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Unbiased IoU for Spherical Image Object Detection
| null |
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 for spherical ones. Some existing methods utilize planar bounding boxes to represent spherical objects. However, they are biased due to the distortions of spherical objects. Others use spherical rectangles as unbiased representations, but they adopt excessive approximate algorithms when computing the IoU. In this paper, we propose an unbiased IoU as a novel evaluation criterion for spherical image object detection, which is based on the unbiased representations and utilize unbiased analytical method for IoU calculation. This is the first time that the absolutely accurate IoU calculation is applied to the evaluation criterion, thus object detection algorithms can be correctly evaluated for spherical images. With the unbiased representation and calculation, we also present Spherical CenterNet, an anchor free object detection algorithm for spherical images. The experiments show that our unbiased IoU gives accurate results and the proposed Spherical CenterNet achieves better performance on one real-world and two synthetic spherical object detection datasets than existing methods.
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Feng Dai, Bin Chen, Hang Xu, Yike Ma, Xiaodong Li, Bailan Feng, Peng Yuan, Chenggang Yan, Qiang Zhao
| null | null | 2,022 |
aaai
|
Attention-Aligned Transformer for Image Captioning
| null |
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 features. In this paper, we present A2 - an attention-aligned Transformer for image captioning, which guides attention learning in a perturbation-based self-supervised manner, without any annotation overhead. Specifically, we add mask operation on image regions through a learnable network to estimate the true function in ultimate description generation. We hypothesize that the necessary image region features, where small disturbance causes an obvious performance degradation, deserve more attention weight. Then, we propose four aligned strategies to use this information to refine attention weight distribution. Under such a pattern, image regions are attended correctly with the output words. Extensive experiments conducted on the MS COCO dataset demonstrate that the proposed A2 Transformer consistently outperforms baselines in both automatic metrics and human evaluation. Trained models and code for reproducing the experiments are publicly available.
|
Zhengcong Fei
| null | null | 2,022 |
aaai
|
Perceptual Quality Assessment of Omnidirectional Images
| null |
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 images is highly desired, since it plays a great role in guaranteeing the immersive quality of experience (IQoE). In this paper, we conduct a comprehensive study on the perceptual quality of omnidirectional images from both subjective and objective perspectives. Specifically, we construct the largest so far subjective omnidirectional image quality database, where we consider several key influential elements, i.e., realistic non-uniform distortion, viewing condition, and viewing behavior, from the user view. In addition to subjective quality scores, we also record head and eye movement data. Besides, we make the first attempt by using the proposed database to train a convolutional neural network (CNN) for blind omnidirectional image quality assessment. To be consistent with the human viewing behavior in the VR device, we extract viewports from each omnidirectional image and incorporate the user viewing conditions naturally in the proposed model. The proposed model is composed of two parts, including a multi-scale CNN-based feature extraction module and a perceptual quality prediction module. The feature extraction module is used to incorporate the multi-scale features, and the perceptual quality prediction module is designed to regress them to perceived quality scores. The experimental results on our database verify that the proposed model achieves the competing performance compared with the state-of-the-art methods.
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Yuming Fang, Liping Huang, Jiebin Yan, Xuelin Liu, Yang Liu
| null | null | 2,022 |
aaai
|
DuMLP-Pin: A Dual-MLP-Dot-Product Permutation-Invariant Network for Set Feature Extraction
| null |
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 like PointNet++ and Point Transformer. It remains an open problem whether there exists a global aggregation method with a simple structure, competitive performance, and even much fewer parameters. In this paper, we propose a novel global aggregation permutation-invariant network based on dual MLP dot-product, called DuMLP-Pin, which is capable of being employed to extract features for set inputs, including unordered or unstructured pixel, attribute, and point cloud data sets. We strictly prove that any permutation-invariant function implemented by DuMLP-Pin can be decomposed into two or more permutation-equivariant ones in a dot-product way as the cardinality of the given input set is greater than a threshold. We also show that the DuMLP-Pin can be viewed as Deep Sets with strong constraints under certain conditions. The performance of DuMLP-Pin is evaluated on several different tasks with diverse data sets. The experimental results demonstrate that our DuMLP-Pin achieves the best results on the two classification problems for pixel sets and attribute sets. On both the point cloud classification and the part segmentation, the accuracy of DuMLP-Pin is very close to the so-far best-performing local aggregation method with only a 1-2% difference, while the number of required parameters is significantly reduced by more than 85% in classification and 69% in segmentation, respectively. The code is publicly available on https://github.com/JaronTHU/DuMLP-Pin.
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Jiajun Fei, Ziyu Zhu, Wenlei Liu, Zhidong Deng, Mingyang Li, Huanjun Deng, Shuo Zhang
| null | null | 2,022 |
aaai
|
OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression
| null |
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 employing the octree structure, a memory-efficient representation for point clouds. Our approach encodes octree symbol sequences in a lossless way by gathering the information of sibling and ancestor nodes. Expressly, we first represent point clouds with octree to reduce spatial redundancy, which is robust for point clouds with different resolutions. We then design a conditional entropy model with a large receptive field that models the sibling and ancestor contexts to exploit the strong dependency among the neighboring nodes and employ an attention mechanism to emphasize the correlated nodes in the context. Furthermore, we introduce a mask operation during training and testing to make a trade-off between encoding time and performance. Compared to the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset (e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based baseline. The code is available at https://github.com/zb12138/OctAttention.
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Chunyang Fu, Ge Li, Rui Song, Wei Gao, Shan Liu
| null | null | 2,022 |
aaai
|
Laneformer: Object-Aware Row-Column Transformers for Lane Detection
| null |
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 lane points and global contexts induced by surrounding objects (e.g., pedestrians, vehicles). Inspired by recent advances of the transformer encoder-decoder architecture in various vision tasks, we move forwards to design a new end-to-end Laneformer architecture that revolutionizes the conventional transformers into better capturing the shape and semantic characteristics of lanes, with minimal overhead in latency. First, coupling with deformable pixel-wise self-attention in the encoder, Laneformer presents two new row and column self-attention operations to efficiently mine point context along with the lane shapes. Second, motivated by the appearing objects would affect the decision of predicting lane segments, Laneformer further includes the detected object instances as extra inputs of multi-head attention blocks in the encoder and decoder to facilitate the lane point detection by sensing semantic contexts. Specifically, the bounding box locations of objects are added into Key module to provide interaction with each pixel and query while the ROI-aligned features are inserted into Value module. Extensive experiments demonstrate our Laneformer achieves state-of-the-art performances on CULane benchmark, in terms of 77.1% F1 score. We hope our simple and effective Laneformer will serve as a strong baseline for future research in self-attention models for lane detection.
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Jianhua Han, Xiajun Deng, Xinyue Cai, Zhen Yang, Hang Xu, Chunjing Xu, Xiaodan Liang
| null | null | 2,022 |
aaai
|
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
| null |
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 methods is far worse than that of many-shot classes, e.g., missing boxes for few-shot classes due to misclassification or inaccurate spatial locations with respect to true objects. To address the noisy proposal problem, we propose a novel meta-learning based FSOD model by jointly optimizing the few-shot proposal generation and fine-grained few-shot proposal classification. To improve proposal generation for few-shot classes, we propose to learn a lightweight metric-learning based prototype matching network, instead of the conventional simple linear object/nonobject classifier, e.g., used in RPN. Our non-linear classifier with the feature fusion network could improve the discriminative prototype matching and the proposal recall for few-shot classes. To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection. Meanwhile we learn a separate Faster R-CNN detection head for many-shot base classes and show strong performance of maintaining base-classes knowledge. Our model achieves state-of-the-art performance on multiple FSOD benchmarks over most of the shots and metrics.
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Guangxing Han, Shiyuan Huang, Jiawei Ma, Yicheng He, Shih-Fu Chang
| null | null | 2,022 |
aaai
|
DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents
| null |
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 summarization, image and text retrieval, slide structure and layout prediction to arrange key elements in a form suitable for presentation. We propose a hierarchical sequence-to-sequence approach to tackle our task in an end-to-end manner. Our approach exploits the inherent structures within documents and slides and incorporates paraphrasing and layout prediction modules to generate slides. To help accelerate research in this domain, we release a dataset about 6K paired documents and slide decks used in our experiments. We show that our approach outperforms strong baselines and produces slides with rich content and aligned imagery.
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Tsu-Jui Fu, William Yang Wang, Daniel McDuff, Yale Song
| null | null | 2,022 |
aaai
|
Guide Local Feature Matching by Overlap Estimation
| null |
Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important. Conventional methods detect and match tentative local features across the whole images, with heuristic consistency checks to guarantee reliable matches. In this paper, we introduce a novel Overlap Estimation method conditioned on image pairs with TRansformer, named OETR, to constrain local feature matching in the commonly visible region. OETR performs overlap estimation in a two step process of feature correlation and then overlap regression. As a preprocessing module, OETR can be plugged into any existing local feature detection and matching pipeline, to mitigate potential view angle or scale variance. Intensive experiments show that OETR can boost state of the art local feature matching performance substantially, especially for image pairs with small shared regions. The code will be publicly available at https://github.com/AbyssGaze/OETR.
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Ying Chen, Dihe Huang, Shang Xu, Jianlin Liu, Yong Liu
| null | null | 2,022 |
aaai
|
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers
| null |
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 solving the above drawbacks. However, those methods focus on explanation and diagnosing possible causes for model prediction, based on which the researchers handle the following optimization of models manually. In this paper, we propose the first completely automatic model diagnosing and treating tool, termed as Model Doctor. Based on two discoveries that 1) each category is only correlated with sparse and specific convolution kernels, and 2) adversarial samples are isolated while normal samples are successive in the feature space, a simple aggregate gradient constraint is devised for effectively diagnosing and optimizing CNN classifiers. The aggregate gradient strategy is a versatile module for mainstream CNN classifiers. Extensive experiments demonstrate that the proposed Model Doctor applies to all existing CNN classifiers, and improves the accuracy of 16 mainstream CNN classifiers by 1%~5%.
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Zunlei Feng, Jiacong Hu, Sai Wu, XiaoTian Yu, Jie Song, Mingli Song
| null | null | 2,022 |
aaai
|
Delving into Probabilistic Uncertainty for Unsupervised Domain Adaptive Person Re-identification
| null |
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 learning. In this paper, we propose an approach named probabilistic uncertainty guided progressive label refinery (P2LR) for domain adaptive person re-identification. First, we propose to model the labeling uncertainty with the probabilistic distance along with ideal single-peak distributions. A quantitative criterion is established to measure the uncertainty of pseudo labels and facilitate the network training. Second, we explore a progressive strategy for refining pseudo labels. With the uncertainty-guided alternative optimization, we balance between the exploration of target domain data and the negative effects of noisy labeling. On top of a strong baseline, we obtain significant improvements and achieve the state-of-the-art performance on four UDA ReID benchmarks. Specifically, our method outperforms the baseline by 6.5% mAP on the Duke2Market task, while surpassing the state-of-the-art method by 2.5% mAP on the Market2MSMT task. Code is available at: https://github.com/JeyesHan/P2LR.
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Jian Han, Ya-Li Li, Shengjin Wang
| null | null | 2,022 |
aaai
|
Text Gestalt: Stroke-Aware Scene Text Image Super-resolution
| null |
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 images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Likewise, when humans observe a low-resolution text image, they will inherently use partial stroke-level details to recover the appearance of holistic characters. Inspired by Gestalt Psychology, we put forward a Stroke-Aware Scene Text Image Super-Resolution method containing a Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures of characters in text images. Specifically, we attempt to design rules for decomposing English characters and digits at stroke-level, then pre-train a text recognizer to provide stroke-level attention maps as positional clues with the purpose of controlling the consistency between the generated super-resolution image and high-resolution ground truth. The extensive experimental results validate that the proposed method can indeed generate more distinguishable images on TextZoom and manually constructed Chinese character dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to provide stroke-level guidance when training, it will not bring any time overhead during the test phase. Code is available at https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt.
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Jingye Chen, Haiyang Yu, Jianqi Ma, Bin Li, Xiangyang Xue
| null | null | 2,022 |
aaai
|
Towards High-Fidelity Face Self-Occlusion Recovery via Multi-View Residual-Based GAN Inversion
| null |
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 existing methods show apparent limitations in expressing high-fidelity, natural, and diverse facial details. To overcome these limitations, we propose in this paper a new generative adversarial network (MvInvert) for natural face self-occlusion recovery without using paired image-texture data. We design a coarse-to-fine generator for photorealistic texture generation. A coarse texture is computed by inpainting the invisible areas in the photorealistic but incomplete texture sampled directly from the 2D image using the unrealistic but complete statistical texture from 3DMM. Then, we design a multi-view Residual-based GAN Inversion, which re-renders and refines multi-view 2D images, which are used for extracting multiple high-fidelity textures. Finally, these high-fidelity textures are fused based on their visibility maps via Poisson blending. To perform adversarial learning to assure the quality of the recovered texture, we design a discriminator consisting of two heads, i.e., one for global and local discrimination between the recovered texture and a small set of real textures in UV space, and the other for discrimination between the input image and the re-rendered 2D face images via pixel-wise, identity, and adversarial losses. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods in face self-occlusion recovery under unconstrained scenarios.
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Jinsong Chen, Hu Han, Shiguang Shan
| null | null | 2,022 |
aaai
|
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
| null |
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 current and junction leakage current, which will affect the temporal correlation of objects, resulting in deteriorated motion estimation performance. Particularly, the existing filter-based denoising methods cannot be directly applied to suppress the noise in event stream, since there is no spatial correlation. To address this issue, this paper presents a novel progressive framework, in which a Motion Estimation (ME) module and an Event Denoising (ED) module are jointly optimized in a mutually reinforced manner. Specifically, based on the maximum sharpness criterion, ME module divides the input event into several segments by adaptive clustering in a motion compensating warp field, and captures the temporal correlation of event stream according to the clustered motion parameters. Taking temporal correlation as guidance, ED module calculates the confidence that each event belongs to real activity events, and transmits it to ME module to update energy function of motion segmentation for noise suppression. The two steps are iteratively updated until stable motion segmentation results are obtained. Extensive experimental results on both synthetic and real datasets demonstrate the superiority of our proposed approaches against the State-Of-The-Art (SOTA) methods.
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Jinze Chen, Yang Wang, Yang Cao, Feng Wu, Zheng-Jun Zha
| null | null | 2,022 |
aaai
|
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization
| null |
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 deviate significantly from the underlying data manifold. As a result, performance is skewed toward certain types of corruption. To address this issue, we propose a multi-source vicinal transfer augmentation (VITA) method for generating diverse on-manifold samples. The proposed VITA consists of two complementary parts: tangent transfer and integration of multi-source vicinal samples. The tangent transfer creates initial augmented samples for improving corruption robustness. The integration employs a generative model to characterize the underlying manifold built by vicinal samples, facilitating the generation of on-manifold samples. Our proposed VITA significantly outperforms the current state-of-the-art augmentation methods, demonstrated in extensive experiments on corruption benchmarks.
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Minghui Chen, Cheng Wen, Feng Zheng, Fengxiang He, Ling Shao
| null | null | 2,022 |
aaai
|
Attacking Video Recognition Models with Bullet-Screen Comments
| null |
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. Compared with images, attacking videos is much more challenging as it needs to consider not only spatial cues but also temporal cues. To close this gap, we introduce a novel adversarial attack in this paper, the bullet-screen comment (BSC) attack, which attacks video recognition models with BSCs. Specifically, adversarial BSCs are generated with a Reinforcement Learning (RL) framework, where the environment is set as the target model and the agent plays the role of selecting the position and transparency of each BSC. By continuously querying the target models and receiving feedback, the agent gradually adjusts its selection strategies in order to achieve a high fooling rate with non-overlapping BSCs. As BSCs can be regarded as a kind of meaningful patch, adding it to a clean video will not affect people’s understanding of the video content, nor will arouse people’s suspicion. We conduct extensive experiments to verify the effectiveness of the proposed method. On both UCF-101 and HMDB-51 datasets, our BSC attack method can achieve about 90% fooling rate when attacking three mainstream video recognition models, while only occluding < 8% areas in the video. Our code is available at https://github.com/kay-ck/BSC-attack.
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Kai Chen, Zhipeng Wei, Jingjing Chen, Zuxuan Wu, Yu-Gang Jiang
| null | null | 2,022 |
aaai
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Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification
| null |
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 knowledge even if it is the first time to see this category. Inspired by this, we propose a novel reasoning-based implicit semantic data augmentation method to borrow transformation directions from other classes. Since the covariance matrix of each category represents the feature transformation directions, we can sample new directions from similar categories to generate definitely different instances. Specifically, the long-tailed distributed data is first adopted to train a backbone and a classifier. Then, a covariance matrix for each category is estimated, and a knowledge graph is constructed to store the relations of any two categories. Finally, tail samples are adaptively enhanced via propagating information from all the similar categories in the knowledge graph. Experimental results on CIFAR-LT-100, ImageNet-LT, and iNaturalist 2018 have demonstrated the effectiveness of our proposed method compared with the state-of-the-art methods.
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Xiaohua Chen, Yucan Zhou, Dayan Wu, Wanqian Zhang, Yu Zhou, Bo Li, Weiping Wang
| null | null | 2,022 |
aaai
|
Structured Semantic Transfer for Multi-Label Recognition with Partial Labels
| null |
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 propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i.e., merely some labels are known while other labels are missing (also called unknown labels) per image. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations to transfer knowledge of known labels to generate pseudo labels for unknown labels. Specifically, an intra-image semantic transfer module learns image-specific label co-occurrence matrix and maps the known labels to complement unknown labels based on this matrix. Meanwhile, a cross-image transfer module learns category-specific feature similarities and helps complement unknown labels with high similarities. Finally, both known and generated labels are used to train the multi-label recognition models. Extensive experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.
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Tianshui Chen, Tao Pu, Hefeng Wu, Yuan Xie, Liang Lin
| null | null | 2,022 |
aaai
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DeTarNet: Decoupling Translation and Rotation by Siamese Network for Point Cloud Registration
| null |
Point cloud registration is a fundamental step for many tasks. In this paper, we propose a neural network named DetarNet to decouple the translation t and rotation R, so as to overcome the performance degradation due to their mutual interference in point cloud registration. First, a Siamese Network based Progressive and Coherent Feature Drift (PCFD) module is proposed to align the source and target points in high-dimensional feature space, and accurately recover translation from the alignment process. Then we propose a Consensus Encoding Unit (CEU) to construct more distinguishable features for a set of putative correspondences. After that, a Spatial and Channel Attention (SCA) block is adopted to build a classification network for finding good correspondences. Finally, the rotation is obtained by Singular Value Decomposition (SVD). In this way, the proposed network decouples the estimation of translation and rotation, resulting in better performance for both of them. Experimental results demonstrate that the proposed DetarNet improves registration performance on both indoor and outdoor scenes. Our code will be available in https://github.com/ZhiChen902/DetarNet.
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Zhi Chen, Fan Yang, Wenbing Tao
| null | null | 2,022 |
aaai
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Deep One-Class Classification via Interpolated Gaussian Descriptor
| null |
One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a one-class Gaussian anomaly classifier trained with adversarially interpolated training samples. The Gaussian anomaly classifier differentiates the training samples based on their distance to the Gaussian centre and the standard deviation of these distances, offering the model a discriminability w.r.t. the given samples during training. The adversarial interpolation is enforced to consistently learn a smooth Gaussian descriptor, even when the training data is small or contaminated with anomalous samples. This enables our model to learn the data description based on the representative normal samples rather than fringe or anomalous samples, resulting in significantly improved normality description. In extensive experiments on diverse popular benchmarks, including MNIST, Fashion MNIST, CIFAR10, MVTec AD and two medical datasets, IGD achieves better detection accuracy than current state-of-the-art models. IGD also shows better robustness in problems with small or contaminated training sets.
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Yuanhong Chen, Yu Tian, Guansong Pang, Gustavo Carneiro
| null | null | 2,022 |
aaai
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(2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering
| null |
Spatio-temporal scene-graph approaches to video-based reasoning tasks, such as video question-answering (QA), typically construct such graphs for every video frame. These approaches often ignore the fact that videos are essentially sequences of 2D ``views'' of events happening in a 3D space, and that the semantics of the 3D scene can thus be carried over from frame to frame. Leveraging this insight, we propose a (2.5+1)D scene graph representation to better capture the spatio-temporal information flows inside the videos. Specifically, we first create a 2.5D (pseudo-3D) scene graph by transforming every 2D frame to have an inferred 3D structure using an off-the-shelf 2D-to-3D transformation module, following which we register the video frames into a shared (2.5+1)D spatio-temporal space and ground each 2D scene graph within it. Such a (2.5+1)D graph is then segregated into a static sub-graph and a dynamic sub-graph, corresponding to whether the objects within them usually move in the world. The nodes in the dynamic graph are enriched with motion features capturing their interactions with other graph nodes. Next, for the video QA task, we present a novel transformer-based reasoning pipeline that embeds the (2.5+1)D graph into a spatio-temporal hierarchical latent space, where the sub-graphs and their interactions are captured at varied granularity. To demonstrate the effectiveness of our approach, we present experiments on the NExT-QA and AVSD-QA datasets. Our results show that our proposed (2.5+1)D representation leads to faster training and inference, while our hierarchical model showcases superior performance on the video QA task versus the state of the art.
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Anoop Cherian, Chiori Hori, Tim K. Marks, Jonathan Le Roux
| null | null | 2,022 |
aaai
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TransZero: Attribute-Guided Transformer for Zero-Shot Learning
| null |
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 features enabling significant visual-semantic interaction. Although few attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected. In this paper, we propose an attribute-guided Transformer network to learn the attribute localization for discriminative visual-semantic embedding representations in ZSL, termed TransZero. Specifically, TransZero takes a feature augmentation encoder to alleviate the cross-dataset bias between ImageNet and ZSL benchmarks and improve the transferability of visual features by reducing the entangled relative geometry relationships among region features. To learn locality-augmented visual features, TransZero employs a visual-semantic decoder to localize the most relevant image regions to each attributes from a given image under the guidance of attribute semantic information. Then, the locality-augmented visual features and semantic vectors are used for conducting effective visual-semantic interaction in a visual-semantic embedding network. Extensive experiments show that TransZero achieves a new state-of-the-art on three ZSL benchmarks. The codes are available at: https://github.com/shiming-chen/TransZero.
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Shiming Chen, Ziming Hong, Yang Liu, Guo-Sen Xie, Baigui Sun, Hao Li, Qinmu Peng, Ke Lu, Xinge You
| null | null | 2,022 |
aaai
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SJDL-Vehicle: Semi-supervised Joint Defogging Learning for Foggy Vehicle Re-identification
| null |
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 problem is still not well-addressed so far. In this paper, to address this problem, we propose a novel training framework called Semi-supervised Joint Defogging Learning (SJDL) framework. First, the fog removal branch and the re-identification branch are integrated to perform simultaneous training. With the collaborative training scheme, defogged features generated by the defogging branch from input images can be shared to learn better representation for the re-identification branch. However, since the fog-free image of real-world data is intractable, this architecture can only be trained on the synthetic data, which may cause the domain gap problem between real-world and synthetic scenarios. To solve this problem, we design a semi-supervised defogging training scheme that can train two kinds of data alternatively in each iteration. Due to the lack of a dataset specialized for vehicle ReID in the foggy weather, we construct a dataset called FVRID which consists of real-world and synthetic foggy images to train and evaluate the performance. Experimental results show that the proposed method is effective and outperforms other existing vehicle ReID methods in the foggy weather. The code and dataset are available in https://github.com/Cihsaing/SJDL-Foggy-Vehicle-Re-Identification--AAAI2022.
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Wei-Ting Chen, I-Hsiang Chen, Chih-Yuan Yeh, Hao-Hsiang Yang, Jian-Jiun Ding, Sy-Yen Kuo
| null | null | 2,022 |
aaai
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Event-Image Fusion Stereo Using Cross-Modality Feature Propagation
| null |
Event cameras asynchronously output the polarity values of pixel-level log intensity alterations. They are robust against motion blur and can be adopted in challenging light conditions. Owing to these advantages, event cameras have been employed in various vision tasks such as depth estimation, visual odometry, and object detection. In particular, event cameras are effective in stereo depth estimation to find correspondence points between two cameras under challenging illumination conditions and/or fast motion. However, because event cameras provide spatially sparse event stream data, it is difficult to obtain a dense disparity map. Although it is possible to estimate disparity from event data at the edge of a structure where intensity changes are likely to occur, estimating the disparity in a region where event occurs rarely is challenging. In this study, we propose a deep network that combines the features of an image with the features of an event to generate a dense disparity map. The proposed network uses images to obtain spatially dense features that are lacking in events. In addition, we propose a spatial multi-scale correlation between two fused feature maps for an accurate disparity map. To validate our method, we conducted experiments using synthetic and real-world datasets.
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Hoonhee Cho, Kuk-Jin Yoon
| null | null | 2,022 |
aaai
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Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition
| null |
Subject-invariant facial action unit (AU) recognition remains challenging for the reason that the data distribution varies among subjects. In this paper, we propose a causal inference framework for subject-invariant facial action unit recognition. To illustrate the causal effect existing in AU recognition task, we formulate the causalities among facial images, subjects, latent AU semantic relations, and estimated AU occurrence probabilities via a structural causal model. By constructing such a causal diagram, we clarify the causal-effect among variables and propose a plug-in causal intervention module, CIS, to deconfound the confounder Subject in the causal diagram. Extensive experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, show the effectiveness of our CIS, and the model with CIS inserted, CISNet, has achieved state-of-the-art performance.
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Yingjie Chen, Diqi Chen, Tao Wang, Yizhou Wang, Yun Liang
| null | null | 2,022 |
aaai
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Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
| null |
We present an extremely simple Ultra-Resolution Style Transfer framework, termed URST, to flexibly process arbitrary high-resolution images (e.g., 10000x10000 pixels) style transfer for the first time. Most of the existing state-of-the-art methods would fall short due to massive memory cost and small stroke size when processing ultra-high resolution images. URST completely avoids the memory problem caused by ultra-high resolution images by (1) dividing the image into small patches and (2) performing patch-wise style transfer with a novel Thumbnail Instance Normalization (TIN). Specifically, TIN can extract thumbnail features' normalization statistics and apply them to small patches, ensuring the style consistency among different patches. Overall, the URST framework has three merits compared to prior arts. (1) We divide input image into small patches and adopt TIN, successfully transferring image style with arbitrary high-resolution. (2) Experiments show that our URST surpasses existing SOTA methods on ultra-high resolution images benefiting from the effectiveness of the proposed stroke perceptual loss in enlarging the stroke size. (3) Our URST can be easily plugged into most existing style transfer methods and directly improve their performance even without training. Code is available at https://git.io/URST.
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Zhe Chen, Wenhai Wang, Enze Xie, Tong Lu, Ping Luo
| null | null | 2,022 |
aaai
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PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation
| null |
Gaze estimation methods learn eye gaze from facial features. However, among rich information in the facial image, real gaze-relevant features only correspond to subtle changes in eye region, while other gaze-irrelevant features like illumination, personal appearance and even facial expression may affect the learning in an unexpected way. This is a major reason why existing methods show significant performance degradation in cross-domain/dataset evaluation. In this paper, we tackle the cross-domain problem in gaze estimation. Different from common domain adaption methods, we propose a domain generalization method to improve the cross-domain performance without touching target samples. The domain generalization is realized by gaze feature purification. We eliminate gaze-irrelevant factors such as illumination and identity to improve the cross-domain performance. We design a plug-and-play self-adversarial framework for the gaze feature purification. The framework enhances not only our baseline but also existing gaze estimation methods directly and significantly. To the best of our knowledge, we are the first to propose domain generalization methods in gaze estimation. Our method achieves not only state-of-the-art performance among typical gaze estimation methods but also competitive results among domain adaption methods. The code is released in https://github.com/yihuacheng/PureGaze.
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Yihua Cheng, Yiwei Bao, Feng Lu
| null | null | 2,022 |
aaai
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Efficient Virtual View Selection for 3D Hand Pose Estimation
| null |
3D hand pose estimation from single depth is a fundamental problem in computer vision, and has wide applications. However, the existing methods still can not achieve satisfactory hand pose estimation results due to view variation and occlusion of human hand. In this paper, we propose a new virtual view selection and fusion module for 3D hand pose estimation from single depth. We propose to automatically select multiple virtual viewpoints for pose estimation and fuse the results of all and find this empirically delivers accurate and robust pose estimation. In order to select most effective virtual views for pose fusion, we evaluate the virtual views based on the confidence of virtual views using a light-weight network via network distillation. Experiments on three main benchmark datasets including NYU, ICVL and Hands2019 demonstrate that our method outperforms the state-of-the-arts on NYU and ICVL, and achieves very competitive performance on Hands2019-Task1, and our proposed virtual view selection and fusion module is both effective for 3D hand pose estimation.
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Jian Cheng, Yanguang Wan, Dexin Zuo, Cuixia Ma, Jian Gu, Ping Tan, Hongan Wang, Xiaoming Deng, Yinda Zhang
| null | null | 2,022 |
aaai
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Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations
| null |
We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide sufficient information for estimating a complete texture map, the proposed model simultaneously generates multi-view hallucinations in the image domain and an estimated texture map in the texture domain. During the generating process, each domain generator exchanges features to the other by a flow-based local attention mechanism. In this manner, the proposed model can estimate a texture map utilizing abundant multi-view image features from which multiview hallucinations are generated. As a result, the estimated texture map contains consistent colors and patterns over the entire region. Experiments show the superiority of our model for estimating a directly render-able texture map, which is applicable to 3D animation rendering. Furthermore, our model also improves an overall generation quality in the image domain for pose and viewpoint transfer tasks.
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Seunggyu Chang, Jungchan Cho, Songhwai Oh
| null | null | 2,022 |
aaai
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Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction
| null |
We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstruction. While augmentations via interpolating feature-label pairs are effective in classification tasks, they fall short in shape predictions potentially due to inconsistencies between interpolated products of two images and volumes when rendering viewpoints are unknown. PADMix targets this issue with two sets of mixup procedures performed sequentially. We first perform an input mixup which, combined with a pose adaptive learning procedure, is helpful in learning 2D feature extraction and pose adaptive latent encoding. The stagewise training allows us to build upon the pose invariant representations to perform a follow-up latent mixup under one-to-one correspondences between features and ground-truth volumes. PADMix significantly outperforms previous literature on few-shot settings over the ShapeNet dataset and sets new benchmarks on the more challenging real-world Pix3D dataset.
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Ta-Ying Cheng, Hsuan-Ru Yang, Niki Trigoni, Hwann-Tzong Chen, Tyng-Luh Liu
| null | null | 2,022 |
aaai
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LUNA: Localizing Unfamiliarity Near Acquaintance for Open-Set Long-Tailed Recognition
| null |
The predefined artificially-balanced training classes in object recognition have limited capability in modeling real-world scenarios where objects are imbalanced-distributed with unknown classes. In this paper, we discuss a promising solution to the Open-set Long-Tailed Recognition (OLTR) task utilizing metric learning. Firstly, we propose a distribution-sensitive loss, which weighs more on the tail classes to decrease the intra-class distance in the feature space. Building upon these concentrated feature clusters, a local-density-based metric is introduced, called Localizing Unfamiliarity Near Acquaintance (LUNA), to measure the novelty of a testing sample. LUNA is flexible with different cluster sizes and is reliable on the cluster boundary by considering neighbors of different properties. Moreover, contrary to most of the existing works that alleviate the open-set detection as a simple binary decision, LUNA is a quantitative measurement with interpretable meanings. Our proposed method exceeds the state-of-the-art algorithm by 4-6% in the closed-set recognition accuracy and 4% in F-measure under the open-set on the public benchmark datasets, including our own newly introduced fine-grained OLTR dataset about marine species (MS-LT), which is the first naturally-distributed OLTR dataset revealing the genuine genetic relationships of the classes.
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Jiarui Cai, Yizhou Wang, Hung-Min Hsu, Jenq-Neng Hwang, Kelsey Magrane, Craig S Rose
| null | null | 2,022 |
aaai
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CF-DETR: Coarse-to-Fine Transformers for End-to-End Object Detection
| null |
The recently proposed DEtection TRansformer (DETR) achieves promising performance for end-to-end object detection. However, it has relatively lower detection performance on small objects and suffers from slow convergence. This paper observed that DETR performs surprisingly well even on small objects when measuring Average Precision (AP) at decreased Intersection-over-Union (IoU) thresholds. Motivated by this observation, we propose a simple way to improve DETR by refining the coarse features and predicted locations. Specifically, we propose a novel Coarse-to-Fine (CF) decoder layer constituted of a coarse layer and a carefully designed fine layer. Within each CF decoder layer, the extracted local information (region of interest feature) is introduced into the flow of global context information from the coarse layer to refine and enrich the object query features via the fine layer. In the fine layer, the multi-scale information can be fully explored and exploited via the Adaptive Scale Fusion(ASF) module and Local Cross-Attention (LCA) module. The multi-scale information can also be enhanced by another proposed Transformer Enhanced FPN (TEF) module to further improve the performance. With our proposed framework (named CF-DETR), the localization accuracy of objects (especially for small objects) can be largely improved. As a byproduct, the slow convergence issue of DETR can also be addressed. The effectiveness of CF-DETR is validated via extensive experiments on the coco benchmark. CF-DETR achieves state-of-the-art performance among end-to-end detectors, e.g., achieving 47.8 AP using ResNet-50 with 36 epochs in the standard 3x training schedule.
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Xipeng Cao, Peng Yuan, Bailan Feng, Kun Niu
| null | null | 2,022 |
aaai
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Handwritten Mathematical Expression Recognition via Attention Aggregation Based Bi-directional Mutual Learning
| null |
Handwritten mathematical expression recognition aims to automatically generate LaTeX sequences from given images. Currently, attention-based encoder-decoder models are widely used in this task. They typically generate target sequences in a left-to-right (L2R) manner, leaving the right-to-left (R2L) contexts unexploited. In this paper, we propose an Attention aggregation based Bi-directional Mutual learning Network (ABM) which consists of one shared encoder and two parallel inverse decoders (L2R and R2L). The two decoders are enhanced via mutual distillation, which involves one-to-one knowledge transfer at each training step, making full use of the complementary information from two inverse directions. Moreover, in order to deal with mathematical symbols in diverse scales, an Attention Aggregation Module (AAM) is proposed to effectively integrate multi-scale coverage attentions. Notably, in the inference phase, given that the model already learns knowledge from two inverse directions, we only use the L2R branch for inference, keeping the original parameter size and inference speed. Extensive experiments demonstrate that our proposed approach achieves the recognition accuracy of 56.85 % on CROHME 2014, 52.92 % on CROHME 2016, and 53.96 % on CROHME 2019 without data augmentation and model ensembling, substantially outperforming the state-of-the-art methods. The source code is available in https://github.com/XH-B/ABM.
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Xiaohang Bian, Bo Qin, Xiaozhe Xin, Jianwu Li, Xuefeng Su, Yanfeng Wang
| null | null | 2,022 |
aaai
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Visual Consensus Modeling for Video-Text Retrieval
| null |
In this paper, we propose a novel method to mine the commonsense knowledge shared between the video and text modalities for video-text retrieval, namely visual consensus modeling. Different from the existing works, which learn the video and text representations and their complicated relationships solely based on the pairwise video-text data, we make the first attempt to model the visual consensus by mining the visual concepts from videos and exploiting their co-occurrence patterns within the video and text modalities with no reliance on any additional concept annotations. Specifically, we build a shareable and learnable graph as the visual consensus, where the nodes denoting the mined visual concepts and the edges connecting the nodes representing the co-occurrence relationships between the visual concepts. Extensive experimental results on the public benchmark datasets demonstrate that our proposed method, with the ability to effectively model the visual consensus, achieves state-of-the-art performances on the bidirectional video-text retrieval task. Our code is available at https://github.com/sqiangcao99/VCM.
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Shuqiang Cao, Bairui Wang, Wei Zhang, Lin Ma
| null | null | 2,022 |
aaai
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SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection
| null |
Although point-based networks are demonstrated to be accurate for 3D point cloud modeling, they are still falling behind their voxel-based competitors in 3D detection. We observe that the prevailing set abstraction design for down-sampling points may maintain too much unimportant background information that can affect feature learning for detecting objects. To tackle this issue, we propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA). Technically, we first add a binary segmentation module as the side output to help identify foreground points. Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling. In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection. Additionally, it is an easy-to-plug-in module and able to boost various point-based detectors, including single-stage and two-stage ones. Extensive experiments on the popular KITTI and nuScenes datasets validate the superiority of SASA, lifting point-based detection models to reach comparable performance to state-of-the-art voxel-based methods. Code is available at https://github.com/blakechen97/SASA.
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Chen Chen, Zhe Chen, Jing Zhang, Dacheng Tao
| null | null | 2,022 |
aaai
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Towards End-to-End Image Compression and Analysis with Transformers
| null |
We propose an end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application. Instead of placing an existing Transformer-based image classification model directly after an image codec, we aim to redesign the Vision Transformer (ViT) model to perform image classification from the compressed features and facilitate image compression with the long-term information from the Transformer. Specifically, we first replace the patchify stem (i.e., image splitting and embedding) of the ViT model with a lightweight image encoder modelled by a convolutional neural network. The compressed features generated by the image encoder are injected convolutional inductive bias and are fed to the Transformer for image classification bypassing image reconstruction. Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction. The aggregated features can obtain the long-term information from the self-attention mechanism of the Transformer and improve the compression performance. The rate-distortion-accuracy optimization problem is finally solved by a two-step training strategy. Experimental results demonstrate the effectiveness of the proposed model in both the image compression and the classification tasks.
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Yuanchao Bai, Xu Yang, Xianming Liu, Junjun Jiang, Yaowei Wang, Xiangyang Ji, Wen Gao
| null | null | 2,022 |
aaai
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Prior Gradient Mask Guided Pruning-Aware Fine-Tuning
| null |
We proposed a Prior Gradient Mask Guided Pruning-aware Fine-Tuning (PGMPF) framework to accelerate deep Convolutional Neural Networks (CNNs). In detail, the proposed PGMPF selectively suppresses the gradient of those ”unimportant” parameters via a prior gradient mask generated by the pruning criterion during fine-tuning. PGMPF has three charming characteristics over previous works: (1) Pruning-aware network fine-tuning. A typical pruning pipeline consists of training, pruning and fine-tuning, which are relatively independent, while PGMPF utilizes a variant of the pruning mask as a prior gradient mask to guide fine-tuning, without complicated pruning criteria. (2) An excellent tradeoff between large model capacity during fine-tuning and stable convergence speed to obtain the final compact model. Previous works preserve more training information of pruned parameters during fine-tuning to pursue better performance, which would incur catastrophic non-convergence of the pruned model for relatively large pruning rates, while our PGMPF greatly stabilizes the fine-tuning phase by gradually constraining the learning rate of those ”unimportant” parameters. (3) Channel-wise random dropout of the prior gradient mask to impose some gradient noise to fine-tuning to further improve the robustness of final compact model. Experimental results on three image classification benchmarks CIFAR10/ 100 and ILSVRC-2012 demonstrate the effectiveness of our method for various CNN architectures, datasets and pruning rates. Notably, on ILSVRC-2012, PGMPF reduces 53.5% FLOPs on ResNet-50 with only 0.90% top-1 accuracy drop and 0.52% top-5 accuracy drop, which has advanced the state-of-the-art with negligible extra computational cost.
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Linhang Cai, Zhulin An, Chuanguang Yang, Yangchun Yan, Yongjun Xu
| null | null | 2,022 |
aaai
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Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation
| null |
Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks. However, current SGG methods usually suffer from sub-optimal scene graph generation because of the long-tailed distribution of training data. To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation. Specifically, RTPB uses a distributed-based prior bias to improve models' detecting ability on less frequent relationships during training, thus improving the model generalizability on tail categories. In addition, to further explore the contextual information of objects and relationships, we design a contextual encoding backbone network, termed as Dual Transformer (DTrans). We perform extensive experiments on a very popular benchmark, VG150, to demonstrate the effectiveness of our method for the unbiased scene graph generation. In specific, our RTPB achieves an improvement of over 10% under the mean recall when applied to current SGG methods. Furthermore, DTrans with RTPB outperforms nearly all state-of-the-art methods with a large margin. Code is available at https://github.com/ChCh1999/RTPB
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Chao Chen, Yibing Zhan, Baosheng Yu, Liu Liu, Yong Luo, Bo Du
| null | null | 2,022 |
aaai
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LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
| null |
Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result in highlighting the most discriminative part of objects while ignoring the entire object extent. Recently, the transformer architecture has been deployed to WSOL to capture the long-range feature dependencies with self-attention mechanism and multilayer perceptron structure. Nevertheless, transformers lack the locality inductive bias inherent to CNNs and therefore may deteriorate local feature details in WSOL. In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies. To this end, we propose a relational patch-attention module (RPAM), which considers cross-patch information on a global basis. We further design a cue digging module (CDM), which utilizes local features to guide the learning trend of the model for highlighting the weak local responses. Finally, comprehensive experiments are carried out on two widely used datasets, ie, CUB-200-2011 and ILSVRC, to verify the effectiveness of our method.
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Zhiwei Chen, Changan Wang, Yabiao Wang, Guannan Jiang, Yunhang Shen, Ying Tai, Chengjie Wang, Wei Zhang, Liujuan Cao
| null | null | 2,022 |
aaai
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A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications
| null |
This paper starts by revealing a surprising finding: without any learning, a randomly initialized CNN can localize objects surprisingly well. That is, a CNN has an inductive bias to naturally focus on objects, named as Tobias ("The object is at sight") in this paper. This empirical inductive bias is further analyzed and successfully applied to self-supervised learning (SSL). A CNN is encouraged to learn representations that focus on the foreground object, by transforming every image into various versions with different backgrounds, where the foreground and background separation is guided by Tobias. Experimental results show that the proposed Tobias significantly improves downstream tasks, especially for object detection. This paper also shows that Tobias has consistent improvements on training sets of different sizes, and is more resilient to changes in image augmentations.
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Yun-Hao Cao, Jianxin Wu
| null | null | 2,022 |
aaai
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Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video
| null |
We present Neural Marionette, an unsupervised approach that discovers the skeletal structure from a dynamic sequence and learns to generate diverse motions that are consistent with the observed motion dynamics. Given a video stream of point cloud observation of an articulated body under arbitrary motion, our approach discovers the unknown low-dimensional skeletal relationship that can effectively represent the movement. Then the discovered structure is utilized to encode the motion priors of dynamic sequences in a latent structure, which can be decoded to the relative joint rotations to represent the full skeletal motion. Our approach works without any prior knowledge of the underlying motion or skeletal structure, and we demonstrate that the discovered structure is even comparable to the hand-labeled ground truth skeleton in representing a 4D sequence of motion. The skeletal structure embeds the general semantics of possible motion space that can generate motions for diverse scenarios. We verify that the learned motion prior is generalizable to the multi-modal sequence generation, interpolation of two poses, and motion retargeting to a different skeletal structure.
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Jinseok Bae, Hojun Jang, Cheol-Hui Min, Hyungun Choi, Young Min Kim
| null | null | 2,022 |
aaai
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Comprehensive Regularization in a Bi-directional Predictive Network for Video Anomaly Detection
| null |
Video anomaly detection aims to automatically identify unusual objects or behaviours by learning from normal videos. Previous methods tend to use simplistic reconstruction or prediction constraints, which leads to the insufficiency of learned representations for normal data. As such, we propose a novel bi-directional architecture with three consistency constraints to comprehensively regularize the prediction task from pixel-wise, cross-modal, and temporal-sequence levels. First, predictive consistency is proposed to consider the symmetry property of motion and appearance in forwards and backwards time, which ensures the highly realistic appearance and motion predictions at the pixel-wise level. Second, association consistency considers the relevance between different modalities and uses one modality to regularize the prediction of another one. Finally, temporal consistency utilizes the relationship of the video sequence and ensures that the predictive network generates temporally consistent frames. During inference, the pattern of abnormal frames is unpredictable and will therefore cause higher prediction errors. Experiments show that our method outperforms advanced anomaly detectors and achieves state-of-the-art results on UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets.
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Chengwei Chen, Yuan Xie, Shaohui Lin, Angela Yao, Guannan Jiang, Wei Zhang, Yanyun Qu, Ruizhi Qiao, Bo Ren, Lizhuang Ma
| null | null | 2,022 |
aaai
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Deformable Part Region Learning for Object Detection
| null |
In a convolutional object detector, the detection accuracy can be degraded often due to the low feature discriminability caused by geometric variation or transformation of an object. In this paper, we propose a deformable part region learning in order to allow decomposed part regions to be deformable according to geometric transformation of an object. To this end, we introduce trainable geometric parameters for the location of each part model. Because the ground truth of the part models is not available, we design classification and mask losses for part models, and learn the geometric parameters by minimizing an integral loss including those part losses. As a result, we can train a deformable part region network without extra super-vision and make each part model deformable according to object scale variation. Furthermore, for improving cascade object detection and instance segmentation, we present a Cascade deformable part region architecture which can refine whole and part detections iteratively in the cascade manner. Without bells and whistles, our implementation of a Cascade deformable part region detector achieves better detection and segmentation mAPs on COCO and VOC datasets, compared to the recent cascade and other state-of-the-art detectors.
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Seung-Hwan Bae
| null | null | 2,022 |
aaai
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DCAN: Improving Temporal Action Detection via Dual Context Aggregation
| null |
Temporal action detection aims to locate the boundaries of action in the video. The current method based on boundary matching enumerates and calculates all possible boundary matchings to generate proposals. However, these methods neglect the long-range context aggregation in boundary prediction. At the same time, due to the similar semantics of adjacent matchings, local semantic aggregation of densely-generated matchings cannot improve semantic richness and discrimination. In this paper, we propose the end-to-end proposal generation method named Dual Context Aggregation Network (DCAN) to aggregate context on two levels, namely, boundary level and proposal level, for generating high-quality action proposals, thereby improving the performance of temporal action detection. Specifically, we design the Multi-Path Temporal Context Aggregation (MTCA) to achieve smooth context aggregation on boundary level and precise evaluation of boundaries. For matching evaluation, Coarse-to-fine Matching (CFM) is designed to aggregate context on the proposal level and refine the matching map from coarse to fine. We conduct extensive experiments on ActivityNet v1.3 and THUMOS-14. DCAN obtains an average mAP of 35.39% on ActivityNet v1.3 and reaches mAP 54.14% at [email protected] on THUMOS-14, which demonstrates DCAN can generate high-quality proposals and achieve state-of-the-art performance. We release the code at https://github.com/cg1177/DCAN.
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Guo Chen, Yin-Dong Zheng, Limin Wang, Tong Lu
| null | null | 2,022 |
aaai
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Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg
| null |
Despite the advances in multi-person pose estimation, state-of-the-art techniques only deliver the human pose structure.Yet, they do not leverage the keypoints of human pose to deliver whole-body shape information for human instance segmentation. This paper presents PosePlusSeg, a joint model designed for both human pose estimation and instance segmentation. For pose estimation, PosePlusSeg first takes a bottom-up approach to detect the soft and hard keypoints of individuals by producing a strong keypoint heat map, then improves the keypoint detection confidence score by producing a body heat map. For instance segmentation, PosePlusSeg generates a mask offset where keypoint is defined as a centroid for the pixels in the embedding space, enabling instance-level segmentation for the human class. Finally, we propose a new pose and instance segmentation algorithm that enables PosePlusSeg to determine the joint structure of the human pose and instance segmentation. Experiments using the COCO challenging dataset demonstrate that PosePlusSeg copes better with challenging scenarios, like occlusions, en-tangled limbs, and overlapped people. PosePlusSeg outperforms state-of-the-art detection-based approaches achieving a 0.728 mAP for human pose estimation and a 0.445 mAP for instance segmentation. Code has been made available at: https://github.com/RaiseLab/PosePlusSeg.
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Niaz Ahmad, Jawad Khan, Jeremy Yuhyun Kim, Youngmoon Lee
| null | null | 2,022 |
aaai
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Logic Rule Guided Attribution with Dynamic Ablation
| null |
With the increasing demands for understanding the internal behaviors of deep networks, Explainable AI (XAI) has been made remarkable progress in interpreting the model's decision. A family of attribution techniques has been proposed, highlighting whether the input pixels are responsible for the model's prediction. However, the existing attribution methods suffer from the lack of rule guidance and require further human interpretations. In this paper, we construct the 'if-then' logic rules that are sufficiently precise locally. Moreover, a novel rule-guided method, dynamic ablation (DA), is proposed to find a minimal bound sufficient in an input image to justify the network's prediction and aggregate iteratively to reach a complete attribution. Both qualitative and quantitative experiments are conducted to evaluate the proposed DA. We demonstrate the advantages of our method in providing clear and explicit explanations that are also easy for human experts to understand. Besides, through the attribution on a series of trained networks with different architectures, we show that more complex networks require less information to make a specific prediction.
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Jianqiao An, Yuandu Lai, Yahong Han
| null | null | 2,022 |
aaai
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Keypoint Message Passing for Video-Based Person Re-identification
| null |
Video-based person re-identification~(re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras. Existing methods are mostly based on convolutional neural networks~(CNNs), whose building blocks either process local neighbor pixels at a time, or, when 3D convolutions are used to model temporal information, suffer from the misalignment problem caused by person movement. In this paper, we propose to overcome the limitations of normal convolutions with a human-oriented graph method. Specifically, features located at person joint keypoints are extracted and connected as a spatial-temporal graph. These keypoint features are then updated by message passing from their connected nodes with a graph convolutional network~(GCN). During training, the GCN can be attached to any CNN-based person re-ID model to assist representation learning on feature maps, whilst it can be dropped after training for better inference speed. Our method brings significant improvements over the CNN-based baseline model on the MARS dataset with generated person keypoints and a newly annotated dataset: PoseTrackReID. It also defines a new state-of-the-art method in terms of top-1 accuracy and mean average precision in comparison to prior works.
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Di Chen, Andreas Doering, Shanshan Zhang, Jian Yang, Juergen Gall, Bernt Schiele
| null | null | 2,022 |
aaai
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ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
| null |
Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Because of the limited information in low-quality images, detecting low-quality deepfake remains an important challenge. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capability in KD to enable a student network to learn discriminative features from low-quality images effectively. In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher’s and student’s tensors under different views to transfer the teacher tensor’s distribution to the student more efficiently. Our extensive experimental results demonstrate that our approach outperforms state-of-the-art baselines in detecting low-quality compressed deepfake images.
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Le Minh Binh, Simon Woo
| null | null | 2,022 |
aaai
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Proximal PanNet: A Model-Based Deep Network for Pansharpening
| null |
Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.
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Xiangyong Cao, Yang Chen, Wenfei Cao
| null | null | 2,022 |
aaai
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Explore Inter-contrast between Videos via Composition for Weakly Supervised Temporal Sentence Grounding
| null |
Weakly supervised temporal sentence grounding aims to temporally localize the target segment corresponding to a given natural language query, where it provides video-query pairs without temporal annotations during training. Most existing methods use the fused visual-linguistic feature to reconstruct the query, where the least reconstruction error determines the target segment. This work introduces a novel approach that explores the inter-contrast between videos in a composed video by selecting components from two different videos and fusing them into a single video. Such a straightforward yet effective composition strategy provides the temporal annotations at multiple composed positions, resulting in numerous videos with temporal ground-truths for training the temporal sentence grounding task. A transformer framework is introduced with multi-tasks training to learn a compact but efficient visual-linguistic space. The experimental results on the public Charades-STA and ActivityNet-Caption dataset demonstrate the effectiveness of the proposed method, where our approach achieves comparable performance over the state-of-the-art weakly-supervised baselines. The code is available at https://github.com/PPjmchen/Composition_WSTG.
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Jiaming Chen, Weixin Luo, Wei Zhang, Lin Ma
| null | null | 2,022 |
aaai
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OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression
| null |
Recently, deep learning has been proven to be a promising approach in standard dynamic range (SDR) image compression. However, due to the wide luminance distribution of high dynamic range (HDR) images and the lack of large standard datasets, developing a deep model for HDR image compression is much more challenging. To tackle this issue, we view HDR data as distributional shifts of SDR data and the HDR image compression can be modeled as an out-of-distribution generalization (OoD) problem. Herein, we propose a novel out-of-distribution (OoD) HDR image compression framework (OoDHDR-codec). It learns the general representation across HDR and SDR environments, and allows the model to be trained effectively using a large set of SDR datases supplemented with much fewer HDR samples. Specifically, OoDHDR-codec consists of two branches to process the data from two environments. The SDR branch is a standard blackbox network. For the HDR branch, we develop a hybrid system that models luminance masking and tone mapping with white-box modules and performs content compression with black-box neural networks. To improve the generalization from SDR training data on HDR data, we introduce an invariance regularization term to learn the common representation for both SDR and HDR compression. Extensive experimental results show that the OoDHDR codec achieves strong competitive in-distribution performance and state-of-the-art OoD performance. To the best of our knowledge, our proposed approach is the first work to model HDR compression as OoD generalization problems and our OoD generalization algorithmic framework can be applied to any deep compression model in addition to the network architectural choice demonstrated in the paper. Code available at https://github.com/caolinfeng/OoDHDR-codec.
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Linfeng Cao, Aofan Jiang, Wei Li, Huaying Wu, Nanyang Ye
| null | null | 2,022 |
aaai
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Geometry-Contrastive Transformer for Generalized 3D Pose Transfer
| null |
We present a customized 3D mesh Transformer model for the pose transfer task. As the 3D pose transfer essentially is a deformation procedure dependent on the given meshes, the intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. Specifically, we propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies across the given meshes. Moreover, locally, a simple yet efficient central geodesic contrastive loss is further proposed to improve the regional geometric-inconsistency learning. At last, we present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task towards unknown spaces. The massive experimental results prove the efficacy of our approach by showing state-of-the-art quantitative performances on SMPL-NPT, FAUST and our new proposed dataset SMG-3D datasets, as well as promising qualitative results on MG-cloth and SMAL datasets. It's demonstrated that our method can achieve robust 3D pose transfer and be generalized to challenging meshes from unknown spaces on cross-dataset tasks. The code and dataset are made available. Code is available: https://github.com/mikecheninoulu/CGT.
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Haoyu Chen, Hao Tang, Zitong Yu, Nicu Sebe, Guoying Zhao
| null | null | 2,022 |
aaai
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Adaptive Image-to-Video Scene Graph Generation via Knowledge Reasoning and Adversarial Learning
| null |
Scene graph in a video conveys a wealth of information about objects and their relationships in the scene, thus benefiting many downstream tasks such as video captioning and visual question answering. Existing methods of scene graph generation require large-scale training videos annotated with objects and relationships in each frame to learn a powerful model. However, such comprehensive annotation is time-consuming and labor-intensive. On the other hand, it is much easier and less cost to annotate images with scene graphs, so we investigate leveraging annotated images to facilitate training a scene graph generation model for unannotated videos, namely image-to-video scene graph generation. This task presents two challenges: 1) infer unseen dynamic relationships in videos from static relationships in images due to the absence of motion information in images; 2) adapt objects and static relationships from images to video frames due to the domain shift between them. To address the first challenge, we exploit external commonsense knowledge to infer the unseen dynamic relationship from the temporal evolution of static relationships. We tackle the second challenge by hierarchical adversarial learning to reduce the data distribution discrepancy between images and video frames. Extensive experiment results on two benchmark video datasets demonstrate the effectiveness of our method.
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Jin Chen, Xiaofeng Ji, Xinxiao Wu
| null | null | 2,022 |
aaai
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Fully Attentional Network for Semantic Segmentation
| null |
Recent non-local self-attention methods have proven to be effective in capturing long-range dependencies for semantic segmentation. These methods usually form a similarity map of R^(CxC) (by compressing spatial dimensions) or R^(HWxHW) (by compressing channels) to describe the feature relations along either channel or spatial dimensions, where C is the number of channels, H and W are the spatial dimensions of the input feature map. However, such practices tend to condense feature dependencies along the other dimensions, hence causing attention missing, which might lead to inferior results for small/thin categories or inconsistent segmentation inside large objects. To address this problem, we propose a new approach, namely Fully Attentional Network (FLANet), to encode both spatial and channel attentions in a single similarity map while maintaining high computational efficiency. Specifically, for each channel map, our FLANet can harvest feature responses from all other channel maps, and the associated spatial positions as well, through a novel fully attentional module. Our new method has achieved state-of-the-art performance on three challenging semantic segmentation datasets, i.e., 83.6%, 46.99%, and 88.5% on the Cityscapes test set, the ADE20K validation set, and the PASCAL VOC test set, respectively.
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Qi Song, Jie Li, Chenghong Li, Hao Guo, Rui Huang
| null | null | 2,022 |
aaai
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Self-Supervised Object Localization with Joint Graph Partition
| null |
Object localization aims to generate a tight bounding box for the target object, which is a challenging problem that has been deeply studied in recent years. Since collecting bounding-box labels is time-consuming and laborious, many researchers focus on weakly supervised object localization (WSOL). As the recent appealing self-supervised learning technique shows its powerful function in visual tasks, in this paper, we take the early attempt to explore unsupervised object localization by self-supervision. Specifically, we adopt different geometric transformations to image and utilize their parameters as pseudo labels for self-supervised learning. Then, the class-agnostic activation map (CAAM) is used to highlight the target object potential regions. However, such attention maps merely focus on the most discriminative part of the objects, which will affect the quality of the predicted bounding box. Based on the motivation that the activation maps of different transformations of the same image should be equivariant, we further design a siamese network that encodes the paired images and propose a joint graph cluster partition mechanism in an unsupervised manner to enhance the object co-occurrent regions. To validate the effectiveness of the proposed method, extensive experiments are conducted on CUB-200-2011, Stanford Cars and FGVC-Aircraft datasets. Experimental results show that our method outperforms state-of-the-art methods using the same level of supervision, even outperforms some weakly-supervised methods.
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Yukun Su, Guosheng Lin, Yun Hao, Yiwen Cao, Wenjun Wang, Qingyao Wu
| null | null | 2,022 |
aaai
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JPV-Net: Joint Point-Voxel Representations for Accurate 3D Object Detection
| null |
Voxel and point representations are widely applied in recent 3D object detection tasks from LiDAR point clouds. Voxel representations contribute to efficiently and rapidly locating objects, whereas point representations are capable of describing intra-object spatial relationship for detection refinement. In this work, we aim to exploit the strengths of both two representations, and present a novel two-stage detector, named Joint Point-Voxel Network (JPV-Net). Specifically, our framework is equipped with a Dual Encoders-Fusion Decoder, which consists of the dual encoders to extract voxel features of sketchy 3D scenes and point features rich in geometric context, respectively, and the Feature Propagation Fusion (FP-Fusion) decoder to attentively fuse them from coarse to fine. By making use of the advantages of these features, the refinement network can effectively eliminate false detection and provide better accuracy. Besides, to further develop the perception characteristics of voxel CNN and point backbone, we design two novel intersection-over-union (IoU) estimation modules for proposal generation and refinement, both of which can alleviate the misalignment between the localization and the classification confidence. Extensive experiments on the KITTI dataset and ONCE dataset demonstrate that our proposed JPV-Net outperforms other state-of-the-art methods with remarkable margins.
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Nan Song, Tianyuan Jiang, Jian Yao
| null | null | 2,022 |
aaai
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Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?
| null |
Transformers have sprung up in the field of computer vision. In this work, we explore whether the core self-attention module in Transformer is the key to achieving excellent performance in image recognition. To this end, we build an attention-free network called sMLPNet based on the existing MLP-based vision models. Specifically, we replace the MLP module in the token-mixing step with a novel sparse MLP (sMLP) module. For 2D image tokens, sMLP applies 1D MLP along the axial directions and the parameters are shared among rows or columns. By sparse connection and weight sharing, sMLP module significantly reduces the number of model parameters and computational complexity, avoiding the common over-fitting problem that plagues the performance of MLP-like models. When only trained on the ImageNet-1K dataset, the proposed sMLPNet achieves 81.9% top-1 accuracy with only 24M parameters, which is much better than most CNNs and vision Transformers under the same model size constraint. When scaling up to 66M parameters, sMLPNet achieves 83.4% top-1 accuracy, which is on par with the state-of-the-art Swin Transformer. The success of sMLPNet suggests that the self-attention mechanism is not necessarily a silver bullet in computer vision. The code and models are publicly available at https://github.com/microsoft/SPACH.
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Chuanxin Tang, Yucheng Zhao, Guangting Wang, Chong Luo, Wenxuan Xie, Wenjun Zeng
| null | null | 2,022 |
aaai
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Self-Supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss
| null |
Recent advances in supervised deep learning methods are enabling remote measurements of photoplethysmography-based physiological signals using facial videos. The performance of these supervised methods, however, are dependent on the availability of large labelled data. Contrastive learning as a self-supervised method has recently achieved state-of-the-art performances in learning representative data features by maximising mutual information between different augmented views. However, existing data augmentation techniques for contrastive learning are not designed to learn physiological signals from videos and often fail when there are complicated noise and subtle and periodic colour/shape variations between video frames. To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data. Firstly, we propose a landmark-based spatial augmentation that splits the face into several informative parts based on the Shafer’s dichromatic reflection model to characterise subtle skin colour fluctuations. We also formulate a sparsity-based temporal augmentation exploiting Nyquist–Shannon sampling theorem to effectively capture periodic temporal changes by modelling physiological signal features. Furthermore, we introduce a constrained spatiotemporal loss which generates pseudo-labels for augmented video clips. It is used to regulate the training process and handle complicated noise. We evaluated our framework on 3 public datasets and demonstrated superior performances than other self-supervised methods and achieved competitive accuracy compared to the state-of-the-art supervised methods. Code is available at https://github.com/Dylan-H-Wang/SLF-RPM.
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Hao Wang, Euijoon Ahn, Jinman Kim
| null | null | 2,022 |
aaai
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Correlation Field for Boosting 3D Object Detection in Structured Scenes
| null |
Data augmentation is an efficient way to elevate 3D object detection performance. In this paper, we propose a simple but effective online crop-and-paste data augmentation pipeline for structured 3D point cloud scenes, named CorrelaBoost. Observing that 3D objects should have reasonable relative positions in a structured scene because of the objects' functionalities and natural relationships, we express this correlation as a kind of interactive force. An energy field called Correlation Field can be calculated correspondingly across the whole 3D space. According to the Correlation Field, we propose two data augmentation strategies to explore highly congruent positions that a designated object may be pasted to: 1) Category Consistent Exchanging and 2) Energy Optimized Transformation. We conduct exhaustive experiments on various popular benchmarks with different detection frameworks and the results illustrate that our method brings huge free-lunch improvement and significantly outperforms state-of-the-art approaches in terms of data augmentation. It is worth noting that the performance of VoteNet with [email protected] is improved by 7.7 on ScanNetV2 dataset and 5.0 on SUN RGB-D dataset. Our method is simple to implement and increases few computational overhead.
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Jianhua Sun, Hao-Shu Fang, Xianghui Zhu, Jiefeng Li, Cewu Lu
| null | null | 2,022 |
aaai
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When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism
| null |
Attention mechanism has been widely believed as the key to success of vision transformers (ViTs), since it provides a flexible and powerful way to model spatial relationships. However, is the attention mechanism truly an indispensable part of ViT? Can it be replaced by some other alternatives? To demystify the role of attention mechanism, we simplify it into an extremely simple case: ZERO FLOP and ZERO parameter. Concretely, we revisit the shift operation. It does not contain any parameter or arithmetic calculation. The only operation is to exchange a small portion of the channels between neighboring features. Based on this simple operation, we construct a new backbone network, namely ShiftViT, where the attention layers in ViT are substituted by shift operations. Surprisingly, ShiftViT works quite well in several mainstream tasks, e.g., classification, detection, and segmentation. The performance is on par with or even better than the strong baseline Swin Transformer. These results suggest that the attention mechanism might not be the vital factor that makes ViT successful. It can be even replaced by a zero-parameter operation. We should pay more attentions to the remaining parts of ViT in the future work. Code is available at github.com/microsoft/SPACH.
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Guangting Wang, Yucheng Zhao, Chuanxin Tang, Chong Luo, Wenjun Zeng
| null | null | 2,022 |
aaai
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Iterative Contrast-Classify for Semi-supervised Temporal Action Segmentation
| null |
Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method for temporal action segmentation. Our method hinges on unsupervised representation learning, which, for temporal action segmentation, poses unique challenges. Actions in untrimmed videos vary in length and have unknown labels and start/end times. Ordering of actions across videos may also vary. We propose a novel way to learn frame-wise representations from temporal convolutional networks (TCNs) by clustering input features with added time-proximity conditions and multi-resolution similarity. By merging representation learning with conventional supervised learning, we develop an "Iterative Contrast-Classify (ICC)'' semi-supervised learning scheme. With more labelled data, ICC progressively improves in performance; ICC semi-supervised learning, with 40% labelled videos, performs similarly to fully-supervised counterparts. Our ICC improves MoF by {+1.8, +5.6, +2.5}% on Breakfast, 50Salads, and GTEA respectively for 100% labelled videos.
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Dipika Singhania, Rahul Rahaman, Angela Yao
| null | null | 2,022 |
aaai
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Dual Contrastive Learning for General Face Forgery Detection
| null |
With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns. Previous works always formulate face forgery detection as a classification problem based on cross-entropy loss, which emphasizes category-level differences rather than the essential discrepancies between real and fake faces, limiting model generalization in unseen domains. To address this issue, we propose a novel face forgery detection framework, named Dual Contrastive Learning (DCL), which specially constructs positive and negative paired data and performs designed contrastive learning at different granularities to learn generalized feature representation. Concretely, combined with the hard sample selection strategy, Inter-Instance Contrastive Learning (Inter-ICL) is first proposed to promote task-related discriminative features learning by especially constructing instance pairs. Moreover, to further explore the essential discrepancies, Intra-Instance Contrastive Learning (Intra-ICL) is introduced to focus on the local content inconsistencies prevalent in the forged faces by constructing local region pairs inside instances. Extensive experiments and visualizations on several datasets demonstrate the generalization of our method against the state-of-the-art competitors. Our Code is available at https://github.com/Tencent/TFace.git.
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Ke Sun, Taiping Yao, Shen Chen, Shouhong Ding, Jilin Li, Rongrong Ji
| null | null | 2,022 |
aaai
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Transfer Learning for Color Constancy via Statistic Perspective
| null |
Color Constancy aims to correct image color casts caused by scene illumination. Recently, although the deep learning approaches have remarkably improved on single-camera data, these models still suffer from the seriously insufficient data problem, resulting in shallow model capacity and degradation in multi-camera settings. In this paper, to alleviate this problem, we present a Transfer Learning Color Constancy (TLCC) method that leverages cross-camera RAW data and massive unlabeled sRGB data to support training. Specifically, TLCC consists of the Statistic Estimation Scheme (SE-Scheme) and Color-Guided Adaption Branch (CGA-Branch). SE-Scheme builds a statistic perspective to map the camera-related illumination labels into camera-agnostic form and produce pseudo labels for sRGB data, which greatly expands data for joint training. Then, CGA-Branch further promotes efficient transfer learning from sRGB to RAW data by extracting color information to regularize the backbone's features adaptively. Experimental results show the TLCC has overcome the data limitation and model degradation, outperforming the state-of-the-art performance on popular benchmarks. Moreover, the experiments also prove the TLCC is capable of learning new scenes information from sRGB data to improve accuracy on the RAW images with similar scenes.
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Yuxiang Tang, Xuejing Kang, Chunxiao Li, Zhaowen Lin, Anlong Ming
| null | null | 2,022 |
aaai
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SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal
| null |
Makeup transfer is not only to extract the makeup style of the reference image, but also to render the makeup style to the semantic corresponding position of the target image. However, most existing methods focus on the former and ignore the latter, resulting in a failure to achieve desired results. To solve the above problems, we propose a unified Symmetric Semantic-Aware Transformer (SSAT) network, which incorporates semantic correspondence learning to realize makeup transfer and removal simultaneously. In SSAT, a novel Symmetric Semantic Corresponding Feature Transfer (SSCFT) module and a weakly supervised semantic loss are proposed to model and facilitate the establishment of accurate semantic correspondence. In the generation process, the extracted makeup features are spatially distorted by SSCFT to achieve semantic alignment with the target image, then the distorted makeup features are combined with unmodified makeup irrelevant features to produce the final result. Experiments show that our method obtains more visually accurate makeup transfer results, and user study in comparison with other state-of-the-art makeup transfer methods reflects the superiority of our method. Besides, we verify the robustness of the proposed method in the difference of expression and pose, object occlusion scenes, and extend it to video makeup transfer.
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Zhaoyang Sun, Yaxiong Chen, Shengwu Xiong
| null | null | 2,022 |
aaai
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Self-Supervised Category-Level 6D Object Pose Estimation with Deep Implicit Shape Representation
| null |
Category-level 6D pose estimation can be better generalized to unseen objects in a category compared with instance-level 6D pose estimation. However, existing category-level 6D pose estimation methods usually require supervised training with a sufficient number of 6D pose annotations of objects which makes them difficult to be applied in real scenarios. To address this problem, we propose a self-supervised framework for category-level 6D pose estimation in this paper. We leverage DeepSDF as a 3D object representation and design several novel loss functions based on DeepSDF to help the self-supervised model predict unseen object poses without any 6D object pose labels and explicit 3D models in real scenarios. Experiments demonstrate that our method achieves comparable performance with the state-of-the-art fully supervised methods on the category-level NOCS benchmark.
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Wanli Peng, Jianhang Yan, Hongtao Wen, Yi Sun
| null | null | 2,022 |
aaai
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MTLDesc: Looking Wider to Describe Better
| null |
Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of global and surrounding spatial context. In this work, we focus on making local descriptors ``look wider to describe better'' by learning local Descriptors with More Than Local information (MTLDesc). Specifically, we resort to context augmentation and spatial attention mechanism to make the descriptors obtain non-local awareness. First, Adaptive Global Context Augmented Module and Diverse Local Context Augmented Module are proposed to construct robust local descriptors with context information from global to local. Second, we propose the Consistent Attention Weighted Triplet Loss to leverage spatial attention awareness in both optimization and matching of local descriptors. Third, Local Features Detection with Feature Pyramid is proposed to obtain more stable and accurate keypoints localization. With the above innovations, the performance of the proposed MTLDesc significantly surpasses the current state-of-the-art local descriptors on HPatches, Aachen Day-Night localization and InLoc indoor localization benchmarks. Our code is available at https://github.com/vignywang/MTLDesc.
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Changwei Wang, Rongtao Xu, Yuyang Zhang, Shibiao Xu, Weiliang Meng, Bin Fan, Xiaopeng Zhang
| null | null | 2,022 |
aaai
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TVT: Three-Way Vision Transformer through Multi-Modal Hypersphere Learning for Zero-Shot Sketch-Based Image Retrieval
| null |
In this paper, we study the zero-shot sketch-based image retrieval (ZS-SBIR) task, which retrieves natural images related to sketch queries from unseen categories. In the literature, convolutional neural networks (CNNs) have become the de-facto standard and they are either trained end-to-end or used to extract pre-trained features for images and sketches. However, CNNs are limited in modeling the global structural information of objects due to the intrinsic locality of convolution operations. To this end, we propose a Transformer-based approach called Three-Way Vision Transformer (TVT) to leverage the ability of Vision Transformer (ViT) to model global contexts due to the global self-attention mechanism. Going beyond simply applying ViT to this task, we propose a token-based strategy of adding fusion and distillation tokens and making them complementary to each other. Specifically, we integrate three ViTs, which are pre-trained on data of each modality, into a three-way pipeline through the processes of distillation and multi-modal hypersphere learning. The distillation process is proposed to supervise fusion ViT (ViT with an extra fusion token) with soft targets from modality-specific ViTs, which prevents fusion ViT from catastrophic forgetting. Furthermore, our method learns a multi-modal hypersphere by performing inter- and intra-modal alignment without loss of uniformity, which aims to bridge the modal gap between modalities of sketch and image and avoid the collapse in dimensions. Extensive experiments on three benchmark datasets, i.e., Sketchy, TU-Berlin, and QuickDraw, demonstrate the superiority of our TVT method over the state-of-the-art ZS-SBIR methods.
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Jialin Tian, Xing Xu, Fumin Shen, Yang Yang, Heng Tao Shen
| null | null | 2,022 |
aaai
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GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference
| null |
Semi-supervised learning is a challenging problem which aims to construct a model by learning from limited labeled examples. Numerous methods for this task focus on utilizing the predictions of unlabeled instances consistency alone to regularize networks. However, treating labeled and unlabeled data separately often leads to the discarding of mass prior knowledge learned from the labeled examples. In this paper, we propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net, by leveraging labeled information to guide the learning of unlabeled instances. Specifically, GuidedMix-Net employs three operations: 1) interpolation of similar labeled-unlabeled image pairs; 2) transfer of mutual information; 3) generalization of pseudo masks. It enables segmentation models can learning the higher-quality pseudo masks of unlabeled data by transfer the knowledge from labeled samples to unlabeled data. Along with supervised learning for labeled data, the prediction of unlabeled data is jointly learned with the generated pseudo masks from the mixed data. Extensive experiments on PASCAL VOC 2012, and Cityscapes demonstrate the effectiveness of our GuidedMix-Net, which achieves competitive segmentation accuracy and significantly improves the mIoU over 7$%$ compared to previous approaches.
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Peng Tu, Yawen Huang, Feng Zheng, Zhenyu He, Liujuan Cao, Ling Shao
| null | null | 2,022 |
aaai
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Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspective
| null |
We revisit Semantic Scene Completion (SSC), a useful task to predict the semantic and occupancy representation of 3D scenes, in this paper. A number of methods for this task are always based on voxelized scene representations. Although voxel representations keep local structures of the scene, these methods suffer from heavy computation redundancy due to the existence of visible empty voxels when the network goes deeper. To address this dilemma, we propose our novel point-voxel aggregation network for this task. We first transfer the voxelized scenes to point clouds by removing these visible empty voxels and adopt a deep point stream to capture semantic information from the scene efficiently. Meanwhile, a light-weight voxel stream containing only two 3D convolution layers preserves local structures of the voxelized scenes. Furthermore, we design an anisotropic voxel aggregation operator to fuse the structure details from the voxel stream into the point stream, and a semantic-aware propagation module to enhance the up-sampling process in the point stream by semantic labels. We demonstrate that our model surpasses state-of-the-arts on two benchmarks by a large margin, with only the depth images as input.
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Jiaxiang Tang, Xiaokang Chen, Jingbo Wang, Gang Zeng
| null | null | 2,022 |
aaai
|
FCA: Learning a 3D Full-Coverage Vehicle Camouflage for Multi-View Physical Adversarial Attack
| null |
Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an individual adversarial patch, which only covers the planar part of the vehicle’s surface and fails to attack the detector in physical scenarios for multi-view, long-distance and partially occluded objects. To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors. Specifically, we first try rendering the nonplanar camouflage texture over the full vehicle surface. To mimic the real-world environment conditions, we then introduce a transformation function to transfer the rendered camouflaged vehicle into a photo-realistic scenario. Finally, we design an efficient loss function to optimize the camouflage texture. Experiments show that the full-coverage camouflage attack can not only outperform state-of-the-art methods under various test cases but also generalize to different environments, vehicles, and object detectors.
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Donghua Wang, Tingsong Jiang, Jialiang Sun, Weien Zhou, Zhiqiang Gong, Xiaoya Zhang, Wen Yao, Xiaoqian Chen
| null | null | 2,022 |
aaai
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Boost Supervised Pretraining for Visual Transfer Learning: Implications of Self-Supervised Contrastive Representation Learning
| null |
Unsupervised pretraining based on contrastive learning has made significant progress recently and showed comparable or even superior transfer learning performance to traditional supervised pretraining on various tasks. In this work, we first empirically investigate when and why unsupervised pretraining surpasses supervised counterparts for image classification tasks with a series of control experiments. Besides the commonly used accuracy, we further analyze the results qualitatively with the class activation maps and assess the learned representations quantitatively with the representation entropy and uniformity. Our core finding is that it is the amount of information effectively perceived by the learning model that is crucial to transfer learning, instead of absolute size of the dataset. Based on this finding, we propose Classification Activation Map guided contrastive (CAMtrast) learning which better utilizes the label supervsion to strengthen supervised pretraining, by making the networks perceive more information from the training images. CAMtrast is evaluated with three fundamental visual learning tasks: image recognition, object detection, and semantic segmentation, on various public datasets. Experimental results show that our CAMtrast effectively improves the performance of supervised pretraining, and that its performance is superior to both unsupervised counterparts and a recent related work which similarly attempted improving supervised pretraining.
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Jinghan Sun, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng
| null | null | 2,022 |
aaai
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Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
| null |
Training the multi-label image recognition models with partial labels, in which merely some labels are known while others are unknown for each image, is a considerably challenging and practical task. To address this task, current algorithms mainly depend on pre-training classification or similarity models to generate pseudo labels for the unknown labels. However, these algorithms depend on sufficient multi-label annotations to train the models, leading to poor performance especially with low known label proportion. In this work, we propose to blend category-specific representation across different images to transfer information of known labels to complement unknown labels, which can get rid of pre-training models and thus does not depend on sufficient annotations. To this end, we design a unified semantic-aware representation blending (SARB) framework that exploits instance-level and prototype-level semantic representation to complement unknown labels by two complementary modules: 1) an instance-level representation blending (ILRB) module blends the representations of the known labels in an image to the representations of the unknown labels in another image to complement these unknown labels. 2) a prototype-level representation blending (PLRB) module learns more stable representation prototypes for each category and blends the representation of unknown labels with the prototypes of corresponding labels to complement these labels. Extensive experiments on the MS-COCO, Visual Genome, Pascal VOC 2007 datasets show that the proposed SARB framework obtains superior performance over current leading competitors on all known label proportion settings, i.e., with the mAP improvement of 4.6%, 4.6%, 2.2% on these three datasets when the known label proportion is 10%. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.
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Tao Pu, Tianshui Chen, Hefeng Wu, Liang Lin
| null | null | 2,022 |
aaai
|
Online-Updated High-Order Collaborative Networks for Single Image Deraining
| null |
Single image deraining is an important and challenging task for some downstream artificial intelligence applications such as video surveillance and self-driving systems. Most of the existing deep-learning-based methods constrain the network to generate derained images but few of them explore features from intermediate layers, different levels, and different modules which are beneficial for rain streaks removal. In this paper, we propose a high-order collaborative network with multi-scale compact constraints and a bidirectional scale-content similarity mining module to exploit features from deep networks externally and internally for rain streaks removal. Externally, we design a deraining framework with three sub-networks trained in a collaborative manner, where the bottom network transmits intermediate features to the middle network which also receives shallower rainy features from the top network and sends back features to the bottom network. Internally, we enforce multi-scale compact constraints on the intermediate layers of deep networks to learn useful features via a Laplacian pyramid. Further, we develop a bidirectional scale-content similarity mining module to explore features at different scales in a down-to-up and up-to-down manner. To improve the model performance on real-world images, we propose an online-update learning approach, which uses real-world rainy images to fine-tune the network and update the deraining results in a self-supervised manner. Extensive experiments demonstrate that our proposed method performs favorably against eleven state-of-the-art methods on five public synthetic datasets and one real-world dataset.
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Cong Wang, Jinshan Pan, Xiao-Ming Wu
| null | null | 2,022 |
aaai
|
Active Boundary Loss for Semantic Segmentation
| null |
This paper proposes a novel active boundary loss for semantic segmentation. It can progressively encourage the alignment between predicted boundaries and ground-truth boundaries during end-to-end training, which is not explicitly enforced in commonly used cross-entropy loss. Based on the predicted boundaries detected from the segmentation results using current network parameters, we formulate the boundary alignment problem as a differentiable direction vector prediction problem to guide the movement of predicted boundaries in each iteration. Our loss is model-agnostic and can be plugged in to the training of segmentation networks to improve the boundary details. Experimental results show that training with the active boundary loss can effectively improve the boundary F-score and mean Intersection-over-Union on challenging image and video object segmentation datasets.
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Chi Wang, Yunke Zhang, Miaomiao Cui, Peiran Ren, Yin Yang, Xuansong Xie, Xian-Sheng Hua, Hujun Bao, Weiwei Xu
| null | null | 2,022 |
aaai
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Beyond Learning Features: Training a Fully-Functional Classifier with ZERO Instance-Level Labels
| null |
We attempt to train deep neural networks for classification without using any labeled data. Existing unsupervised methods, though mine useful clusters or features, require some annotated samples to facilitate the final task-specific predictions. This defeats the true purpose of unsupervised learning and hence we envisage a paradigm of `true' self-supervision, where absolutely no annotated instances are used for training a classifier. The proposed method first pretrains a deep network through self-supervision and performs clustering on the learned features. A classifier layer is then appended to the self-supervised network and is trained by matching the distribution of the predictions to that of a predefined prior. This approach leverages the distribution of labels for supervisory signals and consequently, no image-label pair is needed. Experiments reveal that the method works on major nominal as well as ordinal classification datasets and delivers significant performance.
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Deepak Babu Sam, Abhinav Agarwalla, Venkatesh Babu Radhakrishnan
| null | null | 2,022 |
aaai
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ReX: An Efficient Approach to Reducing Memory Cost in Image Classification
| null |
Exiting simple samples in adaptive multi-exit networks through early modules is an effective way to achieve high computational efficiency. One can observe that deployments of multi-exit architectures on resource-constrained devices are easily limited by high memory footprint of early modules. In this paper, we propose a novel approach named recurrent aggregation operator (ReX), which uses recurrent neural networks (RNNs) to effectively aggregate intra-patch features within a large receptive field to get delicate local representations, while bypassing large early activations. The resulting model, named ReXNet, can be easily extended to dynamic inference by introducing a novel consistency-based early exit criteria, which is based on the consistency of classification decisions over several modules, rather than the entropy of the prediction distribution. Extensive experiments on two benchmark datasets, i.e., Visual Wake Words, ImageNet-1k, demonstrate that our method consistently reduces the peak RAM and average latency of a wide variety of adaptive models on low-power devices.
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Xuwei Qian, Renlong Hang, Qingshan Liu
| null | null | 2,022 |
aaai
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Reference-Guided Pseudo-Label Generation for Medical Semantic Segmentation
| null |
Producing densely annotated data is a difficult and tedious task for medical imaging applications. To address this problem, we propose a novel approach to generate supervision for semi-supervised semantic segmentation. We argue that visually similar regions between labeled and unlabeled images likely contain the same semantics and therefore should share their label. Following this thought, we use a small number of labeled images as reference material and match pixels in an unlabeled image to the semantic of the best fitting pixel in a reference set. This way, we avoid pitfalls such as confirmation bias, common in purely prediction-based pseudo-labeling. Since our method does not require any architectural changes or accompanying networks, one can easily insert it into existing frameworks. We achieve the same performance as a standard fully supervised model on X-ray anatomy segmentation, albeit using 95% fewer labeled images. Aside from an in-depth analysis of different aspects of our proposed method, we further demonstrate the effectiveness of our reference-guided learning paradigm by comparing our approach against existing methods for retinal fluid segmentation with competitive performance as we improve upon recent work by up to 15% mean IoU.
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Constantin Marc Seibold, Simon Reiß, Jens Kleesiek, Rainer Stiefelhagen
| null | null | 2,022 |
aaai
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Information-Theoretic Bias Reduction via Causal View of Spurious Correlation
| null |
We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation, which is effective to identify the feature-level algorithmic bias by taking advantage of conditional mutual information. Although several bias measurement methods have been proposed and widely investigated to achieve algorithmic fairness in various tasks such as face recognition, their accuracy- or logit-based metrics are susceptible to leading to trivial prediction score adjustment rather than fundamental bias reduction. Hence, we design a novel debiasing framework against the algorithmic bias, which incorporates a bias regularization loss derived by the proposed information-theoretic bias measurement approach. In addition, we present a simple yet effective unsupervised debiasing technique based on stochastic label noise, which does not require the explicit supervision of bias information. The proposed bias measurement and debiasing approaches are validated in diverse realistic scenarios through extensive experiments on multiple standard benchmarks.
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Seonguk Seo, Joon-Young Lee, Bohyung Han
| null | null | 2,022 |
aaai
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CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation
| null |
Acquiring the most representative examples via active learning (AL) can benefit many data-dependent computer vision tasks by minimizing efforts of image-level or pixel-wise annotations. In this paper, we propose a novel Collaborative Panoptic-Regional Active Learning framework (CPRAL) to address the semantic segmentation task. For a small batch of images initially sampled with pixel-wise annotations, we employ panoptic information to initially select unlabeled samples. Considering the class imbalance in the segmentation dataset, we import a Regional Gaussian Attention module (RGA) to achieve semantics-biased selection. The subset is highlighted by vote entropy and then attended by Gaussian kernels to maximize the biased regions. We also propose a Contextual Labels Extension (CLE) to boost regional annotations with contextual attention guidance. With the collaboration of semantics-agnostic panoptic matching and region-biased selection and extension, our CPRAL can strike a balance between labeling efforts and performance and compromise the semantics distribution. We perform extensive experiments on Cityscapes and BDD10K datasets and show that CPRAL outperforms the cutting-edge methods with impressive results and less labeling proportion.
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Yu Qiao, Jincheng Zhu, Chengjiang Long, Zeyao Zhang, Yuxin Wang, Zhenjun Du, Xin Yang
| null | null | 2,022 |
aaai
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Deep Implicit Statistical Shape Models for 3D Medical Image Delineation
| null |
3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models (SSMs) that imposed anatomical constraints and produced high quality surfaces were a core technology. Today’s fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach that marries the representation power of deep networks with the benefits of SSMs. DISSMs use an implicit representation to produce compact and descriptive deep surface embeddings that permit statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process (MDP). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than four leading FCN models, including nnU-Net + an adversarial prior: reducing the mean Hausdorff distance (HD) by 7.5-14.3 mm and improving the worst case Dice-Sørensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on an external and highly challenging clinical dataset demonstrate that DISSMs improve the mean DSC and HD by 2.1-5.9% and 9.9-24.5 mm, respectively, and the worst-case DSC by 5.4-7.3%. Supplemental validation on a highly challenging and low-contrast larynx dataset further demonstrate DISSM’s improvements. These improvements are over and above any benefits from representing delineations with high-quality surfaces.
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Ashwin Raju, Shun Miao, Dakai Jin, Le Lu, Junzhou Huang, Adam P. Harrison
| null | null | 2,022 |
aaai
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Learning from Label Proportions with Prototypical Contrastive Clustering
| null |
The use of priors to avoid manual labeling for training machine learning methods has received much attention in the last few years. One of the critical subthemes in this regard is Learning from Label Proportions (LLP), where only the information about class proportions is available for training the models. While various LLP training settings verse in the literature, most approaches focus on bag-level label proportions errors, often leading to suboptimal solutions. This paper proposes a new model that jointly uses prototypical contrastive learning and bag-level cluster proportions to implement efficient LLP classification. Our proposal explicitly relaxes the equipartition constraint commonly used in prototypical contrastive learning methods and incorporates the exact cluster proportions into the optimal transport algorithm used for cluster assignments. At inference time, we compute the clusters' assignment, delivering instance-level classification. We experimented with our method on two widely used image classification benchmarks and report a new state-of-art LLP performance, achieving results close to fully supervised methods.
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Laura Elena Cué La Rosa, Dário Augusto Borges Oliveira
| null | null | 2,022 |
aaai
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Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation
| null |
Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet challenging computer vision task facilitating scene understanding and automatic driving. Most existing methods resort to classification-based Class Activation Maps (CAMs) to play as the initial pseudo labels, which tend to focus on the discriminative image regions and lack customized characteristics for the segmentation task. To alleviate this issue, we propose a novel activation modulation and recalibration (AMR) scheme, which leverages a spotlight branch and a compensation branch to obtain weighted CAMs that can provide recalibration supervision and task-specific concepts. Specifically, an attention modulation module (AMM) is employed to rearrange the distribution of feature importance from the channel-spatial sequential perspective, which helps to explicitly model channel-wise interdependencies and spatial encodings to adaptively modulate segmentation-oriented activation responses. Furthermore, we introduce a cross pseudo supervision for dual branches, which can be regarded as a semantic similar regularization to mutually refine two branches. Extensive experiments show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label. Experiments also reveal that our scheme is plug-and-play and can be incorporated with other approaches to boost their performance. Our code is available at: https://github.com/jieqin-ai/AMR.
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Jie Qin, Jie Wu, Xuefeng Xiao, Lujun Li, Xingang Wang
| null | null | 2,022 |
aaai
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TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework Using Self-Supervised Multi-Task Learning
| null |
In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-supervised reconstruction tasks according to the characteristics of multi-exposure images and conduct these tasks simultaneously using multi-task learning; through this process, the network can learn the characteristics of multi-exposure images and extract more generalized features. In addition, to compensate for the defect in establishing long-range dependencies in CNN-based architectures, we design an encoder that combines a CNN module with a transformer module. This combination enables the network to focus on both local and global information. We evaluated our method and compared it to 11 competitive traditional and deep learning-based methods on the latest released multi-exposure image fusion benchmark dataset, and our method achieved the best performance in both subjective and objective evaluations. Code will be available at https://github.com/miccaiif/TransMEF.
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Linhao Qu, Shaolei Liu, Manning Wang, Zhijian Song
| null | null | 2,022 |
aaai
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Improving Scene Graph Classification by Exploiting Knowledge from Texts
| null |
Training scene graph classification models requires a large amount of annotated image data. Meanwhile, scene graphs represent relational knowledge that can be modeled with symbolic data from texts or knowledge graphs. While image annotation demands extensive labor, collecting textual descriptions of natural scenes requires less effort. In this work, we investigate whether textual scene descriptions can substitute for annotated image data. To this end, we employ a scene graph classification framework that is trained not only from annotated images but also from symbolic data. In our architecture, the symbolic entities are first mapped to their correspondent image-grounded representations and then fed into the relational reasoning pipeline. Even though a structured form of knowledge, such as the form in knowledge graphs, is not always available, we can generate it from unstructured texts using a transformer-based language model. We show that by fine-tuning the classification pipeline with the extracted knowledge from texts, we can achieve ~8x more accurate results in scene graph classification, ~3x in object classification, and ~1.5x in predicate classification, compared to the supervised baselines with only 1% of the annotated images.
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Sahand Sharifzadeh, Sina Moayed Baharlou, Martin Schmitt, Hinrich Schütze, Volker Tresp
| null | null | 2,022 |
aaai
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Explainable Survival Analysis with Convolution-Involved Vision Transformer
| null |
Image-based survival prediction models can facilitate doctors in diagnosing and treating cancer patients. With the advance of digital pathology technologies, the big whole slide images (WSIs) provide increasing resolution and more details for diagnosis. However, the gigabyte-size WSIs would make most models computationally infeasible. To this end, instead of using the complete WSIs, most of existing models only use a pre-selected subset of key patches or patch clusters as input, which might fail to completely capture the patient's tumor morphology. In this work, we aim to develop a novel survival analysis model to fully utilize the complete WSI information. We show that the use of a Vision Transformer (ViT) backbone, together with convolution operations involved in it, is an effective framework to improve the prediction performance. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features, making the model more faithful and the results easier to comprehend by human users. Evaluations on two large cancer datasets show that our proposed model is more effective and has better interpretability for survival prediction.
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Yifan Shen, Li Liu, Zhihao Tang, Zongyi Chen, Guixiang Ma, Jiyan Dong, Xi Zhang, Lin Yang, Qingfeng Zheng
| null | null | 2,022 |
aaai
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Social Interpretable Tree for Pedestrian Trajectory Prediction
| null |
Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications. In this paper, we propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task, where a hand-crafted tree is built depending on the prior information of observed trajectory to model multiple future trajectories. Specifically, a path in the tree from the root to leaf represents an individual possible future trajectory. SIT employs a coarse-to-fine optimization strategy, in which the tree is first built by high-order velocity to balance the complexity and coverage of the tree and then optimized greedily to encourage multimodality. Finally, a teacher-forcing refining operation is used to predict the final fine trajectory. Compared with prior methods which leverage implicit latent variables to represent possible future trajectories, the path in the tree can explicitly explain the rough moving behaviors (e.g., go straight and then turn right), and thus provides better interpretability. Despite the hand-crafted tree, the experimental results on ETH-UCY and Stanford Drone datasets demonstrate that our method is capable of matching or exceeding the performance of state-of-the-art methods. Interestingly, the experiments show that the raw built tree without training outperforms many prior deep neural network based approaches. Meanwhile, our method presents sufficient flexibility in long-term prediction and different best-of-K predictions.
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Liushuai Shi, Le Wang, Chengjiang Long, Sanping Zhou, Fang Zheng, Nanning Zheng, Gang Hua
| null | null | 2,022 |
aaai
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Improving Zero-Shot Phrase Grounding via Reasoning on External Knowledge and Spatial Relations
| null |
Phrase grounding is a multi-modal problem that localizes a particular noun phrase in an image referred to by a text query. In the challenging zero-shot phrase grounding setting, the existing state-of-the-art grounding models have limited capacity in handling the unseen phrases. Humans, however, can ground novel types of objects in images with little effort, significantly benefiting from reasoning with commonsense. In this paper, we design a novel phrase grounding architecture that builds multi-modal knowledge graphs using external knowledge and then performs graph reasoning and spatial relation reasoning to localize the referred nouns phrases. We perform extensive experiments on different zero-shot grounding splits sub-sampled from the Flickr30K Entity and Visual Genome dataset, demonstrating that the proposed framework is orthogonal to backbone image encoders and outperforms the baselines by 2~3% in accuracy, resulting in a significant improvement under the standard evaluation metrics.
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Zhan Shi, Yilin Shen, Hongxia Jin, Xiaodan Zhu
| null | null | 2,022 |
aaai
|
Reliable Inlier Evaluation for Unsupervised Point Cloud Registration
| null |
Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. It is expected to capture the discriminative geometric difference between the source neighborhood and the corresponding pseudo target neighborhood for effective inlier distinction. Specifically, our model consists of a matching map refinement module and an inlier evaluation module. In our matching map refinement module, we improve the point-wise matching map estimation by integrating the matching scores of neighbors into it. The aggregated neighborhood information potentially facilitates the discriminative map construction so that high-quality correspondences can be provided for generating the pseudo target point cloud. Based on the observation that the outlier has the significant structure-wise difference between its source neighborhood and corresponding pseudo target neighborhood while this difference for inlier is small, the inlier evaluation module exploits this difference to score the inlier confidence for each estimated correspondence. In particular, we construct an effective graph representation for capturing this geometric difference between the neighborhoods. Finally, with the learned correspondences and the corresponding inlier confidence, we use the weighted SVD algorithm for transformation estimation.Under the unsupervised setting, we exploit the Huber function based global alignment loss, the local neighborhood consensus loss and spatial consistency loss for model optimization. The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance.
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Yaqi Shen, Le Hui, Haobo Jiang, Jin Xie, Jian Yang
| null | null | 2,022 |
aaai
|
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
| null |
Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods need to iteratively optimize an objective function via a specific deformation model along with meticulous parameter tuning, but which have limited capabilities in registering images with large deformations. While deep learning-based methods can learn the complex mapping from input images to their respective deformation field, it is regression-based and is prone to be stuck at local minima, particularly when large deformations are involved. To this end, we present Stochastic Planner-Actor-Critic (spac), a novel reinforcement learning-based framework that performs step-wise registration. The key notion is warping a moving image successively by each time step to finally align to a fixed image. Considering that it is challenging to handle high dimensional continuous action and state spaces in the conventional reinforcement learning (RL) framework, we introduce a new concept `Plan' to the standard Actor-Critic model, which is of low dimension and can facilitate the actor to generate a tractable high dimensional action. The entire framework is based on unsupervised training and operates in an end-to-end manner. We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations. Our empirical results highlight that our work achieves consistent, significant gains and outperforms state-of-the-art methods.
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Ziwei Luo, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song, Xi Wu, Siwei Lyu
| null | null | 2,022 |
aaai
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Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning
| null |
The recently advanced unsupervised learning approaches use the siamese-like framework to compare two "views" from the same image for learning representations. Making the two views distinctive is a core to guarantee that unsupervised methods can learn meaningful information. However, such frameworks are sometimes fragile on overfitting if the augmentations used for generating two views are not strong enough, causing the over-confident issue on the training data. This drawback hinders the model from learning subtle variance and fine-grained information. To address this, in this work we aim to involve the soft distance concept on label space in the contrastive-based unsupervised learning task and let the model be aware of the soft degree of similarity between positive or negative pairs through mixing the input data space, to further work collaboratively for the input and loss spaces. Despite its conceptual simplicity, we show empirically that with the solution -- Unsupervised image mixtures (Un-Mix), we can learn subtler, more robust and generalized representations from the transformed input and corresponding new label space. Extensive experiments are conducted on CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet and standard ImageNet-1K with popular unsupervised methods SimCLR, BYOL, MoCo V1&V2, SwAV, etc. Our proposed image mixture and label assignment strategy can obtain consistent improvement by 1~3% following exactly the same hyperparameters and training procedures of the base methods. Code is publicly available at https://github.com/szq0214/Un-Mix.
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Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, Eric Xing
| null | null | 2,022 |
aaai
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Towards Bridging Sample Complexity and Model Capacity
| null |
In this paper, we give a new definition for sample complexity, and further develop a theoretical analysis to bridge the gap between sample complexity and model capacity. In contrast to previous works which study on some toy samples, we conduct our analysis on more general data space, and build a qualitative relationship from sample complexity to model capacity required to achieve comparable performance. Besides, we introduce a simple indicator to evaluate the sample complexity based on continuous mapping. Moreover, we further analysis the relationship between sample complexity and data distribution, which paves the way to understand the present representation learning. Extensive experiments on several datasets well demonstrate the effectiveness of our evaluation method.
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Shibin Mei, Chenglong Zhao, Shengchao Yuan, Bingbing Ni
| null | null | 2,022 |
aaai
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Generative Adaptive Convolutions for Real-World Noisy Image Denoising
| null |
Recently, deep learning techniques are soaring and have shown dramatic improvements in real-world noisy image denoising. However, the statistics of real noise generally vary with different camera sensors and in-camera signal processing pipelines. This will induce problems of most deep denoisers for the overfitting or degrading performance due to the noise discrepancy between the training and test sets. To remedy this issue, we propose a novel flexible and adaptive denoising network, coined as FADNet. Our FADNet is equipped with a plane dynamic filter module, which generates weight filters with flexibility that can adapt to the specific input and thereby impedes the FADNet from overfitting to the training data. Specifically, we exploit the advantage of the spatial and channel attention, and utilize this to devise a decoupling filter generation scheme. The generated filters are conditioned on the input and collaboratively applied to the decoded features for representation capability enhancement. We additionally introduce the Fourier transform and its inverse to guide the predicted weight filters to adapt to the noisy input with respect to the image contents. Experimental results demonstrate the superior denoising performances of the proposed FADNet versus the state-of-the-art. In contrast to the existing deep denoisers, our FADNet is not only flexible and efficient, but also exhibits a compelling generalization capability, enjoying tremendous potential for practical usage.
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Ruijun Ma, Shuyi Li, Bob Zhang, Zhengming Li
| null | null | 2,022 |
aaai
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Adaptive Poincaré Point to Set Distance for Few-Shot Classification
| null |
Learning and generalizing from limited examples, i.e., few-shot learning, is of core importance to many real-world vision applications. A principal way of achieving few-shot learning is to realize an embedding where samples from different classes are distinctive. Recent studies suggest that embedding via hyperbolic geometry enjoys low distortion for hierarchical and structured data, making it suitable for few-shot learning. In this paper, we propose to learn a context-aware hyperbolic metric to characterize the distance between a point and a set associated with a learned set to set distance. To this end, we formulate the metric as a weighted sum on the tangent bundle of the hyperbolic space and develop a mechanism to obtain the weights adaptively, based on the constellation of the points. This not only makes the metric local but also dependent on the task in hand, meaning that the metric will adapt depending on the samples that it compares. We empirically show that such metric yields robustness in the presence of outliers and achieves a tangible improvement over baseline models. This includes the state-of-the-art results on five popular few-shot classification benchmarks, namely mini-ImageNet, tiered-ImageNet, Caltech-UCSD Birds-200-2011(CUB), CIFAR-FS, and FC100.
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Rongkai Ma, Pengfei Fang, Tom Drummond, Mehrtash Harandi
| null | null | 2,022 |
aaai
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Can Vision Transformers Learn without Natural Images?
| null |
Is it possible to complete Vision Transformer (ViT) pre-training without natural images and human-annotated labels? This question has become increasingly relevant in recent months because while current ViT pre-training tends to rely heavily on a large number of natural images and human-annotated labels, the recent use of natural images has resulted in problems related to privacy violation, inadequate fairness protection, and the need for labor-intensive annotations. In this paper, we experimentally verify that the results of formula-driven supervised learning (FDSL) framework are comparable with, and can even partially outperform, sophisticated self-supervised learning (SSL) methods like SimCLRv2 and MoCov2 without using any natural images in the pre-training phase. We also consider ways to reorganize FractalDB generation based on our tentative conclusion that there is room for configuration improvements in the iterated function system (IFS) parameter settings of such databases. Moreover, we show that while ViTs pre-trained without natural images produce visualizations that are somewhat different from ImageNet pre-trained ViTs, they can still interpret natural image datasets to a large extent. Finally, in experiments using the CIFAR-10 dataset, we show that our model achieved a performance rate of 97.8, which is comparable to the rate of 97.4 achieved with SimCLRv2 and 98.0 achieved with ImageNet.
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Kodai Nakashima, Hirokatsu Kataoka, Asato Matsumoto, Kenji Iwata, Nakamasa Inoue, Yutaka Satoh
| null | null | 2,022 |
aaai
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Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation
| null |
How deep neural networks (DNNs) learn from noisy labels has been studied extensively in image classification but much less in image segmentation. So far, our understanding of the learning behavior of DNNs trained by noisy segmentation labels remains limited. In this study, we address this deficiency in both binary segmentation of biological microscopy images and multi-class segmentation of natural images. We generate extremely noisy labels by randomly sampling a small fraction (e.g., 10%) or flipping a large fraction (e.g., 90%) of the ground truth labels. When trained with these noisy labels, DNNs provide largely the same segmentation performance as trained by the original ground truth. This indicates that DNNs learn structures hidden in labels rather than pixel-level labels per se in their supervised training for semantic segmentation. We refer to these hidden structures in labels as meta-structures. When DNNs are trained by labels with different perturbations to the meta-structure, we find consistent degradation in their segmentation performance. In contrast, incorporation of meta-structure information substantially improves performance of an unsupervised segmentation model developed for binary semantic segmentation. We define meta-structures mathematically as spatial density distributions and show both theoretically and experimentally how this formulation explains key observed learning behavior of DNNs.
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Yaoru Luo, Guole Liu, Yuanhao Guo, Ge Yang
| null | null | 2,022 |
aaai
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REMOTE: Reinforced Motion Transformation Network for Semi-supervised 2D Pose Estimation in Videos
| null |
Existing approaches for 2D pose estimation in videos often require a large number of dense annotations, which are costly and labor intensive to acquire. In this paper, we propose a semi-supervised REinforced MOtion Transformation nEtwork (REMOTE) to leverage a few labeled frames and temporal pose variations in videos, which enables effective learning of 2D pose estimation in sparsely annotated videos. Specifically, we introduce a Motion Transformer (MT) module to perform cross frame reconstruction, aiming to learn motion dynamic knowledge in videos. Besides, a novel reinforcement learning-based Frame Selection Agent (FSA) is designed within our framework, which is able to harness informative frame pairs on the fly to enhance the pose estimator under our cross reconstruction mechanism. We conduct extensive experiments that show the efficacy of our proposed REMOTE framework.
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Xianzheng Ma, Hossein Rahmani, Zhipeng Fan, Bin Yang, Jun Chen, Jun Liu
| null | null | 2,022 |
aaai
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Federated Learning for Face Recognition with Gradient Correction
| null |
With increasing appealing to privacy issues in face recognition, federated learning has emerged as one of the most prevalent approaches to study the unconstrained face recognition problem with private decentralized data. However, conventional decentralized federated algorithm sharing whole parameters of networks among clients suffers from privacy leakage in face recognition scene. In this work, we introduce a framework, FedGC, to tackle federated learning for face recognition and guarantees higher privacy. We explore a novel idea of correcting gradients from the perspective of backward propagation and propose a softmax-based regularizer to correct gradients of class embeddings by precisely injecting a cross-client gradient term. Theoretically, we show that FedGC constitutes a valid loss function similar to standard softmax. Extensive experiments have been conducted to validate the superiority of FedGC which can match the performance of conventional centralized methods utilizing full training dataset on several popular benchmark datasets.
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Yifan Niu, Weihong Deng
| null | null | 2,022 |
aaai
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TEACh: Task-Driven Embodied Agents That Chat
| null |
Robots operating in human spaces must be able to engage in natural language interaction, both understanding and executing instructions, and using conversation to resolve ambiguity and correct mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human-human, interactive dialogues to complete household tasks in simulation. A Commander with access to oracle information about a task communicates in natural language with a Follower. The Follower navigates through and interacts with the environment to complete tasks varying in complexity from "Make Coffee" to "Prepare Breakfast", asking questions and getting additional information from the Commander. We propose three benchmarks using TEACh to study embodied intelligence challenges, and we evaluate initial models' abilities in dialogue understanding, language grounding, and task execution.
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Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava, Patrick Lange, Anjali Narayan-Chen, Spandana Gella, Robinson Piramuthu, Gokhan Tur, Dilek Hakkani-Tur
| null | null | 2,022 |
aaai
|
P^3-Net: Part Mobility Parsing from Point Cloud Sequences via Learning Explicit Point Correspondence
| null |
Understanding an articulated 3D object with its movable parts is an essential skill for an intelligent agent. This paper presents a novel approach to parse 3D part mobility from point cloud sequences. The key innovation is learning explicit point correspondence from a raw unordered point cloud sequence. We propose a novel deep network called P^3-Net to parallelize trajectory feature extraction and point correspondence establishment, performing joint optimization between them. Specifically, we design a Match-LSTM module to reaggregate point features among different frames by a point correspondence matrix, a.k.a. the matching matrix. To obtain this matrix, an attention module is proposed to calculate the point correspondence. Moreover, we implement a Gumbel-Sinkhorn module to reduce the many-to-one relationship for better point correspondence. We conduct comprehensive evaluations on public benchmarks, including the motion dataset and the PartNet dataset. Results demonstrate that our approach outperforms SOTA methods on various 3D parsing tasks of part mobility, including motion flow prediction, motion part segmentation, and motion attribute (i.e. axis & range) estimation. Moreover, we integrate our approach into a robot perception module to validate its robustness.
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Yahao Shi, Xinyu Cao, Feixiang Lu, Bin Zhou
| null | null | 2,022 |
aaai
|
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals
| null |
It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning. Existing methods usually adopt a large off-the-shelf model to transfer knowledge to the small one via distillation. Despite their effectiveness, distillation-based methods may not be suitable for some resource-restricted scenarios due to the huge computational expenses of deploying a large model. In this paper, we study the issue of training self-supervised small models without distillation signals. We first evaluate the representation spaces of the small models and make two non-negligible observations: (i) the small models can complete the pretext task without overfitting despite their limited capacity and (ii) they universally suffer the problem of over clustering. Then we verify multiple assumptions that are considered to alleviate the over-clustering phenomenon. Finally, we combine the validated techniques and improve the baseline performances of five small architectures with considerable margins, which indicates that training small self-supervised contrastive models is feasible even without distillation signals. The code is available at https://github.com/WOWNICE/ssl-small.
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Haizhou Shi, Youcai Zhang, Siliang Tang, Wenjie Zhu, Yaqian Li, Yandong Guo, Yueting Zhuang
| null | null | 2,022 |
aaai
|
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