Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0.947 $\pm$ 0.044.Comment: ISBI 2019 (Oral

Hypergraph Transformer for Skeleton-based Action Recognition

Skeleton-based action recognition aims to predict human actions given human joint coordinates with skeletal interconnections. To model such off-grid data points and their co-occurrences, Transformer-based formulations would be a natural choice. However, Transformers still lag behind state-of-the-art methods using graph convolutional networks (GCNs). Transformers assume that the input is permutation-invariant and homogeneous (partially alleviated by positional encoding), which ignores an important characteristic of skeleton data, i.e., bone connectivity. Furthermore, each type of body joint has a clear physical meaning in human motion, i.e., motion retains an intrinsic relationship regardless of the joint coordinates, which is not explored in Transformers. In fact, certain re-occurring groups of body joints are often involved in specific actions, such as the subconscious hand movement for keeping balance. Vanilla attention is incapable of describing such underlying relations that are persistent and beyond pair-wise. In this work, we aim to exploit these unique aspects of skeleton data to close the performance gap between Transformers and GCNs. Specifically, we propose a new self-attention (SA) extension, named Hypergraph Self-Attention (HyperSA), to incorporate inherently higher-order relations into the model. The K-hop relative positional embeddings are also employed to take bone connectivity into account. We name the resulting model Hyperformer, and it achieves comparable or better performance w.r.t. accuracy and efficiency than state-of-the-art GCN architectures on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets. On the largest NTU RGB+D 120 dataset, the significantly improved performance reached by our Hyperformer demonstrates the underestimated potential of Transformer models in this field

DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders

Image colorization is a challenging problem due to multi-modal uncertainty and high ill-posedness. Directly training a deep neural network usually leads to incorrect semantic colors and low color richness. While transformer-based methods can deliver better results, they often rely on manually designed priors, suffer from poor generalization ability, and introduce color bleeding effects. To address these issues, we propose DDColor, an end-to-end method with dual decoders for image colorization. Our approach includes a pixel decoder and a query-based color decoder. The former restores the spatial resolution of the image, while the latter utilizes rich visual features to refine color queries, thus avoiding hand-crafted priors. Our two decoders work together to establish correlations between color and multi-scale semantic representations via cross-attention, significantly alleviating the color bleeding effect. Additionally, a simple yet effective colorfulness loss is introduced to enhance the color richness. Extensive experiments demonstrate that DDColor achieves superior performance to existing state-of-the-art works both quantitatively and qualitatively. The codes and models are publicly available at https://github.com/piddnad/DDColor.Comment: ICCV 2023; Code: https://github.com/piddnad/DDColo

Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN

Masked image modeling (MIM), an emerging self-supervised pre-training method, has shown impressive success across numerous downstream vision tasks with Vision transformers (ViTs). Its underlying idea is simple: a portion of the input image is randomly masked out and then reconstructed via the pre-text task. However, the working principle behind MIM is not well explained, and previous studies insist that MIM primarily works for the Transformer family but is incompatible with CNNs. In this paper, we first study interactions among patches to understand what knowledge is learned and how it is acquired via the MIM task. We observe that MIM essentially teaches the model to learn better middle-order interactions among patches and extract more generalized features. Based on this fact, we propose an Architecture-Agnostic Masked Image Modeling framework (A$^2$MIM), which is compatible with both Transformers and CNNs in a unified way. Extensive experiments on popular benchmarks show that our A$^2$MIM learns better representations without explicit design and endows the backbone model with the stronger capability to transfer to various downstream tasks for both Transformers and CNNs.Comment: Preprint under review (update reversion). The source code will be released in https://github.com/Westlake-AI/openmixu

PGformer: Proxy-Bridged Game Transformer for Multi-Person Extremely Interactive Motion Prediction

Multi-person motion prediction is a challenging task, especially for real-world scenarios of densely interacted persons. Most previous works have been devoted to studying the case of weak interactions (e.g., hand-shaking), which typically forecast each human pose in isolation. In this paper, we focus on motion prediction for multiple persons with extreme collaborations and attempt to explore the relationships between the highly interactive persons' motion trajectories. Specifically, a novel cross-query attention (XQA) module is proposed to bilaterally learn the cross-dependencies between the two pose sequences tailored for this situation. Additionally, we introduce and build a proxy entity to bridge the involved persons, which cooperates with our proposed XQA module and subtly controls the bidirectional information flows, acting as a motion intermediary. We then adapt these designs to a Transformer-based architecture and devise a simple yet effective end-to-end framework called proxy-bridged game Transformer (PGformer) for multi-person interactive motion prediction. The effectiveness of our method has been evaluated on the challenging ExPI dataset, which involves highly interactive actions. We show that our PGformer consistently outperforms the state-of-the-art methods in both short- and long-term predictions by a large margin. Besides, our approach can also be compatible with the weakly interacted CMU-Mocap and MuPoTS-3D datasets and achieve encouraging results. Our code will become publicly available upon acceptance

Discovering Physical Interaction Vulnerabilities in IoT Deployments

Internet of Things (IoT) applications drive the behavior of IoT deployments according to installed sensors and actuators. It has recently been shown that IoT deployments are vulnerable to physical interactions, caused by design flaws or malicious intent, that can have severe physical consequences. Yet, extant approaches to securing IoT do not translate the app source code into its physical behavior to evaluate physical interactions. Thus, IoT consumers and markets do not possess the capability to assess the safety and security risks these interactions present. In this paper, we introduce the IoTSeer security service for IoT deployments, which uncovers undesired states caused by physical interactions. IoTSeer operates in four phases (1) translation of each actuation command and sensor event in an app source code into a hybrid I/O automaton that defines an app's physical behavior, (2) combining apps in a novel composite automaton that represents the joint physical behavior of interacting apps, (3) applying grid-based testing and falsification to validate whether an IoT deployment conforms to desired physical interaction policies, and (4) identification of the root cause of policy violations and proposing patches that guide users to prevent them. We use IoTSeer in an actual house with 13 actuators and six sensors with 37 apps and demonstrate its effectiveness and performance

DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving

Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that has yet to be thoroughly explored in existing research. To address this gap, we present DAMO-StreamNet, an optimized framework that combines recent advances from the YOLO series with a comprehensive analysis of spatial and temporal perception mechanisms, delivering a cutting-edge solution. The key innovations of DAMO-StreamNet are: (1) A robust neck structure incorporating deformable convolution, enhancing the receptive field and feature alignment capabilities. (2) A dual-branch structure that integrates short-path semantic features and long-path temporal features, improving motion state prediction accuracy. (3) Logits-level distillation for efficient optimization, aligning the logits of teacher and student networks in semantic space. (4) A real-time forecasting mechanism that updates support frame features with the current frame, ensuring seamless streaming perception during inference. Our experiments demonstrate that DAMO-StreamNet surpasses existing state-of-the-art methods, achieving 37.8% (normal size (600, 960)) and 43.3% (large size (1200, 1920)) sAP without using extra data. This work not only sets a new benchmark for real-time perception but also provides valuable insights for future research. Additionally, DAMO-StreamNet can be applied to various autonomous systems, such as drones and robots, paving the way for real-time perception. The code is available at https://github.com/zhiqic/DAMO-StreamNet

Parallel Aligned Treebank Corpora at LDC: Methodology, Annotation and Integration

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 14-23. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15893

KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration

In the realm of facial analysis, accurate landmark detection is crucial for various applications, ranging from face recognition and expression analysis to animation. Conventional heatmap or coordinate regression-based techniques, however, often face challenges in terms of computational burden and quantization errors. To address these issues, we present the KeyPoint Positioning System (KeyPosS) - a groundbreaking facial landmark detection framework that stands out from existing methods. The framework utilizes a fully convolutional network to predict a distance map, which computes the distance between a Point of Interest (POI) and multiple anchor points. These anchor points are ingeniously harnessed to triangulate the POI's position through the True-range Multilateration algorithm. Notably, the plug-and-play nature of KeyPosS enables seamless integration into any decoding stage, ensuring a versatile and adaptable solution. We conducted a thorough evaluation of KeyPosS's performance by benchmarking it against state-of-the-art models on four different datasets. The results show that KeyPosS substantially outperforms leading methods in low-resolution settings while requiring a minimal time overhead. The code is available at https://github.com/zhiqic/KeyPosS.Comment: Accepted to ACM Multimedia 2023; 10 pages, 7 figures, 6 tables; the code is at https://github.com/zhiqic/KeyPos