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.v36i1.19873 | Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders | https://ojs.aaai.org/index.php/AAAI/article/view/19873 | https://ojs.aaai.org/index.php/AAAI/article/download/19873/19632 | [
"Abhishek Banerjee",
"Uttaran Bhattacharya",
"Aniket Bera"
] | We present a novel generalized zero-shot algorithm to recognize perceived emotions from gestures. Our task is to map gestures to novel emotion categories not encountered in training. We introduce an adversarial autoencoder-based representation learning that correlates 3D motion-captured gesture sequences with the vecto... | main | Cognitive Modeling & Cognitive Systems | 10.1609/aaai.v36i1.19873 | 36 | 1 | 3-10 | official | 2009.08906 | title_snapshot |
10.1609/aaai.v36i1.19874 | Optimized Potential Initialization for Low-Latency Spiking Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/19874 | https://ojs.aaai.org/index.php/AAAI/article/download/19874/19633 | [
"Tong Bu",
"Jianhao Ding",
"Zhaofei Yu",
"Tiejun Huang"
] | Spiking Neural Networks (SNNs) have been attached great importance due to the distinctive properties of low power consumption, biological plausibility, and adversarial robustness. The most effective way to train deep SNNs is through ANN-to-SNN conversion, which have yielded the best performance in deep network structur... | main | Cognitive Modeling & Cognitive Systems | 10.1609/aaai.v36i1.19874 | 36 | 1 | 11-20 | official | 2202.01440 | title_snapshot |
10.1609/aaai.v36i1.19875 | Planning with Biological Neurons and Synapses | https://ojs.aaai.org/index.php/AAAI/article/view/19875 | https://ojs.aaai.org/index.php/AAAI/article/download/19875/19634 | [
"Francesco D'Amore",
"Daniel Mitropolsky",
"Pierluigi Crescenzi",
"Emanuele Natale",
"Christos H. Papadimitriou"
] | We revisit the planning problem in the blocks world, and we implement a known heuristic for this task. Importantly, our implementation is biologically plausible, in the sense that it is carried out exclusively through the spiking of neurons. Even though much has been accomplished in the blocks world over the past five ... | main | Cognitive Modeling & Cognitive Systems | 10.1609/aaai.v36i1.19875 | 36 | 1 | 21-28 | official | 2112.08186 | title_snapshot |
10.1609/aaai.v36i1.19876 | Backprop-Free Reinforcement Learning with Active Neural Generative Coding | https://ojs.aaai.org/index.php/AAAI/article/view/19876 | https://ojs.aaai.org/index.php/AAAI/article/download/19876/19635 | [
"Alexander G. Ororbia",
"Ankur Mali"
] | In humans, perceptual awareness facilitates the fast recognition and extraction of information from sensory input. This awareness largely depends on how the human agent interacts with the environment. In this work, we propose active neural generative coding, a computational framework for learning action-driven generati... | main | Cognitive Modeling & Cognitive Systems | 10.1609/aaai.v36i1.19876 | 36 | 1 | 29-37 | official | 2107.07046 | title_snapshot |
10.1609/aaai.v36i1.19877 | VECA: A New Benchmark and Toolkit for General Cognitive Development | https://ojs.aaai.org/index.php/AAAI/article/view/19877 | https://ojs.aaai.org/index.php/AAAI/article/download/19877/19636 | [
"Kwanyoung Park",
"Hyunseok Oh",
"Youngki Lee"
] | The developmental approach, simulating a cognitive development of a human, arises as a way to nurture a human-level commonsense and overcome the limitations of data-driven approaches. However, neither a virtual environment nor an evaluation platform exists for the overall development of core cognitive skills. We presen... | main | Cognitive Modeling & Cognitive Systems | 10.1609/aaai.v36i1.19877 | 36 | 1 | 38-48 | official | null | null |
10.1609/aaai.v36i1.19878 | Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network | https://ojs.aaai.org/index.php/AAAI/article/view/19878 | https://ojs.aaai.org/index.php/AAAI/article/download/19878/19637 | [
"Yuqi Ren",
"Deyi Xiong"
] | Cognitive processing signals can be used to improve natural language processing (NLP) tasks. However, it is not clear how these signals correlate with linguistic information. Bridging between human language processing and linguistic features has been widely studied in neurolinguistics, usually via single-variable contr... | main | Cognitive Modeling & Cognitive Systems | 10.1609/aaai.v36i1.19878 | 36 | 1 | 49-58 | official | 2112.08831 | title_snapshot |
10.1609/aaai.v36i1.19879 | Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/19879 | https://ojs.aaai.org/index.php/AAAI/article/download/19879/19638 | [
"Duzhen Zhang",
"Tielin Zhang",
"Shuncheng Jia",
"Bo Xu"
] | With the help of deep neural networks (DNNs), deep reinforcement learning (DRL) has achieved great success on many complex tasks, from games to robotic control. Compared to DNNs with partial brain-inspired structures and functions, spiking neural networks (SNNs) consider more biological features, including spiking neur... | main | Cognitive Modeling & Cognitive Systems | 10.1609/aaai.v36i1.19879 | 36 | 1 | 59-67 | official | 2106.07854 | title_judge |
10.1609/aaai.v36i1.20004 | Deep Translation Prior: Test-Time Training for Photorealistic Style Transfer | https://ojs.aaai.org/index.php/AAAI/article/view/20004 | https://ojs.aaai.org/index.php/AAAI/article/download/20004/19763 | [
"Sunwoo Kim",
"Soohyun Kim",
"Seungryong Kim"
] | Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles. To overcome this, we propose a novel framework, dubbed D... | main | Computer Vision | 10.1609/aaai.v36i1.20004 | 36 | 1 | 1183-1191 | official | 2112.06150 | title_snapshot |
10.1609/aaai.v36i1.20005 | PrivateSNN: Privacy-Preserving Spiking Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/20005 | https://ojs.aaai.org/index.php/AAAI/article/download/20005/19764 | [
"Youngeun Kim",
"Yeshwanth Venkatesha",
"Priyadarshini Panda"
] | How can we bring both privacy and energy-efficiency to a neural system? In this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural Networks (SNNs) from a pre-trained ANN model without leaking sensitive information contained in a dataset. Here, we tackle two types of leakage problems: 1) Data lea... | main | Computer Vision | 10.1609/aaai.v36i1.20005 | 36 | 1 | 1192-1200 | official | 2104.03414 | title_snapshot |
10.1609/aaai.v36i1.20006 | NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency | https://ojs.aaai.org/index.php/AAAI/article/view/20006 | https://ojs.aaai.org/index.php/AAAI/article/download/20006/19765 | [
"Yujin Kim",
"Dogyun Park",
"Dohee Kim",
"Suhyun Kim"
] | We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. In NaturalInversion, we propose: (1) a Feature Transfer Pyramid which uses enhanced image prior of the original data by combining the multi-scale feature... | main | Computer Vision | 10.1609/aaai.v36i1.20006 | 36 | 1 | 1201-1209 | official | 2306.16661 | title_snapshot |
10.1609/aaai.v36i1.20007 | Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds | https://ojs.aaai.org/index.php/AAAI/article/view/20007 | https://ojs.aaai.org/index.php/AAAI/article/download/20007/19766 | [
"Junho Koh",
"Jaekyum Kim",
"Jin Hyeok Yoo",
"Yecheol Kim",
"Dongsuk Kum",
"Jun Won Choi"
] | In this paper, we propose a new joint object detection and tracking (JoDT) framework for 3D object detection and tracking based on camera and LiDAR sensors. The proposed method, referred to as 3D DetecTrack, enables the detector and tracker to cooperate to generate a spatio-temporal representation of the camera and LiD... | main | Computer Vision | 10.1609/aaai.v36i1.20007 | 36 | 1 | 1210-1218 | official | 2112.07116 | title_snapshot |
10.1609/aaai.v36i1.19984 | Learning to Model Pixel-Embedded Affinity for Homogeneous Instance Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/19984 | https://ojs.aaai.org/index.php/AAAI/article/download/19984/19743 | [
"Wei Huang",
"Shiyu Deng",
"Chang Chen",
"Xueyang Fu",
"Zhiwei Xiong"
] | Homogeneous instance segmentation aims to identify each instance in an image where all interested instances belong to the same category, such as plant leaves and microscopic cells. Recently, proposal-free methods, which straightforwardly generate instance-aware information to group pixels into different instances, have... | main | Computer Vision | 10.1609/aaai.v36i1.19984 | 36 | 1 | 1007-1015 | official | null | null |
10.1609/aaai.v36i1.19985 | Channelized Axial Attention – considering Channel Relation within Spatial Attention for Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/19985 | https://ojs.aaai.org/index.php/AAAI/article/download/19985/19744 | [
"Ye Huang",
"Di Kang",
"Wenjing Jia",
"Liu Liu",
"Xiangjian He"
] | Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases. In this paper, we... | main | Computer Vision | 10.1609/aaai.v36i1.19985 | 36 | 1 | 1016-1025 | official | 2101.07434 | title_judge |
10.1609/aaai.v36i1.19986 | UFPMP-Det:Toward Accurate and Efficient Object Detection on Drone Imagery | https://ojs.aaai.org/index.php/AAAI/article/view/19986 | https://ojs.aaai.org/index.php/AAAI/article/download/19986/19745 | [
"Yecheng Huang",
"Jiaxin Chen",
"Di Huang"
] | This paper proposes a novel approach to object detection on drone imagery, namely Multi-Proxy Detection Network with Unified Foreground Packing (UFPMP-Det). To deal with the numerous instances of very small scales, different from the common solution that divides the high-resolution input image into quite a number of ch... | main | Computer Vision | 10.1609/aaai.v36i1.19986 | 36 | 1 | 1026-1033 | official | 2112.10415 | title_snapshot |
10.1609/aaai.v36i1.19987 | Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/19987 | https://ojs.aaai.org/index.php/AAAI/article/download/19987/19746 | [
"Zhipeng Huang",
"Jiawei Liu",
"Liang Li",
"Kecheng Zheng",
"Zheng-Jun Zha"
] | RGB-infrared person re-identification is an emerging cross-modality re-identification task, which is very challenging due to significant modality discrepancy between RGB and infrared images. In this work, we propose a novel modality-adaptive mixup and invariant decomposition (MID) approach for RGB-infrared person re-id... | main | Computer Vision | 10.1609/aaai.v36i1.19987 | 36 | 1 | 1034-1042 | official | 2203.01735 | title_snapshot |
10.1609/aaai.v36i1.19988 | MuMu: Cooperative Multitask Learning-Based Guided Multimodal Fusion | https://ojs.aaai.org/index.php/AAAI/article/view/19988 | https://ojs.aaai.org/index.php/AAAI/article/download/19988/19747 | [
"Md Mofijul Islam",
"Tariq Iqbal"
] | Multimodal sensors (visual, non-visual, and wearable) can provide complementary information to develop robust perception systems for recognizing activities accurately. However, it is challenging to extract robust multimodal representations due to the heterogeneous characteristics of data from multimodal sensors and dis... | main | Computer Vision | 10.1609/aaai.v36i1.19988 | 36 | 1 | 1043-1051 | official | null | null |
10.1609/aaai.v36i1.19989 | An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/19989 | https://ojs.aaai.org/index.php/AAAI/article/download/19989/19748 | [
"Haedong Jeong",
"Jiyeon Han",
"Jaesik Choi"
] | Despite significant improvements on the image generation performance of Generative Adversarial Networks (GANs), generations with low visual fidelity still have been observed. As widely used metrics for GANs focus more on the overall performance of the model, evaluation on the quality of individual generations or detect... | main | Computer Vision | 10.1609/aaai.v36i1.19989 | 36 | 1 | 1052-1059 | official | 2112.08814 | title_snapshot |
10.1609/aaai.v36i1.19990 | FrePGAN: Robust Deepfake Detection Using Frequency-Level Perturbations | https://ojs.aaai.org/index.php/AAAI/article/view/19990 | https://ojs.aaai.org/index.php/AAAI/article/download/19990/19749 | [
"Yonghyun Jeong",
"Doyeon Kim",
"Youngmin Ro",
"Jongwon Choi"
] | Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise from the overfitting issue, which we discover from our own analysis and the previous studies to originate from the frequency-level artifacts ... | main | Computer Vision | 10.1609/aaai.v36i1.19990 | 36 | 1 | 1060-1068 | official | 2202.03347 | title_snapshot |
10.1609/aaai.v36i1.19991 | Learning Disentangled Attribute Representations for Robust Pedestrian Attribute Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/19991 | https://ojs.aaai.org/index.php/AAAI/article/download/19991/19750 | [
"Jian Jia",
"Naiyu Gao",
"Fei He",
"Xiaotang Chen",
"Kaiqi Huang"
] | Although various methods have been proposed for pedestrian attribute recognition, most studies follow the same feature learning mechanism, \ie, learning a shared pedestrian image feature to classify multiple attributes. However, this mechanism leads to low-confidence predictions and non-robustness of the model in the i... | main | Computer Vision | 10.1609/aaai.v36i1.19991 | 36 | 1 | 1069-1077 | official | null | null |
10.1609/aaai.v36i1.19992 | Degrade Is Upgrade: Learning Degradation for Low-Light Image Enhancement | https://ojs.aaai.org/index.php/AAAI/article/view/19992 | https://ojs.aaai.org/index.php/AAAI/article/download/19992/19751 | [
"Kui Jiang",
"Zhongyuan Wang",
"Zheng Wang",
"Chen Chen",
"Peng Yi",
"Tao Lu",
"Chia-Wen Lin"
] | Low-light image enhancement aims to improve an image's visibility while keeping its visual naturalness. Different from existing methods, which tend to accomplish the relighting task directly, we investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps. Insp... | main | Computer Vision | 10.1609/aaai.v36i1.19992 | 36 | 1 | 1078-1086 | official | 2103.10621 | title_snapshot |
10.1609/aaai.v36i1.19993 | HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images | https://ojs.aaai.org/index.php/AAAI/article/view/19993 | https://ojs.aaai.org/index.php/AAAI/article/download/19993/19752 | [
"Meirui Jiang",
"Zirui Wang",
"Qi Dou"
] | Multiple medical institutions collaboratively training a model using federated learning (FL) has become a promising solution for maximizing the potential of data-driven models, yet the non-independent and identically distributed (non-iid) data in medical images is still an outstanding challenge in real-world practice. ... | main | Computer Vision | 10.1609/aaai.v36i1.19993 | 36 | 1 | 1087-1095 | official | 2112.10775 | title_snapshot |
10.1609/aaai.v36i1.19994 | Coarse-to-Fine Generative Modeling for Graphic Layouts | https://ojs.aaai.org/index.php/AAAI/article/view/19994 | https://ojs.aaai.org/index.php/AAAI/article/download/19994/19753 | [
"Zhaoyun Jiang",
"Shizhao Sun",
"Jihua Zhu",
"Jian-Guang Lou",
"Dongmei Zhang"
] | Even though graphic layout generation has attracted growing attention recently, it is still challenging to synthesis realistic and diverse layouts, due to the complicated element relationships and varied element arrangements. In this work, we seek to improve the performance of layout generation by incorporating the con... | main | Computer Vision | 10.1609/aaai.v36i1.19994 | 36 | 1 | 1096-1103 | official | null | null |
10.1609/aaai.v36i1.19995 | DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior | https://ojs.aaai.org/index.php/AAAI/article/view/19995 | https://ojs.aaai.org/index.php/AAAI/article/download/19995/19754 | [
"Shuangping Jin",
"Bingbing Yu",
"Minhao Jing",
"Yi Zhou",
"Jiajun Liang",
"Renhe Ji"
] | RGB-NIR fusion is a promising method for low-light imaging. However, high-intensity noise in low-light images amplifies the effect of structure inconsistency between RGB-NIR images, which fails existing algorithms. To handle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net (DVN) with two technical... | main | Computer Vision | 10.1609/aaai.v36i1.19995 | 36 | 1 | 1104-1112 | official | 2303.06834 | title_snapshot |
10.1609/aaai.v36i1.19996 | LAGConv: Local-Context Adaptive Convolution Kernels with Global Harmonic Bias for Pansharpening | https://ojs.aaai.org/index.php/AAAI/article/view/19996 | https://ojs.aaai.org/index.php/AAAI/article/download/19996/19755 | [
"Zi-Rong Jin",
"Tian-Jing Zhang",
"Tai-Xiang Jiang",
"Gemine Vivone",
"Liang-Jian Deng"
] | Pansharpening is a critical yet challenging low-level vision task that aims to obtain a higher-resolution image by fusing a multispectral (MS) image and a panchromatic (PAN) image. While most pansharpening methods are based on convolutional neural network (CNN) architectures with standard convolution operations, few at... | main | Computer Vision | 10.1609/aaai.v36i1.19996 | 36 | 1 | 1113-1121 | official | null | null |
10.1609/aaai.v36i1.19997 | Learning the Dynamics of Visual Relational Reasoning via Reinforced Path Routing | https://ojs.aaai.org/index.php/AAAI/article/view/19997 | https://ojs.aaai.org/index.php/AAAI/article/download/19997/19756 | [
"Chenchen Jing",
"Yunde Jia",
"Yuwei Wu",
"Chuanhao Li",
"Qi Wu"
] | Reasoning is a dynamic process. In cognitive theories, the dynamics of reasoning refers to reasoning states over time after successive state transitions. Modeling the cognitive dynamics is of utmost importance to simulate human reasoning capability. In this paper, we propose to learn the reasoning dynamics of visual re... | main | Computer Vision | 10.1609/aaai.v36i1.19997 | 36 | 1 | 1122-1130 | official | null | null |
10.1609/aaai.v36i1.19998 | Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/19998 | https://ojs.aaai.org/index.php/AAAI/article/download/19998/19757 | [
"Lipeng Ke",
"Kuan-Chuan Peng",
"Siwei Lyu"
] | Graph Convolutional Networks (GCNs) have been widely used to model the high-order dynamic dependencies for skeleton-based action recognition. Most existing approaches do not explicitly embed the high-order spatio-temporal importance to joints’ spatial connection topology and intensity, and they do not have direct objec... | main | Computer Vision | 10.1609/aaai.v36i1.19998 | 36 | 1 | 1131-1139 | official | 2202.02314 | title_snapshot |
10.1609/aaai.v36i1.19999 | MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition | https://ojs.aaai.org/index.php/AAAI/article/view/19999 | https://ojs.aaai.org/index.php/AAAI/article/download/19999/19758 | [
"Zhanghan Ke",
"Jiayu Sun",
"Kaican Li",
"Qiong Yan",
"Rynson W.H. Lau"
] | Existing portrait matting methods either require auxiliary inputs that are costly to obtain or involve multiple stages that are computationally expensive, making them less suitable for real-time applications. In this work, we present a light-weight matting objective decomposition network (MODNet) for portrait matting i... | main | Computer Vision | 10.1609/aaai.v36i1.19999 | 36 | 1 | 1140-1147 | official | 2011.11961 | title_snapshot |
10.1609/aaai.v36i1.20000 | Learning Mixture of Domain-Specific Experts via Disentangled Factors for Autonomous Driving | https://ojs.aaai.org/index.php/AAAI/article/view/20000 | https://ojs.aaai.org/index.php/AAAI/article/download/20000/19759 | [
"Inhan Kim",
"Joonyeong Lee",
"Daijin Kim"
] | Since human drivers only consider the driving-related factors that affect vehicle control depending on the situation, they can drive safely even in diverse driving environments. To mimic this behavior, we propose an autonomous driving framework based on the two-stage representation learning that initially splits the la... | main | Computer Vision | 10.1609/aaai.v36i1.20000 | 36 | 1 | 1148-1156 | official | null | null |
10.1609/aaai.v36i1.20001 | Towards Versatile Pedestrian Detector with Multisensory-Matching and Multispectral Recalling Memory | https://ojs.aaai.org/index.php/AAAI/article/view/20001 | https://ojs.aaai.org/index.php/AAAI/article/download/20001/19760 | [
"Jung Uk Kim",
"Sungjune Park",
"Yong Man Ro"
] | Recently, automated surveillance cameras can change a visible sensor and a thermal sensor for all-day operation. However, existing single-modal pedestrian detectors mainly focus on detecting pedestrians in only one specific modality (i.e., visible or thermal), so they cannot cope with other modal inputs. In addition, r... | main | Computer Vision | 10.1609/aaai.v36i1.20001 | 36 | 1 | 1157-1165 | official | null | null |
10.1609/aaai.v36i1.20002 | Semantic Feature Extraction for Generalized Zero-Shot Learning | https://ojs.aaai.org/index.php/AAAI/article/view/20002 | https://ojs.aaai.org/index.php/AAAI/article/download/20002/19761 | [
"Junhan Kim",
"Kyuhong Shim",
"Byonghyo Shim"
] | Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes using the attribute. In this paper, we put forth a new GZSL technique that improves the GZSL classification performance greatly. Key idea of the proposed approach, henceforth referred to as semantic feature ex... | main | Computer Vision | 10.1609/aaai.v36i1.20002 | 36 | 1 | 1166-1173 | official | 2112.14478 | title_snapshot |
10.1609/aaai.v36i1.20003 | Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip Reading | https://ojs.aaai.org/index.php/AAAI/article/view/20003 | https://ojs.aaai.org/index.php/AAAI/article/download/20003/19762 | [
"Minsu Kim",
"Jeong Hun Yeo",
"Yong Man Ro"
] | Recognizing speech from silent lip movement, which is called lip reading, is a challenging task due to 1) the inherent information insufficiency of lip movement to fully represent the speech, and 2) the existence of homophenes that have similar lip movement with different pronunciations. In this paper, we try to allevi... | main | Computer Vision | 10.1609/aaai.v36i1.20003 | 36 | 1 | 1174-1182 | official | 2204.01725 | title_snapshot |
10.1609/aaai.v36i1.19964 | RRL: Regional Rotate Layer in Convolutional Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/19964 | https://ojs.aaai.org/index.php/AAAI/article/download/19964/19723 | [
"Zongbo Hao",
"Tao Zhang",
"Mingwang Chen",
"Zou Kaixu"
] | Convolutional Neural Networks (CNNs) perform very well in image classification and object detection in recent years, but even the most advanced models have limited rotation invariance. Known solutions include the enhancement of training data and the increase of rotation invariance by globally merging the rotation equiv... | main | Computer Vision | 10.1609/aaai.v36i1.19964 | 36 | 1 | 826-833 | official | 2202.12509 | title_judge |
10.1609/aaai.v36i1.19965 | QueryProp: Object Query Propagation for High-Performance Video Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/19965 | https://ojs.aaai.org/index.php/AAAI/article/download/19965/19724 | [
"Fei He",
"Naiyu Gao",
"Jian Jia",
"Xin Zhao",
"Kaiqi Huang"
] | Video object detection has been an important yet challenging topic in computer vision. Traditional methods mainly focus on designing the image-level or box-level feature propagation strategies to exploit temporal information. This paper argues that with a more effective and efficient feature propagation framework, vide... | main | Computer Vision | 10.1609/aaai.v36i1.19965 | 36 | 1 | 834-842 | official | 2207.10959 | title_snapshot |
10.1609/aaai.v36i1.19966 | Flow-Based Unconstrained Lip to Speech Generation | https://ojs.aaai.org/index.php/AAAI/article/view/19966 | https://ojs.aaai.org/index.php/AAAI/article/download/19966/19725 | [
"Jinzheng He",
"Zhou Zhao",
"Yi Ren",
"Jinglin Liu",
"Baoxing Huai",
"Nicholas Yuan"
] | Unconstrained lip-to-speech aims to generate corresponding speeches based on silent facial videos with no restriction to head pose or vocabulary. It is desirable to generate intelligible and natural speech with a fast speed in unconstrained settings. Currently, to handle the more complicated scenarios, most existing me... | main | Computer Vision | 10.1609/aaai.v36i1.19966 | 36 | 1 | 843-851 | official | null | null |
10.1609/aaai.v36i1.19967 | TransFG: A Transformer Architecture for Fine-Grained Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/19967 | https://ojs.aaai.org/index.php/AAAI/article/download/19967/19726 | [
"Ju He",
"Jie-Neng Chen",
"Shuai Liu",
"Adam Kortylewski",
"Cheng Yang",
"Yutong Bai",
"Changhu Wang"
] | Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences. Most existing works mainly tackle this problem by reusing the backbone network to extract features of detected discriminative regions. However, ... | main | Computer Vision | 10.1609/aaai.v36i1.19967 | 36 | 1 | 852-860 | official | 2103.07976 | title_snapshot |
10.1609/aaai.v36i1.19968 | Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions | https://ojs.aaai.org/index.php/AAAI/article/view/19968 | https://ojs.aaai.org/index.php/AAAI/article/download/19968/19727 | [
"Pan He",
"Patrick Emami",
"Sanjay Ranka",
"Anand Rangarajan"
] | In this paper, we present a new self-supervised scene flow estimation approach for a pair of consecutive point clouds. The key idea of our approach is to represent discrete point clouds as continuous probability density functions using Gaussian mixture models. Scene flow estimation is therefore converted into the probl... | main | Computer Vision | 10.1609/aaai.v36i1.19968 | 36 | 1 | 861-869 | official | 2203.12193 | title_snapshot |
10.1609/aaai.v36i1.19969 | SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds | https://ojs.aaai.org/index.php/AAAI/article/view/19969 | https://ojs.aaai.org/index.php/AAAI/article/download/19969/19728 | [
"Qingdong He",
"Zhengning Wang",
"Hao Zeng",
"Yi Zeng",
"Yijun Liu"
] | Accurate 3D object detection from point clouds has become a crucial component in autonomous driving. However, the volumetric representations and the projection methods in previous works fail to establish the relationships between the local point sets. In this paper, we propose Sparse Voxel-Graph Attention Network (SVGA... | main | Computer Vision | 10.1609/aaai.v36i1.19969 | 36 | 1 | 870-878 | official | 2006.04043 | title_snapshot |
10.1609/aaai.v36i1.19970 | SECRET: Self-Consistent Pseudo Label Refinement for Unsupervised Domain Adaptive Person Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/19970 | https://ojs.aaai.org/index.php/AAAI/article/download/19970/19729 | [
"Tao He",
"Leqi Shen",
"Yuchen Guo",
"Guiguang Ding",
"Zhenhua Guo"
] | Unsupervised domain adaptive person re-identification aims at learning on an unlabeled target domain with only labeled data in source domain. Currently, the state-of-the-arts usually solve this problem by pseudo-label-based clustering and fine-tuning in target domain. However, the reason behind the noises of pseudo lab... | main | Computer Vision | 10.1609/aaai.v36i1.19970 | 36 | 1 | 879-887 | official | null | null |
10.1609/aaai.v36i1.19971 | Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/19971 | https://ojs.aaai.org/index.php/AAAI/article/download/19971/19730 | [
"Yue He",
"Chen Chen",
"Jing Zhang",
"Juhua Liu",
"Fengxiang He",
"Chaoyue Wang",
"Bo Du"
] | Existing Scene Text Recognition (STR) methods typically use a language model to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) model, which ignore the 2D spatial context of visual semantics within and between character instances, making them not generalize well to arb... | main | Computer Vision | 10.1609/aaai.v36i1.19971 | 36 | 1 | 888-896 | official | 2112.12916 | title_snapshot |
10.1609/aaai.v36i1.19972 | Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives | https://ojs.aaai.org/index.php/AAAI/article/view/19972 | https://ojs.aaai.org/index.php/AAAI/article/download/19972/19731 | [
"David T. Hoffmann",
"Nadine Behrmann",
"Juergen Gall",
"Thomas Brox",
"Mehdi Noroozi"
] | This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples. In contrast to the standard InfoNCE loss, which requires a strict binary separation of the training pairs into similar and dissimilar samples, RINCE... | main | Computer Vision | 10.1609/aaai.v36i1.19972 | 36 | 1 | 897-905 | official | 2201.11736 | title_snapshot |
10.1609/aaai.v36i1.19973 | Uncertainty-Driven Dehazing Network | https://ojs.aaai.org/index.php/AAAI/article/view/19973 | https://ojs.aaai.org/index.php/AAAI/article/download/19973/19732 | [
"Ming Hong",
"Jianzhuang Liu",
"Cuihua Li",
"Yanyun Qu"
] | Deep learning has made remarkable achievements for single image haze removal. However, existing deep dehazing models only give deterministic results without discussing the uncertainty of them. There exist two types of uncertainty in the dehazing models: aleatoric uncertainty that comes from noise inherent in the observ... | main | Computer Vision | 10.1609/aaai.v36i1.19973 | 36 | 1 | 906-913 | official | null | null |
10.1609/aaai.v36i1.19974 | Shadow Generation for Composite Image in Real-World Scenes | https://ojs.aaai.org/index.php/AAAI/article/view/19974 | https://ojs.aaai.org/index.php/AAAI/article/download/19974/19733 | [
"Yan Hong",
"Li Niu",
"Jianfu Zhang"
] | Image composition targets at inserting a foreground object into a background image. Most previous image composition methods focus on adjusting the foreground to make it compatible with background while ignoring the shadow effect of foreground on the background. In this work, we focus on generating plausible shadow for ... | main | Computer Vision | 10.1609/aaai.v36i1.19974 | 36 | 1 | 914-922 | official | 2104.10338 | title_snapshot |
10.1609/aaai.v36i1.19975 | Shape-Adaptive Selection and Measurement for Oriented Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/19975 | https://ojs.aaai.org/index.php/AAAI/article/download/19975/19734 | [
"Liping Hou",
"Ke Lu",
"Jian Xue",
"Yuqiu Li"
] | The development of detection methods for oriented object detection remains a challenging task. A considerable obstacle is the wide variation in the shape (e.g., aspect ratio) of objects. Sample selection in general object detection has been widely studied as it plays a crucial role in the performance of the detection m... | main | Computer Vision | 10.1609/aaai.v36i1.19975 | 36 | 1 | 923-932 | official | null | null |
10.1609/aaai.v36i1.19976 | H^2-MIL: Exploring Hierarchical Representation with Heterogeneous Multiple Instance Learning for Whole Slide Image Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/19976 | https://ojs.aaai.org/index.php/AAAI/article/download/19976/19735 | [
"Wentai Hou",
"Lequan Yu",
"Chengxuan Lin",
"Helong Huang",
"Rongshan Yu",
"Jing Qin",
"Liansheng Wang"
] | Current representation learning methods for whole slide image (WSI) with pyramidal resolutions are inherently homogeneous and flat, which cannot fully exploit the multiscale and heterogeneous diagnostic information of different structures for comprehensive analysis. This paper presents a novel graph neural network-base... | main | Computer Vision | 10.1609/aaai.v36i1.19976 | 36 | 1 | 933-941 | official | null | null |
10.1609/aaai.v36i1.19977 | Elastic-Link for Binarized Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/19977 | https://ojs.aaai.org/index.php/AAAI/article/download/19977/19736 | [
"Jie Hu",
"Ziheng Wu",
"Vince Tan",
"Zhilin Lu",
"Mengze Zeng",
"Enhua Wu"
] | Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrained devices. However, in comparison to their full-precision counterparts, BNNs suffer from severe accuracy degradation. Research aiming to r... | main | Computer Vision | 10.1609/aaai.v36i1.19977 | 36 | 1 | 942-950 | official | null | null |
10.1609/aaai.v36i1.19978 | FInfer: Frame Inference-Based Deepfake Detection for High-Visual-Quality Videos | https://ojs.aaai.org/index.php/AAAI/article/view/19978 | https://ojs.aaai.org/index.php/AAAI/article/download/19978/19737 | [
"Juan Hu",
"Xin Liao",
"Jinwen Liang",
"Wenbo Zhou",
"Zheng Qin"
] | Deepfake has ignited hot research interests in both academia and industry due to its potential security threats. Many countermeasures have been proposed to mitigate such risks. Current Deepfake detection methods achieve superior performances in dealing with low-visual-quality Deepfake media which can be distinguished b... | main | Computer Vision | 10.1609/aaai.v36i1.19978 | 36 | 1 | 951-959 | official | null | null |
10.1609/aaai.v36i1.19979 | Bi-volution: A Static and Dynamic Coupled Filter | https://ojs.aaai.org/index.php/AAAI/article/view/19979 | https://ojs.aaai.org/index.php/AAAI/article/download/19979/19738 | [
"Xiwei Hu",
"Xuanhong Chen",
"Bingbing Ni",
"Teng Li",
"Yutian Liu"
] | Dynamic convolution has achieved significant gain in performance and computational complexity, thanks to its powerful representation capability given limited filter number/layers. However, SOTA dynamic convolution operators are sensitive to input noises (e.g., Gaussian noise, shot noise, e.t.c.) and lack sufficient spa... | main | Computer Vision | 10.1609/aaai.v36i1.19979 | 36 | 1 | 960-968 | official | null | null |
10.1609/aaai.v36i1.19980 | AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds | https://ojs.aaai.org/index.php/AAAI/article/view/19980 | https://ojs.aaai.org/index.php/AAAI/article/download/19980/19739 | [
"Yihan Hu",
"Zhuangzhuang Ding",
"Runzhou Ge",
"Wenxin Shao",
"Li Huang",
"Kun Li",
"Qiang Liu"
] | There have been two streams in the 3D detection from point clouds: single-stage methods and two-stage methods. While the former is more computationally efficient, the latter usually provides better detection accuracy. By carefully examining the two-stage approaches, we have found that if appropriately designed, the fir... | main | Computer Vision | 10.1609/aaai.v36i1.19980 | 36 | 1 | 969-979 | official | 2112.09205 | title_snapshot |
10.1609/aaai.v36i1.19981 | Divide-and-Regroup Clustering for Domain Adaptive Person Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/19981 | https://ojs.aaai.org/index.php/AAAI/article/download/19981/19740 | [
"Zhengdong Hu",
"Yifan Sun",
"Yi Yang",
"Jianguang Zhou"
] | Clustering is important for domain adaptive person re-identification(re-ID). A majority of unsupervised domain adaptation (UDA) methods conduct clustering on the target domain and then use the generated pseudo labels for adaptive training. Albeit important, the clustering pipeline adopted by current literature is quite... | main | Computer Vision | 10.1609/aaai.v36i1.19981 | 36 | 1 | 980-988 | official | null | null |
10.1609/aaai.v36i1.19982 | CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes | https://ojs.aaai.org/index.php/AAAI/article/view/19982 | https://ojs.aaai.org/index.php/AAAI/article/download/19982/19741 | [
"Hao Huang",
"Yongtao Wang",
"Zhaoyu Chen",
"Yuze Zhang",
"Yuheng Li",
"Zhi Tang",
"Wei Chu",
"Jingdong Chen",
"Weisi Lin",
"Kai-Kuang Ma"
] | Malicious applications of deepfakes (i.e., technologies generating target facial attributes or entire faces from facial images) have posed a huge threat to individuals' reputation and security. To mitigate these threats, recent studies have proposed adversarial watermarks to combat deepfake models, leading them to gene... | main | Computer Vision | 10.1609/aaai.v36i1.19982 | 36 | 1 | 989-997 | official | 2105.10872 | title_snapshot |
10.1609/aaai.v36i1.19983 | Deconfounded Visual Grounding | https://ojs.aaai.org/index.php/AAAI/article/view/19983 | https://ojs.aaai.org/index.php/AAAI/article/download/19983/19742 | [
"Jianqiang Huang",
"Yu Qin",
"Jiaxin Qi",
"Qianru Sun",
"Hanwang Zhang"
] | We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck. For example, the grounding process is usually a trivial languagelocation association without visual reasoning, e.g., grounding any language query contain... | main | Computer Vision | 10.1609/aaai.v36i1.19983 | 36 | 1 | 998-1006 | official | 2112.15324 | title_snapshot |
10.1609/aaai.v36i1.19944 | Unsupervised Underwater Image Restoration: From a Homology Perspective | https://ojs.aaai.org/index.php/AAAI/article/view/19944 | https://ojs.aaai.org/index.php/AAAI/article/download/19944/19703 | [
"Zhenqi Fu",
"Huangxing Lin",
"Yan Yang",
"Shu Chai",
"Liyan Sun",
"Yue Huang",
"Xinghao Ding"
] | Underwater images suffer from degradation due to light scattering and absorption. It remains challenging to restore such degraded images using deep neural networks since real-world paired data is scarcely available while synthetic paired data cannot approximate real-world data perfectly. In this paper, we propose an Un... | main | Computer Vision | 10.1609/aaai.v36i1.19944 | 36 | 1 | 643-651 | official | null | null |
10.1609/aaai.v36i1.19945 | Playing Lottery Tickets with Vision and Language | https://ojs.aaai.org/index.php/AAAI/article/view/19945 | https://ojs.aaai.org/index.php/AAAI/article/download/19945/19704 | [
"Zhe Gan",
"Yen-Chun Chen",
"Linjie Li",
"Tianlong Chen",
"Yu Cheng",
"Shuohang Wang",
"Jingjing Liu",
"Lijuan Wang",
"Zicheng Liu"
] | Large-scale pre-training has recently revolutionized vision-and-language (VL) research. Models such as LXMERT and UNITER have significantly lifted the state of the art over a wide range of VL tasks. However, the large number of parameters in such models hinders their application in practice. In parallel, work on the lo... | main | Computer Vision | 10.1609/aaai.v36i1.19945 | 36 | 1 | 652-660 | official | 2104.11832 | title_snapshot |
10.1609/aaai.v36i1.19946 | Feature Distillation Interaction Weighting Network for Lightweight Image Super-resolution | https://ojs.aaai.org/index.php/AAAI/article/view/19946 | https://ojs.aaai.org/index.php/AAAI/article/download/19946/19705 | [
"Guangwei Gao",
"Wenjie Li",
"Juncheng Li",
"Fei Wu",
"Huimin Lu",
"Yi Yu"
] | Convolutional neural networks based single-image superresolution (SISR) has made great progress in recent years. However, it is difficult to apply these methods to real-world scenarios due to the computational and memory cost. Meanwhile, how to take full advantage of the intermediate features under the constraints of l... | main | Computer Vision | 10.1609/aaai.v36i1.19946 | 36 | 1 | 661-669 | official | 2112.08655 | title_snapshot |
10.1609/aaai.v36i1.19947 | Weakly-Supervised Salient Object Detection Using Point Supervision | https://ojs.aaai.org/index.php/AAAI/article/view/19947 | https://ojs.aaai.org/index.php/AAAI/article/download/19947/19706 | [
"Shuyong Gao",
"Wei Zhang",
"Yan Wang",
"Qianyu Guo",
"Chenglong Zhang",
"Yangji He",
"Wenqiang Zhang"
] | Current state-of-the-art saliency detection models rely heavily on large datasets of accurate pixel-wise annotations, but manually labeling pixels is time-consuming and labor-intensive. There are some weakly supervised methods developed for alleviating the problem, such as image label, bounding box label, and scribble ... | main | Computer Vision | 10.1609/aaai.v36i1.19947 | 36 | 1 | 670-678 | official | 2203.11652 | title_snapshot |
10.1609/aaai.v36i1.19948 | Latent Space Explanation by Intervention | https://ojs.aaai.org/index.php/AAAI/article/view/19948 | https://ojs.aaai.org/index.php/AAAI/article/download/19948/19707 | [
"Itai Gat",
"Guy Lorberbom",
"Idan Schwartz",
"Tamir Hazan"
] | The success of deep neural nets heavily relies on their ability to encode complex relations between their input and their output. While this property serves to fit the training data well, it also obscures the mechanism that drives prediction. This study aims to reveal hidden concepts by employing an intervention mechan... | main | Computer Vision | 10.1609/aaai.v36i1.19948 | 36 | 1 | 679-687 | official | 2112.04895 | title_snapshot |
10.1609/aaai.v36i1.19949 | Lifelong Person Re-identification by Pseudo Task Knowledge Preservation | https://ojs.aaai.org/index.php/AAAI/article/view/19949 | https://ojs.aaai.org/index.php/AAAI/article/download/19949/19708 | [
"Wenhang Ge",
"Junlong Du",
"Ancong Wu",
"Yuqiao Xian",
"Ke Yan",
"Feiyue Huang",
"Wei-Shi Zheng"
] | In real world, training data for person re-identification (Re-ID) is collected discretely with spatial and temporal variations, which requires a model to incrementally learn new knowledge without forgetting old knowledge. This problem is called lifelong person re-identification (LReID). Variations of illumination and b... | main | Computer Vision | 10.1609/aaai.v36i1.19949 | 36 | 1 | 688-696 | official | null | null |
10.1609/aaai.v36i1.19950 | Adversarial Robustness in Multi-Task Learning: Promises and Illusions | https://ojs.aaai.org/index.php/AAAI/article/view/19950 | https://ojs.aaai.org/index.php/AAAI/article/download/19950/19709 | [
"Salah Ghamizi",
"Maxime Cordy",
"Mike Papadakis",
"Yves Le Traon"
] | Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets, very little research has considered complex multi-task models that are common in real applications. In this paper, we evaluate the design c... | main | Computer Vision | 10.1609/aaai.v36i1.19950 | 36 | 1 | 697-705 | official | 2110.15053 | title_snapshot |
10.1609/aaai.v36i1.19951 | Deep Confidence Guided Distance for 3D Partial Shape Registration | https://ojs.aaai.org/index.php/AAAI/article/view/19951 | https://ojs.aaai.org/index.php/AAAI/article/download/19951/19710 | [
"Dvir Ginzburg",
"Dan Raviv"
] | We present a novel non-iterative learnable method for partial-to-partial 3D shape registration. The partial alignment task is extremely complex, as it jointly tries to match between points, and identify which points do not appear in the corresponding shape, causing the solution to be non-unique and ill-posed in most ca... | main | Computer Vision | 10.1609/aaai.v36i1.19951 | 36 | 1 | 706-714 | official | 2201.11379 | title_snapshot |
10.1609/aaai.v36i1.19952 | Predicting Physical World Destinations for Commands Given to Self-Driving Cars | https://ojs.aaai.org/index.php/AAAI/article/view/19952 | https://ojs.aaai.org/index.php/AAAI/article/download/19952/19711 | [
"Dusan Grujicic",
"Thierry Deruyttere",
"Marie-Francine Moens",
"Matthew B. Blaschko"
] | In recent years, we have seen significant steps taken in the development of self-driving cars. Multiple companies are starting to roll out impressive systems that work in a variety of settings. These systems can sometimes give the impression that full self-driving is just around the corner and that we would soon build ... | main | Computer Vision | 10.1609/aaai.v36i1.19952 | 36 | 1 | 715-725 | official | 2112.05419 | title_snapshot |
10.1609/aaai.v36i1.19953 | Towards Light-Weight and Real-Time Line Segment Detection | https://ojs.aaai.org/index.php/AAAI/article/view/19953 | https://ojs.aaai.org/index.php/AAAI/article/download/19953/19712 | [
"Geonmo Gu",
"Byungsoo Ko",
"SeoungHyun Go",
"Sung-Hyun Lee",
"Jingeun Lee",
"Minchul Shin"
] | Previous deep learning-based line segment detection (LSD) suffers from the immense model size and high computational cost for line prediction. This constrains them from real-time inference on computationally restricted environments. In this paper, we propose a real-time and light-weight line segment detector for resour... | main | Computer Vision | 10.1609/aaai.v36i1.19953 | 36 | 1 | 726-734 | official | 2106.00186 | title_snapshot |
10.1609/aaai.v36i1.19954 | Exploiting Fine-Grained Face Forgery Clues via Progressive Enhancement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/19954 | https://ojs.aaai.org/index.php/AAAI/article/download/19954/19713 | [
"Qiqi Gu",
"Shen Chen",
"Taiping Yao",
"Yang Chen",
"Shouhong Ding",
"Ran Yi"
] | With the rapid development of facial forgery techniques, forgery detection has attracted more and more attention due to security concerns. Existing approaches attempt to use frequency information to mine subtle artifacts under high-quality forged faces. However, the exploitation of frequency information is coarse-grain... | main | Computer Vision | 10.1609/aaai.v36i1.19954 | 36 | 1 | 735-743 | official | 2112.13977 | title_snapshot |
10.1609/aaai.v36i1.19955 | Delving into the Local: Dynamic Inconsistency Learning for DeepFake Video Detection | https://ojs.aaai.org/index.php/AAAI/article/view/19955 | https://ojs.aaai.org/index.php/AAAI/article/download/19955/19714 | [
"Zhihao Gu",
"Yang Chen",
"Taiping Yao",
"Shouhong Ding",
"Jilin Li",
"Lizhuang Ma"
] | The rapid development of facial manipulation techniques has aroused public concerns in recent years. Existing deepfake video detection approaches attempt to capture the discrim- inative features between real and fake faces based on tem- poral modelling. However, these works impose supervisions on sparsely sampled video... | main | Computer Vision | 10.1609/aaai.v36i1.19955 | 36 | 1 | 744-752 | official | null | null |
10.1609/aaai.v36i1.19956 | Assessing a Single Image in Reference-Guided Image Synthesis | https://ojs.aaai.org/index.php/AAAI/article/view/19956 | https://ojs.aaai.org/index.php/AAAI/article/download/19956/19715 | [
"Jiayi Guo",
"Chaoqun Du",
"Jiangshan Wang",
"Huijuan Huang",
"Pengfei Wan",
"Gao Huang"
] | Assessing the performance of Generative Adversarial Networks (GANs) has been an important topic due to its practical significance. Although several evaluation metrics have been proposed, they generally assess the quality of the whole generated image distribution. For Reference-guided Image Synthesis (RIS) tasks, i.e., ... | main | Computer Vision | 10.1609/aaai.v36i1.19956 | 36 | 1 | 753-761 | official | 2112.04163 | title_snapshot |
10.1609/aaai.v36i1.19957 | Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/19957 | https://ojs.aaai.org/index.php/AAAI/article/download/19957/19716 | [
"Tianyu Guo",
"Hong Liu",
"Zhan Chen",
"Mengyuan Liu",
"Tao Wang",
"Runwei Ding"
] | In recent years, self-supervised representation learning for skeleton-based action recognition has been developed with the advance of contrastive learning methods. The existing contrastive learning methods use normal augmentations to construct similar positive samples, which limits the ability to explore novel movement... | main | Computer Vision | 10.1609/aaai.v36i1.19957 | 36 | 1 | 762-770 | official | 2112.03590 | title_snapshot |
10.1609/aaai.v36i1.19958 | Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition | https://ojs.aaai.org/index.php/AAAI/article/view/19958 | https://ojs.aaai.org/index.php/AAAI/article/download/19958/19717 | [
"Marawan Gamal Abdel Hameed",
"Marzieh S. Tahaei",
"Ali Mosleh",
"Vahid Partovi Nia"
] | Modern Convolutional Neural Network (CNN) architectures, despite their superiority in solving various problems, are generally too large to be deployed on resource constrained edge devices. In this paper, we reduce memory usage and floating-point operations required by convolutional layers in CNNs. We compress these lay... | main | Computer Vision | 10.1609/aaai.v36i1.19958 | 36 | 1 | 771-779 | official | 2109.14710 | title_snapshot |
10.1609/aaai.v36i1.19959 | Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment | https://ojs.aaai.org/index.php/AAAI/article/view/19959 | https://ojs.aaai.org/index.php/AAAI/article/download/19959/19718 | [
"Guangxing Han",
"Shiyuan Huang",
"Jiawei Ma",
"Yicheng He",
"Shih-Fu Chang"
] | Few-shot object detection (FSOD) aims to detect objects using only a few examples. How to adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object proposal is a key ingredient in modern object detectors. However, the quality of proposals generated for few-shot classes using existing me... | main | Computer Vision | 10.1609/aaai.v36i1.19959 | 36 | 1 | 780-789 | official | 2104.07719 | title_snapshot |
10.1609/aaai.v36i1.19960 | Delving into Probabilistic Uncertainty for Unsupervised Domain Adaptive Person Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/19960 | https://ojs.aaai.org/index.php/AAAI/article/download/19960/19719 | [
"Jian Han",
"Ya-Li Li",
"Shengjin Wang"
] | Clustering-based unsupervised domain adaptive (UDA) person re-identification (ReID) reduces exhaustive annotations. However, owing to unsatisfactory feature embedding and imperfect clustering, pseudo labels for target domain data inherently contain an unknown proportion of wrong ones, which would mislead feature learni... | main | Computer Vision | 10.1609/aaai.v36i1.19960 | 36 | 1 | 790-798 | official | 2112.14025 | title_snapshot |
10.1609/aaai.v36i1.19961 | Laneformer: Object-Aware Row-Column Transformers for Lane Detection | https://ojs.aaai.org/index.php/AAAI/article/view/19961 | https://ojs.aaai.org/index.php/AAAI/article/download/19961/19720 | [
"Jianhua Han",
"Xiajun Deng",
"Xinyue Cai",
"Zhen Yang",
"Hang Xu",
"Chunjing Xu",
"Xiaodan Liang"
] | We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in autonomous driving. The dominant paradigms rely on purely CNN-based architectures which often fail in incorporating relations of long-range... | main | Computer Vision | 10.1609/aaai.v36i1.19961 | 36 | 1 | 799-807 | official | 2203.09830 | title_snapshot |
10.1609/aaai.v36i1.19962 | Modify Self-Attention via Skeleton Decomposition for Effective Point Cloud Transformer | https://ojs.aaai.org/index.php/AAAI/article/view/19962 | https://ojs.aaai.org/index.php/AAAI/article/download/19962/19721 | [
"Jiayi Han",
"Longbin Zeng",
"Liang Du",
"Xiaoqing Ye",
"Weiyang Ding",
"Jianfeng Feng"
] | Although considerable progress has been achieved regarding the transformers in recent years, the large number of parameters, quadratic computational complexity, and memory cost conditioned on long sequences make the transformers hard to train and implement, especially in edge computing configurations. In this case, a d... | main | Computer Vision | 10.1609/aaai.v36i1.19962 | 36 | 1 | 808-816 | official | null | null |
10.1609/aaai.v36i1.19963 | Generalizable Person Re-identification via Self-Supervised Batch Norm Test-Time Adaption | https://ojs.aaai.org/index.php/AAAI/article/view/19963 | https://ojs.aaai.org/index.php/AAAI/article/download/19963/19722 | [
"Ke Han",
"Chenyang Si",
"Yan Huang",
"Liang Wang",
"Tieniu Tan"
] | In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they neglect that BN is se... | main | Computer Vision | 10.1609/aaai.v36i1.19963 | 36 | 1 | 817-825 | official | 2203.00672 | title_snapshot |
10.1609/aaai.v36i1.19924 | Style-Guided and Disentangled Representation for Robust Image-to-Image Translation | https://ojs.aaai.org/index.php/AAAI/article/view/19924 | https://ojs.aaai.org/index.php/AAAI/article/download/19924/19683 | [
"Jaewoong Choi",
"Daeha Kim",
"Byung Cheol Song"
] | Recently, various image-to-image translation (I2I) methods have improved mode diversity and visual quality in terms of neural networks or regularization terms. However, conventional I2I methods relies on a static decision boundary and the encoded representations in those methods are entangled with each other, so they o... | main | Computer Vision | 10.1609/aaai.v36i1.19924 | 36 | 1 | 463-471 | official | null | null |
10.1609/aaai.v36i1.19925 | Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation | https://ojs.aaai.org/index.php/AAAI/article/view/19925 | https://ojs.aaai.org/index.php/AAAI/article/download/19925/19684 | [
"Tong Chu",
"Yahao Liu",
"Jinhong Deng",
"Wen Li",
"Lixin Duan"
] | Source-Free Unsupervised Domain Adaptation(SFUDA) aims to adapt a pre-trained source model to an unlabeled target domain without access to the original labeled source domain samples. Many existing SFUDA approaches apply the self-training strategy, which involves iteratively selecting confidently predicted target sample... | main | Computer Vision | 10.1609/aaai.v36i1.19925 | 36 | 1 | 472-480 | official | null | null |
10.1609/aaai.v36i1.19926 | Model-Based Image Signal Processors via Learnable Dictionaries | https://ojs.aaai.org/index.php/AAAI/article/view/19926 | https://ojs.aaai.org/index.php/AAAI/article/download/19926/19685 | [
"Marcos V. Conde",
"Steven McDonagh",
"Matteo Maggioni",
"Ales Leonardis",
"Eduardo Pérez-Pellitero"
] | Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due to the inherent hardware design, but also due to the appealing simplicity of no... | main | Computer Vision | 10.1609/aaai.v36i1.19926 | 36 | 1 | 481-489 | official | 2201.03210 | title_snapshot |
10.1609/aaai.v36i1.19927 | MMA: Multi-Camera Based Global Motion Averaging | https://ojs.aaai.org/index.php/AAAI/article/view/19927 | https://ojs.aaai.org/index.php/AAAI/article/download/19927/19686 | [
"Hainan Cui",
"Shuhan Shen"
] | In order to fully perceive the surrounding environment, many intelligent robots and self-driving cars are equipped with a multi-camera system. Based on this system, the structure-from-motion (SfM) technology is used to realize scene reconstruction, but the fixed relative poses between cameras in the multi-camera system... | main | Computer Vision | 10.1609/aaai.v36i1.19927 | 36 | 1 | 490-498 | official | null | null |
10.1609/aaai.v36i1.19928 | GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data | https://ojs.aaai.org/index.php/AAAI/article/view/19928 | https://ojs.aaai.org/index.php/AAAI/article/download/19928/19687 | [
"Kaiwen Cui",
"Jiaxing Huang",
"Zhipeng Luo",
"Gongjie Zhang",
"Fangneng Zhan",
"Shijian Lu"
] | Training effective Generative Adversarial Networks (GANs) requires large amounts of training data, without which the trained models are usually sub-optimal with discriminator over-fitting. Several prior studies address this issue by expanding the distribution of the limited training data via massive and hand-crafted da... | main | Computer Vision | 10.1609/aaai.v36i1.19928 | 36 | 1 | 499-507 | official | 2110.01254 | title_snapshot |
10.1609/aaai.v36i1.19929 | Unbiased IoU for Spherical Image Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/19929 | https://ojs.aaai.org/index.php/AAAI/article/download/19929/19688 | [
"Feng Dai",
"Bin Chen",
"Hang Xu",
"Yike Ma",
"Xiaodong Li",
"Bailan Feng",
"Peng Yuan",
"Chenggang Yan",
"Qiang Zhao"
] | As one of the fundamental components of object detection, intersection-over-union (IoU) calculations between two bounding boxes play an important role in samples selection, NMS operation and evaluation of object detection algorithms. This procedure is well-defined and solved for planar images, while it is challenging f... | main | Computer Vision | 10.1609/aaai.v36i1.19929 | 36 | 1 | 508-515 | official | 2108.08029 | title_snapshot |
10.1609/aaai.v36i1.19930 | InsCLR: Improving Instance Retrieval with Self-Supervision | https://ojs.aaai.org/index.php/AAAI/article/view/19930 | https://ojs.aaai.org/index.php/AAAI/article/download/19930/19689 | [
"Zelu Deng",
"Yujie Zhong",
"Sheng Guo",
"Weilin Huang"
] | This work aims at improving instance retrieval with self-supervision. We find that fine-tuning using the recently developed self-supervised learning (SSL) methods, such as SimCLR and MoCo, fails to improve the performance of instance retrieval. In this work, we identify that the learnt representations for instance retr... | main | Computer Vision | 10.1609/aaai.v36i1.19930 | 36 | 1 | 516-524 | official | 2112.01390 | title_snapshot |
10.1609/aaai.v36i1.19931 | Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/19931 | https://ojs.aaai.org/index.php/AAAI/article/download/19931/19690 | [
"Ziluo Ding",
"Rui Zhao",
"Jiyuan Zhang",
"Tianxiao Gao",
"Ruiqin Xiong",
"Zhaofei Yu",
"Tiejun Huang"
] | Event camera has offered promising alternative for visual perception, especially in high speed and high dynamic range scenes. Recently, many deep learning methods have shown great success in providing model-free solutions to many event-based problems, such as optical flow estimation. However, existing deep learning met... | main | Computer Vision | 10.1609/aaai.v36i1.19931 | 36 | 1 | 525-533 | official | 2109.04871 | title_snapshot |
10.1609/aaai.v36i1.19932 | Construct Effective Geometry Aware Feature Pyramid Network for Multi-Scale Object Detection | https://ojs.aaai.org/index.php/AAAI/article/view/19932 | https://ojs.aaai.org/index.php/AAAI/article/download/19932/19691 | [
"Jinpeng Dong",
"Yuhao Huang",
"Songyi Zhang",
"Shitao Chen",
"Nanning Zheng"
] | Feature Pyramid Network (FPN) has been widely adopted to exploit multi-scale features for scale variation in object detection. However, intrinsic defects in most of the current methods with FPN make it difficult to adapt to the feature of different geometric objects. To address this issue, we introduce geometric prior ... | main | Computer Vision | 10.1609/aaai.v36i1.19932 | 36 | 1 | 534-541 | official | null | null |
10.1609/aaai.v36i1.19933 | Complementary Attention Gated Network for Pedestrian Trajectory Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/19933 | https://ojs.aaai.org/index.php/AAAI/article/download/19933/19692 | [
"Jinghai Duan",
"Le Wang",
"Chengjiang Long",
"Sanping Zhou",
"Fang Zheng",
"Liushuai Shi",
"Gang Hua"
] | Pedestrian trajectory prediction is crucial in many practical applications due to the diversity of pedestrian movements, such as social interactions and individual motion behaviors. With similar observable trajectories and social environments, different pedestrians may make completely different future decisions. Howeve... | main | Computer Vision | 10.1609/aaai.v36i1.19933 | 36 | 1 | 542-550 | official | null | null |
10.1609/aaai.v36i1.19934 | SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/19934 | https://ojs.aaai.org/index.php/AAAI/article/download/19934/19693 | [
"Zhaoxin Fan",
"Zhenbo Song",
"Hongyan Liu",
"Zhiwu Lu",
"Jun He",
"Xiaoyong Du"
] | Simultaneous Localization and Mapping (SLAM) and Autonomous Driving are becoming increasingly more important in recent years. Point cloud-based large scale place recognition is the spine of them. While many models have been proposed and have achieved acceptable performance by learning short-range local features, they a... | main | Computer Vision | 10.1609/aaai.v36i1.19934 | 36 | 1 | 551-560 | official | 2105.00149 | title_snapshot |
10.1609/aaai.v36i1.19935 | Backdoor Attacks on the DNN Interpretation System | https://ojs.aaai.org/index.php/AAAI/article/view/19935 | https://ojs.aaai.org/index.php/AAAI/article/download/19935/19694 | [
"Shihong Fang",
"Anna Choromanska"
] | Interpretability is crucial to understand the inner workings of deep neural networks (DNNs). Many interpretation methods help to understand the decision-making of DNNs by generating saliency maps that highlight parts of the input image that contribute the most to the prediction made by the DNN. In this paper we design ... | main | Computer Vision | 10.1609/aaai.v36i1.19935 | 36 | 1 | 561-570 | official | 2011.10698 | title_snapshot |
10.1609/aaai.v36i1.19936 | Learning to Learn Transferable Attack | https://ojs.aaai.org/index.php/AAAI/article/view/19936 | https://ojs.aaai.org/index.php/AAAI/article/download/19936/19695 | [
"Shuman Fang",
"Jie Li",
"Xianming Lin",
"Rongrong Ji"
] | Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. However, the transferability of perturbations from existing methods is still limited, since the adversarial perturbations a... | main | Computer Vision | 10.1609/aaai.v36i1.19936 | 36 | 1 | 571-579 | official | 2112.06658 | title_snapshot |
10.1609/aaai.v36i1.19937 | Perceptual Quality Assessment of Omnidirectional Images | https://ojs.aaai.org/index.php/AAAI/article/view/19937 | https://ojs.aaai.org/index.php/AAAI/article/download/19937/19696 | [
"Yuming Fang",
"Liping Huang",
"Jiebin Yan",
"Xuelin Liu",
"Yang Liu"
] | Omnidirectional images, also called 360◦images, have attracted extensive attention in recent years, due to the rapid development of virtual reality (VR) technologies. During omnidirectional image processing including capture, transmission, consumption, and so on, measuring the perceptual quality of omnidirectional imag... | main | Computer Vision | 10.1609/aaai.v36i1.19937 | 36 | 1 | 580-588 | official | 2207.02674 | title_snapshot |
10.1609/aaai.v36i1.19938 | PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/19938 | https://ojs.aaai.org/index.php/AAAI/article/download/19938/19697 | [
"Mojtaba Faramarzi",
"Mohammad Amini",
"Akilesh Badrinaaraayanan",
"Vikas Verma",
"Sarath Chandar"
] | Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data. A recent class of methods to address this problem uses various ways to construct a new training sample by mixing a pair (or more) of training samples. We propose PatchUp, a hidde... | main | Computer Vision | 10.1609/aaai.v36i1.19938 | 36 | 1 | 589-597 | official | 2006.07794 | title_snapshot |
10.1609/aaai.v36i1.19939 | DuMLP-Pin: A Dual-MLP-Dot-Product Permutation-Invariant Network for Set Feature Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/19939 | https://ojs.aaai.org/index.php/AAAI/article/download/19939/19698 | [
"Jiajun Fei",
"Ziyu Zhu",
"Wenlei Liu",
"Zhidong Deng",
"Mingyang Li",
"Huanjun Deng",
"Shuo Zhang"
] | Existing permutation-invariant methods can be divided into two categories according to the aggregation scope, i.e. global aggregation and local one. Although the global aggregation methods, e. g., PointNet and Deep Sets, get involved in simpler structures, their performance is poorer than the local aggregation ones lik... | main | Computer Vision | 10.1609/aaai.v36i1.19939 | 36 | 1 | 598-606 | official | 2203.04007 | title_snapshot |
10.1609/aaai.v36i1.19940 | Attention-Aligned Transformer for Image Captioning | https://ojs.aaai.org/index.php/AAAI/article/view/19940 | https://ojs.aaai.org/index.php/AAAI/article/download/19940/19699 | [
"Zhengcong Fei"
] | Recently, attention-based image captioning models, which are expected to ground correct image regions for proper word generations, have achieved remarkable performance. However, some researchers have argued “deviated focus” problem of existing attention mechanisms in determining the effective and influential image feat... | main | Computer Vision | 10.1609/aaai.v36i1.19940 | 36 | 1 | 607-615 | official | null | null |
10.1609/aaai.v36i1.19941 | Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers | https://ojs.aaai.org/index.php/AAAI/article/view/19941 | https://ojs.aaai.org/index.php/AAAI/article/download/19941/19700 | [
"Zunlei Feng",
"Jiacong Hu",
"Sai Wu",
"XiaoTian Yu",
"Jie Song",
"Mingli Song"
] | Recently, Convolutional Neural Network (CNN) has achieved excellent performance in the classification task. It is widely known that CNN is deemed as a 'blackbox', which is hard for understanding the prediction mechanism and debugging the wrong prediction. Some model debugging and explanation works are developed for sol... | main | Computer Vision | 10.1609/aaai.v36i1.19941 | 36 | 1 | 616-624 | official | 2112.04934 | title_snapshot |
10.1609/aaai.v36i1.19942 | OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression | https://ojs.aaai.org/index.php/AAAI/article/view/19942 | https://ojs.aaai.org/index.php/AAAI/article/download/19942/19701 | [
"Chunyang Fu",
"Ge Li",
"Rui Song",
"Wei Gao",
"Shan Liu"
] | In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework called OctAttention em... | main | Computer Vision | 10.1609/aaai.v36i1.19942 | 36 | 1 | 625-633 | official | 2202.06028 | title_snapshot |
10.1609/aaai.v36i1.19943 | DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents | https://ojs.aaai.org/index.php/AAAI/article/view/19943 | https://ojs.aaai.org/index.php/AAAI/article/download/19943/19702 | [
"Tsu-Jui Fu",
"William Yang Wang",
"Daniel McDuff",
"Yale Song"
] | Creating presentation materials requires complex multimodal reasoning skills to summarize key concepts and arrange them in a logical and visually pleasing manner. Can machines learn to emulate this laborious process? We present a novel task and approach for document-to-slide generation. Solving this involves document s... | main | Computer Vision | 10.1609/aaai.v36i1.19943 | 36 | 1 | 634-642 | official | 2101.11796 | title_snapshot |
10.1609/aaai.v36i1.19904 | Text Gestalt: Stroke-Aware Scene Text Image Super-resolution | https://ojs.aaai.org/index.php/AAAI/article/view/19904 | https://ojs.aaai.org/index.php/AAAI/article/download/19904/19663 | [
"Jingye Chen",
"Haiyang Yu",
"Jianqi Ma",
"Bin Li",
"Xiangyang Xue"
] | In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general... | main | Computer Vision | 10.1609/aaai.v36i1.19904 | 36 | 1 | 285-293 | official | 2112.08171 | title_snapshot |
10.1609/aaai.v36i1.19905 | Towards High-Fidelity Face Self-Occlusion Recovery via Multi-View Residual-Based GAN Inversion | https://ojs.aaai.org/index.php/AAAI/article/view/19905 | https://ojs.aaai.org/index.php/AAAI/article/download/19905/19664 | [
"Jinsong Chen",
"Hu Han",
"Shiguang Shan"
] | Face self-occlusions are inevitable due to the 3D nature of the human face and the loss of information in the projection process from 3D to 2D images. While recovering face self-occlusions based on 3D face reconstruction, e.g., 3D Morphable Model (3DMM) and its variants provides an effective solution, most of the exist... | main | Computer Vision | 10.1609/aaai.v36i1.19905 | 36 | 1 | 294-302 | official | null | null |
10.1609/aaai.v36i1.19906 | ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/19906 | https://ojs.aaai.org/index.php/AAAI/article/download/19906/19665 | [
"Jinze Chen",
"Yang Wang",
"Yang Cao",
"Feng Wu",
"Zheng-Jun Zha"
] | Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting apparent motion of objects with microsecond resolution, and shows great application potential in monitoring and other fields. However, the output event stream of existing DVS inevitably contains background activity noise (BA noise) due to dark ... | main | Computer Vision | 10.1609/aaai.v36i1.19906 | 36 | 1 | 303-311 | official | 2203.11732 | title_snapshot |
10.1609/aaai.v36i1.19907 | Attacking Video Recognition Models with Bullet-Screen Comments | https://ojs.aaai.org/index.php/AAAI/article/view/19907 | https://ojs.aaai.org/index.php/AAAI/article/download/19907/19666 | [
"Kai Chen",
"Zhipeng Wei",
"Jingjing Chen",
"Zuxuan Wu",
"Yu-Gang Jiang"
] | Recent research has demonstrated that Deep Neural Networks (DNNs) are vulnerable to adversarial patches which introduce perceptible but localized changes to the input. Nevertheless, existing approaches have focused on generating adversarial patches on images, their counterparts in videos have been less explored. Compar... | main | Computer Vision | 10.1609/aaai.v36i1.19907 | 36 | 1 | 312-320 | official | 2110.15629 | title_snapshot |
10.1609/aaai.v36i1.19908 | VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization | https://ojs.aaai.org/index.php/AAAI/article/view/19908 | https://ojs.aaai.org/index.php/AAAI/article/download/19908/19667 | [
"Minghui Chen",
"Cheng Wen",
"Feng Zheng",
"Fengxiang He",
"Ling Shao"
] | Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been the major approach in improving the robustness against common corruptions. However, the samples produced by popular augmentation strategies d... | main | Computer Vision | 10.1609/aaai.v36i1.19908 | 36 | 1 | 321-329 | official | 2204.11531 | title_snapshot |
10.1609/aaai.v36i1.19909 | TransZero: Attribute-Guided Transformer for Zero-Shot Learning | https://ojs.aaai.org/index.php/AAAI/article/view/19909 | https://ojs.aaai.org/index.php/AAAI/article/download/19909/19668 | [
"Shiming Chen",
"Ziming Hong",
"Yang Liu",
"Guo-Sen Xie",
"Baigui Sun",
"Hao Li",
"Qinmu Peng",
"Ke Lu",
"Xinge You"
] | Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descriptions shared between different classes, which are strong prior for localization of object attribute for representing discriminative region fea... | main | Computer Vision | 10.1609/aaai.v36i1.19909 | 36 | 1 | 330-338 | official | 2112.01683 | title_snapshot |
10.1609/aaai.v36i1.19910 | Structured Semantic Transfer for Multi-Label Recognition with Partial Labels | https://ojs.aaai.org/index.php/AAAI/article/view/19910 | https://ojs.aaai.org/index.php/AAAI/article/download/19910/19669 | [
"Tianshui Chen",
"Tao Pu",
"Hefeng Wu",
"Yuan Xie",
"Liang Lin"
] | Multi-label image recognition is a fundamental yet practical task because real-world images inherently possess multiple semantic labels. However, it is difficult to collect large-scale multi-label annotations due to the complexity of both the input images and output label spaces. To reduce the annotation cost, we propo... | main | Computer Vision | 10.1609/aaai.v36i1.19910 | 36 | 1 | 339-346 | official | 2112.10941 | title_snapshot |
10.1609/aaai.v36i1.19911 | SJDL-Vehicle: Semi-supervised Joint Defogging Learning for Foggy Vehicle Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/19911 | https://ojs.aaai.org/index.php/AAAI/article/download/19911/19670 | [
"Wei-Ting Chen",
"I-Hsiang Chen",
"Chih-Yuan Yeh",
"Hao-Hsiang Yang",
"Jian-Jiun Ding",
"Sy-Yen Kuo"
] | Vehicle re-identification (ReID) has attracted considerable attention in computer vision. Although several methods have been proposed to achieve state-of-the-art performance on this topic, re-identifying vehicle in foggy scenes remains a great challenge due to the degradation of visibility. To our knowledge, this probl... | main | Computer Vision | 10.1609/aaai.v36i1.19911 | 36 | 1 | 347-355 | official | null | null |
10.1609/aaai.v36i1.19912 | Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification | https://ojs.aaai.org/index.php/AAAI/article/view/19912 | https://ojs.aaai.org/index.php/AAAI/article/download/19912/19671 | [
"Xiaohua Chen",
"Yucan Zhou",
"Dayan Wu",
"Wanqian Zhang",
"Yu Zhou",
"Bo Li",
"Weiping Wang"
] | Real-world data often follows a long-tailed distribution, which makes the performance of existing classification algorithms degrade heavily. A key issue is that the samples in tail categories fail to depict their intra-class diversity. Humans can imagine a sample in new poses, scenes and view angles with their prior kn... | main | Computer Vision | 10.1609/aaai.v36i1.19912 | 36 | 1 | 356-364 | official | 2112.07928 | title_snapshot |
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