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.v35i2.16180 | Semantic MapNet: Building Allocentric Semantic Maps and Representations from Egocentric Views | https://ojs.aaai.org/index.php/AAAI/article/view/16180 | https://ojs.aaai.org/index.php/AAAI/article/download/16180/15987 | [
"Vincent Cartillier",
"Zhile Ren",
"Neha Jain",
"Stefan Lee",
"Irfan Essa",
"Dhruv Batra"
] | We study the task of semantic mapping – specifically, an embodied agent (a robot or an egocentric AI assistant) is given a tour of a new environment and asked to build an allocentric top-down semantic map (‘what is where?’) from egocentric observations of an RGB-D camera with known pose (via localization sensors). Impo... | main | Computer Vision | 10.1609/aaai.v35i2.16180 | 35 | 2 | 964-972 | official | 2010.01191 | title_snapshot |
10.1609/aaai.v35i2.16181 | Understanding Deformable Alignment in Video Super-Resolution | https://ojs.aaai.org/index.php/AAAI/article/view/16181 | https://ojs.aaai.org/index.php/AAAI/article/download/16181/15988 | [
"Kelvin C.K. Chan",
"Xintao Wang",
"Ke Yu",
"Chao Dong",
"Chen Change Loy"
] | Deformable convolution, originally proposed for the adaptation to geometric variations of objects, has recently shown compelling performance in aligning multiple frames and is increasingly adopted for video super-resolution. Despite its remarkable performance, its underlying mechanism for alignment remains unclear. In ... | main | Computer Vision | 10.1609/aaai.v35i2.16181 | 35 | 2 | 973-981 | official | 2009.07265 | title_snapshot |
10.1609/aaai.v35i2.16182 | Deep Metric Learning with Graph Consistency | https://ojs.aaai.org/index.php/AAAI/article/view/16182 | https://ojs.aaai.org/index.php/AAAI/article/download/16182/15989 | [
"Binghui Chen",
"Pengyu Li",
"Zhaoyi Yan",
"Biao Wang",
"Lei Zhang"
] | Deep Metric Learning (DML) has been more attractive and widely applied in many computer vision tasks, in which a discriminative embedding is requested such that the image features belonging to the same class are gathered together and the ones belonging to different classes are pushed apart. Most existing works insist t... | main | Computer Vision | 10.1609/aaai.v35i2.16182 | 35 | 2 | 982-990 | official | null | null |
10.1609/aaai.v35i3.16363 | BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation | https://ojs.aaai.org/index.php/AAAI/article/view/16363 | https://ojs.aaai.org/index.php/AAAI/article/download/16363/16170 | [
"Haisheng Su",
"Weihao Gan",
"Wei Wu",
"Yu Qiao",
"Junjie Yan"
] | Generating human action proposals in untrimmed videos is an important yet challenging task with wide applications. Current methods often suffer from the noisy boundary locations and the inferior quality of confidence scores used for proposal retrieving. In this paper, we present BSN++, a new framework which exploits co... | main | Computer Vision | 10.1609/aaai.v35i3.16363 | 35 | 3 | 2602-2610 | official | 2009.07641 | title_snapshot |
10.1609/aaai.v35i3.16364 | MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing | https://ojs.aaai.org/index.php/AAAI/article/view/16364 | https://ojs.aaai.org/index.php/AAAI/article/download/16364/16171 | [
"Hao Su",
"Jianwei Niu",
"Xuefeng Liu",
"Qingfeng Li",
"Jiahe Cui",
"Ji Wan"
] | Manga is a world popular comic form originated in Japan, which typically employs black-and-white stroke lines and geometric exaggeration to describe humans' appearances, poses, and actions. In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga tra... | main | Computer Vision | 10.1609/aaai.v35i3.16364 | 35 | 3 | 2611-2619 | official | 2004.10634 | title_snapshot |
10.1609/aaai.v35i3.16365 | MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16365 | https://ojs.aaai.org/index.php/AAAI/article/download/16365/16172 | [
"Guanxiong Sun",
"Yang Hua",
"Guosheng Hu",
"Neil Robertson"
] | State-of-the-art video object detection methods maintain a memory structure, either a sliding window or a memory queue, to enhance the current frame using attention mechanisms. However, we argue that these memory structures are not efficient or sufficient because of two implied operations: (1) concatenating all feature... | main | Computer Vision | 10.1609/aaai.v35i3.16365 | 35 | 3 | 2620-2627 | official | 2401.09923 | title_snapshot |
10.1609/aaai.v35i3.16366 | Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging | https://ojs.aaai.org/index.php/AAAI/article/view/16366 | https://ojs.aaai.org/index.php/AAAI/article/download/16366/16173 | [
"He Sun",
"Katherine L. Bouman"
] | Computational image reconstruction algorithms generally produce a single image without any measure of uncertainty or confidence. Regularized Maximum Likelihood (RML) and feed-forward deep learning approaches for inverse problems typically focus on recovering a point estimate. This is a serious limitation when working w... | main | Computer Vision | 10.1609/aaai.v35i3.16366 | 35 | 3 | 2628-2637 | official | 2010.14462 | title_snapshot |
10.1609/aaai.v35i3.16367 | Domain General Face Forgery Detection by Learning to Weight | https://ojs.aaai.org/index.php/AAAI/article/view/16367 | https://ojs.aaai.org/index.php/AAAI/article/download/16367/16174 | [
"Ke Sun",
"Hong Liu",
"Qixiang Ye",
"Yue Gao",
"Jianzhuang Liu",
"Ling Shao",
"Rongrong Ji"
] | In this paper, we propose a domain-general model, termed learning-to-weight (LTW), that guarantees face detection performance across multiple domains, particularly the target domains that are never seen before. However, various face forgery methods cause complex and biased data distributions, making it challenging to d... | main | Computer Vision | 10.1609/aaai.v35i3.16367 | 35 | 3 | 2638-2646 | official | null | null |
10.1609/aaai.v35i3.16368 | Object-Centric Image Generation from Layouts | https://ojs.aaai.org/index.php/AAAI/article/view/16368 | https://ojs.aaai.org/index.php/AAAI/article/download/16368/16175 | [
"Tristan Sylvain",
"Pengchuan Zhang",
"Yoshua Bengio",
"R Devon Hjelm",
"Shikhar Sharma"
] | We begin with the hypothesis that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes with multiple objects well. Our layout-to-image-generation method, which we call Object-Centric Generative Adversarial Network (or OC-GAN), relies on a novel Scen... | main | Computer Vision | 10.1609/aaai.v35i3.16368 | 35 | 3 | 2647-2655 | official | 2003.07449 | title_snapshot |
10.1609/aaai.v35i3.16369 | Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation | https://ojs.aaai.org/index.php/AAAI/article/view/16369 | https://ojs.aaai.org/index.php/AAAI/article/download/16369/16176 | [
"Jilin Tang",
"Yi Yuan",
"Tianjia Shao",
"Yong Liu",
"Mengmeng Wang",
"Kun Zhou"
] | In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the source appearance. Given the inefficiency of standard CNNs in handling large spatial transformation, we propose a structure-aware flow based... | main | Computer Vision | 10.1609/aaai.v35i3.16369 | 35 | 3 | 2656-2664 | official | 2102.02972 | title_snapshot |
10.1609/aaai.v35i3.16370 | Gradient Regularized Contrastive Learning for Continual Domain Adaptation | https://ojs.aaai.org/index.php/AAAI/article/view/16370 | https://ojs.aaai.org/index.php/AAAI/article/download/16370/16177 | [
"Shixiang Tang",
"Peng Su",
"Dapeng Chen",
"Wanli Ouyang"
] | Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algorithms remains a challenge. To better understand this issue, we study the problem of continual domain adaptation, where the model is presented... | main | Computer Vision | 10.1609/aaai.v35i3.16370 | 35 | 3 | 2665-2673 | official | 2103.12294 | title_snapshot |
10.1609/aaai.v35i3.16371 | Adversarial Training Reduces Information and Improves Transferability | https://ojs.aaai.org/index.php/AAAI/article/view/16371 | https://ojs.aaai.org/index.php/AAAI/article/download/16371/16178 | [
"Matteo Terzi",
"Alessandro Achille",
"Marco Maggipinto",
"Gian Antonio Susto"
] | Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility. The latter property may seem counter-intuitive as it is widely accepted by the community that classification models should only capture the minimal info... | main | Computer Vision | 10.1609/aaai.v35i3.16371 | 35 | 3 | 2674-2682 | official | 2007.11259 | title_snapshot |
10.1609/aaai.v35i3.16372 | Adversarial Turing Patterns from Cellular Automata | https://ojs.aaai.org/index.php/AAAI/article/view/16372 | https://ojs.aaai.org/index.php/AAAI/article/download/16372/16179 | [
"Nurislam Tursynbek",
"Ilya Vilkoviskiy",
"Maria Sindeeva",
"Ivan Oseledets"
] | State-of-the-art deep classifiers are intriguingly vulnerable to universal adversarial perturbations: single disturbances of small magnitude that lead to misclassification of most inputs. This phenomena may potentially result in a serious security problem. Despite the extensive research in this area, there is a lack of... | main | Computer Vision | 10.1609/aaai.v35i3.16372 | 35 | 3 | 2683-2691 | official | 2011.09393 | title_snapshot |
10.1609/aaai.v35i3.16373 | Artificial Dummies for Urban Dataset Augmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16373 | https://ojs.aaai.org/index.php/AAAI/article/download/16373/16180 | [
"Antonín Vobecký",
"David Hurych",
"Michal Uřičář",
"Patrick Pérez",
"Josef Sivic"
] | Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are too difficult to capture due to safety reasons, or they are very unlikely to happen. The strict safety requirements in assisted and autonom... | main | Computer Vision | 10.1609/aaai.v35i3.16373 | 35 | 3 | 2692-2700 | official | 2012.08274 | title_snapshot |
10.1609/aaai.v35i3.16374 | SCNet: Training Inference Sample Consistency for Instance Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16374 | https://ojs.aaai.org/index.php/AAAI/article/download/16374/16181 | [
"Thang Vu",
"Haeyong Kang",
"Chang D. Yoo"
] | Cascaded architectures have brought significant performance improvement in object detection and instance segmentation. However, there are lingering issues regarding the disparity in the Intersection-over-Union (IoU) distribution of the samples between training and inference. This disparity can potentially exacerbate de... | main | Computer Vision | 10.1609/aaai.v35i3.16374 | 35 | 3 | 2701-2709 | official | 2012.10150 | title_snapshot |
10.1609/aaai.v35i3.16375 | Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16375 | https://ojs.aaai.org/index.php/AAAI/article/download/16375/16182 | [
"Chaoqun Wang",
"Xuejin Chen",
"Shaobo Min",
"Xiaoyan Sun",
"Houqiang Li"
] | Generalized Zero-Shot Learning (GZSL) targets recognizing new categories by learning transferable image representations. Existing methods find that, by aligning image representations with corresponding semantic labels, the semantic-aligned representations can be transferred to unseen categories. However, supervised by ... | main | Computer Vision | 10.1609/aaai.v35i3.16375 | 35 | 3 | 2710-2718 | official | 2104.01832 | title_snapshot |
10.1609/aaai.v35i3.16343 | CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval | https://ojs.aaai.org/index.php/AAAI/article/view/16343 | https://ojs.aaai.org/index.php/AAAI/article/download/16343/16150 | [
"Hai X. Pham",
"Ricardo Guerrero",
"Vladimir Pavlovic",
"Jiatong Li"
] | Despite the abundance of multi-modal data, such as image-text pairs, there has been little effort in understanding the individual entities and their different roles in the construction of these data instances. In this work, we endeavour to discover the entities and their corresponding importance in cooking recipes auto... | main | Computer Vision | 10.1609/aaai.v35i3.16343 | 35 | 3 | 2423-2430 | official | 2102.02547 | title_snapshot |
10.1609/aaai.v35i3.16344 | Explainable Models with Consistent Interpretations | https://ojs.aaai.org/index.php/AAAI/article/view/16344 | https://ojs.aaai.org/index.php/AAAI/article/download/16344/16151 | [
"Vipin Pillai",
"Hamed Pirsiavash"
] | Given the widespread deployment of black box deep neural networks in computer vision applications, the interpretability aspect of these black box systems has recently gained traction. Various methods have been proposed to explain the results of such deep neural networks. However, some recent works have shown that such ... | main | Computer Vision | 10.1609/aaai.v35i3.16344 | 35 | 3 | 2431-2439 | official | null | null |
10.1609/aaai.v35i3.16345 | Dual Adversarial Graph Neural Networks for Multi-label Cross-modal Retrieval | https://ojs.aaai.org/index.php/AAAI/article/view/16345 | https://ojs.aaai.org/index.php/AAAI/article/download/16345/16152 | [
"Shengsheng Qian",
"Dizhan Xue",
"Huaiwen Zhang",
"Quan Fang",
"Changsheng Xu"
] | Cross-modal retrieval has become an active study field with the expanding scale of multimodal data. To date, most existing methods transform multimodal data into a common representation space where semantic similarities between items can be directly measured across different modalities. However, these methods typically... | main | Computer Vision | 10.1609/aaai.v35i3.16345 | 35 | 3 | 2440-2448 | official | null | null |
10.1609/aaai.v35i3.16346 | KGDet: Keypoint-Guided Fashion Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16346 | https://ojs.aaai.org/index.php/AAAI/article/download/16346/16153 | [
"Shenhan Qian",
"Dongze Lian",
"Binqiang Zhao",
"Tong Liu",
"Bohui Zhu",
"Hai Li",
"Shenghua Gao"
] | Locating and classifying clothes, usually referred to as clothing detection, is a fundamental task in fashion analysis. Motivated by the strong structural characteristics of clothes, we pursue a detection method enhanced by clothing keypoints, which is a compact and effective representation of structures. To incorporat... | main | Computer Vision | 10.1609/aaai.v35i3.16346 | 35 | 3 | 2449-2457 | official | null | null |
10.1609/aaai.v35i3.16347 | Learning Modulated Loss for Rotated Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16347 | https://ojs.aaai.org/index.php/AAAI/article/download/16347/16154 | [
"Wen Qian",
"Xue Yang",
"Silong Peng",
"Junchi Yan",
"Yue Guo"
] | Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) or eight parameters (coordinates of four vertices) to describe the rotated bounding box and l1 loss as the loss function. In this paper, we argue that the aforementioned integration can ca... | main | Computer Vision | 10.1609/aaai.v35i3.16347 | 35 | 3 | 2458-2466 | official | 1911.08299 | title_snapshot |
10.1609/aaai.v35i3.16348 | MANGO: A Mask Attention Guided One-Stage Scene Text Spotter | https://ojs.aaai.org/index.php/AAAI/article/view/16348 | https://ojs.aaai.org/index.php/AAAI/article/download/16348/16155 | [
"Liang Qiao",
"Ying Chen",
"Zhanzhan Cheng",
"Yunlu Xu",
"Yi Niu",
"Shiliang Pu",
"Fei Wu"
] | Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods attempt to develop various region of interest (RoI) operations to concatenate the detection part and the sequence recognition part into a t... | main | Computer Vision | 10.1609/aaai.v35i3.16348 | 35 | 3 | 2467-2476 | official | 2012.04350 | title_snapshot |
10.1609/aaai.v35i3.16349 | REFINE: Prediction Fusion Network for Panoptic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16349 | https://ojs.aaai.org/index.php/AAAI/article/download/16349/16156 | [
"Jiawei Ren",
"Cunjun Yu",
"Zhongang Cai",
"Mingyuan Zhang",
"Chongsong Chen",
"Haiyu Zhao",
"Shuai Yi",
"Hongsheng Li"
] | Panoptic segmentation aims at generating pixel-wise class and instance predictions for each pixel in the input image, which is a challenging task and far more complicated than naively fusing the semantic and instance segmentation results. Prediction fusion is therefore important to achieve accurate panoptic segmentatio... | main | Computer Vision | 10.1609/aaai.v35i3.16349 | 35 | 3 | 2477-2485 | official | null | null |
10.1609/aaai.v35i3.16350 | AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16350 | https://ojs.aaai.org/index.php/AAAI/article/download/16350/16157 | [
"Youngmin Ro",
"Jin Young Choi"
] | Existing fine-tuning methods use a single learning rate over all layers. In this paper, first, we discuss that trends of layer-wise weight variations by fine-tuning using a single learning rate do not match the well-known notion that lower-level layers extract general features and higher-level layers extract specific f... | main | Computer Vision | 10.1609/aaai.v35i3.16350 | 35 | 3 | 2486-2494 | official | 2002.06048 | title_snapshot |
10.1609/aaai.v35i3.16351 | DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch | https://ojs.aaai.org/index.php/AAAI/article/view/16351 | https://ojs.aaai.org/index.php/AAAI/article/download/16351/16158 | [
"Xiaofeng Ruan",
"Yufan Liu",
"Bing Li",
"Chunfeng Yuan",
"Weiming Hu"
] | Filter pruning is a commonly used method for compressing Convolutional Neural Networks (ConvNets), due to its friendly hardware supporting and flexibility. However, existing methods mostly need a cumbersome procedure, which brings many extra hyper-parameters and training epochs. This is because only using sparsity and ... | main | Computer Vision | 10.1609/aaai.v35i3.16351 | 35 | 3 | 2495-2503 | official | null | null |
10.1609/aaai.v35i3.16352 | Efficient Certification of Spatial Robustness | https://ojs.aaai.org/index.php/AAAI/article/view/16352 | https://ojs.aaai.org/index.php/AAAI/article/download/16352/16159 | [
"Anian Ruoss",
"Maximilian Baader",
"Mislav Balunović",
"Martin Vechev"
] | Recent work has exposed the vulnerability of computer vision models to vector field attacks. Due to the widespread usage of such models in safety-critical applications, it is crucial to quantify their robustness against such spatial transformations. However, existing work only provides empirical robustness quantificati... | main | Computer Vision | 10.1609/aaai.v35i3.16352 | 35 | 3 | 2504-2513 | official | 2009.09318 | title_snapshot |
10.1609/aaai.v35i3.16353 | Semantic Grouping Network for Video Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/16353 | https://ojs.aaai.org/index.php/AAAI/article/download/16353/16160 | [
"Hobin Ryu",
"Sunghun Kang",
"Haeyong Kang",
"Chang D. Yoo"
] | This paper considers a video caption generating network referred to as Semantic Grouping Network (SGN) that attempts (1) to group video frames with discriminating word phrases of partially decoded caption and then (2) to decode those semantically aligned groups in predicting the next word. As consecutive frames are not... | main | Computer Vision | 10.1609/aaai.v35i3.16353 | 35 | 3 | 2514-2522 | official | 2102.00831 | title_snapshot |
10.1609/aaai.v35i3.16354 | Audio-Visual Localization by Synthetic Acoustic Image Generation | https://ojs.aaai.org/index.php/AAAI/article/view/16354 | https://ojs.aaai.org/index.php/AAAI/article/download/16354/16161 | [
"Valentina Sanguineti",
"Pietro Morerio",
"Alessio Del Bue",
"Vittorio Murino"
] | Acoustic images constitute an emergent data modality for multimodal scene understanding. Such images have the peculiarity to distinguish the spectral signature of sounds coming from different directions in space, thus providing richer information than the one derived from mono and binaural microphones. However, acousti... | main | Computer Vision | 10.1609/aaai.v35i3.16354 | 35 | 3 | 2523-2531 | official | null | null |
10.1609/aaai.v35i3.16355 | Enhanced Regularizers for Attributional Robustness | https://ojs.aaai.org/index.php/AAAI/article/view/16355 | https://ojs.aaai.org/index.php/AAAI/article/download/16355/16162 | [
"Anindya Sarkar",
"Anirban Sarkar",
"Vineeth N Balasubramanian"
] | Deep neural networks are the default choice of learning models for computer vision tasks. Extensive work has been carried out in recent years on explaining deep models for vision tasks such as classification. However, recent work has shown that it is possible for these models to produce substantially different attribut... | main | Computer Vision | 10.1609/aaai.v35i3.16355 | 35 | 3 | 2532-2540 | official | 2012.14395 | title_snapshot |
10.1609/aaai.v35i3.16356 | Progressive Network Grafting for Few-Shot Knowledge Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/16356 | https://ojs.aaai.org/index.php/AAAI/article/download/16356/16163 | [
"Chengchao Shen",
"Xinchao Wang",
"Youtan Yin",
"Jie Song",
"Sihui Luo",
"Mingli Song"
] | Knowledge distillation has demonstrated encouraging performances in deep model compression. Most existing approaches, however, require massive labeled data to accomplish the knowledge transfer, making the model compression a cumbersome and costly process. In this paper, we investigate the practical few-shot knowledge d... | main | Computer Vision | 10.1609/aaai.v35i3.16356 | 35 | 3 | 2541-2549 | official | 2012.04915 | title_snapshot |
10.1609/aaai.v35i3.16357 | Social-DPF: Socially Acceptable Distribution Prediction of Futures | https://ojs.aaai.org/index.php/AAAI/article/view/16357 | https://ojs.aaai.org/index.php/AAAI/article/download/16357/16164 | [
"Xiaodan Shi",
"Xiaowei Shao",
"Guangming Wu",
"Haoran Zhang",
"Zhiling Guo",
"Renhe Jiang",
"Ryosuke Shibasaki"
] | We consider long-term path forecasting problems in crowds, where future sequence trajectories are generated given a short observation. Recent methods for this problem have focused on modeling social interactions and predicting multi-modal futures. However, it is not easy for machines to successfully consider social int... | main | Computer Vision | 10.1609/aaai.v35i3.16357 | 35 | 3 | 2550-2557 | official | null | null |
10.1609/aaai.v35i3.16358 | Robust Knowledge Transfer via Hybrid Forward on the Teacher-Student Model | https://ojs.aaai.org/index.php/AAAI/article/view/16358 | https://ojs.aaai.org/index.php/AAAI/article/download/16358/16165 | [
"Liangchen Song",
"Jialian Wu",
"Ming Yang",
"Qian Zhang",
"Yuan Li",
"Junsong Yuan"
] | When adopting deep neural networks for a new vision task, a common practice is to start with fine-tuning some off-the-shelf well-trained network models from the community. Since a new task may require training a different network architecture with new domain data, taking advantage of off-the-shelf models is not trivial... | main | Computer Vision | 10.1609/aaai.v35i3.16358 | 35 | 3 | 2558-2566 | official | null | null |
10.1609/aaai.v35i3.16359 | AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing | https://ojs.aaai.org/index.php/AAAI/article/view/16359 | https://ojs.aaai.org/index.php/AAAI/article/download/16359/16166 | [
"Qi Song",
"Kangfu Mei",
"Rui Huang"
] | Two factors have proven to be very important to the performance of semantic segmentation models: global context and multi-level semantics. However, generating features that capture both factors always leads to high computational complexity, which is problematic in real-time scenarios. In this paper, we propose a new mo... | main | Computer Vision | 10.1609/aaai.v35i3.16359 | 35 | 3 | 2567-2575 | official | 2103.05930 | title_snapshot |
10.1609/aaai.v35i3.16360 | To Choose or to Fuse? Scale Selection for Crowd Counting | https://ojs.aaai.org/index.php/AAAI/article/view/16360 | https://ojs.aaai.org/index.php/AAAI/article/download/16360/16167 | [
"Qingyu Song",
"Changan Wang",
"Yabiao Wang",
"Ying Tai",
"Chengjie Wang",
"Jilin Li",
"Jian Wu",
"Jiayi Ma"
] | In this paper, we address the large scale variation problem in crowd counting by taking full advantage of the multi-scale feature representations in a multi-level network. We implement such an idea by keeping the counting error of a patch as small as possible with a proper feature level selection strategy, since a spec... | main | Computer Vision | 10.1609/aaai.v35i3.16360 | 35 | 3 | 2576-2583 | official | null | null |
10.1609/aaai.v35i3.16361 | Image Captioning with Context-Aware Auxiliary Guidance | https://ojs.aaai.org/index.php/AAAI/article/view/16361 | https://ojs.aaai.org/index.php/AAAI/article/download/16361/16168 | [
"Zeliang Song",
"Xiaofei Zhou",
"Zhendong Mao",
"Jianlong Tan"
] | Image captioning is a challenging computer vision task, which aims to generate a natural language description of an image. Most recent researches follow the encoder-decoder framework which depends heavily on the previous generated words for the current prediction. Such methods can not effectively take advantage of the ... | main | Computer Vision | 10.1609/aaai.v35i3.16361 | 35 | 3 | 2584-2592 | official | 2012.05545 | title_snapshot |
10.1609/aaai.v35i3.16362 | Unsupervised Model Adaptation for Continual Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16362 | https://ojs.aaai.org/index.php/AAAI/article/download/16362/16169 | [
"Serban Stan",
"Mohammad Rostami"
] | We develop an algorithm for adapting a semantic segmentation model that is trained using a labeled source domain to generalize well in an unlabeled target domain. A similar problem has been studied extensively in the unsupervised domain adaptation (UDA) literature, but existing UDA algorithms require access to both the... | main | Computer Vision | 10.1609/aaai.v35i3.16362 | 35 | 3 | 2593-2601 | official | 2009.12518 | title_snapshot |
10.1609/aaai.v35i3.16323 | Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context | https://ojs.aaai.org/index.php/AAAI/article/view/16323 | https://ojs.aaai.org/index.php/AAAI/article/download/16323/16130 | [
"Ziyi Liu",
"Le Wang",
"Wei Tang",
"Junsong Yuan",
"Nanning Zheng",
"Gang Hua"
] | Weakly-supervised Temporal Action Localization (WS-TAL) methods learn to localize temporal starts and ends of action instances in a video under only video-level supervision. Existing WS-TAL methods rely on deep features learned for action recognition. However, due to the mismatch between classification and localization... | main | Computer Vision | 10.1609/aaai.v35i3.16323 | 35 | 3 | 2242-2250 | official | 2103.16155 | title_snapshot |
10.1609/aaai.v35i3.16324 | PointINet: Point Cloud Frame Interpolation Network | https://ojs.aaai.org/index.php/AAAI/article/view/16324 | https://ojs.aaai.org/index.php/AAAI/article/download/16324/16131 | [
"Fan Lu",
"Guang Chen",
"Sanqing Qu",
"Zhijun Li",
"Yinlong Liu",
"Alois Knoll"
] | LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras. To overcome the temporal limitations of LiDAR sensors, a novel task named P... | main | Computer Vision | 10.1609/aaai.v35i3.16324 | 35 | 3 | 2251-2259 | official | 2012.10066 | title_snapshot |
10.1609/aaai.v35i3.16325 | A Global Occlusion-Aware Approach to Self-Supervised Monocular Visual Odometry | https://ojs.aaai.org/index.php/AAAI/article/view/16325 | https://ojs.aaai.org/index.php/AAAI/article/download/16325/16132 | [
"Yao Lu",
"Xiaoli Xu",
"Mingyu Ding",
"Zhiwu Lu",
"Tao Xiang"
] | Self-Supervised monocular visual odometry (VO) is often cast into a view synthesis problem based on depth and camera pose estimation. One of the key challenges is to accurately and robustly estimate depth with occlusions and moving objects in the scene. Existing methods simply detect and mask out regions of occlusions ... | main | Computer Vision | 10.1609/aaai.v35i3.16325 | 35 | 3 | 2260-2268 | official | null | null |
10.1609/aaai.v35i3.16326 | PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos | https://ojs.aaai.org/index.php/AAAI/article/view/16326 | https://ojs.aaai.org/index.php/AAAI/article/download/16326/16133 | [
"Tianyu Luan",
"Yali Wang",
"Junhao Zhang",
"Zhe Wang",
"Zhipeng Zhou",
"Yu Qiao"
] | The end-to-end Human Mesh Recovery (HMR) approach has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human body by directly learning mesh parameters from images or videos, while lacking explicit guidance of 3D human pose in visual data. As a result, the generated mesh ... | main | Computer Vision | 10.1609/aaai.v35i3.16326 | 35 | 3 | 2269-2276 | official | 2103.09009 | title_snapshot |
10.1609/aaai.v35i3.16327 | DeepDT: Learning Geometry From Delaunay Triangulation for Surface Reconstruction | https://ojs.aaai.org/index.php/AAAI/article/view/16327 | https://ojs.aaai.org/index.php/AAAI/article/download/16327/16134 | [
"Yiming Luo",
"Zhenxing Mi",
"Wenbing Tao"
] | In this paper, a novel learning-based network, named DeepDT, is proposed to reconstruct the surface from Delaunay triangulation of point cloud. DeepDT learns to predict inside/outside labels of Delaunay tetrahedrons directly from a point cloud and corresponding Delaunay triangulation. The local geometry features are fi... | main | Computer Vision | 10.1609/aaai.v35i3.16327 | 35 | 3 | 2277-2285 | official | 2101.10353 | title_snapshot |
10.1609/aaai.v35i3.16328 | Dual-level Collaborative Transformer for Image Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/16328 | https://ojs.aaai.org/index.php/AAAI/article/download/16328/16135 | [
"Yunpeng Luo",
"Jiayi Ji",
"Xiaoshuai Sun",
"Liujuan Cao",
"Yongjian Wu",
"Feiyue Huang",
"Chia-Wen Lin",
"Rongrong Ji"
] | Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning. However, they are still criticized for the lack of contextual information and fine-grained details, which in contrast are the merits of traditional grid features. In this pape... | main | Computer Vision | 10.1609/aaai.v35i3.16328 | 35 | 3 | 2286-2293 | official | 2101.06462 | title_snapshot |
10.1609/aaai.v35i3.16329 | HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16329 | https://ojs.aaai.org/index.php/AAAI/article/download/16329/16136 | [
"Xiaoyang Lyu",
"Liang Liu",
"Mengmeng Wang",
"Xin Kong",
"Lina Liu",
"Yong Liu",
"Xinxin Chen",
"Yi Yuan"
] | Self-supervised learning shows great potential in monocular depth estimation, using image sequences as the only source of supervision. Although people try to use the high-resolution image for depth estimation, the accuracy of prediction has not been significantly improved. In this work, we find the core reason comes fr... | main | Computer Vision | 10.1609/aaai.v35i3.16329 | 35 | 3 | 2294-2301 | official | 2012.07356 | title_snapshot |
10.1609/aaai.v35i3.16330 | SMIL: Multimodal Learning with Severely Missing Modality | https://ojs.aaai.org/index.php/AAAI/article/view/16330 | https://ojs.aaai.org/index.php/AAAI/article/download/16330/16137 | [
"Mengmeng Ma",
"Jian Ren",
"Long Zhao",
"Sergey Tulyakov",
"Cathy Wu",
"Xi Peng"
] | A common assumption in multimodal learning is the completeness of training data, i.e., full modalities are available in all training examples. Although there exists research endeavor in developing novel methods to tackle the incompleteness of testing data, e.g., modalities are partially missing in testing examples, few... | main | Computer Vision | 10.1609/aaai.v35i3.16330 | 35 | 3 | 2302-2310 | official | 2103.05677 | title_snapshot |
10.1609/aaai.v35i3.16331 | Pyramidal Feature Shrinking for Salient Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16331 | https://ojs.aaai.org/index.php/AAAI/article/download/16331/16138 | [
"Mingcan Ma",
"Changqun Xia",
"Jia Li"
] | Recently, we have witnessed the great progress of salient object detection (SOD), which benefits from the effectiveness of various feature aggregation strategies. However, existing methods usually aggregate the low-level features containing details and the high-level features containing semantics over a large span, whi... | main | Computer Vision | 10.1609/aaai.v35i3.16331 | 35 | 3 | 2311-2318 | official | null | null |
10.1609/aaai.v35i3.16332 | Learning to Count via Unbalanced Optimal Transport | https://ojs.aaai.org/index.php/AAAI/article/view/16332 | https://ojs.aaai.org/index.php/AAAI/article/download/16332/16139 | [
"Zhiheng Ma",
"Xing Wei",
"Xiaopeng Hong",
"Hui Lin",
"Yunfeng Qiu",
"Yihong Gong"
] | Counting dense crowds through computer vision technology has attracted widespread attention. Most crowd counting datasets use point annotations. In this paper, we formulate crowd counting as a measure regression problem to minimize the distance between two measures with different supports and unequal total mass. Specif... | main | Computer Vision | 10.1609/aaai.v35i3.16332 | 35 | 3 | 2319-2327 | official | null | null |
10.1609/aaai.v35i3.16333 | Scene Graph Embeddings Using Relative Similarity Supervision | https://ojs.aaai.org/index.php/AAAI/article/view/16333 | https://ojs.aaai.org/index.php/AAAI/article/download/16333/16140 | [
"Paridhi Maheshwari",
"Ritwick Chaudhry",
"Vishwa Vinay"
] | Scene graphs are a powerful structured representation of the underlying content of images, and embeddings derived from them have been shown to be useful in multiple downstream tasks. In this work, we employ a graph convolutional network to exploit structure in scene graphs and produce image embeddings useful for semant... | main | Computer Vision | 10.1609/aaai.v35i3.16333 | 35 | 3 | 2328-2336 | official | 2104.02381 | title_snapshot |
10.1609/aaai.v35i3.16334 | Few-Shot Lifelong Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16334 | https://ojs.aaai.org/index.php/AAAI/article/download/16334/16141 | [
"Pratik Mazumder",
"Pravendra Singh",
"Piyush Rai"
] | Many real-world classification problems often have classes with very few labeled training samples. Moreover, all possible classes may not be initially available for training, and may be given incrementally. Deep learning models need to deal with this two-fold problem in order to perform well in real-life situations. In... | main | Computer Vision | 10.1609/aaai.v35i3.16334 | 35 | 3 | 2337-2345 | official | 2103.00991 | title_snapshot |
10.1609/aaai.v35i3.16335 | CARPe Posterum: A Convolutional Approach for Real-Time Pedestrian Path Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/16335 | https://ojs.aaai.org/index.php/AAAI/article/download/16335/16142 | [
"Matias Mendieta",
"Hamed Tabkhi"
] | Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize ... | main | Computer Vision | 10.1609/aaai.v35i3.16335 | 35 | 3 | 2346-2354 | official | 2005.12469 | title_snapshot |
10.1609/aaai.v35i3.16336 | Dynamic Anchor Learning for Arbitrary-Oriented Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16336 | https://ojs.aaai.org/index.php/AAAI/article/download/16336/16143 | [
"Qi Ming",
"Zhiqiang Zhou",
"Lingjuan Miao",
"Hongwei Zhang",
"Linhao Li"
] | Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote sensing images, etc., and thus arbitrary-oriented object detection has received considerable attention. Many current rotation detectors use plenty of anchors with different orientations to achieve spatial alignment with ground truth ... | main | Computer Vision | 10.1609/aaai.v35i3.16336 | 35 | 3 | 2355-2363 | official | 2012.04150 | title_snapshot |
10.1609/aaai.v35i3.16337 | Terrace-based Food Counting and Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16337 | https://ojs.aaai.org/index.php/AAAI/article/download/16337/16144 | [
"Huu-Thanh Nguyen",
"Chong-Wah Ngo"
] | This paper represents object instance as a terrace, where the height of terrace corresponds to object attention while the evolution of layers from peak to sea level represents the complexity in drawing the finer boundary of an object. A multitask neural network is presented to learn the terrace representation. The atte... | main | Computer Vision | 10.1609/aaai.v35i3.16337 | 35 | 3 | 2364-2372 | official | null | null |
10.1609/aaai.v35i3.16338 | Embodied Visual Active Learning for Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16338 | https://ojs.aaai.org/index.php/AAAI/article/download/16338/16145 | [
"David Nilsson",
"Aleksis Pirinen",
"Erik Gärtner",
"Cristian Sminchisescu"
] | We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding by actively selecting views for which to request annotation. While accurate on some benchmarks, today's deep visual recognition pipelines tend to not generalize wel... | main | Computer Vision | 10.1609/aaai.v35i3.16338 | 35 | 3 | 2373-2383 | official | 2012.09503 | title_snapshot |
10.1609/aaai.v35i3.16339 | TDAF: Top-Down Attention Framework for Vision Tasks | https://ojs.aaai.org/index.php/AAAI/article/view/16339 | https://ojs.aaai.org/index.php/AAAI/article/download/16339/16146 | [
"Bo Pang",
"Yizhuo Li",
"Jiefeng Li",
"Muchen Li",
"Hanwen Cao",
"Cewu Lu"
] | Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-down attentions, which can be easily adopted in most existing models. The designed Recursive Dual-Directional Nested Structure in it forms t... | main | Computer Vision | 10.1609/aaai.v35i3.16339 | 35 | 3 | 2384-2392 | official | 2012.07248 | title_snapshot |
10.1609/aaai.v35i3.16340 | Few-shot Font Generation with Localized Style Representations and Factorization | https://ojs.aaai.org/index.php/AAAI/article/view/16340 | https://ojs.aaai.org/index.php/AAAI/article/download/16340/16147 | [
"Song Park",
"Sanghyuk Chun",
"Junbum Cha",
"Bado Lee",
"Hyunjung Shim"
] | Automatic few-shot font generation is a practical and widely studied problem because manual designs are expensive and sensitive to the expertise of designers. Existing few-shot font generation methods aim to learn to disentangle the style and content element from a few reference glyphs, and mainly focus on a universal ... | main | Computer Vision | 10.1609/aaai.v35i3.16340 | 35 | 3 | 2393-2402 | official | 2009.11042 | title_snapshot |
10.1609/aaai.v35i3.16341 | Learning Disentangled Representation for Fair Facial Attribute Classification via Fairness-aware Information Alignment | https://ojs.aaai.org/index.php/AAAI/article/view/16341 | https://ojs.aaai.org/index.php/AAAI/article/download/16341/16148 | [
"Sungho Park",
"Sunhee Hwang",
"Dohyung Kim",
"Hyeran Byun"
] | Although AI systems archive a great success in various societal fields, there still exists a challengeable issue of outputting discriminatory results with respect to protected attributes (e.g., gender and age). The popular approach to solving the issue is to remove protected attribute information in the decision proces... | main | Computer Vision | 10.1609/aaai.v35i3.16341 | 35 | 3 | 2403-2411 | official | null | null |
10.1609/aaai.v35i3.16342 | Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation | https://ojs.aaai.org/index.php/AAAI/article/view/16342 | https://ojs.aaai.org/index.php/AAAI/article/download/16342/16149 | [
"Sunghyun Park",
"Kangyeol Kim",
"Junsoo Lee",
"Jaegul Choo",
"Joonseok Lee",
"Sookyung Kim",
"Edward Choi"
] | Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e.g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames). To resolve the restricted nature of e... | main | Computer Vision | 10.1609/aaai.v35i3.16342 | 35 | 3 | 2412-2422 | official | 2010.08188 | title_snapshot |
10.1609/aaai.v35i3.16303 | Single View Point Cloud Generation via Unified 3D Prototype | https://ojs.aaai.org/index.php/AAAI/article/view/16303 | https://ojs.aaai.org/index.php/AAAI/article/download/16303/16110 | [
"Yu Lin",
"Yigong Wang",
"Yi-Fan Li",
"Zhuoyi Wang",
"Yang Gao",
"Latifur Khan"
] | As 3D point clouds become the representation of choice for multiple vision and graphics applications, such as autonomous driving, robotics, etc., the generation of them by deep neural networks has attracted increasing attention in the research community. Despite the recent success of deep learning models in classificat... | main | Computer Vision | 10.1609/aaai.v35i3.16303 | 35 | 3 | 2064-2072 | official | null | null |
10.1609/aaai.v35i3.16304 | Self-Supervised Sketch-to-Image Synthesis | https://ojs.aaai.org/index.php/AAAI/article/view/16304 | https://ojs.aaai.org/index.php/AAAI/article/download/16304/16111 | [
"Bingchen Liu",
"Yizhe Zhu",
"Kunpeng Song",
"Ahmed Elgammal"
] | Imagining a colored realistic image from an arbitrary-drawn sketch is one of human capabilities that we eager machines to mimic. Unlike previous methods that either require the sketch-image pairs or utilize low-quantity detected edges as sketches, we study the exemplar-based sketch-to-image (s2i) synthesis task in a se... | main | Computer Vision | 10.1609/aaai.v35i3.16304 | 35 | 3 | 2073-2081 | official | 2012.09290 | title_snapshot |
10.1609/aaai.v35i3.16305 | TIME: Text and Image Mutual-Translation Adversarial Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16305 | https://ojs.aaai.org/index.php/AAAI/article/download/16305/16112 | [
"Bingchen Liu",
"Kunpeng Song",
"Yizhe Zhu",
"Gerard De Melo",
"Ahmed Elgammal"
] | Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework. While previous methods tackle the T2I ... | main | Computer Vision | 10.1609/aaai.v35i3.16305 | 35 | 3 | 2082-2090 | official | 2005.13192 | title_snapshot |
10.1609/aaai.v35i3.16306 | SA-BNN: State-Aware Binary Neural Network | https://ojs.aaai.org/index.php/AAAI/article/view/16306 | https://ojs.aaai.org/index.php/AAAI/article/download/16306/16113 | [
"Chunlei Liu",
"Peng Chen",
"Bohan Zhuang",
"Chunhua Shen",
"Baochang Zhang",
"Wenrui Ding"
] | Binary Neural Networks (BNNs) have received significant attention due to the memory and computation efficiency recently. However, the considerable accuracy gap between BNNs and their full-precision counterparts hinders BNNs to be deployed to resource-constrained platforms. One of the main reasons for the performance ga... | main | Computer Vision | 10.1609/aaai.v35i3.16306 | 35 | 3 | 2091-2099 | official | null | null |
10.1609/aaai.v35i3.16307 | Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16307 | https://ojs.aaai.org/index.php/AAAI/article/download/16307/16114 | [
"Daizong Liu",
"Shuangjie Xu",
"Xiao-Yang Liu",
"Zichuan Xu",
"Wei Wei",
"Pan Zhou"
] | This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these approaches extract the best proposal by a greedy strategy, which may lose the local patch details outside the chosen candidate. In this pape... | main | Computer Vision | 10.1609/aaai.v35i3.16307 | 35 | 3 | 2100-2108 | official | 2012.05499 | title_snapshot |
10.1609/aaai.v35i3.16308 | F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16308 | https://ojs.aaai.org/index.php/AAAI/article/download/16308/16115 | [
"Daizong Liu",
"Dongdong Yu",
"Changhu Wang",
"Pan Zhou"
] | Although deep learning based methods have achieved great progress in unsupervised video object segmentation, difficult scenarios (e.g., visual similarity, occlusions, and appearance changing) are still no well-handled. To alleviate these issues, we propose a novel Focus on Foreground Network (F2Net), which delves into ... | main | Computer Vision | 10.1609/aaai.v35i3.16308 | 35 | 3 | 2109-2117 | official | 2012.02534 | title_snapshot |
10.1609/aaai.v35i3.16309 | Toward Realistic Virtual Try-on Through Landmark Guided Shape Matching | https://ojs.aaai.org/index.php/AAAI/article/view/16309 | https://ojs.aaai.org/index.php/AAAI/article/download/16309/16116 | [
"Guoqiang Liu",
"Dan Song",
"Ruofeng Tong",
"Min Tang"
] | Image-based virtual try-on aims to synthesize the customer image with an in-shop clothes image to acquire seamless and natural try-on results, which have attracted increasing attentions. The main procedures of image-based virtual try-on usually consist of clothes image generation and try-on image synthesis, whereas pri... | main | Computer Vision | 10.1609/aaai.v35i3.16309 | 35 | 3 | 2118-2126 | official | null | null |
10.1609/aaai.v35i3.16310 | Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling | https://ojs.aaai.org/index.php/AAAI/article/view/16310 | https://ojs.aaai.org/index.php/AAAI/article/download/16310/16117 | [
"Hongying Liu",
"Peng Zhao",
"Zhubo Ruan",
"Fanhua Shang",
"Yuanyuan Liu"
] | Video super-resolution (VSR) aims at restoring a video in low-resolution (LR) and improving it to higher-resolution (HR). Due to the characteristics of video tasks, it is very important that motion information among frames should be well concerned, summarized and utilized for guidance in a VSR algorithm. Especially, wh... | main | Computer Vision | 10.1609/aaai.v35i3.16310 | 35 | 3 | 2127-2135 | official | 2103.11744 | title_snapshot |
10.1609/aaai.v35i3.16311 | FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion | https://ojs.aaai.org/index.php/AAAI/article/view/16311 | https://ojs.aaai.org/index.php/AAAI/article/download/16311/16118 | [
"Lina Liu",
"Xibin Song",
"Xiaoyang Lyu",
"Junwei Diao",
"Mengmeng Wang",
"Yong Liu",
"Liangjun Zhang"
] | Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate the depth completion as a one-stage end-to-end learning task, which outputs dense depth maps directly. However, the feature extraction and supervision in one-stage f... | main | Computer Vision | 10.1609/aaai.v35i3.16311 | 35 | 3 | 2136-2144 | official | 2012.08270 | title_snapshot |
10.1609/aaai.v35i3.16312 | Activity Image-to-Video Retrieval by Disentangling Appearance and Motion | https://ojs.aaai.org/index.php/AAAI/article/view/16312 | https://ojs.aaai.org/index.php/AAAI/article/download/16312/16119 | [
"Liu Liu",
"Jiangtong Li",
"Li Niu",
"Ruicong Xu",
"Liqing Zhang"
] | With the rapid emergence of video data, image-to-video retrieval has attracted much attention. There are two types of image-to-video retrieval: instance-based and activity-based. The former task aims to retrieve videos containing the same main objects as the query image, while the latter focuses on finding the similar ... | main | Computer Vision | 10.1609/aaai.v35i3.16312 | 35 | 3 | 2145-2153 | official | null | null |
10.1609/aaai.v35i3.16313 | Adaptive Pattern-Parameter Matching for Robust Pedestrian Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16313 | https://ojs.aaai.org/index.php/AAAI/article/download/16313/16120 | [
"Mengyin Liu",
"Chao Zhu",
"Jun Wang",
"Xu-Cheng Yin"
] | Pedestrians with challenging patterns, e.g. small scale or heavy occlusion, appear frequently in practical applications like autonomous driving, which remains tremendous obstacle to higher robustness of detectors. Although plenty of previous works have been dedicated to these problems, properly matching patterns of ped... | main | Computer Vision | 10.1609/aaai.v35i3.16313 | 35 | 3 | 2154-2162 | official | null | null |
10.1609/aaai.v35i3.16314 | Temporal Segmentation of Fine-gained Semantic Action: A Motion-Centered Figure Skating Dataset | https://ojs.aaai.org/index.php/AAAI/article/view/16314 | https://ojs.aaai.org/index.php/AAAI/article/download/16314/16121 | [
"Shenglan Liu",
"Aibin Zhang",
"Yunheng Li",
"Jian Zhou",
"Li Xu",
"Zhuben Dong",
"Renhao Zhang"
] | Temporal Action Segmentation (TAS) has achieved great success in many fields such as exercise rehabilitation, movie editing, etc. Currently, task-driven TAS is a central topic in human action analysis. However, motion-centered TAS, as an important topic, is little researched due to unavailable datasets. In order to exp... | main | Computer Vision | 10.1609/aaai.v35i3.16314 | 35 | 3 | 2163-2171 | official | null | null |
10.1609/aaai.v35i3.16315 | Learning Hybrid Relationships for Person Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/16315 | https://ojs.aaai.org/index.php/AAAI/article/download/16315/16122 | [
"Shuang Liu",
"Wenmin Huang",
"Zhong Zhang"
] | Recently, the relationship among individual pedestrian images and the relationship among pairwise pedestrian images have become attractive for person re-identification (re-ID) as they effectively improve the ability of feature representation. In this paper, we propose a novel method named Hybrid Relationship Network (H... | main | Computer Vision | 10.1609/aaai.v35i3.16315 | 35 | 3 | 2172-2179 | official | null | null |
10.1609/aaai.v35i3.16316 | Translate the Facial Regions You Like Using Self-Adaptive Region Translation | https://ojs.aaai.org/index.php/AAAI/article/view/16316 | https://ojs.aaai.org/index.php/AAAI/article/download/16316/16123 | [
"Wenshuang Liu",
"Wenting Chen",
"Zhanjia Yang",
"Linlin Shen"
] | With the progression of Generative Adversarial Networks (GANs), image translation methods has achieved increasingly remarkable performance. However, most available methods can only achieve image level translation, which is unable to precisely control the regions to be translated. In this paper, we propose a novel self-... | main | Computer Vision | 10.1609/aaai.v35i3.16316 | 35 | 3 | 2180-2188 | official | 2007.14615 | title_judge |
10.1609/aaai.v35i3.16317 | Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis | https://ojs.aaai.org/index.php/AAAI/article/view/16317 | https://ojs.aaai.org/index.php/AAAI/article/download/16317/16124 | [
"Xiaofeng Liu",
"Xiongchang Liu",
"Bo Hu",
"Wenxuan Ji",
"Fangxu Xing",
"Jun Lu",
"Jane You",
"C.-C. Jay Kuo",
"Georges El Fakhri",
"Jonghye Woo"
] | Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype struct... | main | Computer Vision | 10.1609/aaai.v35i3.16317 | 35 | 3 | 2189-2197 | official | 2101.00318 | title_snapshot |
10.1609/aaai.v35i3.16318 | FontRL: Chinese Font Synthesis via Deep Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16318 | https://ojs.aaai.org/index.php/AAAI/article/download/16318/16125 | [
"Yitian Liu",
"Zhouhui Lian"
] | Automatic generation of Chinese fonts is a valuable but challenging task in areas of AI and Computer Graphics, mainly due to the huge amount of Chinese characters and their complex glyph structures. In this paper, we propose FontRL, a novel method for Chinese font synthesis by using deep reinforcement learning. Specifi... | main | Computer Vision | 10.1609/aaai.v35i3.16318 | 35 | 3 | 2198-2206 | official | null | null |
10.1609/aaai.v35i3.16319 | Hierarchical Information Passing Based Noise-Tolerant Hybrid Learning for Semi-Supervised Human Parsing | https://ojs.aaai.org/index.php/AAAI/article/view/16319 | https://ojs.aaai.org/index.php/AAAI/article/download/16319/16126 | [
"Yunan Liu",
"Shanshan Zhang",
"Jian Yang",
"PongChi Yuen"
] | Deep learning based human parsing methods usually require a large amount of training data to reach high performance. However, it is costly and time-consuming to obtain manually annotated high quality labels for a large scale dataset. To alleviate annotation efforts, we propose a new semi-supervised human parsing method... | main | Computer Vision | 10.1609/aaai.v35i3.16319 | 35 | 3 | 2207-2215 | official | null | null |
10.1609/aaai.v35i3.16320 | Delving into Variance Transmission and Normalization: Shift of Average Gradient Makes the Network Collapse | https://ojs.aaai.org/index.php/AAAI/article/view/16320 | https://ojs.aaai.org/index.php/AAAI/article/download/16320/16127 | [
"Yuxiang Liu",
"Jidong Ge",
"Chuanyi Li",
"Jie Gui"
] | Normalization operations are essential for state-of-the-art neural networks and enable us to train a network from scratch with a large learning rate (LR). We attempt to explain the real effect of Batch Normalization (BN) from the perspective of variance transmission by investigating the relationship between BN and Weig... | main | Computer Vision | 10.1609/aaai.v35i3.16320 | 35 | 3 | 2216-2224 | official | 2103.11590 | title_snapshot |
10.1609/aaai.v35i3.16321 | Aggregated Multi-GANs for Controlled 3D Human Motion Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/16321 | https://ojs.aaai.org/index.php/AAAI/article/download/16321/16128 | [
"Zhenguang Liu",
"Kedi Lyu",
"Shuang Wu",
"Haipeng Chen",
"Yanbin Hao",
"Shouling Ji"
] | Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted future motion is confined within the same activity. One can neither generate predictions that differ from the current activity, nor manipulate th... | main | Computer Vision | 10.1609/aaai.v35i3.16321 | 35 | 3 | 2225-2232 | official | 2103.09755 | title_snapshot |
10.1609/aaai.v35i3.16322 | ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization | https://ojs.aaai.org/index.php/AAAI/article/view/16322 | https://ojs.aaai.org/index.php/AAAI/article/download/16322/16129 | [
"Ziyi Liu",
"Le Wang",
"Qilin Zhang",
"Wei Tang",
"Junsong Yuan",
"Nanning Zheng",
"Gang Hua"
] | The object of Weakly-supervised Temporal Action Localization (WS-TAL) is to localize all action instances in an untrimmed video with only video-level supervision. Due to the lack of frame-level annotations during training, current WS-TAL methods rely on attention mechanisms to localize the foreground snippets or frames... | main | Computer Vision | 10.1609/aaai.v35i3.16322 | 35 | 3 | 2233-2241 | official | 2103.15088 | title_snapshot |
10.1609/aaai.v35i3.16283 | Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus | https://ojs.aaai.org/index.php/AAAI/article/view/16283 | https://ojs.aaai.org/index.php/AAAI/article/download/16283/16090 | [
"Marius Leordeanu",
"Mihai Cristian Pîrvu",
"Dragos Costea",
"Alina E Marcu",
"Emil Slusanschi",
"Rahul Sukthankar"
] | We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. Each graph node is a scene interpretation layer, while each edge is a deep net that transforms one layer at one node into another from a diffe... | main | Computer Vision | 10.1609/aaai.v35i3.16283 | 35 | 3 | 1882-1892 | official | 2010.01086 | title_snapshot |
10.1609/aaai.v35i3.16284 | Static-Dynamic Interaction Networks for Offline Signature Verification | https://ojs.aaai.org/index.php/AAAI/article/view/16284 | https://ojs.aaai.org/index.php/AAAI/article/download/16284/16091 | [
"Huan Li",
"Ping Wei",
"Ping Hu"
] | Offline signature verification is a challenging issue that is widely used in various fields. Previous approaches model this task as a static feature matching or distance metric problem of two images. In this paper, we propose a novel Static-Dynamic Interaction Network (SDINet) model which introduces sequential represen... | main | Computer Vision | 10.1609/aaai.v35i3.16284 | 35 | 3 | 1893-1901 | official | null | null |
10.1609/aaai.v35i3.16285 | Proposal-Free Video Grounding with Contextual Pyramid Network | https://ojs.aaai.org/index.php/AAAI/article/view/16285 | https://ojs.aaai.org/index.php/AAAI/article/download/16285/16092 | [
"Kun Li",
"Dan Guo",
"Meng Wang"
] | The challenge of video grounding - localizing activities in an untrimmed video via a natural language query - is to tackle the semantics of vision and language consistently along the temporal dimension. Most existing proposal-based methods are trapped by computational cost with extensive candidate proposals. In this pa... | main | Computer Vision | 10.1609/aaai.v35i3.16285 | 35 | 3 | 1902-1910 | official | null | null |
10.1609/aaai.v35i3.16286 | Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation | https://ojs.aaai.org/index.php/AAAI/article/view/16286 | https://ojs.aaai.org/index.php/AAAI/article/download/16286/16093 | [
"Lincheng Li",
"Suzhen Wang",
"Zhimeng Zhang",
"Yu Ding",
"Yixing Zheng",
"Xin Yu",
"Changjie Fan"
] | In this paper, we propose a novel text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions in accordance with contextual sentiments as well as speech rhythm and pauses. To be specific, our framework consists of a speaker-independent stage and a speaker-specif... | main | Computer Vision | 10.1609/aaai.v35i3.16286 | 35 | 3 | 1911-1920 | official | 2104.07995 | title_snapshot |
10.1609/aaai.v35i3.16287 | Exploiting Learnable Joint Groups for Hand Pose Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16287 | https://ojs.aaai.org/index.php/AAAI/article/download/16287/16094 | [
"Moran Li",
"Yuan Gao",
"Nong Sang"
] | In this paper, we propose to estimate 3D hand pose by recovering the 3D coordinates of joints in a group-wise manner, where less-related joints are automatically categorized into different groups and exhibit different features. This is different from the previous methods where all the joints are considered holistically... | main | Computer Vision | 10.1609/aaai.v35i3.16287 | 35 | 3 | 1921-1929 | official | 2012.09496 | title_snapshot |
10.1609/aaai.v35i3.16288 | RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving | https://ojs.aaai.org/index.php/AAAI/article/view/16288 | https://ojs.aaai.org/index.php/AAAI/article/download/16288/16095 | [
"Peixuan Li",
"Shun Su",
"Huaici Zhao"
] | Although the recent image-based 3D object detection methods using Pseudo-LiDAR representation have shown great capabilities, a notable gap in efficiency and accuracy still exist compared with LiDAR-based methods. Besides, over-reliance on the stand-alone depth estimator, requiring a large number of pixel-wise annotatio... | main | Computer Vision | 10.1609/aaai.v35i3.16288 | 35 | 3 | 1930-1939 | official | 2012.15072 | title_snapshot |
10.1609/aaai.v35i3.16289 | Adversarial Pose Regression Network for Pose-Invariant Face Recognitions | https://ojs.aaai.org/index.php/AAAI/article/view/16289 | https://ojs.aaai.org/index.php/AAAI/article/download/16289/16096 | [
"Pengyu Li",
"Biao Wang",
"Lei Zhang"
] | Face recognition has achieved significant progress in recent years. However, the large pose variation between face images remains a challenge in face recognition. We observe that the pose variation in the hidden feature maps is one of the most critical factors to hinder the representations from being pose-invariant. Ba... | main | Computer Vision | 10.1609/aaai.v35i3.16289 | 35 | 3 | 1940-1948 | official | null | null |
10.1609/aaai.v35i3.16290 | Category Dictionary Guided Unsupervised Domain Adaptation for Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16290 | https://ojs.aaai.org/index.php/AAAI/article/download/16290/16097 | [
"Shuai Li",
"Jianqiang Huang",
"Xian-Sheng Hua",
"Lei Zhang"
] | Unsupervised domain adaption (UDA) is a promising solution to enhance the generalization ability of a model from a source domain to a target domain without manually annotating labels for target data. Recent works in cross-domain object detection mostly resort to adversarial feature adaptation to match the marginal dist... | main | Computer Vision | 10.1609/aaai.v35i3.16290 | 35 | 3 | 1949-1957 | official | null | null |
10.1609/aaai.v35i3.16291 | Joint Semantic-geometric Learning for Polygonal Building Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16291 | https://ojs.aaai.org/index.php/AAAI/article/download/16291/16098 | [
"Weijia Li",
"Wenqian Zhao",
"Huaping Zhong",
"Conghui He",
"Dahua Lin"
] | Building extraction from aerial or satellite images has been an important research issue in remote sensing and computer vision domains for decades. Compared with pixel-wise semantic segmentation models that output raster building segmentation map, polygonal building segmentation approaches produce more realistic buildi... | main | Computer Vision | 10.1609/aaai.v35i3.16291 | 35 | 3 | 1958-1965 | official | null | null |
10.1609/aaai.v35i3.16292 | Generalized Zero-Shot Learning via Disentangled Representation | https://ojs.aaai.org/index.php/AAAI/article/view/16292 | https://ojs.aaai.org/index.php/AAAI/article/download/16292/16099 | [
"Xiangyu Li",
"Zhe Xu",
"Kun Wei",
"Cheng Deng"
] | Zero-Shot Learning (ZSL) aims to recognize images belonging to unseen classes that are unavailable in the training process, while Generalized Zero-Shot Learning (GZSL) is a more realistic variant that both seen and unseen classes appear during testing. Most GZSL approaches achieve knowledge transfer based on the featur... | main | Computer Vision | 10.1609/aaai.v35i3.16292 | 35 | 3 | 1966-1974 | official | null | null |
10.1609/aaai.v35i3.16293 | Learning Omni-Frequency Region-adaptive Representations for Real Image Super-Resolution | https://ojs.aaai.org/index.php/AAAI/article/view/16293 | https://ojs.aaai.org/index.php/AAAI/article/download/16293/16100 | [
"Xin Li",
"Xin Jin",
"Tao Yu",
"Simeng Sun",
"Yingxue Pang",
"Zhizheng Zhang",
"Zhibo Chen"
] | Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i.e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-resolution (LR) images due to the complicated realistic degradations. The key to solving this more challeng... | main | Computer Vision | 10.1609/aaai.v35i3.16293 | 35 | 3 | 1975-1983 | official | 2012.06131 | title_snapshot |
10.1609/aaai.v35i3.16294 | Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16294 | https://ojs.aaai.org/index.php/AAAI/article/download/16294/16101 | [
"Xueyi Li",
"Tianfei Zhou",
"Jianwu Li",
"Yi Zhou",
"Zhaoxiang Zhang"
] | Acquiring sufficient ground-truth supervision to train deep vi- sual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated in some structured prediction tasks, such as semantic segmen- tation, which requires pixel-level annotations. This work ad- dresses weakly ... | main | Computer Vision | 10.1609/aaai.v35i3.16294 | 35 | 3 | 1984-1992 | official | 2012.05007 | title_snapshot |
10.1609/aaai.v35i3.16295 | Inference Fusion with Associative Semantics for Unseen Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16295 | https://ojs.aaai.org/index.php/AAAI/article/download/16295/16102 | [
"Yanan Li",
"Pengyang Li",
"Han Cui",
"Donghui Wang"
] | We study the problem of object detection when training and test objects are disjoint, i.e. no training examples of the target classes are available. Existing unseen object detection approaches usually combine generic detection frameworks with a single-path unseen classifier, by aligning object regions with semantic cla... | main | Computer Vision | 10.1609/aaai.v35i3.16295 | 35 | 3 | 1993-2001 | official | null | null |
10.1609/aaai.v35i3.16296 | Deep Unsupervised Image Hashing by Maximizing Bit Entropy | https://ojs.aaai.org/index.php/AAAI/article/view/16296 | https://ojs.aaai.org/index.php/AAAI/article/download/16296/16103 | [
"Yunqiang Li",
"Jan Van Gemert"
] | Unsupervised hashing is important for indexing huge image or video collections without having expensive annotations available. Hashing aims to learn short binary codes for compact storage and efficient semantic retrieval. We propose an unsupervised deep hashing layer called Bi-Half Net that maximizes entropy of the bin... | main | Computer Vision | 10.1609/aaai.v35i3.16296 | 35 | 3 | 2002-2010 | official | 2012.12334 | title_snapshot |
10.1609/aaai.v35i3.16297 | Sequential End-to-end Network for Efficient Person Search | https://ojs.aaai.org/index.php/AAAI/article/view/16297 | https://ojs.aaai.org/index.php/AAAI/article/download/16297/16104 | [
"Zhengjia Li",
"Duoqian Miao"
] | Person search aims at jointly solving Person Detection and Person Re-identification (re-ID). Existing works have designed end-to-end networks based on Faster R-CNN. However, due to the parallel structure of Faster R-CNN, the extracted features come from the low-quality proposals generated by the Region Proposal Network... | main | Computer Vision | 10.1609/aaai.v35i3.16297 | 35 | 3 | 2011-2019 | official | 2103.10148 | title_snapshot |
10.1609/aaai.v35i3.16298 | SD-Pose: Semantic Decomposition for Cross-Domain 6D Object Pose Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16298 | https://ojs.aaai.org/index.php/AAAI/article/download/16298/16105 | [
"Zhigang Li",
"Yinlin Hu",
"Mathieu Salzmann",
"Xiangyang Ji"
] | The current leading 6D object pose estimation methods rely heavily on annotated real data, which is highly costly to acquire. To overcome this, many works have proposed to introduce computer-generated synthetic data. However, bridging the gap between the synthetic and real data remains a severe problem. Images depictin... | main | Computer Vision | 10.1609/aaai.v35i3.16298 | 35 | 3 | 2020-2028 | official | null | null |
10.1609/aaai.v35i3.16299 | Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision | https://ojs.aaai.org/index.php/AAAI/article/view/16299 | https://ojs.aaai.org/index.php/AAAI/article/download/16299/16106 | [
"Rongqin Liang",
"Yuanman Li",
"Xia Li",
"Yi Tang",
"Jiantao Zhou",
"Wenbin Zou"
] | Predicting human motion behavior in a crowd is important for many applications, ranging from the natural navigation of autonomous vehicles to intelligent security systems of video surveillance. All the previous works model and predict the trajectory with a single resolution, which is relatively ineffective and difficul... | main | Computer Vision | 10.1609/aaai.v35i3.16299 | 35 | 3 | 2029-2037 | official | 2012.01884 | title_snapshot |
10.1609/aaai.v35i3.16300 | Query-Memory Re-Aggregation for Weakly-supervised Video Object Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16300 | https://ojs.aaai.org/index.php/AAAI/article/download/16300/16107 | [
"Fanchao Lin",
"Hongtao Xie",
"Yan Li",
"Yongdong Zhang"
] | Weakly-supervised video object segmentation (WVOS) is an emerging video task that can track and segment the target given a simple bounding box label. However, existing WVOS methods are still unsatisfied in either speed or accuracy, since they only use the exemplar frame to guide the prediction while they neglect the re... | main | Computer Vision | 10.1609/aaai.v35i3.16300 | 35 | 3 | 2038-2046 | official | null | null |
10.1609/aaai.v35i3.16301 | Augmented Partial Mutual Learning with Frame Masking for Video Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/16301 | https://ojs.aaai.org/index.php/AAAI/article/download/16301/16108 | [
"Ke Lin",
"Zhuoxin Gan",
"Liwei Wang"
] | Recent video captioning work improves greatly due to the invention of various elaborate model architectures. If multiple captioning models are combined into a unified framework not only by simple more ensemble, and each model can benefit from each other, the final captioning might be boosted further. Jointly training o... | main | Computer Vision | 10.1609/aaai.v35i3.16301 | 35 | 3 | 2047-2055 | official | null | null |
10.1609/aaai.v35i3.16302 | Exploiting Audio-Visual Consistency with Partial Supervision for Spatial Audio Generation | https://ojs.aaai.org/index.php/AAAI/article/view/16302 | https://ojs.aaai.org/index.php/AAAI/article/download/16302/16109 | [
"Yan-Bo Lin",
"Yu-Chiang Frank Wang"
] | Human perceives rich auditory experience with distinct sound heard by ears. Videos recorded with binaural audio particular simulate how human receives ambient sound. However, a large number of videos are with monaural audio only, which would degrade the user experience due to the lack of ambient information. To address... | main | Computer Vision | 10.1609/aaai.v35i3.16302 | 35 | 3 | 2056-2063 | official | 2105.00708 | title_snapshot |
10.1609/aaai.v35i3.16274 | Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16274 | https://ojs.aaai.org/index.php/AAAI/article/download/16274/16081 | [
"Minsu Kim",
"Sunghun Joung",
"Seungryong Kim",
"JungIn Park",
"Ig-Jae Kim",
"Kwanghoon Sohn"
] | Existing techniques to adapt semantic segmentation networks across source and target domains within deep convolutional neural networks (CNNs) deal with all the samples from the two domains in a global or category-aware manner. They do not consider an inter-class variation within the target domain itself or estimated ca... | main | Computer Vision | 10.1609/aaai.v35i3.16274 | 35 | 3 | 1799-1807 | official | 2012.08226 | title_snapshot |
10.1609/aaai.v35i3.16275 | Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16275 | https://ojs.aaai.org/index.php/AAAI/article/download/16275/16082 | [
"Soopil Kim",
"Sion An",
"Philip Chikontwe",
"Sang Hyun Park"
] | Segmentation of organs of interest in 3D medical images is necessary for accurate diagnosis and longitudinal studies. Though recent advances using deep learning have shown success for many segmentation tasks, large datasets are required for high performance and the annotation process is both time consuming and labor in... | main | Computer Vision | 10.1609/aaai.v35i3.16275 | 35 | 3 | 1808-1816 | official | 2011.09608 | title_snapshot |
10.1609/aaai.v35i3.16276 | DASZL: Dynamic Action Signatures for Zero-shot Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16276 | https://ojs.aaai.org/index.php/AAAI/article/download/16276/16083 | [
"Tae Soo Kim",
"Jonathan Jones",
"Michael Peven",
"Zihao Xiao",
"Jin Bai",
"Yi Zhang",
"Weichao Qiu",
"Alan Yuille",
"Gregory D. Hager"
] | There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large. This makes end-to-end supervised training of a recognition system impractical as no training set is practically able to encompass the entire label set. In this paper, we present an ap... | main | Computer Vision | 10.1609/aaai.v35i3.16276 | 35 | 3 | 1817-1826 | official | 1912.03613 | title_snapshot |
10.1609/aaai.v35i3.16277 | Multi-level Distance Regularization for Deep Metric Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16277 | https://ojs.aaai.org/index.php/AAAI/article/download/16277/16084 | [
"Yonghyun Kim",
"Wonpyo Park"
] | We propose a novel distance-based regularization method for deep metric learning called Multi-level Distance Regularization (MDR). MDR explicitly disturbs a learning procedure by regularizing pairwise distances between embedding vectors into multiple levels that represents a degree of similarity between a pair. In the ... | main | Computer Vision | 10.1609/aaai.v35i3.16277 | 35 | 3 | 1827-1835 | official | 2102.04223 | title_snapshot |
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