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Found 11 papers, 3 papers with code
Unified Language-driven Zero-shot Domain Adaptation
This paper introduces Unified Language-driven Zero-shot Domain Adaptation (ULDA), a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge.
LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model
While LISA effectively bridges the gap between segmentation and large language models to enable reasoning segmentation, it poses certain limitations: unable to distinguish different instances of the target region, and constrained by the pre-defined textual response formats.
LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding
In this paper, we introduce LiDAR-LLM, which takes raw LiDAR data as input and harnesses the remarkable reasoning capabilities of LLMs to gain a comprehensive understanding of outdoor 3D scenes.
Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation
To tackle these issues, we propose a continual self-supervised method, Adaptive Distribution Masked Autoencoders (ADMA), which enhances the extraction of target domain knowledge while mitigating the accumulation of distribution shifts.
Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation
In this paper, we propose a distribution-aware tuning (DAT) method to make the semantic segmentation CTTA efficient and practical in real-world applications.
PM-DETR: Domain Adaptive Prompt Memory for Object Detection with Transformers
PDM comprehensively leverages the prompt memory to extract domain-specific knowledge and explicitly constructs a long-term memory space for the data distribution, which represents better domain diversity compared to existing methods.
ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation
Note that, our method can be regarded as a novel transfer paradigm for large-scale models, delivering promising results in adaptation to continually changing distributions.
Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction
The visual prompts have provided an efficient manner in addressing visual cross-domain problems.
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All in One Classifier
The SCL loss weakens the adverse effects of the data augmentation view-noise problem which is amplified in domain transfer tasks.
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Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier
The SCL loss weakens the adverse effects of the data augmentation view-noise problem which is amplified in domain transfer tasks.
UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection
We propose that the ideal DR approach combines both FS and FP into a unified end-to-end manifold learning framework, simultaneously performing fundamental feature discovery while maintaining the intrinsic relationships between data samples in the latent space.
