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Found 20 papers, 13 papers with code
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
Graph Neural Networks (GNNs) have shown promising in analyzing brain networks, but there are two major challenges in using GNNs: (1) distribution shifts in multi-site brain network data, leading to poor Out-of-Distribution (OOD) generalization, and (2) limited interpretability in identifying key brain regions critical to neurological disorders.
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
Another challenge lies in aligning the domain-specific and cross-domain sequences.
GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding
Remote sensing (RS) visual grounding aims to use natural language expression to locate specific objects (in the form of the bounding box or segmentation mask) in RS images, enhancing human interaction with intelligent RS interpretation systems.
Text4Seg: Reimagining Image Segmentation as Text Generation
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains a significant challenge.
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
Some recent methods have proposed utilizing multiple atlases, but they neglect consistency across atlases and lack ROI-level information exchange.
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
It generates a prior-knowledge-enhanced contrast graph to address the distribution shifts across sub-populations by a two-stream attention mechanism.
ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation
ProxyCLIP leverages the spatial feature correspondence from VFMs as a form of proxy attention to augment CLIP, thereby inheriting the VFMs' robust local consistency and maintaining CLIP's exceptional zero-shot transfer capacity.
Ranked #2 on
Unsupervised Semantic Segmentation with Language-image Pre-training
on PASCAL Context-60
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+2
ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference
Despite the success of large-scale pretrained Vision-Language Models (VLMs) especially CLIP in various open-vocabulary tasks, their application to semantic segmentation remains challenging, producing noisy segmentation maps with mis-segmented regions.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+1
Towards Vision-Language Geo-Foundation Model: A Survey
Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding.
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization.
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions.
Union Subgraph Neural Networks
We first investigate different kinds of connectivities existing in a local neighborhood and identify a substructure called union subgraph, which is able to capture the complete picture of the 1-hop neighborhood of an edge.
Data-Driven Network Neuroscience: On Data Collection and Benchmark
This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics.
A Class-Aware Representation Refinement Framework for Graph Classification
In this paper, we propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification.
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions
Neural marked point processes possess good interpretability of probabilistic models as well as the representational power of neural networks.
Subdomain Adaptation with Manifolds Discrepancy Alignment
Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains.
Thinning for Accelerating the Learning of Point Processes
Experimental results on synthetic and real-world datasets validate the effectiveness of thinning in the tasks of parameter and gradient estimation, as well as stochastic optimization.
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression
A key challenge in multi-source transfer learning is to capture the diverse inter-domain similarities.
Stochastic Variance Reduced Riemannian Eigensolver
We generalize it to Riemannian manifolds and realize it to solve the non-convex eigen-decomposition problem.
