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.v35i2.16264 | What to Select: Pursuing Consistent Motion Segmentation from Multiple Geometric Models | https://ojs.aaai.org/index.php/AAAI/article/view/16264 | https://ojs.aaai.org/index.php/AAAI/article/download/16264/16071 | [
"Yangbangyan Jiang",
"Qianqian Xu",
"Ke Ma",
"Zhiyong Yang",
"Xiaochun Cao",
"Qingming Huang"
] | Motion segmentation aims at separating motions of different moving objects in a video sequence. Facing the complicated real-world scenes, recent studies reveal that combining multiple geometric models would be a more effective way than just employing a single one. This motivates a new wave of model-fusion based motion ... | main | Computer Vision | 10.1609/aaai.v35i2.16264 | 35 | 2 | 1708-1716 | official | null | null |
10.1609/aaai.v35i2.16265 | Asynchronous Teacher Guided Bit-wise Hard Mining for Online Hashing | https://ojs.aaai.org/index.php/AAAI/article/view/16265 | https://ojs.aaai.org/index.php/AAAI/article/download/16265/16072 | [
"Sheng Jin",
"Qin Zhou",
"Hongxun Yao",
"Yao Liu",
"Xian-Sheng Hua"
] | Online hashing for streaming data has attracted increasing attention recently. However, most existing algorithms focus on batch inputs and instance-balanced optimization, which is limited in the single datum input case and does not match the dynamic training in online hashing. Furthermore, constantly updating the onlin... | main | Computer Vision | 10.1609/aaai.v35i2.16265 | 35 | 2 | 1717-1724 | official | null | null |
10.1609/aaai.v35i2.16266 | Deep Low-Contrast Image Enhancement using Structure Tensor Representation | https://ojs.aaai.org/index.php/AAAI/article/view/16266 | https://ojs.aaai.org/index.php/AAAI/article/download/16266/16073 | [
"Hyungjoo Jung",
"Hyunsung Jang",
"Namkoo Ha",
"Kwanghoon Sohn"
] | We present a new deep learning framework for low-contrast image enhancement, which trains the network using the multi-exposure sequences rather than explicit ground-truth images. The purpose of our method is to enhance a low-contrast image so as to contain abundant details in various exposure levels. To realize this, w... | main | Computer Vision | 10.1609/aaai.v35i2.16266 | 35 | 2 | 1725-1733 | official | null | null |
10.1609/aaai.v35i2.16267 | Spectral Distribution Aware Image Generation | https://ojs.aaai.org/index.php/AAAI/article/view/16267 | https://ojs.aaai.org/index.php/AAAI/article/download/16267/16074 | [
"Steffen Jung",
"Margret Keuper"
] | Recent advances in deep generative models for photo-realistic images have led to high quality visual results. Such models learn to generate data from a given training distribution such that generated images can not be easily distinguished from real images by the human eye. Yet, recent work on the detection of such fake... | main | Computer Vision | 10.1609/aaai.v35i2.16267 | 35 | 2 | 1734-1742 | official | 2012.03110 | title_snapshot |
10.1609/aaai.v35i2.16268 | StarNet: towards Weakly Supervised Few-Shot Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16268 | https://ojs.aaai.org/index.php/AAAI/article/download/16268/16075 | [
"Leonid Karlinsky",
"Joseph Shtok",
"Amit Alfassy",
"Moshe Lichtenstein",
"Sivan Harary",
"Eli Schwartz",
"Sivan Doveh",
"Prasanna Sattigeri",
"Rogerio Feris",
"Alex Bronstein",
"Raja Giryes"
] | Few-shot detection and classification have advanced significantly in recent years. Yet, detection approaches require strong annotation (bounding boxes) both for pre-training and for adaptation to novel classes, and classification approaches rarely provide localization of objects in the scene. In this paper, we introduc... | main | Computer Vision | 10.1609/aaai.v35i2.16268 | 35 | 2 | 1743-1753 | official | 2003.06798 | title_snapshot |
10.1609/aaai.v35i2.16269 | Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16269 | https://ojs.aaai.org/index.php/AAAI/article/download/16269/16076 | [
"Beomyoung Kim",
"Sangeun Han",
"Junmo Kim"
] | Weakly-supervised semantic segmentation (WSSS) using image-level labels has recently attracted much attention for reducing annotation costs. Existing WSSS methods utilize localization maps from the classification network to generate pseudo segmentation labels. However, since localization maps obtained from the classifi... | main | Computer Vision | 10.1609/aaai.v35i2.16269 | 35 | 2 | 1754-1761 | official | 2103.07246 | title_snapshot |
10.1609/aaai.v35i2.16270 | Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images | https://ojs.aaai.org/index.php/AAAI/article/view/16270 | https://ojs.aaai.org/index.php/AAAI/article/download/16270/16077 | [
"Hak Gu Kim",
"Minho Park",
"Sangmin Lee",
"Seongyeop Kim",
"Yong Man Ro"
] | Depth adjustment aims to enhance the visual experience of stereoscopic 3D (S3D) images, which accompanied with improving visual comfort and depth perception. For a human expert, the depth adjustment procedure is a sequence of iterative decision making. The human expert iteratively adjusted the depth until he is satisfi... | main | Computer Vision | 10.1609/aaai.v35i2.16270 | 35 | 2 | 1762-1770 | official | 2104.06782 | title_snapshot |
10.1609/aaai.v35i2.16271 | Dual Compositional Learning in Interactive Image Retrieval | https://ojs.aaai.org/index.php/AAAI/article/view/16271 | https://ojs.aaai.org/index.php/AAAI/article/download/16271/16078 | [
"Jongseok Kim",
"Youngjae Yu",
"Hoeseong Kim",
"Gunhee Kim"
] | We present an approach named Dual Composition Network (DCNet) for interactive image retrieval that searches for the best target image for a natural language query and a reference image. To accomplish this task, existing methods have focused on learning a composite representation of the reference image and the text quer... | main | Computer Vision | 10.1609/aaai.v35i2.16271 | 35 | 2 | 1771-1779 | official | null | null |
10.1609/aaai.v35i2.16272 | End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Images | https://ojs.aaai.org/index.php/AAAI/article/view/16272 | https://ojs.aaai.org/index.php/AAAI/article/download/16272/16079 | [
"Junghee Kim",
"Siyeong Lee",
"Suk-Ju Kang"
] | Recently, high dynamic range (HDR) image reconstruction based on the multiple exposure stack from a given single exposure utilizes a deep learning framework to generate high-quality HDR images. These conventional networks focus on the exposure transfer task to reconstruct the multi-exposure stack. Therefore, they often... | main | Computer Vision | 10.1609/aaai.v35i2.16272 | 35 | 2 | 1780-1788 | official | 2006.15833 | title_snapshot |
10.1609/aaai.v35i2.16273 | Structured Co-reference Graph Attention for Video-grounded Dialogue | https://ojs.aaai.org/index.php/AAAI/article/view/16273 | https://ojs.aaai.org/index.php/AAAI/article/download/16273/16080 | [
"Junyeong Kim",
"Sunjae Yoon",
"Dahyun Kim",
"Chang D. Yoo"
] | A video-grounded dialogue system referred to as the Structured Co-reference Graph Attention (SCGA) is presented for decoding the answer sequence to a question regarding a given video while keeping track of the dialogue context. Although recent efforts have made great strides in improving the quality of the response, pe... | main | Computer Vision | 10.1609/aaai.v35i2.16273 | 35 | 2 | 1789-1797 | official | 2103.13361 | title_snapshot |
10.1609/aaai.v35i2.16243 | Progressive One-shot Human Parsing | https://ojs.aaai.org/index.php/AAAI/article/view/16243 | https://ojs.aaai.org/index.php/AAAI/article/download/16243/16050 | [
"Haoyu He",
"Jing Zhang",
"Bhavani Thuraisingham",
"Dacheng Tao"
] | Prior human parsing models are limited to parsing humans into classes pre-defined in the training data, which is not flexible to generalize to unseen classes, e.g., new clothing in fashion analysis. In this paper, we propose a new problem named one-shot human parsing (OSHP) that requires to parse human into an open set... | main | Computer Vision | 10.1609/aaai.v35i2.16243 | 35 | 2 | 1522-1530 | official | 2012.11810 | title_snapshot |
10.1609/aaai.v35i2.16244 | Consistent-Separable Feature Representation for Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16244 | https://ojs.aaai.org/index.php/AAAI/article/download/16244/16051 | [
"Xingjian He",
"Jing Liu",
"Jun Fu",
"Xinxin Zhu",
"Jinqiao Wang",
"Hanqing Lu"
] | Cross-entropy loss combined with softmax is one of the most commonly used supervision components in most existing segmentation methods. The softmax loss is typically good at optimizing the inter-class difference, but not good at reducing the intra-class variation, which can be suboptimal for semantic segmentation task.... | main | Computer Vision | 10.1609/aaai.v35i2.16244 | 35 | 2 | 1531-1539 | official | null | null |
10.1609/aaai.v35i2.16245 | Error-Aware Density Isomorphism Reconstruction for Unsupervised Cross-Domain Crowd Counting | https://ojs.aaai.org/index.php/AAAI/article/view/16245 | https://ojs.aaai.org/index.php/AAAI/article/download/16245/16052 | [
"Yuhang He",
"Zhiheng Ma",
"Xing Wei",
"Xiaopeng Hong",
"Wei Ke",
"Yihong Gong"
] | This paper focuses on the unsupervised domain adaptation problem for video-based crowd counting, in which we use labeled data as source domain and unlabelled video data as target domain. It is challenging as there is a huge gap between the source and the target domain and no annotations of samples are available in the ... | main | Computer Vision | 10.1609/aaai.v35i2.16245 | 35 | 2 | 1540-1548 | official | null | null |
10.1609/aaai.v35i2.16246 | DropLoss for Long-Tail Instance Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16246 | https://ojs.aaai.org/index.php/AAAI/article/download/16246/16053 | [
"Ting-I Hsieh",
"Esther Robb",
"Hwann-Tzong Chen",
"Jia-Bin Huang"
] | Long-tailed class distributions are prevalent among the practical applications of object detection and instance segmentation. Prior work in long-tail instance segmentation addresses the imbalance of losses between rare and frequent categories by reducing the penalty for a model incorrectly predicting a rare class label... | main | Computer Vision | 10.1609/aaai.v35i2.16246 | 35 | 2 | 1549-1557 | official | 2104.06402 | title_snapshot |
10.1609/aaai.v35i2.16247 | Hand-Model-Aware Sign Language Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16247 | https://ojs.aaai.org/index.php/AAAI/article/download/16247/16054 | [
"Hezhen Hu",
"Wengang Zhou",
"Houqiang Li"
] | Hand gestures play a dominant role in the expression of sign language. Current deep-learning based video sign language recognition (SLR) methods usually follow a data-driven paradigm under the supervision of the category label. However, those methods suffer limited interpretability and may encounter the overfitting iss... | main | Computer Vision | 10.1609/aaai.v35i2.16247 | 35 | 2 | 1558-1566 | official | null | null |
10.1609/aaai.v35i2.16248 | Stratified Rule-Aware Network for Abstract Visual Reasoning | https://ojs.aaai.org/index.php/AAAI/article/view/16248 | https://ojs.aaai.org/index.php/AAAI/article/download/16248/16055 | [
"Sheng Hu",
"Yuqing Ma",
"Xianglong Liu",
"Yanlu Wei",
"Shihao Bai"
] | reasoning refers to the ability to analyze information, discover rules at an intangible level, and solve problems in innovative ways. Raven's Progressive Matrices (RPM) test is typically used to examine the capability of abstract reasoning. The subject is asked to identify the correct choice from the answer set to fill... | main | Computer Vision | 10.1609/aaai.v35i2.16248 | 35 | 2 | 1567-1574 | official | 2002.06838 | title_snapshot |
10.1609/aaai.v35i2.16249 | VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/16249 | https://ojs.aaai.org/index.php/AAAI/article/download/16249/16056 | [
"Xiaowei Hu",
"Xi Yin",
"Kevin Lin",
"Lei Zhang",
"Jianfeng Gao",
"Lijuan Wang",
"Zicheng Liu"
] | It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps). In this challenge, no additional image-caption training data, other than COCO Captions, i... | main | Computer Vision | 10.1609/aaai.v35i2.16249 | 35 | 2 | 1575-1583 | official | 2009.13682 | title_snapshot |
10.1609/aaai.v35i2.16250 | Exploiting Relationship for Complex-scene Image Generation | https://ojs.aaai.org/index.php/AAAI/article/view/16250 | https://ojs.aaai.org/index.php/AAAI/article/download/16250/16057 | [
"Tianyu Hua",
"Hongdong Zheng",
"Yalong Bai",
"Wei Zhang",
"Xiao-Ping Zhang",
"Tao Mei"
] | The significant progress on Generative Adversarial Networks (GANs) has facilitated realistic single-object image generation based on language input. However, complex-scene generation (with various interactions among multiple objects) still suffers from messy layouts and object distortions, due to diverse configurations... | main | Computer Vision | 10.1609/aaai.v35i2.16250 | 35 | 2 | 1584-1592 | official | 2104.00356 | title_snapshot |
10.1609/aaai.v35i2.16251 | Modeling Deep Learning Based Privacy Attacks on Physical Mail | https://ojs.aaai.org/index.php/AAAI/article/view/16251 | https://ojs.aaai.org/index.php/AAAI/article/download/16251/16058 | [
"Bingyao Huang",
"Ruyi Lian",
"Dimitris Samaras",
"Haibin Ling"
] | Mail privacy protection aims to prevent unauthorized access to hidden content within an envelope since normal paper envelopes are not as safe as we think. In this paper, for the first time, we show that with a well designed deep learning model, the hidden content may be largely recovered without opening the envelope. W... | main | Computer Vision | 10.1609/aaai.v35i2.16251 | 35 | 2 | 1593-1601 | official | 2012.11803 | title_snapshot |
10.1609/aaai.v35i2.16252 | PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16252 | https://ojs.aaai.org/index.php/AAAI/article/download/16252/16059 | [
"Huaxi Huang",
"Junjie Zhang",
"Jian Zhang",
"Qiang Wu",
"Chang Xu"
] | The predicament in semi-supervised few-shot learning (SSFSL) is to maximize the value of the extra unlabeled data to boost the few-shot learner. In this paper, we propose a Poisson Transfer Network (PTN) to mine the unlabeled information for SSFSL from two aspects. First, the Poisson Merriman–Bence–Osher (MBO) model bu... | main | Computer Vision | 10.1609/aaai.v35i2.16252 | 35 | 2 | 1602-1609 | official | 2012.10844 | title_snapshot |
10.1609/aaai.v35i2.16253 | Text-Guided Graph Neural Networks for Referring 3D Instance Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16253 | https://ojs.aaai.org/index.php/AAAI/article/download/16253/16060 | [
"Pin-Hao Huang",
"Han-Hung Lee",
"Hwann-Tzong Chen",
"Tyng-Luh Liu"
] | This paper addresses a new task called referring 3D instance segmentation, which aims to segment out the target instance in a 3D scene given a query sentence. Previous work on scene understanding has explored visual grounding with natural language guidance, yet the emphasis is mostly constrained on images and videos. W... | main | Computer Vision | 10.1609/aaai.v35i2.16253 | 35 | 2 | 1610-1618 | official | null | null |
10.1609/aaai.v35i2.16254 | Initiative Defense against Facial Manipulation | https://ojs.aaai.org/index.php/AAAI/article/view/16254 | https://ojs.aaai.org/index.php/AAAI/article/download/16254/16061 | [
"Qidong Huang",
"Jie Zhang",
"Wenbo Zhou",
"Weiming Zhang",
"Nenghai Yu"
] | Benefiting from the development of generative adversarial networks (GAN), facial manipulation has achieved significant progress in both academia and industry recently. It inspires an increasing number of entertainment applications but also incurs severe threats to individual privacy and even political security meanwhil... | main | Computer Vision | 10.1609/aaai.v35i2.16254 | 35 | 2 | 1619-1627 | official | 2112.10098 | title_snapshot |
10.1609/aaai.v35i2.16255 | SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data | https://ojs.aaai.org/index.php/AAAI/article/view/16255 | https://ojs.aaai.org/index.php/AAAI/article/download/16255/16062 | [
"Shaoli Huang",
"Xinchao Wang",
"Dacheng Tao"
] | Data mixing augmentation has proved effective in training deep models. Recent methods mix labels mainly according to the mixture proportion of image pixels. Due to the major discriminative information of a fine-grained image usually resides in subtle regions, these methods tend to introduce heavy label noise in fine-gr... | main | Computer Vision | 10.1609/aaai.v35i2.16255 | 35 | 2 | 1628-1636 | official | 2012.04846 | title_snapshot |
10.1609/aaai.v35i2.16256 | A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization | https://ojs.aaai.org/index.php/AAAI/article/view/16256 | https://ojs.aaai.org/index.php/AAAI/article/download/16256/16063 | [
"Ashraful Islam",
"Chengjiang Long",
"Richard Radke"
] | Weakly supervised temporal action localization is a challenging vision task due to the absence of ground-truth temporal locations of actions in the training videos. With only video-level supervision during training, most existing methods rely on a Multiple Instance Learning (MIL) framework to predict the start and end ... | main | Computer Vision | 10.1609/aaai.v35i2.16256 | 35 | 2 | 1637-1645 | official | 2101.00545 | title_snapshot |
10.1609/aaai.v35i2.16257 | Context-Aware Graph Convolution Network for Target Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/16257 | https://ojs.aaai.org/index.php/AAAI/article/download/16257/16064 | [
"Deyi Ji",
"Haoran Wang",
"Hanzhe Hu",
"Weihao Gan",
"Wei Wu",
"Junjie Yan"
] | Most existing re-identification methods focus on learning robust and discriminative features with deep convolution networks. However, many of them consider content similarity separately and fail to utilize the context information of the query and gallery sets, e.g. probe-gallery and gallery-gallery relations, thus hard... | main | Computer Vision | 10.1609/aaai.v35i2.16257 | 35 | 2 | 1646-1654 | official | 2012.04298 | title_snapshot |
10.1609/aaai.v35i2.16258 | Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network | https://ojs.aaai.org/index.php/AAAI/article/view/16258 | https://ojs.aaai.org/index.php/AAAI/article/download/16258/16065 | [
"Jiayi Ji",
"Yunpeng Luo",
"Xiaoshuai Sun",
"Fuhai Chen",
"Gen Luo",
"Yongjian Wu",
"Yue Gao",
"Rongrong Ji"
] | Transformer-based architectures have shown great success in image captioning, where object regions are encoded and then attended into the vectorial representations to guide the caption decoding. However, such vectorial representations only contain region-level information without considering the global information refl... | main | Computer Vision | 10.1609/aaai.v35i2.16258 | 35 | 2 | 1655-1663 | official | 2012.07061 | title_snapshot |
10.1609/aaai.v35i2.16259 | Frequency Consistent Adaptation for Real World Super Resolution | https://ojs.aaai.org/index.php/AAAI/article/view/16259 | https://ojs.aaai.org/index.php/AAAI/article/download/16259/16066 | [
"Xiaozhong Ji",
"Guangpin Tao",
"Yun Cao",
"Ying Tai",
"Tong Lu",
"Chengjie Wang",
"Jilin Li",
"Feiyue Huang"
] | Recent deep-learning based Super-Resolution (SR) methods have achieved remarkable performance on images with known degradation. However, these methods always fail in real-world scene, since the Low-Resolution (LR) images after the ideal degradation (e.g., bicubic down-sampling) deviate from real source domain. The doma... | main | Computer Vision | 10.1609/aaai.v35i2.16259 | 35 | 2 | 1664-1672 | official | 2012.10102 | title_snapshot |
10.1609/aaai.v35i2.16260 | Matching on Sets: Conquer Occluded Person Re-identification Without Alignment | https://ojs.aaai.org/index.php/AAAI/article/view/16260 | https://ojs.aaai.org/index.php/AAAI/article/download/16260/16067 | [
"Mengxi Jia",
"Xinhua Cheng",
"Yunpeng Zhai",
"Shijian Lu",
"Siwei Ma",
"Yonghong Tian",
"Jian Zhang"
] | Occluded person re-identification (re-ID) is a challenging task as different human parts may become invisible in cluttered scenes, making it hard to match person images of different identities. Most existing methods address this challenge by aligning spatial features of body parts according to semantic information (e.g... | main | Computer Vision | 10.1609/aaai.v35i2.16260 | 35 | 2 | 1673-1681 | official | null | null |
10.1609/aaai.v35i2.16261 | GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates | https://ojs.aaai.org/index.php/AAAI/article/view/16261 | https://ojs.aaai.org/index.php/AAAI/article/download/16261/16068 | [
"Qifei Jia",
"Shikui Wei",
"Tao Ruan",
"Yufeng Zhao",
"Yao Zhao"
] | Weakly-Supervised Object Detection (WSOD) aims at training a model with limited and coarse annotations for precisely locating the regions of objects. Existing works solve the WSOD problem by using a two-stage framework, i.e., generating candidate bounding boxes with weak supervision information and then refining them b... | main | Computer Vision | 10.1609/aaai.v35i2.16261 | 35 | 2 | 1682-1690 | official | null | null |
10.1609/aaai.v35i2.16262 | SSN3D: Self-Separated Network to Align Parts for 3D Convolution in Video Person Re-Identification | https://ojs.aaai.org/index.php/AAAI/article/view/16262 | https://ojs.aaai.org/index.php/AAAI/article/download/16262/16069 | [
"Xiaoke Jiang",
"Yu Qiao",
"Junjie Yan",
"Qichen Li",
"Wanrong Zheng",
"Dapeng Chen"
] | Temporal appearance misalignment is a crucial problem in video person re-identification. The same part of person (e.g. head or hand) appearing on different locations in video sequence weakens its discriminative ability, especially when we apply standard temporal aggregation such as 3D convolution or LSTM. To address th... | main | Computer Vision | 10.1609/aaai.v35i2.16262 | 35 | 2 | 1691-1699 | official | null | null |
10.1609/aaai.v35i2.16223 | Learning Complex 3D Human Self-Contact | https://ojs.aaai.org/index.php/AAAI/article/view/16223 | https://ojs.aaai.org/index.php/AAAI/article/download/16223/16030 | [
"Mihai Fieraru",
"Mihai Zanfir",
"Elisabeta Oneata",
"Alin-Ionut Popa",
"Vlad Olaru",
"Cristian Sminchisescu"
] | Monocular estimation of three dimensional human self-contact is fundamental for detailed scene analysis including body language understanding and behaviour modeling. Existing 3d reconstruction methods do not focus on body regions in self-contact and consequently recover configurations that are either far from each othe... | main | Computer Vision | 10.1609/aaai.v35i2.16223 | 35 | 2 | 1343-1351 | official | 2012.10366 | title_snapshot |
10.1609/aaai.v35i2.16224 | Rain Streak Removal via Dual Graph Convolutional Network | https://ojs.aaai.org/index.php/AAAI/article/view/16224 | https://ojs.aaai.org/index.php/AAAI/article/download/16224/16031 | [
"Xueyang Fu",
"Qi Qi",
"Zheng-Jun Zha",
"Yurui Zhu",
"Xinghao Ding"
] | Deep convolutional neural networks (CNNs) have become dominant in the single image de-raining area. However, most deep CNNs-based de-raining methods are designed by stacking vanilla convolutional layers, which can only be used to model local relations. Therefore, long-range contextual information is rarely considered f... | main | Computer Vision | 10.1609/aaai.v35i2.16224 | 35 | 2 | 1352-1360 | official | null | null |
10.1609/aaai.v35i2.16225 | CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16225 | https://ojs.aaai.org/index.php/AAAI/article/download/16225/16032 | [
"Yang Fu",
"Linjie Yang",
"Ding Liu",
"Thomas S. Huang",
"Humphrey Shi"
] | Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features for the detection, segmentation, and tracking of objects and they suffer in the video scenario due to several distinct challenges such as m... | main | Computer Vision | 10.1609/aaai.v35i2.16225 | 35 | 2 | 1361-1369 | official | 2012.03400 | title_snapshot |
10.1609/aaai.v35i2.16226 | Deep Metric Learning with Self-Supervised Ranking | https://ojs.aaai.org/index.php/AAAI/article/view/16226 | https://ojs.aaai.org/index.php/AAAI/article/download/16226/16033 | [
"Zheren Fu",
"Yan Li",
"Zhendong Mao",
"Quan Wang",
"Yongdong Zhang"
] | Deep metric learning aims to learn a deep embedding space, where similar objects are pushed towards together and different objects are repelled against. Existing approaches typically use inter-class characteristics, e.g. class-level information or instance-level similarity, to obtain semantic relevance of data points a... | main | Computer Vision | 10.1609/aaai.v35i2.16226 | 35 | 2 | 1370-1378 | official | null | null |
10.1609/aaai.v35i2.16227 | A Systematic Evaluation of Object Detection Networks for Scientific Plots | https://ojs.aaai.org/index.php/AAAI/article/view/16227 | https://ojs.aaai.org/index.php/AAAI/article/download/16227/16034 | [
"Pritha Ganguly",
"Nitesh S Methani",
"Mitesh M. Khapra",
"Pratyush Kumar"
] | Are existing object detection methods adequate for detecting text and visual elements in scientific plots which are arguably different than the objects found in natural images? To answer this question, we train and compare the accuracy of Fast/Faster R-CNN, SSD, YOLO and RetinaNet on the PlotQA dataset with over 220,00... | main | Computer Vision | 10.1609/aaai.v35i2.16227 | 35 | 2 | 1379-1387 | official | 2007.02240 | title_snapshot |
10.1609/aaai.v35i2.16228 | The Complexity of Object Association in Multiple Object Tracking | https://ojs.aaai.org/index.php/AAAI/article/view/16228 | https://ojs.aaai.org/index.php/AAAI/article/download/16228/16035 | [
"Robert Ganian",
"Thekla Hamm",
"Sebastian Ordyniak"
] | Object association, i.e., the identification of which observations correspond to the same object, is a central task for the area of multiple object tracking. Two prominent models capturing this task have been introduced in the literature: the Lifted Multicut model and the more recent Lifted Paths model. Here, we carry ... | main | Computer Vision | 10.1609/aaai.v35i2.16228 | 35 | 2 | 1388-1396 | official | null | null |
10.1609/aaai.v35i2.16229 | Learning Local Neighboring Structure for Robust 3D Shape Representation | https://ojs.aaai.org/index.php/AAAI/article/view/16229 | https://ojs.aaai.org/index.php/AAAI/article/download/16229/16036 | [
"Zhongpai Gao",
"Junchi Yan",
"Guangtao Zhai",
"Juyong Zhang",
"Yiyan Yang",
"Xiaokang Yang"
] | Mesh is a powerful data structure for 3D shapes. Representation learning for 3D meshes is important in many computer vision and graphics applications. The recent success of convolutional neural networks (CNNs) for structured data (e.g., images) suggests the value of adapting insight from CNN for 3D shapes. However, 3D ... | main | Computer Vision | 10.1609/aaai.v35i2.16229 | 35 | 2 | 1397-1405 | official | 2004.09995 | title_snapshot |
10.1609/aaai.v35i2.16230 | Semantic-guided Reinforced Region Embedding for Generalized Zero-Shot Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16230 | https://ojs.aaai.org/index.php/AAAI/article/download/16230/16037 | [
"Jiannan Ge",
"Hongtao Xie",
"Shaobo Min",
"Yongdong Zhang"
] | Generalized zero-shot Learning (GZSL) aims to recognize images from either seen or unseen domain, mainly by learning a joint embedding space to associate image features with the corresponding category descriptions. Recent methods have proved that localizing important object regions can effectively bridge the semantic-v... | main | Computer Vision | 10.1609/aaai.v35i2.16230 | 35 | 2 | 1406-1414 | official | null | null |
10.1609/aaai.v35i2.16231 | Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers | https://ojs.aaai.org/index.php/AAAI/article/view/16231 | https://ojs.aaai.org/index.php/AAAI/article/download/16231/16038 | [
"Shijie Geng",
"Peng Gao",
"Moitreya Chatterjee",
"Chiori Hori",
"Jonathan Le Roux",
"Yongfeng Zhang",
"Hongsheng Li",
"Anoop Cherian"
] | Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content. This task thus poses a challenging multi-modal representation learning and reasoning scenario, advancemen... | main | Computer Vision | 10.1609/aaai.v35i2.16231 | 35 | 2 | 1415-1423 | official | 2007.03848 | title_snapshot |
10.1609/aaai.v35i2.16232 | Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds | https://ojs.aaai.org/index.php/AAAI/article/view/16232 | https://ojs.aaai.org/index.php/AAAI/article/download/16232/16039 | [
"Jingyu Gong",
"Jiachen Xu",
"Xin Tan",
"Jie Zhou",
"Yanyun Qu",
"Yuan Xie",
"Lizhuang Ma"
] | Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation where ambiguous features might be generated in feature extraction, leading to misclassification in the transition area between two objects. In this paper, firstly, we propose a Boundary Pre... | main | Computer Vision | 10.1609/aaai.v35i2.16232 | 35 | 2 | 1424-1432 | official | 2101.02381 | title_snapshot |
10.1609/aaai.v35i2.16233 | Analogical Image Translation for Fog Generation | https://ojs.aaai.org/index.php/AAAI/article/view/16233 | https://ojs.aaai.org/index.php/AAAI/article/download/16233/16040 | [
"Rui Gong",
"Dengxin Dai",
"Yuhua Chen",
"Wen Li",
"Danda Pani Paudel",
"Luc Van Gool"
] | Image-to-image translation is to map images from a given style to another given style. While exceptionally successful, current methods assume the availability of training images in both source and target domains, which does not always hold in practice. Inspired by humans' reasoning capability of analogy, we propose ana... | main | Computer Vision | 10.1609/aaai.v35i2.16233 | 35 | 2 | 1433-1441 | official | 2006.15618 | title_snapshot |
10.1609/aaai.v35i2.16234 | Temporal ROI Align for Video Object Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16234 | https://ojs.aaai.org/index.php/AAAI/article/download/16234/16041 | [
"Tao Gong",
"Kai Chen",
"Xinjiang Wang",
"Qi Chu",
"Feng Zhu",
"Dahua Lin",
"Nenghai Yu",
"Huamin Feng"
] | Video object detection is challenging in the presence of appearance deterioration in certain video frames. Therefore, it is a natural choice to aggregate temporal information from other frames of the same video into the current frame. However, ROI Align, as one of the most core procedures of video detectors, still rema... | main | Computer Vision | 10.1609/aaai.v35i2.16234 | 35 | 2 | 1442-1450 | official | 2109.03495 | title_snapshot |
10.1609/aaai.v35i2.16235 | SMART Frame Selection for Action Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16235 | https://ojs.aaai.org/index.php/AAAI/article/download/16235/16042 | [
"Shreyank N Gowda",
"Marcus Rohrbach",
"Laura Sevilla-Lara"
] | Video classification is computationally expensive. In this paper, we address theproblem of frame selection to reduce the computational cost of video classification.Recent work has successfully leveraged frame selection for long, untrimmed videos,where much of the content is not relevant, and easy to discard. In this wo... | main | Computer Vision | 10.1609/aaai.v35i2.16235 | 35 | 2 | 1451-1459 | official | 2012.10671 | title_snapshot |
10.1609/aaai.v35i2.16236 | Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16236 | https://ojs.aaai.org/index.php/AAAI/article/download/16236/16043 | [
"Geonmo Gu",
"Byungsoo Ko",
"Han-Gyu Kim"
] | One of the main purposes of deep metric learning is to construct an embedding space that has well-generalized embeddings on both seen (training) classes and unseen (test) classes. Most existing works have tried to achieve this using different types of metric objectives and hard sample mining strategies with given train... | main | Computer Vision | 10.1609/aaai.v35i2.16236 | 35 | 2 | 1460-1468 | official | 2103.15454 | title_snapshot |
10.1609/aaai.v35i2.16237 | Interpretable Graph Capsule Networks for Object Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16237 | https://ojs.aaai.org/index.php/AAAI/article/download/16237/16044 | [
"Jindong Gu"
] | Capsule Networks, as alternatives to Convolutional Neural Networks, have been proposed to recognize objects from images. The current literature demonstrates many advantages of CapsNets over CNNs. However, how to create explanations for individual classifications of CapsNets has not been well explored. The widely used s... | main | Computer Vision | 10.1609/aaai.v35i2.16237 | 35 | 2 | 1469-1477 | official | 2012.01674 | title_snapshot |
10.1609/aaai.v35i2.16238 | Class-Incremental Instance Segmentation via Multi-Teacher Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16238 | https://ojs.aaai.org/index.php/AAAI/article/download/16238/16045 | [
"Yanan Gu",
"Cheng Deng",
"Kun Wei"
] | Although deep neural networks have achieved amazing results on instance segmentation, they are still ill-equipped when they are required to learn new tasks incrementally. Concretely, they suffer from “catastrophic forgetting”, an abrupt degradation of performance on old classes with the initial training data missing. M... | main | Computer Vision | 10.1609/aaai.v35i2.16238 | 35 | 2 | 1478-1486 | official | null | null |
10.1609/aaai.v35i2.16239 | EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining | https://ojs.aaai.org/index.php/AAAI/article/view/16239 | https://ojs.aaai.org/index.php/AAAI/article/download/16239/16046 | [
"Qing Guo",
"Jingyang Sun",
"Felix Juefei-Xu",
"Lei Ma",
"Xiaofei Xie",
"Wei Feng",
"Yang Liu",
"Jianjun Zhao"
] | Single-image deraining is rather challenging due to the unknown rain model. Existing methods often make specific assumptions of the rain model, which can hardly cover many diverse circumstances in the real world, compelling them to employ complex optimization or progressive refinement. This, however, significantly affe... | main | Computer Vision | 10.1609/aaai.v35i2.16239 | 35 | 2 | 1487-1495 | official | 2009.09238 | title_snapshot |
10.1609/aaai.v35i2.16240 | Order Regularization on Ordinal Loss for Head Pose, Age and Gaze Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16240 | https://ojs.aaai.org/index.php/AAAI/article/download/16240/16047 | [
"Tianchu Guo",
"Hui Zhang",
"ByungIn Yoo",
"Yongchao Liu",
"Youngjun Kwak",
"Jae-Joon Han"
] | Ordinal loss is widely used in solving regression problems with deep learning technologies. Its basic idea is to convert regression to classification while preserving the natural order. However, the order constraint is enforced only by ordinal label implicitly, leading to the real output values not strictly in order. I... | main | Computer Vision | 10.1609/aaai.v35i2.16240 | 35 | 2 | 1496-1504 | official | null | null |
10.1609/aaai.v35i2.16241 | Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search | https://ojs.aaai.org/index.php/AAAI/article/view/16241 | https://ojs.aaai.org/index.php/AAAI/article/download/16241/16048 | [
"Chuchu Han",
"Zhedong Zheng",
"Changxin Gao",
"Nong Sang",
"Yi Yang"
] | The goal of person search is to localize and match query persons from scene images. For high efficiency, one-step methods have been developed to jointly handle the pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. One is the mutu... | main | Computer Vision | 10.1609/aaai.v35i2.16241 | 35 | 2 | 1505-1512 | official | 2102.10795 | title_snapshot |
10.1609/aaai.v35i2.16242 | Spherical Image Generation from a Single Image by Considering Scene Symmetry | https://ojs.aaai.org/index.php/AAAI/article/view/16242 | https://ojs.aaai.org/index.php/AAAI/article/download/16242/16049 | [
"Takayuki Hara",
"Yusuke Mukuta",
"Tatsuya Harada"
] | Spherical images taken in all directions (360 degrees by 180 degrees) allow the full surroundings of a subject to be represented, providing an immersive experience to viewers. Generating a spherical image from a single normal-field-of-view (NFOV) image is convenient and expands the usage scenarios considerably without ... | main | Computer Vision | 10.1609/aaai.v35i2.16242 | 35 | 2 | 1513-1521 | official | null | null |
10.1609/aaai.v35i2.16203 | DramaQA: Character-Centered Video Story Understanding with Hierarchical QA | https://ojs.aaai.org/index.php/AAAI/article/view/16203 | https://ojs.aaai.org/index.php/AAAI/article/download/16203/16010 | [
"Seongho Choi",
"Kyoung-Woon On",
"Yu-Jung Heo",
"Ahjeong Seo",
"Youwon Jang",
"Minsu Lee",
"Byoung-Tak Zhang"
] | Despite recent progress on computer vision and natural language processing, developing a machine that can understand video story is still hard to achieve due to the intrinsic difficulty of video story. Moreover, researches on how to evaluate the degree of video understanding based on human cognitive process have not pr... | main | Computer Vision | 10.1609/aaai.v35i2.16203 | 35 | 2 | 1166-1174 | official | 2005.03356 | title_snapshot |
10.1609/aaai.v35i2.16204 | DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16204 | https://ojs.aaai.org/index.php/AAAI/article/download/16204/16011 | [
"Bo Cui",
"Guyue Hu",
"Shan Yu"
] | An important challenge for neural networks is to learn incrementally, i.e., learn new classes without catastrophic forgetting. To overcome this problem, generative replay technique has been suggested, which can generate samples belonging to learned classes while learning new ones. However, such generative models usuall... | main | Computer Vision | 10.1609/aaai.v35i2.16204 | 35 | 2 | 1175-1183 | official | null | null |
10.1609/aaai.v35i2.16205 | Split then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal | https://ojs.aaai.org/index.php/AAAI/article/view/16205 | https://ojs.aaai.org/index.php/AAAI/article/download/16205/16012 | [
"Xiaodong Cun",
"Chi-Man Pun"
] | Digital watermark is a commonly used technique to protect the copyright of medias. Simultaneously, to increase the robustness of watermark, attacking technique, such as watermark removal, also gets the attention from the community. Previous watermark removal methods require to gain the watermark location from users or ... | main | Computer Vision | 10.1609/aaai.v35i2.16205 | 35 | 2 | 1184-1192 | official | 2012.07007 | title_snapshot |
10.1609/aaai.v35i2.16206 | RSGNet: Relation based Skeleton Graph Network for Crowded Scenes Pose Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16206 | https://ojs.aaai.org/index.php/AAAI/article/download/16206/16013 | [
"Yan Dai",
"Xuanhan Wang",
"Lianli Gao",
"Jingkuan Song",
"Heng Tao Shen"
] | Despite of the recent great progress on multi-person pose estimation, existing solutions still remain challenging under the condition of "crowded scenes'', where RGB images capture complex real-world scenes with highly-overlapped people, severe occlusions and diverse postures. In this work, we focus on two main problem... | main | Computer Vision | 10.1609/aaai.v35i2.16206 | 35 | 2 | 1193-1200 | official | null | null |
10.1609/aaai.v35i2.16207 | Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16207 | https://ojs.aaai.org/index.php/AAAI/article/download/16207/16014 | [
"Jiajun Deng",
"Shaoshuai Shi",
"Peiwei Li",
"Wengang Zhou",
"Yanyong Zhang",
"Houqiang Li"
] | Recent advances on 3D object detection heavily rely on how the 3D data are represented, i.e., voxel-based or point-based representation. Many existing high performance 3D detectors are point-based because this structure can better retain precise point positions. Nevertheless, point-level features lead to high computati... | main | Computer Vision | 10.1609/aaai.v35i2.16207 | 35 | 2 | 1201-1209 | official | 2012.15712 | title_snapshot |
10.1609/aaai.v35i2.16208 | Arbitrary Video Style Transfer via Multi-Channel Correlation | https://ojs.aaai.org/index.php/AAAI/article/view/16208 | https://ojs.aaai.org/index.php/AAAI/article/download/16208/16015 | [
"Yingying Deng",
"Fan Tang",
"Weiming Dong",
"Haibin Huang",
"Chongyang Ma",
"Changsheng Xu"
] | Video style transfer is attracting increasing attention from the artificial intelligence community because of its numerous applications, such as augmented reality and animation production. Relative to traditional image style transfer, video style transfer presents new challenges, including how to effectively generate s... | main | Computer Vision | 10.1609/aaai.v35i2.16208 | 35 | 2 | 1210-1217 | official | 2009.08003 | title_snapshot |
10.1609/aaai.v35i2.16209 | Similarity Reasoning and Filtration for Image-Text Matching | https://ojs.aaai.org/index.php/AAAI/article/view/16209 | https://ojs.aaai.org/index.php/AAAI/article/download/16209/16016 | [
"Haiwen Diao",
"Ying Zhang",
"Lin Ma",
"Huchuan Lu"
] | Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words. However, how to make the most of these alignments to infer more accurate matching scores is still ... | main | Computer Vision | 10.1609/aaai.v35i2.16209 | 35 | 2 | 1218-1226 | official | 2101.01368 | title_snapshot |
10.1609/aaai.v35i2.16210 | Spatio-Temporal Difference Descriptor for Skeleton-Based Action Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16210 | https://ojs.aaai.org/index.php/AAAI/article/download/16210/16017 | [
"Chongyang Ding",
"Kai Liu",
"Jari Korhonen",
"Evgeny Belyaev"
] | In skeletal representation, intra-frame differences between body joints, as well as inter-frame dynamics between body skeletons contain discriminative information for action recognition. Conventional methods for modeling human skeleton sequences generally depend on motion trajectory and body joint dependency informatio... | main | Computer Vision | 10.1609/aaai.v35i2.16210 | 35 | 2 | 1227-1235 | official | null | null |
10.1609/aaai.v35i2.16211 | Towards Universal Physical Attacks on Single Object Tracking | https://ojs.aaai.org/index.php/AAAI/article/view/16211 | https://ojs.aaai.org/index.php/AAAI/article/download/16211/16018 | [
"Li Ding",
"Yongwei Wang",
"Kaiwen Yuan",
"Minyang Jiang",
"Ping Wang",
"Hua Huang",
"Z. Jane Wang"
] | Recent studies show that small perturbations in video frames could misguide single object trackers. However, such attacks have been mainly designed for digital-domain videos (i.e., perturbation on full images), which makes them practically infeasible to evaluate the adversarial vulnerability of trackers in real-world s... | main | Computer Vision | 10.1609/aaai.v35i2.16211 | 35 | 2 | 1236-1245 | official | null | null |
10.1609/aaai.v35i2.16212 | Modeling the Probabilistic Distribution of Unlabeled Data for One-shot Medical Image Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16212 | https://ojs.aaai.org/index.php/AAAI/article/download/16212/16019 | [
"Yuhang Ding",
"Xin Yu",
"Yi Yang"
] | Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires sophisticated expert knowledge. Thus, it is more desirable to employ only a few labeled data in pursuing high segmentation performance. In this ... | main | Computer Vision | 10.1609/aaai.v35i2.16212 | 35 | 2 | 1246-1254 | official | 2102.02033 | title_judge |
10.1609/aaai.v35i2.16213 | Few-Shot Class-Incremental Learning via Relation Knowledge Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/16213 | https://ojs.aaai.org/index.php/AAAI/article/download/16213/16020 | [
"Songlin Dong",
"Xiaopeng Hong",
"Xiaoyu Tao",
"Xinyuan Chang",
"Xing Wei",
"Yihong Gong"
] | In this paper, we focus on the challenging few-shot class incremental learning (FSCIL) problem, which requires to transfer knowledge from old tasks to new ones and solves catastrophic forgetting. We propose the exemplar relation distillation incremental learning framework to balance the tasks of old-knowledge preservin... | main | Computer Vision | 10.1609/aaai.v35i2.16213 | 35 | 2 | 1255-1263 | official | null | null |
10.1609/aaai.v35i2.16214 | MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems | https://ojs.aaai.org/index.php/AAAI/article/view/16214 | https://ojs.aaai.org/index.php/AAAI/article/download/16214/16021 | [
"Xuan Dong",
"Xiaoyan Hu",
"Weixin Li",
"Xiaojie Wang",
"Yunhong Wang"
] | We study the High Dynamic Range (HDR) imaging problem using two Low Dynamic Range (LDR) images that are shot from dual-lens systems in a single shot time with different exposures. In most of the related HDR imaging methods, the problem is usually solved by Multiple Images Merging, i.e. the final HDR image is fused from... | main | Computer Vision | 10.1609/aaai.v35i2.16214 | 35 | 2 | 1264-1272 | official | null | null |
10.1609/aaai.v35i2.16215 | Boosting Image-based Mutual Gaze Detection using Pseudo 3D Gaze | https://ojs.aaai.org/index.php/AAAI/article/view/16215 | https://ojs.aaai.org/index.php/AAAI/article/download/16215/16022 | [
"Bardia Doosti",
"Ching-Hui Chen",
"Raviteja Vemulapalli",
"Xuhui Jia",
"Yukun Zhu",
"Bradley Green"
] | Mutual gaze detection, i.e., predicting whether or not two people are looking at each other, plays an important role in understanding human interactions. In this work, we focus on the task of image-based mutual gaze detection, and propose a simple and effective approach to boost the performance by using an auxiliary 3D... | main | Computer Vision | 10.1609/aaai.v35i2.16215 | 35 | 2 | 1273-1281 | official | 2010.07811 | title_snapshot |
10.1609/aaai.v35i2.16216 | How to Save your Annotation Cost for Panoptic Segmentation? | https://ojs.aaai.org/index.php/AAAI/article/view/16216 | https://ojs.aaai.org/index.php/AAAI/article/download/16216/16023 | [
"Xuefeng Du",
"ChenHan Jiang",
"Hang Xu",
"Gengwei Zhang",
"Zhenguo Li"
] | How to properly reduce the annotation cost for panoptic segmentation? How to leverage and optimize the cost-quality trade-off for training data and model? These questions are key challenges towards a label-efficient and scalable panoptic segmentation system due to its expensive instance/semantic pixel-level annotation ... | main | Computer Vision | 10.1609/aaai.v35i2.16216 | 35 | 2 | 1282-1290 | official | null | null |
10.1609/aaai.v35i2.16217 | DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16217 | https://ojs.aaai.org/index.php/AAAI/article/download/16217/16024 | [
"Hao-Shu Fang",
"Yichen Xie",
"Dian Shao",
"Cewu Lu"
] | Recent years, human-object interaction (HOI) detection has achieved impressive advances. However, conventional two-stage methods are usually slow in inference. On the other hand, existing one-stage methods mainly focus on the union regions of interactions, which introduce unnecessary visual information as disturbances ... | main | Computer Vision | 10.1609/aaai.v35i2.16217 | 35 | 2 | 1291-1299 | official | 2010.01005 | title_snapshot |
10.1609/aaai.v35i2.16218 | DecAug: Augmenting HOI Detection via Decomposition | https://ojs.aaai.org/index.php/AAAI/article/view/16218 | https://ojs.aaai.org/index.php/AAAI/article/download/16218/16025 | [
"Hao-Shu Fang",
"Yichen Xie",
"Dian Shao",
"Yong-Lu Li",
"Cewu Lu"
] | Human-object interaction (HOI) detection requires a large amount of annotated data. Current algorithms suffer from insufficient training samples and category imbalance within datasets. To increase data efficiency, in this paper, we propose an efficient and effective data augmentation method called DecAug for HOI detect... | main | Computer Vision | 10.1609/aaai.v35i2.16218 | 35 | 2 | 1300-1308 | official | 2010.01007 | title_snapshot |
10.1609/aaai.v35i2.16219 | Partially Non-Autoregressive Image Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/16219 | https://ojs.aaai.org/index.php/AAAI/article/download/16219/16026 | [
"Zhengcong Fei"
] | Current state-of-the-art image captioning systems usually generated descriptions autoregressively, i.e., every forward step conditions on the given image and previously produced words. The sequential attribution causes a unavoidable decoding latency. Non-autoregressive image captioning, on the other hand, predicts the ... | main | Computer Vision | 10.1609/aaai.v35i2.16219 | 35 | 2 | 1309-1316 | official | null | null |
10.1609/aaai.v35i2.16220 | Memory-Augmented Image Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/16220 | https://ojs.aaai.org/index.php/AAAI/article/download/16220/16027 | [
"Zhengcong Fei"
] | Current deep learning-based image captioning systems have been proven to store practical knowledge with their parameters and achieve competitive performances in the public datasets. Nevertheless, their ability to access and precisely manipulate the mastered knowledge is still limited. Besides, providing evidence for de... | main | Computer Vision | 10.1609/aaai.v35i2.16220 | 35 | 2 | 1317-1324 | official | null | null |
10.1609/aaai.v35i2.16221 | Edge-competing Pathological Liver Vessel Segmentation with Limited Labels | https://ojs.aaai.org/index.php/AAAI/article/view/16221 | https://ojs.aaai.org/index.php/AAAI/article/download/16221/16028 | [
"Zunlei Feng",
"Zhonghua Wang",
"Xinchao Wang",
"Xiuming Zhang",
"Lechao Cheng",
"Jie Lei",
"Yuexuan Wang",
"Mingli Song"
] | The microvascular invasion (MVI) is a major prognostic factor in hepatocellular carcinoma, which is one of the malignant tumors with the highest mortality rate. The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily ... | main | Computer Vision | 10.1609/aaai.v35i2.16221 | 35 | 2 | 1325-1333 | official | 2108.00384 | title_snapshot |
10.1609/aaai.v35i2.16222 | Visual Boundary Knowledge Translation for Foreground Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16222 | https://ojs.aaai.org/index.php/AAAI/article/download/16222/16029 | [
"Zunlei Feng",
"Lechao Cheng",
"Xinchao Wang",
"Xiang Wang",
"Ya Jie Liu",
"Xiangtong Du",
"Mingli Song"
] | When confronted with objects of unknown types in an image, humans can effortlessly and precisely tell their visual boundaries. This recognition mechanism and underlying generalization capability seem to contrast to state-of-the-art image segmentation networks that rely on large-scale category-aware annotated training s... | main | Computer Vision | 10.1609/aaai.v35i2.16222 | 35 | 2 | 1334-1342 | official | 2108.00379 | title_snapshot |
10.1609/aaai.v35i2.16183 | CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/16183 | https://ojs.aaai.org/index.php/AAAI/article/download/16183/15990 | [
"Boyo Chen",
"Buo-Fu Chen",
"Chun Min Hsiao"
] | Convolutional neural networks (CNN) have achieved great success in analyzing tropical cyclones (TC) with satellite images in several tasks, such as TC intensity estimation. In contrast, TC structure, which is conventionally described by a few parameters estimated subjectively by meteorology specialists, is still hard t... | main | Computer Vision | 10.1609/aaai.v35i2.16183 | 35 | 2 | 991-998 | official | 2010.15158 | title_snapshot |
10.1609/aaai.v35i2.16184 | Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling | https://ojs.aaai.org/index.php/AAAI/article/view/16184 | https://ojs.aaai.org/index.php/AAAI/article/download/16184/15991 | [
"Hong Chen",
"Yifei Huang",
"Hiroya Takamura",
"Hideki Nakayama"
] | Visual storytelling is a task of generating relevant and interesting stories for given image sequences. In this work we aim at increasing the diversity of the generated stories while preserving the informative content from the images. We propose to foster the diversity and informativeness of a generated story by using ... | main | Computer Vision | 10.1609/aaai.v35i2.16184 | 35 | 2 | 999-1008 | official | 2102.02963 | title_snapshot |
10.1609/aaai.v35i2.16185 | Attention-based Multi-Level Fusion Network for Light Field Depth Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16185 | https://ojs.aaai.org/index.php/AAAI/article/download/16185/15992 | [
"Jiaxin Chen",
"Shuo Zhang",
"Youfang Lin"
] | Depth estimation from Light Field (LF) images is a crucial basis for LF related applications. Since multiple views with abundant information are available, how to effectively fuse features of these views is a key point for accurate LF depth estimation. In this paper, we propose a novel attention-based multi-level fusio... | main | Computer Vision | 10.1609/aaai.v35i2.16185 | 35 | 2 | 1009-1017 | official | null | null |
10.1609/aaai.v35i2.16186 | Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty | https://ojs.aaai.org/index.php/AAAI/article/view/16186 | https://ojs.aaai.org/index.php/AAAI/article/download/16186/15993 | [
"Jierun Chen",
"Song Wen",
"S.-H. Gary Chan"
] | Image demosaicking and denoising are the two key fundamental steps in digital camera pipelines, aiming to reconstruct clean color images from noisy luminance readings. In this paper, we propose and study Wild-JDD, a novel learning framework for joint demosaicking and denoising in the wild. In contrast to previous works... | main | Computer Vision | 10.1609/aaai.v35i2.16186 | 35 | 2 | 1018-1026 | official | 2101.04442 | title_snapshot |
10.1609/aaai.v35i2.16187 | Spatial-temporal Causal Inference for Partial Image-to-video Adaptation | https://ojs.aaai.org/index.php/AAAI/article/view/16187 | https://ojs.aaai.org/index.php/AAAI/article/download/16187/15994 | [
"Jin Chen",
"Xinxiao Wu",
"Yao Hu",
"Jiebo Luo"
] | Image-to-video adaptation leverages off-the-shelf learned models in labeled images to help classification in unlabeled videos, thus alleviating the high computation overhead of training a video classifier from scratch. This task is very challenging since there exist two types of domain shifts between images and videos:... | main | Computer Vision | 10.1609/aaai.v35i2.16187 | 35 | 2 | 1027-1035 | official | null | null |
10.1609/aaai.v35i2.16188 | Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding | https://ojs.aaai.org/index.php/AAAI/article/view/16188 | https://ojs.aaai.org/index.php/AAAI/article/download/16188/15995 | [
"Long Chen",
"Wenbo Ma",
"Jun Xiao",
"Hanwang Zhang",
"Shih-Fu Chang"
] | The prevailing framework for solving referring expression grounding is based on a two-stage process: 1) detecting proposals with an object detector and 2) grounding the referent to one of the proposals. Existing two-stage solutions mostly focus on the grounding step, which aims to align the expressions with the proposa... | main | Computer Vision | 10.1609/aaai.v35i2.16188 | 35 | 2 | 1036-1044 | official | 2009.01449 | title_snapshot |
10.1609/aaai.v35i2.16189 | RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16189 | https://ojs.aaai.org/index.php/AAAI/article/download/16189/15996 | [
"Peihao Chen",
"Deng Huang",
"Dongliang He",
"Xiang Long",
"Runhao Zeng",
"Shilei Wen",
"Mingkui Tan",
"Chuang Gan"
] | We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such as action recognition. This task, however, is extremely challenging due to 1) the highly complex spatial-temporal information in videos and... | main | Computer Vision | 10.1609/aaai.v35i2.16189 | 35 | 2 | 1045-1053 | official | 2011.07949 | title_snapshot |
10.1609/aaai.v35i2.16190 | Dual Distribution Alignment Network for Generalizable Person Re-Identification | https://ojs.aaai.org/index.php/AAAI/article/view/16190 | https://ojs.aaai.org/index.php/AAAI/article/download/16190/15997 | [
"Peixian Chen",
"Pingyang Dai",
"Jianzhuang Liu",
"Feng Zheng",
"Mingliang Xu",
"Qi Tian",
"Rongrong Ji"
] | Domain generalization (DG) offers a preferable real-world setting for Person Re-Identification (Re-ID), which trains a model using multiple source domain datasets and expects it to perform well in an unseen target domain without any model updating. Unfortunately, most DG approaches are designed explicitly for classific... | main | Computer Vision | 10.1609/aaai.v35i2.16190 | 35 | 2 | 1054-1062 | official | 2007.13249 | title_snapshot |
10.1609/aaai.v35i2.16191 | RGB-D Salient Object Detection via 3D Convolutional Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16191 | https://ojs.aaai.org/index.php/AAAI/article/download/16191/15998 | [
"Qian Chen",
"Ze Liu",
"Yi Zhang",
"Keren Fu",
"Qijun Zhao",
"Hongwei Du"
] | RGB-D salient object detection (SOD) recently has attracted increasing research interest and many deep learning methods based on encoder-decoder architectures have emerged. However, most existing RGB-D SOD models conduct feature fusion either in the single encoder or the decoder stage, which hardly guarantees sufficien... | main | Computer Vision | 10.1609/aaai.v35i2.16191 | 35 | 2 | 1063-1071 | official | 2101.10241 | title_snapshot |
10.1609/aaai.v35i2.16192 | Mind-the-Gap! Unsupervised Domain Adaptation for Text-Video Retrieval | https://ojs.aaai.org/index.php/AAAI/article/view/16192 | https://ojs.aaai.org/index.php/AAAI/article/download/16192/15999 | [
"Qingchao Chen",
"Yang Liu",
"Samuel Albanie"
] | When can we expect a text-video retrieval system to work effectively on datasets that differ from its training domain? In this work, we investigate this question through the lens of unsupervised domain adaptation in which the objective is to match natural language queries and video content in the presence of domain shi... | main | Computer Vision | 10.1609/aaai.v35i2.16192 | 35 | 2 | 1072-1080 | official | null | null |
10.1609/aaai.v35i2.16193 | Local Relation Learning for Face Forgery Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16193 | https://ojs.aaai.org/index.php/AAAI/article/download/16193/16000 | [
"Shen Chen",
"Taiping Yao",
"Yang Chen",
"Shouhong Ding",
"Jilin Li",
"Rongrong Ji"
] | With the rapid development of facial manipulation techniques, face forgery has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision. How... | main | Computer Vision | 10.1609/aaai.v35i2.16193 | 35 | 2 | 1081-1088 | official | 2105.02577 | title_snapshot |
10.1609/aaai.v35i2.16194 | Deductive Learning for Weakly-Supervised 3D Human Pose Estimation via Uncalibrated Cameras | https://ojs.aaai.org/index.php/AAAI/article/view/16194 | https://ojs.aaai.org/index.php/AAAI/article/download/16194/16001 | [
"Xipeng Chen",
"Pengxu Wei",
"Liang Lin"
] | Without prohibitive and laborious 3D annotations, weakly-supervised 3D human pose methods mainly employ the model regularization with geometric projection consistency or geometry estimation from multi-view images. Nevertheless, those approaches explicitly need known parameters of calibrated cameras, exhibiting a limite... | main | Computer Vision | 10.1609/aaai.v35i2.16194 | 35 | 2 | 1089-1096 | official | null | null |
10.1609/aaai.v35i2.16195 | A Unified Multi-Scenario Attacking Network for Visual Object Tracking | https://ojs.aaai.org/index.php/AAAI/article/view/16195 | https://ojs.aaai.org/index.php/AAAI/article/download/16195/16002 | [
"Xuesong Chen",
"Canmiao Fu",
"Feng Zheng",
"Yong Zhao",
"Hongsheng Li",
"Ping Luo",
"Guo-Jun Qi"
] | Existing methods of adversarial attacks successfully generate adversarial examples to confuse Deep Neural Networks (DNNs) of image classification and object detection, resulting in wrong predictions. However, these methods are difficult to attack models of video object tracking, because the tracking algorithms could ha... | main | Computer Vision | 10.1609/aaai.v35i2.16195 | 35 | 2 | 1097-1104 | official | null | null |
10.1609/aaai.v35i2.16196 | SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains | https://ojs.aaai.org/index.php/AAAI/article/view/16196 | https://ojs.aaai.org/index.php/AAAI/article/download/16196/16003 | [
"Yuanqi Chen",
"Ge Li",
"Cece Jin",
"Shan Liu",
"Thomas Li"
] | This paper observes that there is an issue of high frequencies missing in the discriminator of standard GAN, and we reveal it stems from downsampling layers employed in the network architecture. This issue makes the generator lack the incentive from the discriminator to learn high-frequency content of data, resulting i... | main | Computer Vision | 10.1609/aaai.v35i2.16196 | 35 | 2 | 1105-1112 | official | 2012.05535 | title_snapshot |
10.1609/aaai.v35i2.16197 | Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16197 | https://ojs.aaai.org/index.php/AAAI/article/download/16197/16004 | [
"Zhan Chen",
"Sicheng Li",
"Bing Yang",
"Qinghan Li",
"Hong Liu"
] | Graph convolutional networks have been widely used for skeleton-based action recognition due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a local operation, it can only utilize the short-range joint dependencies and short-term trajectory but fails to directly model the distant ... | main | Computer Vision | 10.1609/aaai.v35i2.16197 | 35 | 2 | 1113-1122 | official | 2206.13028 | title_snapshot |
10.1609/aaai.v35i2.16198 | Cascade Network with Guided Loss and Hybrid Attention for Finding Good Correspondences | https://ojs.aaai.org/index.php/AAAI/article/view/16198 | https://ojs.aaai.org/index.php/AAAI/article/download/16198/16005 | [
"Zhi Chen",
"Fan Yang",
"Wenbing Tao"
] | Finding good correspondences is a critical prerequisite in many feature based tasks. Given a putative correspondence set of an image pair, we propose a neural network which finds correct correspondences by a binary-class classifier and estimates relative pose through classified correspondences. First, we analyze that d... | main | Computer Vision | 10.1609/aaai.v35i2.16198 | 35 | 2 | 1123-1131 | official | 2102.00411 | title_snapshot |
10.1609/aaai.v35i2.16199 | Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing | https://ojs.aaai.org/index.php/AAAI/article/view/16199 | https://ojs.aaai.org/index.php/AAAI/article/download/16199/16006 | [
"Zhihong Chen",
"Taiping Yao",
"Kekai Sheng",
"Shouhong Ding",
"Ying Tai",
"Jilin Li",
"Feiyue Huang",
"Xinyu Jin"
] | Face anti-spoofing approach based on domain generalization (DG) has drawn growing attention due to its robustness for unseen scenarios. Existing DG methods assume that the domain label is known. However, in real-world applications, the collected dataset always contains mixture domains, where the domain label is unknown... | main | Computer Vision | 10.1609/aaai.v35i2.16199 | 35 | 2 | 1132-1139 | official | 2105.02453 | title_snapshot |
10.1609/aaai.v35i2.16200 | SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network | https://ojs.aaai.org/index.php/AAAI/article/view/16200 | https://ojs.aaai.org/index.php/AAAI/article/download/16200/16007 | [
"Mingmei Cheng",
"Le Hui",
"Jin Xie",
"Jian Yang"
] | Point cloud semantic segmentation is a crucial task in 3D scene understanding. Existing methods mainly focus on employing a large number of annotated labels for supervised semantic segmentation. Nonetheless, manually labeling such large point clouds for the supervised segmentation task is time-consuming. In order to re... | main | Computer Vision | 10.1609/aaai.v35i2.16200 | 35 | 2 | 1140-1147 | official | 2104.07861 | title_snapshot |
10.1609/aaai.v35i2.16201 | Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification | https://ojs.aaai.org/index.php/AAAI/article/view/16201 | https://ojs.aaai.org/index.php/AAAI/article/download/16201/16008 | [
"Siyuan Cheng",
"Yingqi Liu",
"Shiqing Ma",
"Xiangyu Zhang"
] | Trojan (backdoor) attack is a form of adversarial attack on deep neural networks where the attacker provides victims with a model trained/retrained on malicious data. The backdoor can be activated when a normal input is stamped with a certain pattern called trigger, causing misclassification. Many existing trojan attac... | main | Computer Vision | 10.1609/aaai.v35i2.16201 | 35 | 2 | 1148-1156 | official | 2012.11212 | title_snapshot |
10.1609/aaai.v35i2.16202 | Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos | https://ojs.aaai.org/index.php/AAAI/article/view/16202 | https://ojs.aaai.org/index.php/AAAI/article/download/16202/16009 | [
"Yu Cheng",
"Bo Wang",
"Bo Yang",
"Robby T. Tan"
] | Despite the recent progress, 3D multi-person pose estimation from monocular videos is still challenging due to the commonly encountered problem of missing information caused by occlusion, partially out-of-frame target persons, and inaccurate person detection. To tackle this problem, we propose a novel framework integra... | main | Computer Vision | 10.1609/aaai.v35i2.16202 | 35 | 2 | 1157-1165 | official | 2012.11806 | title_snapshot |
10.1609/aaai.v35i2.16170 | Localization in the Crowd with Topological Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/16170 | https://ojs.aaai.org/index.php/AAAI/article/download/16170/15977 | [
"Shahira Abousamra",
"Minh Hoai",
"Dimitris Samaras",
"Chao Chen"
] | We address the problem of crowd localization, i.e., the prediction of dots corresponding to people in a crowded scene. Due to various challenges, a localization method is prone to spatial semantic errors, i.e., predicting multiple dots within a same person or collapsing multiple dots in a cluttered region. We propose a... | main | Computer Vision | 10.1609/aaai.v35i2.16170 | 35 | 2 | 872-881 | official | 2012.12482 | title_snapshot |
10.1609/aaai.v35i2.16171 | Deep Event Stereo Leveraged by Event-to-Image Translation | https://ojs.aaai.org/index.php/AAAI/article/view/16171 | https://ojs.aaai.org/index.php/AAAI/article/download/16171/15978 | [
"Soikat Hasan Ahmed",
"Hae Woong Jang",
"S M Nadim Uddin",
"Yong Ju Jung"
] | Depth estimation in real-world applications requires precise responses to fast motion and challenging lighting conditions. Event cameras use bio-inspired event-driven sensors that provide instantaneous and asynchronous information of pixel-level log intensity changes, which makes them suitable for depth estimation in s... | main | Computer Vision | 10.1609/aaai.v35i2.16171 | 35 | 2 | 882-890 | official | null | null |
10.1609/aaai.v35i2.16172 | Optical Flow Estimation from a Single Motion-blurred Image | https://ojs.aaai.org/index.php/AAAI/article/view/16172 | https://ojs.aaai.org/index.php/AAAI/article/download/16172/15979 | [
"Dawit Mureja Argaw",
"Junsik Kim",
"Francois Rameau",
"Jae Won Cho",
"In So Kweon"
] | In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in fundamental computer vision problems. In this work, we propose a novel framework to estimate optical flow from a single motion-blurred imag... | main | Computer Vision | 10.1609/aaai.v35i2.16172 | 35 | 2 | 891-900 | official | 2103.02996 | title_snapshot |
10.1609/aaai.v35i2.16173 | Motion-blurred Video Interpolation and Extrapolation | https://ojs.aaai.org/index.php/AAAI/article/view/16173 | https://ojs.aaai.org/index.php/AAAI/article/download/16173/15980 | [
"Dawit Mureja Argaw",
"Junsik Kim",
"Francois Rameau",
"In So Kweon"
] | Abrupt motion of camera or objects in a scene result in a blurry video, and therefore recovering high quality video requires two types of enhancements: visual enhancement and temporal upsampling. A broad range of research attempted to recover clean frames from blurred image sequences or temporally upsample frames by in... | main | Computer Vision | 10.1609/aaai.v35i2.16173 | 35 | 2 | 901-910 | official | 2103.02984 | title_snapshot |
10.1609/aaai.v35i2.16174 | Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/16174 | https://ojs.aaai.org/index.php/AAAI/article/download/16174/15981 | [
"Inhwan Bae",
"Hae-Gon Jeon"
] | Pedestrian trajectory prediction is one of the important tasks required for autonomous navigation and social robots in human environments. Previous studies focused on estimating social forces among individual pedestrians. However, they did not consider the social forces of groups on pedestrians, which results in over-c... | main | Computer Vision | 10.1609/aaai.v35i2.16174 | 35 | 2 | 911-919 | official | null | null |
10.1609/aaai.v35i2.16175 | Dense Events Grounding in Video | https://ojs.aaai.org/index.php/AAAI/article/view/16175 | https://ojs.aaai.org/index.php/AAAI/article/download/16175/15982 | [
"Peijun Bao",
"Qian Zheng",
"Yadong Mu"
] | This paper explores a novel setting of temporal sentence grounding for the first time, dubbed as dense events grounding. Given an untrimmed video and a paragraph description, dense events grounding aims to jointly localize temporal moments of multiple events described in the paragraph. Our main motivating fact is that ... | main | Computer Vision | 10.1609/aaai.v35i2.16175 | 35 | 2 | 920-928 | official | null | null |
10.1609/aaai.v35i2.16176 | Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification | https://ojs.aaai.org/index.php/AAAI/article/view/16176 | https://ojs.aaai.org/index.php/AAAI/article/download/16176/15983 | [
"Ardhendu Behera",
"Zachary Wharton",
"Pradeep R P G Hewage",
"Asish Bera"
] | Deep convolutional neural networks (CNNs) have shown a strong ability in mining discriminative object pose and parts information for image recognition. For fine-grained recognition, context-aware rich feature representation of object/scene plays a key role since it exhibits a significant variance in the same subcategor... | main | Computer Vision | 10.1609/aaai.v35i2.16176 | 35 | 2 | 929-937 | official | 2101.06635 | title_snapshot |
10.1609/aaai.v35i2.16177 | Appearance-Motion Memory Consistency Network for Video Anomaly Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16177 | https://ojs.aaai.org/index.php/AAAI/article/download/16177/15984 | [
"Ruichu Cai",
"Hao Zhang",
"Wen Liu",
"Shenghua Gao",
"Zhifeng Hao"
] | Abnormal event detection in the surveillance video is an essential but challenging task, and many methods have been proposed to deal with this problem. The previous methods either only consider the appearance information or directly integrate the results of appearance and motion information without considering their en... | main | Computer Vision | 10.1609/aaai.v35i2.16177 | 35 | 2 | 938-946 | official | null | null |
10.1609/aaai.v35i2.16178 | Rethinking Object Detection in Retail Stores | https://ojs.aaai.org/index.php/AAAI/article/view/16178 | https://ojs.aaai.org/index.php/AAAI/article/download/16178/15985 | [
"Yuanqiang Cai",
"Longyin Wen",
"Libo Zhang",
"Dawei Du",
"Weiqiang Wang"
] | The conventional standard for object detection uses a bounding box to represent each individual object instance. However, it is not practical in the industry-relevant applications in the context of warehouses due to severe occlusions among groups of instances of the same categories. In this paper, we propose a new task... | main | Computer Vision | 10.1609/aaai.v35i2.16178 | 35 | 2 | 947-954 | official | 2003.08230 | title_snapshot |
10.1609/aaai.v35i2.16179 | YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design | https://ojs.aaai.org/index.php/AAAI/article/view/16179 | https://ojs.aaai.org/index.php/AAAI/article/download/16179/15986 | [
"Yuxuan Cai",
"Hongjia Li",
"Geng Yuan",
"Wei Niu",
"Yanyu Li",
"Xulong Tang",
"Bin Ren",
"Yanzhi Wang"
] | The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight... | main | Computer Vision | 10.1609/aaai.v35i2.16179 | 35 | 2 | 955-963 | official | 2009.05697 | title_snapshot |
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