19 accepted papers
Existing human attention modeling methods persist as highly fragmented across modalities, scenes, and task formulations. Consequently, even with increasing model capacity and data scale, current models predominantly remain scene-dependent and task-specific, failing to practically generalize in real-…
Camouflaged scene understanding (CSU) has attracted significant attention due to its broad practical implications. However, in this field, robust image-text cross-modal alignment remains under-explored, hindering deeper understanding of camouflaged scenarios and their related applications. To this e
Recent research in Category-Agnostic Pose Estimation (CAPE) has adopted fixed textual keypoint description as semantic prior for two-stage pose matching frameworks. While this paradigm enhances robustness and flexibility by disentangling the dependency of support images, our critical analysis reveal
Large 3D reconstruction models have revolutionized the 3D content generation field, enabling broad applications in virtual reality and gaming. Just like other large models, large 3D reconstruction models suffer from hallucinations as well, introducing structural outliers (e.g., odd holes or protrusi
Non-Exemplar Class Incremental Learning (NECIL) strives to preserve classification performance in an evolving data stream without revisiting old-class exemplars. Current methods mitigate catastrophic forgetting by replaying and augmenting historical prototypes as surrogates for old classes. However,
High-precision dichotomous image segmentation (DIS) is a task of extracting fine-grained objects from high-resolution images.Existing methods trade efficiency for accuracy: non-diffusion methods are fast but suffer from weak semantics and unstable spatial priors, causing false detections; diffusion-
The high cost of synthetic aperture radar (SAR) data acquisition motivates SAR image generation research. However, the data scarcity and SAR
The core challenge in camouflaged object detection (COD) is identifying objects that blend seamlessly with their surroundings. Existing methods emulate the strategies biological organisms break camouflage by manually constructing modules with expert knowledge from existing segmentation tasks, making…
The contrastive learning paradigm has been widely used for image-language pre-training and extended to video-text tuning. These approaches aim to maximize the similarity between positive sample pairs while minimizing that of negative ones through an alignment objective. Their performance is highly a…
Despite the promising results obtained by existing deepfake face detection methods for within-dataset detection, they often fail to generalize effectively to new datasets. We hypothesize that identity, a significant feature in facial recognition, is a key factor affecting deepfake detection models’…
RGB-D Video Salient Object Detection has gained increasing interest, but existing models often struggle to balance efficiency and accuracy, hindering their applications on resource-constrained devices. A key challenge in designing lightweight models is maintaining accuracy while reducing parameters.…
Metal implants in patients cause severe streaking artifacts in computed tomography (CT) images, significantly compromising image quality. Deep learning methods have been successfully applied to metal artifact reduction (MAR) in CT, but often result in overly smooth images, failing to reconstruct com…
Existing salient object detection (SOD) models primarily resort to convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs and quadratic computational complexity of transformers both constrain the performance of current models on discovering attention-gra…
Shape generation is the practice of producing 3D shapes as various representations for 3D content creation. Previous studies on 3D shape generation have focused on shape quality and structure, without or less considering the importance of semantic information. Consequently, such generative models of…
Estimating homography from an image pair is a fundamental problem in image alignment. Unsupervised learning methods have received increasing attention in this field due to their promising performance and label-free training. However, existing methods do not explicitly consider the problem of plane i…
RGB-D salient object detection (SOD) recently has attracted increasing research interest and many deep learning methods based on encoder-decoder architectures have emerged. However, most existing RGB-D SOD models conduct feature fusion either in the single encoder or the decoder stage, which hardly…
This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constra…
Modern crowd counting methods usually employ deep neural networks (DNN) to estimate crowd counts via density regression. Despite their significant improvements, the regression-based methods are incapable of providing the detection of individuals in crowds. The detection-based methods, on the other h…
This paper proposes an encoder-decoder network to disentangle shape features during 3D face shape reconstruction from single 2D images, such that the tasks of learning discriminative shape features for face recognition and reconstructing accurate 3D face shapes can be done simultaneously. Unlike exi…