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Found 12 papers, 9 papers with code
BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification
In this paper, we present BrainPrompt, an innovative framework that enhances Graph Neural Networks (GNNs) by integrating Large Language Models (LLMs) with knowledge-driven prompts, enabling more effective capture of complex, non-imaging information and external knowledge for neurological disease identification.
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.
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
Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach
As such, the key issues of WS-PDA are: 1) how to sufficiently discover the knowledge from the noisy labeled source domain and the unlabeled target domain, and 2) how to successfully adapt the knowledge across domains.
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction
The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner.
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces.
