Search Results for author:
Found 12 papers, 9 papers with code
CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation
The field of Remote Sensing Domain Generalization (RSDG) has emerged as a critical and valuable research frontier, focusing on developing models that generalize effectively across diverse scenarios.
When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models
Hence, with this paper, we aim to chart a course for future research that explores and expands the capabilities of 3D-LLMs in understanding and interacting with the complex 3D world.
CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning
SAVPT features a novel metric Severity that divides all adverse scene images into low-severity and high-severity images.
Ranked #1 on
Domain Adaptation
on Cityscapes-to-FoggyDriving
Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption
The standard Neural Radiance Fields (NeRF) paradigm employs a viewer-centered methodology, entangling the aspects of illumination and material reflectance into emission solely from 3D points.
Unifying Image Processing as Visual Prompting Question Answering
To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc.
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video.
Ranked #5 on
3D Question Answering (3D-QA)
on 3D MM-Vet
Personalize Segment Anything Model with One Shot
Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models.
Ranked #2 on
Personalized Segmentation
on PerSeg
Aleth-NeRF: Low-light Condition View Synthesis with Concealing Fields
Common capture low-light scenes are challenging for most computer vision techniques, including Neural Radiance Fields (NeRF).
Matching Is Not Enough: A Two-Stage Framework for Category-Agnostic Pose Estimation
To calibrate the inaccurate matching results, we introduce a two-stage framework, where matched keypoints from the first stage are viewed as similarity-aware position proposals.
Ranked #5 on
2D Pose Estimation
on MP-100
Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation
Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions.
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention
Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with great transferability, which achieves promising accuracy for zero-shot classification.
Ranked #5 on
Training-free 3D Point Cloud Classification
on ScanObjectNN
(using extra training data)
Training-free 3D Point Cloud Classification
Transfer Learning
+1
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding
Unlike previous methods that mainly focus on closing the domain gap caused by fog -- defogging the foggy images or fogging the clear images, we propose to alleviate the domain gap by considering fog influence and style variation simultaneously.
Ranked #4 on
Domain Adaptation
on Cityscapes-to-FoggyZurich
