Search Results for author:
Found 38 papers, 12 papers with code
ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection
Extensive experiments on the PASCAL VOC and COCO datasets show that our ECEA module can assist the few-shot detector to completely predict the object despite some regions failing to appear in the training samples and achieve the new state of the art compared with existing FSOD methods.
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.
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning
However, existing GAN-based generative ZSL methods are based on hand-crafted models, which cannot adapt to various datasets/scenarios and fails to model instability.
Evolving Semantic Prototype Improves Generative Zero-Shot Learning
After alignment, synthesized sample features from unseen classes are closer to the real sample features and benefit DSP to improve existing generative ZSL methods by 8. 5\%, 8. 0\%, and 9. 7\% on the standard CUB, SUN AWA2 datasets, the significant performance improvement indicates that evolving semantic prototype explores a virgin field in ZSL.
Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization
Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species.
CDLT: A Dataset with Concept Drift and Long-Tailed Distribution for Fine-Grained Visual Categorization
Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC).
Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums
In this paper, we bring a new ``view'' for trajectory prediction to model and forecast trajectories hierarchically according to different frequency portions from the spectral domain to learn to forecast trajectories by considering their frequency responses.
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.
Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval
In this paper, instead of using an extra NLP model to define a common space beforehand, we consider a totally different way to construct (or learn) a common hamming space from an information-theoretic perspective.
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.
Leachable Component Clustering
Clustering attempts to partition data instances into several distinctive groups, while the similarities among data belonging to the common partition can be principally reserved.
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.
R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction
In this paper, we present a novel approach for FGVC, which can simultaneously make use of partial yet sufficient discriminative information in environmental cues and also compress the redundant information in class-token with respect to the target.
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++: Cross Attribute-Guided Transformer for Zero-Shot Learning
Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features.
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.
Adaptive Matching of Kernel Means
As a promising step, the performance of data analysis and feature learning are able to be improved if certain pattern matching mechanism is available.
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.
CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Network
In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multi-objective optimization.
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.
Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling
It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations.
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.
Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition
Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning.
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.
Multi-view Common Component Discriminant Analysis for Cross-view Classification
Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision.
Multi-view Hybrid Embedding: A Divide-and-Conquer Approach
We present a novel cross-view classification algorithm where the gallery and probe data come from different views.
Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning
In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD^2L) approach for SR person re-identification.
