Search Results for author:
Found 28 papers, 15 papers with code
Region Graph Embedding Network for Zero-Shot Learning
To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.
Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification
On the other hand, mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks.
LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection
To address these limitations, we introduce a new task, Reasoning Logical Anomaly Detection (RLAD), which extends traditional anomaly detection by incorporating logical reasoning.
SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation
In contrast, the large-scale visual model SAM, pre-trained on tens of millions of images from various domains and classes, possesses excellent generalizability.
Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection
Specifically, a kernel-aware hierarchical graph is built by taking the different layer features focusing on anomalous regions of different sizes as nodes, meanwhile, the relationships between arbitrary pairs of nodes stand for the edges of the graph.
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation
Therefore, in this paper, we utilize the more challenging image-level annotations and propose an adaptive frequency-aware network (AFANet) for weakly-supervised few-shot semantic segmentation (WFSS).
Visual-Semantic Graph Matching Net for Zero-Shot Learning
However, methods trained under this paradigm often struggle to learn robust embedding space because they align the two modalities in an isolated manner among classes, which ignore the crucial class relationship during the alignment process.
When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation
This study presents a comprehensive study on SAM2's ability in VCOS.
AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition
Therefore, we propose a novel Adapt-Focused bi-Propagating Prototype learning (AdaFPP) framework to jointly recognize individual, group, and global activities in panoramic activity scenes by learning an adapt-focused detector and multi-granularity prototypes as the pretext tasks in an end-to-end way.
MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition
Recent few-shot action recognition (FSAR) methods typically perform semantic matching on learned discriminative features to achieve promising performance.
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues.
Holistic Prototype Attention Network for Few-Shot VOS
Existing methods have demonstrated that the domain agent-based attention mechanism is effective in FSVOS by learning the correlation between support images and query frames.
Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning
In particular, we propose a novel Attack-Augmentation Mixing-Contrastive skeletal representation learning (A$^2$MC) to contrast hard positive features and hard negative features for learning more robust skeleton representations.
Learning Anchor Transformations for 3D Garment Animation
Once the anchor transformations are found, per-vertex nonlinear displacements of the garment template can be regressed in a canonical space, which reduces the complexity of deformation space learning.
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS).
Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation
Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes.
Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation
Specifically, in DPCN, a dynamic convolution module (DCM) is firstly proposed to generate dynamic kernels from support foreground, then information interaction is achieved by convolution operations over query features using these kernels.
Ranked #36 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
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.
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.
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.
Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation
However, they fail to fully leverage the high-order appearance relationships between multi-scale features among the support-query image pairs, thus leading to an inaccurate localization of the query objects.
Few-Shot Semantic Segmentation With Cyclic Memory Network
Specifically, we first generate N pairs (key and value) of multi-resolution query features guided by the support feature and its mask.
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.
Attentive Region Embedding Network for Zero-Shot Learning
Zero-shot learning (ZSL) aims to classify images from unseen categories, by merely utilizing seen class images as the training data.
SADIH: Semantic-Aware DIscrete Hashing
Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.
Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation
Learned from a large-scale training dataset, CNN features are much more discriminative and accurate than the hand-crafted features.
Task-Driven Feature Pooling for Image Classification
Feature pooling is an important strategy to achieve high performance in image classification.
