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Found 29 papers, 8 papers with code
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs
Besides, the existing methods primarily focus on anomaly detection in static graphs, and little effort was paid to consider the continuous evolution characteristic of graphs over time (dynamic graphs).
Graph Anomaly Detection
Semi-supervised Anomaly Detection
+1
Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Prediction
Understanding and anticipating human movement has become more critical and challenging in diverse applications such as autonomous driving and surveillance.
Towards Cross-domain Few-shot Graph Anomaly Detection
Few-shot graph anomaly detection (GAD) has recently garnered increasing attention, which aims to discern anomalous patterns among abundant unlabeled test nodes under the guidance of a limited number of labeled training nodes.
Generalized Sparse Additive Model with Unknown Link Function
The proposed GSAMUL can realize both variable selection and hidden interaction.
What Happens Without Background? Constructing Foreground-Only Data for Fine-Grained Tasks
Fine-grained recognition, a pivotal task in visual signal processing, aims to distinguish between similar subclasses based on discriminative information present in samples.
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions
To address this issue, we propose a novel approach termed the detail reinforcement diffusion model~(DRDM), which leverages the rich knowledge of large models for fine-grained data augmentation and comprises two key components including discriminative semantic recombination (DSR) and spatial knowledge reference~(SKR).
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing
In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks.
Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums
More and more trajectories with different forms, such as coordinates, bounding boxes, and even high-dimensional human skeletons, need to be analyzed and forecasted.
Semantic-visual Guided Transformer for Few-shot Class-incremental Learning
And then, a text encoder is introduced to automatically generate the corresponding semantic (text) labels for each image from the base classes.
Ranked #3 on
Few-Shot Class-Incremental Learning
on CUB-200-2011
class-incremental learning
Few-Shot Class-Incremental Learning
+2
Filter Pruning based on Information Capacity and Independence
For the amount of information contained in each filter, a new metric called information capacity is proposed.
Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features
Some recent methods have been proposed to reconstruct the missing node features by the information propagation among nodes with known and unknown attributes.
Deep Manifold Hashing: A Divide-and-Conquer Approach for Semi-Paired Unsupervised Cross-Modal Retrieval
Hashing that projects data into binary codes has shown extraordinary talents in cross-modal retrieval due to its low storage usage and high query speed.
Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis
Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i. e., hashing-based cross-modal medical data retrieval), provides a new view to promot computeraided diagnosis.
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning
Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e. g., attribute semantics) between visual and attribute features.
CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces
The current methods are dedicated to studying the agents' future trajectories under the social interaction and the sceneries' physical constraints.
TransZero: Attribute-guided Transformer for Zero-Shot Learning
Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected.
View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums
Different frequency bands in the trajectory spectrums could hierarchically reflect agents' motion preferences at different scales.
Ranked #2 on
Trajectory Prediction
on ETH/UCY
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i. e., structure adaptation and distribution adaptation.
FREE: Feature Refinement for Generalized Zero-Shot Learning
FREE employs a feature refinement (FR) module that incorporates \textit{semantic$\rightarrow$visual} mapping into a unified generative model to refine the visual features of seen and unseen class samples.
MSN: Multi-Style Network for Trajectory Prediction
Then, we assume that the target agents may plan their future behaviors according to each of these categorized styles, thus utilizing different style channels to make predictions with significant style differences in parallel.
BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction
Visual images usually contain the informative context of the environment, thereby helping to predict agents' behaviors.
Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning
Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution.
A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction
It remains challenging to automatically predict the multi-agent trajectory due to multiple interactions including agent to agent interaction and scene to agent interaction.
Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis
Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge.
Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation
Weakly-supervised semantic segmentation aims to assign each pixel a semantic category under weak supervisions, such as image-level tags.
Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network
High angular resolution diffusion imaging (HARDI) demands a lager amount of data measurements compared to diffusion tensor imaging, restricting its use in practice.
Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network
It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.
Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.
Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery
Low-Rank Matrix Recovery (LRMR) has recently been applied to saliency detection by decomposing image features into a low-rank component associated with background and a sparse component associated with visual salient regions.
