LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep LiDAR point cloud, the accurate semantic segmentation is non-trivial to achieve. In this paper, we propose a novel sparse LiDAR point cloud ... | main | Computer Vision | 10.1609/aaai.v35i4.16419 | 35 | 4 | 3101-3109 | official | 2012.03762 | title_snapshot |
10.1609/aaai.v35i4.16420 | Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16420 | https://ojs.aaai.org/index.php/AAAI/article/download/16420/16227 | [
"Xudong Yan",
"Huaidong Zhang",
"Xuemiao Xu",
"Xiaowei Hu",
"Pheng-Ann Heng"
] | Unsupervised anomaly detection aims to identify data samples that have low probability density from a set of input samples, and only the normal samples are provided for model training. The inference of abnormal regions on the input image requires an understanding of the surrounding semantic context. This work presents ... | main | Computer Vision | 10.1609/aaai.v35i4.16420 | 35 | 4 | 3110-3118 | official | null | null |
10.1609/aaai.v35i4.16421 | Non-Autoregressive Coarse-to-Fine Video Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/16421 | https://ojs.aaai.org/index.php/AAAI/article/download/16421/16228 | [
"Bang Yang",
"Yuexian Zou",
"Fenglin Liu",
"Can Zhang"
] | It is encouraged to see that progress has been made to bridge videos and natural language. However, mainstream video captioning methods suffer from slow inference speed due to the sequential manner of autoregressive decoding, and prefer generating generic descriptions due to the insufficient training of visual words (e... | main | Computer Vision | 10.1609/aaai.v35i4.16421 | 35 | 4 | 3119-3127 | official | 1911.12018 | title_snapshot |
10.1609/aaai.v35i4.16422 | Learning to Attack Real-World Models for Person Re-identification via Virtual-Guided Meta-Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16422 | https://ojs.aaai.org/index.php/AAAI/article/download/16422/16229 | [
"Fengxiang Yang",
"Zhun Zhong",
"Hong Liu",
"Zheng Wang",
"Zhiming Luo",
"Shaozi Li",
"Nicu Sebe",
"Shin'ichi Satoh"
] | Recent advances in person re-identification (re-ID) have led to impressive retrieval accuracy. However, existing re-ID models are challenged by the adversarial examples crafted by adding quasi-imperceptible perturbations. Moreover, re-ID systems face the domain shift issue that training and testing domains are not cons... | main | Computer Vision | 10.1609/aaai.v35i4.16422 | 35 | 4 | 3128-3135 | official | null | null |
10.1609/aaai.v35i4.16383 | PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network | https://ojs.aaai.org/index.php/AAAI/article/view/16383 | https://ojs.aaai.org/index.php/AAAI/article/download/16383/16190 | [
"Pengfei Wang",
"Chengquan Zhang",
"Fei Qi",
"Shanshan Liu",
"Xiaoqiang Zhang",
"Pengyuan Lyu",
"Junyu Han",
"Jingtuo Liu",
"Errui Ding",
"Guangming Shi"
] | The reading of arbitrarily-shaped text has received increasing research attention. However, existing text spotters are mostly built on two-stage frameworks or character-based methods, which suffer from either Non-Maximum Suppression (NMS), Region-of-Interest (RoI) operations, or character-level annotations. In this pap... | main | Computer Vision | 10.1609/aaai.v35i4.16383 | 35 | 4 | 2782-2790 | official | 2104.05458 | title_snapshot |
10.1609/aaai.v35i4.16384 | Dynamic Position-aware Network for Fine-grained Image Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16384 | https://ojs.aaai.org/index.php/AAAI/article/download/16384/16191 | [
"Shijie Wang",
"Haojie Li",
"Zhihui Wang",
"Wanli Ouyang"
] | Most weakly supervised fine-grained image recognition (WFGIR) approaches predominantly focus on learning the discriminative details which contain the visual variances and position clues. The position clues can be indirectly learnt by utilizing context information of discriminative visual content. However, this will cau... | main | Computer Vision | 10.1609/aaai.v35i4.16384 | 35 | 4 | 2791-2799 | official | null | null |
10.1609/aaai.v35i4.16385 | Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16385 | https://ojs.aaai.org/index.php/AAAI/article/download/16385/16192 | [
"Tiancai Wang",
"Tong Yang",
"Jiale Cao",
"Xiangyu Zhang"
] | Object detectors usually achieve promising results with the supervision of complete instance annotations. However, their performance is far from satisfactory with sparse instance annotations. Most existing methods for sparsely annotated object detection either re-weight the loss of hard negative samples or convert the ... | main | Computer Vision | 10.1609/aaai.v35i4.16385 | 35 | 4 | 2800-2808 | official | 2012.01950 | title_snapshot |
10.1609/aaai.v35i4.16386 | Very Important Person Localization in Unconstrained Conditions: A New Benchmark | https://ojs.aaai.org/index.php/AAAI/article/view/16386 | https://ojs.aaai.org/index.php/AAAI/article/download/16386/16193 | [
"Xiao Wang",
"Zheng Wang",
"Toshihiko Yamasaki",
"Wenjun Zeng"
] | This paper presents a new high-quality dataset for Very Important Person Localization (VIPLoc), named Unconstrained-7k. Generally, current datasets: 1) are limited in scale; 2) built under simple and constrained conditions, where the number of disturbing non-VIPs is not large, the scene is relatively simple, and the fa... | main | Computer Vision | 10.1609/aaai.v35i4.16386 | 35 | 4 | 2809-2816 | official | null | null |
10.1609/aaai.v35i4.16387 | Teacher Guided Neural Architecture Search for Face Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16387 | https://ojs.aaai.org/index.php/AAAI/article/download/16387/16194 | [
"Xiaobo Wang"
] | Knowledge distillation is an effective tool to compress large pre-trained convolutional neural networks (CNNs) or their ensembles into models applicable to mobile and embedded devices. However, with expected flops or latency, existing methods are hand-crafted heuristics. They propose to pre-define the target student ne... | main | Computer Vision | 10.1609/aaai.v35i4.16387 | 35 | 4 | 2817-2825 | official | null | null |
10.1609/aaai.v35i4.16388 | Deep Multi-Task Learning for Diabetic Retinopathy Grading in Fundus Images | https://ojs.aaai.org/index.php/AAAI/article/view/16388 | https://ojs.aaai.org/index.php/AAAI/article/download/16388/16195 | [
"Xiaofei Wang",
"Mai Xu",
"Jicong Zhang",
"Lai Jiang",
"Liu Li"
] | Recent years have witnessed the growing interest in disease severity grading, especially for ocular diseases based on fundus images. The existing grading methods are usually trained with high resolution (HR) images. However, the grading performance decreases a lot given low resolution (LR) images, which are common in p... | main | Computer Vision | 10.1609/aaai.v35i4.16388 | 35 | 4 | 2826-2834 | official | null | null |
10.1609/aaai.v35i4.16389 | Confidence-aware Non-repetitive Multimodal Transformers for TextCaps | https://ojs.aaai.org/index.php/AAAI/article/view/16389 | https://ojs.aaai.org/index.php/AAAI/article/download/16389/16196 | [
"Zhaokai Wang",
"Renda Bao",
"Qi Wu",
"Si Liu"
] | When describing an image, reading text in the visual scene is crucial to understand the key information. Recent work explores the TextCaps task, i.e. image captioning with reading Optical Character Recognition (OCR) tokens, which requires models to read text and cover them in generated captions. Existing approaches fai... | main | Computer Vision | 10.1609/aaai.v35i4.16389 | 35 | 4 | 2835-2843 | official | 2012.03662 | title_snapshot |
10.1609/aaai.v35i4.16390 | Geodesic-HOF: 3D Reconstruction Without Cutting Corners | https://ojs.aaai.org/index.php/AAAI/article/view/16390 | https://ojs.aaai.org/index.php/AAAI/article/download/16390/16197 | [
"Ziyun Wang",
"Eric A. Mitchell",
"Volkan Isler",
"Daniel D. Lee"
] | Single-view 3D object reconstruction is a challenging fundamental problem in machine perception, largely due to the morphological diversity of objects in the natural world. In particular, high curvature regions are not always represented accurately by methods trained with common set-based loss functions such as Chamfer... | main | Computer Vision | 10.1609/aaai.v35i4.16390 | 35 | 4 | 2844-2851 | official | 2006.07981 | title_snapshot |
10.1609/aaai.v35i4.16391 | C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer | https://ojs.aaai.org/index.php/AAAI/article/view/16391 | https://ojs.aaai.org/index.php/AAAI/article/download/16391/16198 | [
"Dongxu Wei",
"Xiaowei Xu",
"Haibin Shen",
"Kejie Huang"
] | Human video motion transfer (HVMT) aims to synthesize videos that one person imitates other persons' actions. Although existing GAN-based HVMT methods have achieved great success, they either fail to preserve appearance details due to the loss of spatial consistency between synthesized and exemplary images, or generate... | main | Computer Vision | 10.1609/aaai.v35i4.16391 | 35 | 4 | 2852-2860 | official | 2012.08976 | title_snapshot |
10.1609/aaai.v35i4.16392 | Semantic Consistency Networks for 3D Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16392 | https://ojs.aaai.org/index.php/AAAI/article/download/16392/16199 | [
"Wenwen Wei",
"Ping Wei",
"Nanning Zheng"
] | Detecting 3D objects from point clouds is a significant yet challenging issue in many applications. While most existing approaches seek to leverage geometric information of point clouds, few studies accommodate the inherent semantic characteristics of each point and the consistency between the geometric and semantic cu... | main | Computer Vision | 10.1609/aaai.v35i4.16392 | 35 | 4 | 2861-2869 | official | null | null |
10.1609/aaai.v35i4.16393 | Holistic Multi-View Building Analysis in the Wild with Projection Pooling | https://ojs.aaai.org/index.php/AAAI/article/view/16393 | https://ojs.aaai.org/index.php/AAAI/article/download/16393/16200 | [
"Zbigniew Wojna",
"Krzysztof Maziarz",
"Łukasz Jocz",
"Robert Pałuba",
"Robert Kozikowski",
"Iason Kokkinos"
] | We address six different classification tasks related to fine-grained building attributes: construction type, number of floors, pitch and geometry of the roof, facade material, and occupancy class. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urba... | main | Computer Vision | 10.1609/aaai.v35i4.16393 | 35 | 4 | 2870-2878 | official | 2008.10041 | title_snapshot |
10.1609/aaai.v35i4.16394 | Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations | https://ojs.aaai.org/index.php/AAAI/article/view/16394 | https://ojs.aaai.org/index.php/AAAI/article/download/16394/16201 | [
"Alex Wong",
"Mukund Mundhra",
"Stefano Soatto"
] | We study the effect of adversarial perturbations of images on the estimates of disparity by deep learning models trained for stereo. We show that imperceptible additive perturbations can significantly alter the disparity map, and correspondingly the perceived geometry of the scene. These perturbations not only affect t... | main | Computer Vision | 10.1609/aaai.v35i4.16394 | 35 | 4 | 2879-2888 | official | 2009.10142 | title_snapshot |
10.1609/aaai.v35i4.16395 | Generalising without Forgetting for Lifelong Person Re-Identification | https://ojs.aaai.org/index.php/AAAI/article/view/16395 | https://ojs.aaai.org/index.php/AAAI/article/download/16395/16202 | [
"Guile Wu",
"Shaogang Gong"
] | Existing person re-identification (Re-ID) methods mostly prepare all training data in advance, while real-world Re-ID data are inherently captured over time or from different locations, which requires a model to be incrementally generalised from sequential learning of piecemeal new data without forgetting what is alrea... | main | Computer Vision | 10.1609/aaai.v35i4.16395 | 35 | 4 | 2889-2897 | official | null | null |
10.1609/aaai.v35i4.16396 | Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification | https://ojs.aaai.org/index.php/AAAI/article/view/16396 | https://ojs.aaai.org/index.php/AAAI/article/download/16396/16203 | [
"Guile Wu",
"Shaogang Gong"
] | Deep learning has been successful for many computer vision tasks due to the availability of shared and centralised large-scale training data. However, increasing awareness of privacy concerns poses new challenges to deep learning, especially for human subject related recognition such as person re-identification (Re-ID)... | main | Computer Vision | 10.1609/aaai.v35i4.16396 | 35 | 4 | 2898-2906 | official | 2006.04150 | title_snapshot |
10.1609/aaai.v35i4.16397 | Region-aware Global Context Modeling for Automatic Nerve Segmentation from Ultrasound Images | https://ojs.aaai.org/index.php/AAAI/article/view/16397 | https://ojs.aaai.org/index.php/AAAI/article/download/16397/16204 | [
"Huisi Wu",
"Jiasheng Liu",
"Wei Wang",
"Zhenkun Wen",
"Jing Qin"
] | We present a novel deep learning model equipped with a new region-aware global context modeling technique for automatic nerve segmentation from ultrasound images, which is a challenging task due to (1) the large variation and blurred boundaries of targets, (2) the large amount of speckle noise in ultrasound images, and... | main | Computer Vision | 10.1609/aaai.v35i4.16397 | 35 | 4 | 2907-2915 | official | null | null |
10.1609/aaai.v35i4.16398 | Precise Yet Efficient Semantic Calibration and Refinement in ConvNets for Real-time Polyp Segmentation from Colonoscopy Videos | https://ojs.aaai.org/index.php/AAAI/article/view/16398 | https://ojs.aaai.org/index.php/AAAI/article/download/16398/16205 | [
"Huisi Wu",
"Jiafu Zhong",
"Wei Wang",
"Zhenkun Wen",
"Jing Qin"
] | We propose a novel convolutional neural network (ConvNet) equipped with two new semantic calibration and refinement approaches for automatic polyp segmentation from colonoscopy videos. While ConvNets set state-of-the-are performance for this task, it is still difficult to achieve satisfactory results in a real-time man... | main | Computer Vision | 10.1609/aaai.v35i4.16398 | 35 | 4 | 2916-2924 | official | null | null |
10.1609/aaai.v35i4.16399 | Graph-to-Graph: Towards Accurate and Interpretable Online Handwritten Mathematical Expression Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16399 | https://ojs.aaai.org/index.php/AAAI/article/download/16399/16206 | [
"Jin-Wen Wu",
"Fei Yin",
"Yan-Ming Zhang",
"Xu-Yao Zhang",
"Cheng-Lin Liu"
] | Recent handwritten mathematical expression recognition (HMER) approaches treat the problem as an image-to-markup generation task where the handwritten formula is translated into a sequence (e.g. LaTeX). The encoder-decoder framework is widely used to solve this image-to-sequence problem. However, (i) for structured mat... | main | Computer Vision | 10.1609/aaai.v35i4.16399 | 35 | 4 | 2925-2933 | official | null | null |
10.1609/aaai.v35i4.16400 | Learning Comprehensive Motion Representation for Action Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16400 | https://ojs.aaai.org/index.php/AAAI/article/download/16400/16207 | [
"Mingyu Wu",
"Boyuan Jiang",
"Donghao Luo",
"Junchi Yan",
"Yabiao Wang",
"Ying Tai",
"Chengjie Wang",
"Jilin Li",
"Feiyue Huang",
"Xiaokang Yang"
] | For action recognition learning, 2D CNN-based methods are efficient but may yield redundant features due to applying the same 2D convolution kernel to each frame. Recent efforts attempt to capture motion information by establishing inter-frame connections while still suffering the limited temporal receptive field or hi... | main | Computer Vision | 10.1609/aaai.v35i4.16400 | 35 | 4 | 2934-2942 | official | 2103.12278 | title_snapshot |
10.1609/aaai.v35i4.16401 | MVFNet: Multi-View Fusion Network for Efficient Video Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16401 | https://ojs.aaai.org/index.php/AAAI/article/download/16401/16208 | [
"Wenhao Wu",
"Dongliang He",
"Tianwei Lin",
"Fu Li",
"Chuang Gan",
"Errui Ding"
] | Conventionally, spatiotemporal modeling network and its complexity are the two most concentrated research topics in video action recognition. Existing state-of-the-art methods have achieved excellent accuracy regardless of the complexity meanwhile efficient spatiotemporal modeling solutions are slightly inferior in per... | main | Computer Vision | 10.1609/aaai.v35i4.16401 | 35 | 4 | 2943-2951 | official | 2012.06977 | title_snapshot |
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