38 accepted papers
Time-series forecasting is critical in various scenarios, such as energy, transportation, and public health. However, most existing forecasters rely primarily on one-way inference, \textit{i.e.}, mapping \textbf{history} to \textbf{target}, and overlook the structural information provided by a revis…
Learning from continuous-time trajectories requires modeling multivariate sensor measurements generated by underlying physical or dynamical processes. Under extreme data compression and heterogeneous sampling, directly optimizing synthetic signals as discrete sample values becomes fundamentally misa…
The rapid growth of visual data under stringent storage and bandwidth constraints makes extremely low-bitrate image compression increasingly important. While Vector Quantization (VQ) offers strong structural fidelity, existing methods lack a principled mechanism for joint rate-distortion (RD) optimi
Recent advances have demonstrated that coded aperture snapshot spectral imaging (CASSI) systems show great potential for capturing 3D hyperspectral images (HSIs) from a single 2D measurement. Despite the inherent spectral continuity of scenes captured by CASSI, most existing reconstruction methods a…
Multi-view clustering aims to utilize information from multiple feature representations to uncover underlying data structures. Most existing methods emphasize learning a consensus representation by enforcing consistency across views. However, those structures that cannot be directly incorporated int
WiFi sensing offers passive and privacy-preserving perception that complements vision-based sensing, but its performance degrades sharply under domain shifts caused by changes in environment, subjects, or hardware. This challenge is exacerbated in real-world deployments where source data are unavail
Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relatio…
WiFi systems offer enormous potential for device-free human intrusion detection. Current methods often require routers to be deployed in multiple adjacent rooms on the same floor, which is redundant and costly. To solve this, we introduce the first work on intrusion detection in the crossing-floor s…
Curvilinear structure segmentation using deep neural networks is often limited by the high cost of annotation. Semi-supervised learning (SSL) helps mitigate this dependency on extensive annotated data. State-of-the-art SSL approaches generate pseudo-labels for unlabeled data, which are then used for…
Alzheimer’s disease (AD) is an incurable neurodegenerative disease that involves structural changes in the brain. Early diagnosis of AD helps provide timely treatment and delay its progressive process. Many studies have been conducted based on brain images to detect AD. However, these works are most…
Recent advancements have suggested that neural radiance fields (NeRFs) show great potential in color editing within the 3D domain. However, most existing NeRF-based editing methods continue to face significant challenges in local region editing, which usually lead to imprecise local object boundarie…
Recent advancements in deep neural networks have driven significant progress in image enhancement (IE). However, deploying deep learning models on resource-constrained platforms, such as mobile devices, remains challenging due to high computation and memory demands. To address these challenges and f…
Unmanned Aerial Vehicle (UAV) object detection faces significant challenges due to complex environmental conditions and different imaging conditions. These factors introduce significant changes in scale and appearance, particularly for small objects that occupy limited pixels and exhibit limited inf…
Parameter-efficient fine-tuning (PEFT) adapts pre-trained models to new tasks by updating only a small subset of parameters, achieving efficiency but still facing significant inference costs driven by input token length. This challenge is even more pronounced in pixel-level tasks, which require long…
Multi-view tensor clustering (MVTC) has gained much attention for its effectiveness in capturing global high-order correlations across views. However, current MVTC methods suffer from two limitations: 1) adopting a two-stage process to learn the latent features for clustering, and 2) either ignoring…
Heterogeneous Federated Learning (HFL) has received widespread attention due to its adaptability to different models and data. The HFL approach utilizing auxiliary models for knowledge transfer enhances flexibility. However, existing frameworks face the challenges of aggregation bias and local over…
We address the challenge of WiFi-based temporal activity detection and propose an efficient Dual Pyramid Network that integrates Temporal Signal Semantic Encoders and Local Sensitive Response Encoders. The Temporal Signal Semantic Encoder splits feature learning into high and low-frequency componen…
In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniques are VAE-based with an autoregressive entropy model, which obviously promotes the RD performance by utilizing the dec…
Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity.…
Dysphagia is a prevalent symptom in numerous neurological disorders among older adults. Current dysphagia diagnostic systems either involve invasive procedures or necessitate the ingestion of liquids. Some researchers have devised automatic dysphagia detection methods based on vowels that are easy t…
Phase retrieval seeks to reconstruct a series of image sequences from measurements that only capture their magnitudes. Current approaches either flatten and stack the image sequences, disregarding their multidimensional structural information, or fail to account for errors within the sensing vectors…
Three-dimensional object detection is one of the key tasks in autonomous driving. To reduce costs in practice low-cost multi-view cameras for 3D object detection are proposed to replace the expansive LiDAR sensors. However relying solely on cameras is difficult to achieve highly accurate and robust…
Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However current methods mainly seek the consensus embedding feature for clustering by exploring global correlations between anchor graphs or projection matrices.In…
Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a nove…
Camera pose estimation is a fundamental module for many vision tasks. It is usually based on feature correspondences, i.e., feature matches across different images. However, correspondences always contain non-negligible outliers, which may negatively affect pose estimation efficiency and accuracy. T…
Transformer-based methods have exhibited remarkable potential in single image super-resolution (SISR) by effectively extracting long-range dependencies. However, most of the current research in this area has prioritized the design of transformer blocks to capture global information, while overlookin…
Incomplete multi-view clustering (IMVC), based on imputation and clustering unification, has received wide attention due to its ability to exploit hidden information from missing views. However, current methods mainly consider inter/intra-view correlations, ignoring the structural information of sam…
Nonlocal low-rank (LR) tensor modeling has shown great potential in hyperspectral image (HSI) denoising, which first uses the nonlocal self-similarity (NSS) prior to search for many similar full-band patches to form three-dimensional nonlocal full-band groups (tensors), and then usually enforces an…
Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. However, the existing methods are not robust enough…
Multitask learning (MTL) can utilize the relatedness between multiple tasks for performance improvement. The advent of multimodal data allows tasks to be referenced by multiple indices. High-order tensors are capable of providing efficient representations for such tasks, while preserving structural…
Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions. The key to success is how to solve the many-to-many mapping problem. However, the existing approaches either do not consider constrai…
Poisson noise is a common electronic noise, which has widely occurred in various photo-limited imaging systems. However, due to signal-dependent and multiplicative characteristics for Poisson noise, Poisson denoising is still an open problem. In this paper, we propose a novel approach using simultan…
Inspired by the image nonlocal self-similarity (NSS) prior, structural sparse representation (SSR) models exploit each group as the basic unit for sparse representation, which have achieved promising results in various image restoration applications. However, conventional SSR models only exploited t…
Machine learning models are vulnerable to adversarial examples. For the black-box setting, current substitute attacks need pre-trained models to generate adversarial examples. However, pre-trained models are hard to obtain in real-world tasks. In this paper, we propose a data-free substitute trainin…
While being the de facto standard coordinate representation for human pose estimation, heatmap has not been investigated in-depth. This work fills this gap. For the first time, we find that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is…
Age estimation is a classic learning problem in computer vision. Many larger and deeper CNNs have been proposed with promising performance, such as AlexNet, VggNet, GoogLeNet and ResNet. However, these models are not practical for the embedded/mobile devices. Recently, MobileNets and ShuffleNets hav…
Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were…
Tensor principal component analysis (TPCA) is a multi-linear extension of principal component analysis which converts a set of correlated measurements into several principal components. In this paper, we propose a new robust TPCA method to extract the principal components of the multi-way data based…