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Found 23 papers, 6 papers with code
M$^3$amba: CLIP-driven Mamba Model for Multi-modal Remote Sensing Classification
Multi-modal fusion holds great promise for integrating information from different modalities.
Computational Efficiency
Hyperspectral Image Classification
+1
Visual Generation Without Guidance
It retains the same maximum likelihood objective as CFG and differs mainly in the parameterization of conditional models.
DiffCLIP: Few-shot Language-driven Multimodal Classifier
To tackle this issue, we introduce DiffCLIP, a novel framework that extends CLIP to effectively convey comprehensive language-driven semantic information for accurate classification of high-dimensional multimodal remote sensing images.
A Survey on Vision Autoregressive Model
Autoregressive models have demonstrated great performance in natural language processing (NLP) with impressive scalability, adaptability and generalizability.
RS-MoE: Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering
We fine-tuned our model on the RSICap dataset using our proposed training strategy.
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization
Generalizing to out-of-distribution data while being aware of model fairness is a significant and challenging problem in meta-learning.
Open-Vocabulary Object Detection via Language Hierarchy
LHST expands the image-level labels with language hierarchy and enables co-regularization between the expanded labels and self-training.
Ensemble BERT: A student social network text sentiment classification model based on ensemble learning and BERT architecture
The mental health assessment of middle school students has always been one of the focuses in the field of education.
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior
The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e. g., $\ell_{2, 1}$-norm).
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
Theoretical analysis yields sub-linear upper bounds for both loss regret and the cumulative violation of fairness constraints.
DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception
Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space.
Domain Adaptation for Large-Vocabulary Object Detectors
Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data.
Learning to Prompt Segment Anything Models
SAMs work with two types of prompts including spatial prompts (e. g., points) and semantic prompts (e. g., texts), which work together to prompt SAMs to segment anything on downstream datasets.
Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image
Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research.
Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey
Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture, arising two limitations: (1) it leads to task-specific models, which require multiple models for different tasks and restrict the potential synergies from diverse tasks; (2) it leads to a pre-defined and fixed model interface that has limited interactivity and adaptability in following user' task instructions.
Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
The endeavor to preserve the generalization of a fair and invariant classifier across domains, especially in the presence of distribution shifts, becomes a significant and intricate challenge in machine learning.
Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective
In this study, we introduce a novel problem, semantic OOD detection across domains, which simultaneously addresses both distributional shifts.
Towards Fair Disentangled Online Learning for Changing Environments
To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor.
Toward Stable, Interpretable, and Lightweight Hyperspectral Super-Resolution
Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework.
A multi-category inverse design neural network and its application to diblock copolymers
The neural network model consists of two parts, a classifier and Structure-Parameter-Mapping (SPM) subnets.
Adaptive Fairness-Aware Online Meta-Learning for Changing Environments
Furthermore, to determine a good model parameter at each round, we propose a novel adaptive fairness-aware online meta-learning algorithm, namely FairSAOML, which is able to adapt to changing environments in both bias control and model precision.
2nd Place Solution to Instance Segmentation of IJCAI 3D AI Challenge 2020
Compared with MS-COCO, the dataset for the competition has a larger proportion of large objects which area is greater than 96x96 pixels.
Reinforcement Learning with Goal-Distance Gradient
But the rewards in the actual environment are sparse, and even some environments will not rewards.
