The line is a common feature in the man-made environment, inherently encoding spatial structural information. We propose an accurate and robust bundle adjustment (BA) solution that estimates the 6-DoF pose with an independent RS model of the camera and the geometry of the environment based on the line among the images captured by rolling shutter (RS) camera. However, the nonlinear nature of camera motion curves the projection of each 3D straight line on rolling shutter images, exacerbating the complexity of the original problem. To tackle the challenges, we first establish the RS line projection theory using the Plücker line parameterization and derive a series of line-to-line errors that avoid time-consuming curve-to-line (c2l) errors and exhibit the degeneration-resistant ability. We also provide complete degeneration-resistant proof of our method. Extensive synthetic and real data experiments demonstrate that our method achieves comparable efficiency with higher accuracy over existing point-based RSBA solutions.
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Large-scale self-supervised pre-training has paved the way for one foundation model to handle many different vision tasks. Most pre-training methodologies train a single model of a certain size at one time. Nevertheless, various computation or storage constraints in real-world scenarios require substantial efforts to develop a series of models with different sizes to deploy. Thus, in this study, we propose a novel tri-branch self-supervised training framework, termed as POA (Pre-training Once for All), to tackle this aforementioned issue. Our approach introduces an innovative elastic student branch into a modern self-distillation paradigm. At each pre-training step, we randomly sample a sub-network from the original student to form the elastic student and train all branches in a self-distilling fashion. Once pre-trained, POA allows the extraction of pre-trained models of diverse sizes for downstream tasks. Remarkably, the elastic student facilitates the simultaneous pre-training of multiple models with different sizes, which also acts as an additional ensemble of models of various sizes to enhance representation learning. Extensive experiments, including k-nearest neighbors, linear probing evaluation and assessments on downstream tasks demonstrate the effectiveness and advantages of our POA. It achieves state-of-the-art performance using ViT, Swin Transformer and ResNet backbones, producing around a hundred models with different sizes through a single pre-training session. We will release the code and pre-trained models of POA to share with community in the future.
Neural Radiance Fields (NeRF) have achieved remarkable progress on dynamic scenes with deformable objects. Nonetheless, most of the previous works required multi-view inputs or long training time (several hours), making it hard to apply them for real-world scenarios. Recently, a series of works have been dedicated to addressing the blurry artifact present in synthesized novel views given a monocular input of dynamic scenes. However, they may fail to predict stable and accurate deformation while keeping high-frequency details when rendering at various resolutions. To this end, we introduce a novel framework DMiT (Deformable Mipmapped Tri-Plane) that adopts the mipmaps to render dynamic scenes with various resolutions from novel views. With the help of hierarchical mipmapped triplanes, we incorporate an MLP to effectively predict a mapping between the observation space and the canonical space, enabling not only high-fidelity dynamic scene rendering but also high-performance training and inference. Moreover, a training scheme for joint geometry and deformation refinement is designed for canonical regularization to reconstruct high-quality geometries. Extensive experiments on both synthetic and real dynamic scenes demonstrate the efficacy and efficiency of our method.
The Segment Anything model (SAM) has brought significant changes to the segmentation field with its superior performance, but its extensive computational resource requirements remain a limiting factor. Many works, such as MobileSAM, Edge-SAM, and MobileSAM-v2, have explored lightweight solutions. However, their use of traditional Grid Search sampling strategies or two-stage concatenation methods, which do not allow for end-to-end training, severely limit the performance of segment everything (SegEvery). This paper introduces Lite-SAM, an efficient end-to-end solution for the SegEvery task designed to reduce computational costs and redundancy. Lite-SAM is composed of four main components: a streamlined CNN-Transformer hybrid encoder (LiteViT), an automated prompt proposal network (AutoPPN), a traditional prompt encoder, and a mask decoder. All these components are integrated within the SAM framework. Our LiteViT, a high-performance lightweight backbone network, has only 1.16M parameters, which is a 23% reduction compared to the lightest existing backbone network Shufflenet. We also introduce AutoPPN, an innovative end-to-end method for prompt word generation. This is an improvement over traditional grid search sampling methods, and its unique design allows for easy integration into any SAM series algorithm, extending its usability. we have thoroughly benchmarked Lite-SAM across a plethora of both public and private datasets. The evaluation encompassed a broad spectrum of universal metrics, including the number of parameters, SegEvery execution time, and accuracy. The findings reveal that Lite-SAM, operating with a lean 4.2M parameters, significantly outpaces its counterparts, demonstrating performance improvements of 43x, 31x, 20x, 21x, and 1.6x over SAM, MobileSAM, Edge-SAM, EfficientViT-SAM, and MobileSAM-v2 respectively, all the while maintaining competitive accuracy. This underscores Lite-SAM's prowess in achieving an optimal equilibrium between performance and precision, thereby setting a new state-of-the-art(SOTA) benchmark in the domain.
Integer-Valued Training and Spike-driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection
PDF ↗Brain-inspired Spiking Neural Networks (SNNs) have bio-plausibility and low-power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are currently limited to simple classification tasks because of their poor performance. In this work, we focus on bridging the performance gap between ANNs and SNNs on object detection. Our design revolves around network architecture and spiking neuron. First, the overly complex module design causes spike degradation when the YOLO series is converted to the corresponding spiking version. We design a SpikeYOLO architecture to solve this problem by simplifying the vanilla YOLO and incorporating meta SNN blocks. Second, object detection is more sensitive to quantization errors in the conversion of membrane potentials into binary spikes by spiking neurons. To address this challenge, we design a new spiking neuron that activates Integer values during training while maintaining spike-driven by extending virtual timestep during inference. The proposed method is validated on both static and neuromorphic object detection datasets. On the static COCO dataset, we obtain 66.2% mAP@50 and 48.9% mAP@50:95, which is +15.0% and +18.7% higher than the prior state-of-the-art SNN, respectively. On the neuromorphic Gen1 dataset, we achieve 67.2% mAP@50, which is +8.2% and +2.5% greater than the existing best SNN model and ANN with equivalent architecture, respectively, and the energy efficiency is improved by 5.7 times.
Diffusion models have achieved remarkable success in the domain of text-guided image generation and, more recently, in text-guided image editing. A commonly adopted strategy for editing real images involves inverting the diffusion process to obtain a noisy representation of the original image, which is then denoised to achieve the desired edits. However, current methods for diffusion inversion often struggle to produce edits that are both faithful to the specified text prompt and closely resemble the source image. To overcome these limitations, we introduce a novel and adaptable diffusion inversion technique for real image editing, which is grounded in a theoretical analysis of the role of $\eta$ in the DDIM sampling equation for enhanced editability. By designing a universal diffusion inversion method with a time- and region-dependent $\eta$ function, we enable flexible control over the editing extent. Through a comprehensive series of quantitative and qualitative assessments, involving a comparison with a broad array of recent methods, we demonstrate the superiority of our approach. Our method not only sets a new benchmark in the field but also significantly outperforms existing strategies.
In various domains such as transportation, resource management, and weather forecasting, there is an urgent need for methods that can provide predictions over a sufficiently long time horizon to encompass the period required for decision-making and implementation. Compared to traditional time series forecasting, ultra-long time series forecasting requires enhancing the model's ability to infer long time series, while maintaining inference costs within an acceptable range. To address this challenge, we propose the Boundary-Aware Periodicity-based sparsification strategy for Ultra-Long time series forecasting (BAP-UL).This method effectively captures periodic features in time series and reorganizes inputs and outputs into shorter sub-sequences for improved prediction accuracy. In the paper, we investigate several commonly used benchmark datasets and demonstrate that the proposed method can yield comparable performance across them.
Learning semantic-rich representations from unlabeled time series data with intricate dynamics is a notable challenge. Traditional contrastive learning techniques predominantly focus on segment-level augmentations through time slicing, a practice that, while valuable, often results in sampling bias and suboptimal performance due to the loss of global context. Furthermore, they typically disregard the vital frequency information to enrich data representations. To this end, we propose a novel self-supervised general-purpose framework called Temporal-Frequency and Contextual Consistency (TFCC). Specifically, this framework first performs two instance-level augmentation families over the entire series to capture nuanced representations alongside critical long-term dependencies. Then, TFCC advances by initiating dual cross-view forecasting tasks between the original series and its augmented counterpart in both time and frequency domains to learn robust representations. Finally, three specially designed consistency modules 'temporal, frequency, and temporal-frequency' aid in further developing discriminative representations on top of the learned robust representations. Extensive experiments on multiple benchmarks demonstrate TFCC's superiority over the state-of-the-art classification and forecasting methods and exhibit exceptional efficiency in semi-supervised and transfer learning scenarios.
Streaming videos from resource-constrained front-end devices over networks to resource-rich cloud servers has long been a common practice for surveillance and analytics. Most existing live video analytics (LVA) systems, however, have been built over terrestrial networks, limiting their applications during natural disasters and in remote areas that desperately call for real-time visual data delivery and scene analysis. With the recent advent of space networking, in particular, Low Earth Orbit (LEO) satellite constellations such as Starlink, high-speed truly global Internet access is becoming available and affordable. This paper examines the challenges and potentials of LVA over modern LEO satellite networking (LSN). Using Starlink as the testbed, we have carried out extensive in-the-wild measurements to gain insights into its achievable performance for LVA. The results reveal that the uplink bottleneck in today's LSN, together with the volatile network conditions, can significantly affect the service quality of LVA and necessitate prompt adaptation. We accordingly develop StarStream, a novel LSN-adaptive streaming framework for LVA. At its core, StarStream is empowered by a Transformer-based network performance predictor tailored for LSN and a content-aware configuration optimizer. We discuss a series of key design and implementation issues of StarStream and demonstrate its effectiveness and superiority through trace-driven experiments with real-world network and video processing data.
Scanpath generation in 360° images aims to model the realistic trajectories of gaze points that viewers follow when exploring panoramic environments. Existing methods for scanpath genera- tion suffer from various limitations, including a lack of global atten-tion to panoramic environments, insufficient diversity in generated scanpaths, and inadequate consideration of the temporal sequence of gaze points. To address these challenges, we propose a novel approach, named ScanTD, which employs a conditional Diffusion Model-based method to generate multiple scanpaths. Notably, a transformer-based time-series (TTS) module with a novel attention mechanism is integrated into ScanTD to capture the temporal de- pendency of gaze points effectively. Additionally, ScanTD utilizes a Vision Transformer-based method for image feature extraction, en- abling better learning of scene semantic information. Experimental results demonstrate that our approach outperforms state-of-the-art methods across three datasets. We further demonstrate its general- izability by applying it to the 360° saliency detection task.
With the increasing spatial and temporal resolutions of obtained remote sensing (RS) images, effective compression becomes critical for storage, transmission, and large-scale in-memory processing. Although image compression methods achieve a series of breakthroughs for daily images, a straightforward application of these methods to RS domain underutilizes the properties of the RS images, such as content duplication, homogeneity, and temporal redundancy. This paper proposes a Spatial-Temporal Context model (STCM) for RS image compression, jointly leveraging context from a broader spatial scope and across different temporal images. Specifically, we propose a stacked diagonal masked module to expand the contextual reference scope, which is stackable and maintains its parallel capability. Furthermore, we propose spatial-temporal contextual adaptive coding to enable the entropy estimation to reference context across different temporal RS images at the same geographic location. Experiments show that our method outperforms previous state-of-the-art compression methods on rate-distortion (RD) performance. For downstream tasks validation, our method reduces the bitrate by 52 times for single temporal images in the scene classification task while maintaining accuracy.
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition
PDF ↗Depression recognition based on physiological signals such as functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) has made considerable progress. However, most existing studies ignore the complementarity and semantic consistency of multimodal physiological signals under the same stimulation task in complex spatio-temporal patterns. In this paper, we introduce a multimodal physiological signals representation learning framework using Siamese architecture via multiscale contrasting for depression recognition (MRLMC). First, fNIRS and EEG are transformed into different but correlated data based on a time-domain data augmentation strategy. Then, we design a spatio-temporal contrasting module to learn the representation of fNIRS and EEG through weight-sharing multiscale spatio-temporal convolution. Furthermore, to enhance the learning of semantic representation associated with stimulation tasks, a semantic consistency contrast module is proposed, aiming to maximize the semantic similarity of fNIRS and EEG. Extensive experiments on publicly available and self-collected multimodal physiological signals datasets indicate that MRLMC outperforms the state-of-the-art models. Moreover, our proposed framework is capable of transferring to multimodal time series downstream tasks.
Long-term time series forecasting is a long-standing challenge in various applications. A central issue in time series forecasting is that methods should expressively capture long-term dependency. Furthermore, time series forecasting methods should be flexible when applied to different scenarios. Although Fourier analysis offers an alternative to effectively capture reusable and periodic patterns to achieve long-term forecasting in different scenarios, existing methods often assume high-frequency components represent noise and should be discarded in time series forecasting. However, we conduct a series of motivation experiments and discover that the role of certain frequencies varies depending on the scenarios. In some scenarios, removing high-frequency components from the original time series can improve the forecasting performance, while in others scenarios, removing them is harmful to forecasting performance. Therefore, it is necessary to treat the frequencies differently according to specific scenarios. To achieve this, we first reformulate the time series forecasting problem as learning a transfer function of each frequency in the Fourier domain. Further, we design Frequency Dynamic Fusion (FreDF), which individually predicts each Fourier component, and dynamically fuses the output of different frequencies. Moreover, we provide a novel insight into the generalization ability of time series forecasting and propose the generalization bound of time series forecasting. Then we prove FreDF has a lower bound, indicating that FreDF has better generalization ability. Extensive experiments conducted on multiple benchmark datasets and ablation studies demonstrate the effectiveness of FreDF.
Federated learning (FL) is undergoing significant traction due to its ability to perform privacy-preserving training on decentralized data. In this work, we focus on sensitive time series data collected by distributed sensors in real-world applications. However, time series data introduce the challenge of dual spatial-temporal feature skew due to their dynamic changes across domains and time, differing from computer vision. This key challenge includes inter-client spatial feature skew caused by heterogeneous sensor collection and intra-client temporal feature skew caused by dynamics in time series distribution. We follow the framework of Personalized Federated Learning (pFL) to handle dual feature drifts to enhance the capabilities of customized local models. Therefore, in this paper, we propose a method FedST to solve key challenges through orthogonal feature decoupling and regularization in both training and testing stages. During training, we collaborate time view and frequency view of time series data to enrich the mutual information and adopt orthogonal projection to disentangle and align the shared and personalized features between views, and between clients. During testing, we apply prototype-based predictions and model-based predictions to achieve model consistency based on shared features. Extensive experiments on multiple real-world classification datasets and multimodal time series datasets show our method consistently outperforms state-of-the-art baselines with clear advantages.
Human Activity Recognition (HAR) as an emerging research field has attracted widespread academic attention due to its wide range of practical applications in areas such as healthcare, environmental monitoring, and sports training. Given the high cost of annotating sensor data, many unsupervised and semi-supervised methods have been applied to HAR to alleviate the problem of limited data. In this paper, we propose a novel video-enhanced cross-modal collaborative learning method, Vi2ACT, to address the issue of few-shot HAR. We introduce a new data augmentation approach that utilizes a text-to-video generation model to generate class-related videos. Subsequently, a large quantity of video semantic representations are obtained through fine-tuning the video encoder for cross-modal co-learning. Furthermore, to effectively align video semantic representations and time series representations, we enhance HAR at the representation-level using conditional Generative Adversarial Nets (cGAN). We design a novel Representation Conditional Discriminator that is trained to assess samples as originating from video representations rather than those generated by the time series encoder as accurately as possible. We conduct extensive experiments on four commonly used HAR datasets. The experimental results demonstrate that our method outperforms other baseline models in all few-shot scenarios.
Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series forecasting framework. Specifically, we introduce a Unified Meal Representation Learning (UMRL) module to extract representations for each meal. Additionally, we design a diet-aware loss function to associate food intake with weight variations. By conducting experiments on the DietDiary dataset with two state-of-the-art time series forecasting models, NLinear and iTransformer, we demonstrate that our proposed framework achieves superior performance compared to the original models. We make our dataset, code, and models publicly available at: https://yxg1005.github.io/weight-prediction.
In light of the remarkable advancements made in time-series anomaly detection(TSAD), recent emphasis has been placed on exploiting the frequency domain as well as the time domain to address the difficulties in precisely detecting pattern-wise anomalies. However, in terms of anomaly scores, the window granularity of the frequency domain is inherently distinct from the data-point granularity of the time domain. Owing to this discrepancy, the anomaly information in the frequency domain has not been utilized to its full potential for TSAD. In this paper, we propose a TSAD framework, Dual-TF, that simultaneously uses both the time and frequency domains while breaking the time-frequency granularity discrepancy. To this end, our framework employs nested-sliding windows, with the outer and inner windows responsible for the time and frequency domains, respectively, and aligns the anomaly scores of the two domains. As a result of the high resolution of the aligned scores, the boundaries of pattern-wise anomalies can be identified more precisely. In six benchmark datasets, our framework outperforms state-of-the-art methods by 12.0--147%, as demonstrated by experimental results.
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
PDF ↗Most of current anomaly detection models assume that the normal pattern remains the same all the time. However, the normal patterns of web services can change dramatically and frequently over time. The model trained on old-distribution data becomes outdated and ineffective after such changes. Retraining the whole model whenever the pattern is changed is computationally expensive. Further, at the beginning of normal pattern changes, there is not enough observation data from the new distribution. Retraining a large neural network model with limited data is vulnerable to overfitting. Thus, we propose a Light Anti-overfitting Retraining Approach (LARA) based on deep variational auto-encoders for time series anomaly detection. In LARA we make the following three major contributions: 1) the retraining process is designed as a convex problem such that overfitting is prevented and the retraining process can converge fast; 2) a novel ruminate block is introduced, which can leverage the historical data without the need to store them; 3) we mathematically and experimentally prove that when fine-tuning the latent vector and reconstructed data, the linear formations can achieve the least adjusting errors between the ground truths and the fine-tuned ones. Moreover, we have performed many experiments to verify that retraining LARA with even a limited amount of data from new distribution can achieve competitive performance in comparison with the state-of-the-art anomaly detection models trained with sufficient data. Besides, we verify its light computational overhead.
Subsequence clustering of time series is an essential task in data mining, and interpreting the resulting clusters is also crucial since we generally do not have prior knowledge of the data. Thus, given a large collection of tensor time series consisting of multiple modes, including timestamps, how can we achieve subsequence clustering for tensor time series and provide interpretable insights? In this paper, we propose a new method, Dynamic Multi-network Mining (DMM), that converts a tensor time series into a set of segment groups of various lengths (i.e., clusters) characterized by a dependency network constrained with l1-norm. Our method has the following properties. (a) Interpretable: it characterizes the cluster with multiple networks, each of which is a sparse dependency network of a corresponding non-temporal mode, and thus provides visible and interpretable insights into the key relationships. (b) Accurate : it discovers the clusters with distinct networks from tensor time series according to the minimum description length (MDL). (c) Scalable: it scales linearly in terms of the input data size when solving a non-convex problem to optimize the number of segments and clusters, and thus it is applicable to long-range and high-dimensional tensors. Extensive experiments with synthetic datasets confirm that our method outperforms the state-of-the-art methods in terms of clustering accuracy. We then use real datasets to demonstrate that DMM is useful for providing interpretable insights from tensor time series.
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems. Previous data-driven RCA methods, particularly those employing causal discovery techniques, have primarily focused on constructing dependency or causal graphs for backtracking the root causes. However, these methods often fall short as they rely solely on data from a single modality, thereby resulting in suboptimal solutions. In this work, we propose Mulan, a unified multi-modal causal structure learning method designed to identify root causes in microservice systems. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. To explore intricate relationships across different modalities, we propose a contrastive learning-based approach to extract modality-invariant and modality-specific representations within a shared latent space. Additionally, we introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph. Finally, we employ random walk with restart to simulate system fault propagation and identify potential root causes. Extensive experiments on three real-world datasets validate the effectiveness of our proposed method.