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Found 32 papers, 18 papers with code
Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection
In short, current methods struggle to integrate low-level visual and high-level action features, leading to poor anomaly detection in varied and complex scenes.
Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey
Colorectal polyp segmentation (CPS), an essential problem in medical image analysis, has garnered growing research attention.
Fine-Grained Bipartite Concept Factorization for Clustering
In this paper we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph which provides comprehensive and complementary neighbor information of the data.
Arbitrary Motion Style Transfer with Multi-condition Motion Latent Diffusion Model
Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory motion content and motion style.
HOIAnimator: Generating Text-prompt Human-object Animations using Novel Perceptive Diffusion Models
To date the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research.
Rethinking Object Saliency Ranking: A Novel Whole-flow Processing Paradigm
To conquer, this paper proposes a new paradigm for saliency ranking, which aims to completely focus on ranking salient objects by their "importance order".
WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning
The main reason is that there always exist "blind zooms" when using HMD to collect fixations since the users cannot keep spinning their heads to explore the entire panoptic scene all the time.
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset.
Synthetic Data Supervised Salient Object Detection
In this paper, we propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs requiring only a few labeled data, and these synthesized pairs can replace the human-labeled DUTS-TR to train any off-the-shelf SOD model.
Boundary-Guided Camouflaged Object Detection
Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task.
A Comprehensive Survey on Video Saliency Detection with Auditory Information: the Audio-visual Consistency Perceptual is the Key!
Video saliency detection (VSD) aims at fast locating the most attractive objects/things/patterns in a given video clip.
A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection
The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames.
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations.
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively.
Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception
Moreover, we distill knowledge from these regions to obtain complete new spatial-temporal-audio (STA) fixation prediction (FP) networks, enabling broad applications in cases where video tags are not available.
From Semantic Categories to Fixations: A Novel Weakly-Supervised Visual-Auditory Saliency Detection Approach
Thanks to the rapid advances in the deep learning techniques and the wide availability of large-scale training sets, the performances of video saliency detection models have been improving steadily and significantly.
Mutual Graph Learning for Camouflaged Object Detection
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models.
Kernel Two-Dimensional Ridge Regression for Subspace Clustering
It directly uses 2D data as inputs such that the learning of representations benefits from inherent structures and relationships of the data.
Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter
In sharp contrast to the state-of-the-art (SOTA) methods that focus on learning pixel-wise saliency in "single image" using perceptual clues mainly, our method has investigated the "object-level semantic ranks between multiple images", of which the methodology is more consistent with the real human attention mechanism.
Full Reference Screen Content Image Quality Assessment by Fusing Multi-level Structure Similarity
The screen content images (SCIs) usually comprise various content types with sharp edges, in which the artifacts or distortions can be well sensed by the vanilla structure similarity measurement in a full reference manner.
Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections.
Depth Quality Aware Salient Object Detection
The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D).
Ranked #17 on
RGB-D Salient Object Detection
on NJU2K
A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception
Consequently, we can achieve a significant performance improvement by using this new training set to start a new round of network training.
A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset
Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets.
Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection
Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely.
Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection
Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network architecture.
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter.
A Plug-and-play Scheme to Adapt Image Saliency Deep Model for Video Data
With the rapid development of deep learning techniques, image saliency deep models trained solely by spatial information have occasionally achieved detection performance for video data comparable to that of the models trained by both spatial and temporal information.
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering
In particular, projection matrices are sought under the guidance of building new data representations, such that the spatial information is retained and projections are enhanced by the goal of clustering, which helps construct optimal projection directions.
Nonnegative Matrix Factorization with Local Similarity Learning
Existing nonnegative matrix factorization methods focus on learning global structure of the data to construct basis and coefficient matrices, which ignores the local structure that commonly exists among data.
RES-PCA: A Scalable Approach to Recovering Low-rank Matrices
This drawback has limited the application of RPCA in solving real world problems.
