25 accepted papers
The rapid proliferation of smart-city ecosystems has significantly amplified the demand for Li-ion batteries, which now serve as the primary energy source for sustainable transportation systems such as e-bikes. Ensuring battery safety and optimal performance is crucial, yet challenging due to comple
Humans perceive the world through diverse modalities that operate synergistically to support a holistic understanding of their surroundings. However, existing omnimodal models still exhibit substantial performance degradation on visual tasks when the audio modality is incorporated. We identify this …
Recent advances in text-to-video (T2V) generation highlight the critical role of high-quality video-text pairs in training models capable of producing coherent and instruction-aligned videos. However, strategies for optimizing video captions specifically for T2V training remain underexplored. In thi
"Recently, transformer-based large vision models, , the Segment Anything Model (SAM) and Stable Diffusion (SD), have achieved remarkable success in the computer vision field. However, the quartic complexity within the transformer’s Multi-Head Attention (MHA) leads to substantial computational costs…
Correlation filter (CF)-based approaches have gained widespread attention in the field of unmanned aerial vehicle (UAV) visual tracking due to their light-weight characteristics. However, CFs are prone to generating low-quality response in challenging UAV scenarios, e.g., fast motion and background…
Video storytelling is engaging multimedia content that utilizes video and its accompanying narration to share a story and attract the audience, where a key challenge is creating narrations for recorded visual scenes. Previous studies on dense video captioning and video story generation have made som…
Skeleton-based action recognition has recently received considerable attention. Current approaches to skeleton-based action recognition are typically formulated as one-hot classification tasks and do not fully exploit the semantic relations between actions. For example, "make victory sign" and "thum…
Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace d…
End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods have been explored, most of them still suffer from longer training time and more complex deployment, which cannot be deployed in the…
Aerial-aquatic vehicles are capable to move in the two most dominant fluids, making them more promising for a wide range of applications. We propose a prototype with special designs for propulsion and thruster configuration to cope with the vast differences in the fluid properties of water and air.…
In this work, we present a new computer vision task named video object of interest segmentation (VOIS). Given a video and a target image of interest, our objective is to simultaneously segment and track all objects in the video that are relevant to the target image. This problem combines the traditi…
The decomposition of multi-subject fMRI data using rank-(L,L,1,1) block term decomposition (BTD) can preserve higher-way data structure and is more robust to noise effects by decomposing shared spatial maps (SMs) into a product of two rank-L loading matrices. However, since the number of whole-brain…
The ultimate goal of semi-supervised object detection (SSOD) is to facilitate the utilization and deployment of detectors in actual applications with the help of a large amount of unlabeled data. Although a few works have proposed various self-training-based methods or consistency-regularization-bas…
In this letter, we present a volumetric mapping system that effectively calculates Occupancy Grid Maps (OGMs) and Euclidean Distance Transforms (EDTs) with parallel computing. Unlike these mappers for high-precision structural reconstruction, our system incrementally constructs global EDT and output
The goal of video highlight detection is to select the most attractive segments from a long video to depict the most interesting parts of the video. Existing methods typically focus on modeling relationship between different video segments in order to learning a model that can assign highlight score…
"Image animation aims to animate a source image by using motion learned from a driving video. Current state-of-the-art methods typically use convolutional neural networks (CNNs) to predict motion information, such as motion keypoints and corresponding local transformations. However, these CNN based…
"Motion transfer aims to transfer the motion of a driving video to a source image. When there are considerable differences between object in the driving video and that in the source image, traditional single domain motion transfer approaches often produce notable artifacts; for example, the synthesi…
"Transformer-based methods have recently achieved great advancement on 2D image-based vision tasks. For 3D video-based tasks such as action recognition, however, directly applying spatiotemporal transformers on video data will bring heavy computation and memory burdens due to the largely increased n…
Given a source image and a driving video depicting the same object type, the motion transfer task aims to generate a video by learning the motion from the driving video while preserving the appearance from the source image. In this paper, we propose a novel structure-aware motion modeling approach,…
Face recognition has achieved significant progress in recent years. However, the large pose variation between face images remains a challenge in face recognition. We observe that the pose variation in the hidden feature maps is one of the most critical factors to hinder the representations from bein…
Deep Metric Learning (DML) has been more attractive and widely applied in many computer vision tasks, in which a discriminative embedding is requested such that the image features belonging to the same class are gathered together and the ones belonging to different classes are pushed apart. Most exi…
The goal of semi-supervised object detection is to learn a detection model using only a few labeled data and large amounts of unlabeled data, thereby reducing the cost of data labeling. Although a few studies have proposed various self-training-based methods or consistency regularization-based metho…
In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc. Thus, for learning a general model, training with data from multiple different datasets might b…
Recently, exploiting the effect of the unlabeled data for face recognition attracts increasing attention. However, there are still few works considering the situation that the unlabeled data is shallow which widely exists in real-world scenarios. The existing semi-supervised face recognition methods…
Recently, deep face recognition has achieved significant progress because of Convolutional Neural Networks (CNNs) and large-scale datasets. However, training CNNs on a large-scale face recognition dataset with limited computational resources is still a challenge. This is because the classification p…