19 accepted papers
The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing anomaly generation approaches to augment the anomaly dataset. However, existing anomaly generation methods suffer from limi…
Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) platforms requires efficient motion modeling. This is because UAV-MOT faces both local object motion and global camera motion. Motion blur also increases the difficulty of detecting large moving objects. Previous UAV motion modeling a…
Open-vocabulary detection (OVD) aims to detect objects beyond a predefined set of categories. As a pioneering model incorporating the YOLO series into OVD, YOLO-World is well-suited for scenarios prioritizing speed and efficiency. However, its performance is hindered by its neck feature fusion mecha…
The rapid development of Unmanned Aerial Vehicle (UAV) technology has facilitated the widespread use of UAVs in daily life, this advancement brings huge regulatory demand for UAVs. Automatic identification of UAVs using radio frequency (RF) signals can effectively reduce regulatory costs. Previous s…
Video diffusion models have shown great potential in generating high-quality videos, making them an increasingly popular focus. However, their inherent iterative nature leads to substantial computational and time costs. Although techniques such as consistency distillation and adversarial training ha…
The increasing complexity of industrial anomaly detection (IAD) has positioned multimodal detection methods as a focal area of machine vision research. However, dedicated multimodal datasets specifically tailored for IAD remain limited. Pioneering datasets like MVTec 3D have laid essential groundwor…
With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing. Although existing methods can guide generation beyond text prompts, the challenge of effectively combining multiple conditional inputs while maintainin…
Classifying an entire Whole Slide Image (WSI) in a single forward pass is challenging due to its vast resolution. Consequently, current effort on WSI classification resorts to multiple instance learning (MIL), using patch-wise instances to predict categories under image-wise supervision. However, re…
The recent successful detection of gravitational waves (GWs) at nanohertz based on pulsar timing arrays has underscored the growing significance of searching for new pulsars, which serve as valuable probes for GWs. However, one of the challenges in this endeavor is the lack of labeled data, which ca…
With the increasing customization of spectrometers, spectral unmixing has become a widely used technique in fields such as remote sensing, textiles, and environmental protection. However, endmember variability is a common issue for unmixing, where changes in lighting, atmospheric, temporal condition…
Audio-Visual Segmentation (AVS) is a challenging task, which aims to segment sounding objects in video frames by exploring audio signals. Generally AVS faces two key challenges: (1) Audio signals inherently exhibit a high degree of information density, as sounds produced by multiple objects are enta…
The problem of signal detection and classification of multiple UAVs can be solved using object detection techniques in computer vision. However, this requires collecting and labeling a large amount of reliable raw data. Since the UAV signal dataset cannot be directly applied to object detection, we…
Textile composition identification (TCI) is an essential basic link in the textile industry. Methods based on computer vision or near-infrared (NIR) signal processing have shown potential for the nondestructive TCI task. However, these methods ignore that the integration of NIR signals and visual in…
Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative feature descriptors of a single image, while the diff…
Bipartite graphs have been widely used in many applications such as recommender systems, search engines and so on. Recent works consider bipartite graphs as homogeneous graphs and apply graph convolution networks for link prediction or node classification. However, in bipartite graphs, there are two…
Near Infrared (NIR) Spectral signal has been successfully applied to fabric component analysis (FCA), which is used to identify the category of the textile (defined as a classification task) and its corresponding content for that category (defined as a regression problem). Unlike conventional classi…
The discovery of pulsars is of great importance to human understanding of the universe. Deep learning has exploited to find pulsars based on radio astronomical folded data, which includes time-phase and frequency-phase images and dispersion curve (DM). In this paper, a hierarchical signal fusion net…
Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning started to attract more attention due to its suitability in transferring model in a more practical way with limited ann…
Human-Object Interaction Detection devotes to infer a triplet < human, verb, object > between human and objects. In this paper, we propose a novel model, i.e., Relation Parsing Neural Network (RPNN), to detect human-object interactions. Specifically, the network is represented by two graphs, i.e., O…