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Found 40 papers, 22 papers with code
A comprehensive review of remote sensing in wetland classification and mapping
Although some review articles summarized the development of this field, there is a lack of a thorough and in-depth understanding of wetland classification and mapping: (1) the scientific importance of wetlands, (2) major data, methods used in wetland classification and mapping, (3) driving factors of wetland changes, (4) current research paradigm and limitations, (5) challenges and opportunities in wetland classification and mapping under the context of technological innovation and global environmental change.
Process-Supervised LLM Recommenders via Flow-guided Tuning
While large language models (LLMs) are increasingly adapted for recommendation systems via supervised fine-tuning (SFT), this approach amplifies popularity bias due to its likelihood maximization objective, compromising recommendation diversity and fairness.
SPRec: Leveraging Self-Play to Debias Preference Alignment for Large Language Model-based Recommendations
Large language models (LLMs) have attracted significant attention in recommendation systems.
Training Over a Distribution of Hyperparameters for Enhanced Performance and Adaptability on Imbalanced Classification
Although binary classification is a well-studied problem, training reliable classifiers under severe class imbalance remains a challenge.
ITPatch: An Invisible and Triggered Physical Adversarial Patch against Traffic Sign Recognition
Physical adversarial patches have emerged as a key adversarial attack to cause misclassification of traffic sign recognition (TSR) systems in the real world.
DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems
These sub-tasks are trained independently and inferred sequentially according to user-defined control numbers, ensuring more precise control over diversity.
Beyond Full Labels: Energy-Double-Guided Single-Point Prompt for Infrared Small Target Label Generation
We pioneer a learning-based single-point prompt paradigm for infrared small target label generation (IRSTLG) to lobber annotation burdens.
TALEC: Teach Your LLM to Evaluate in Specific Domain with In-house Criteria by Criteria Division and Zero-shot Plus Few-shot
We also propose a prompt paradigm and an engineering approach to adjust and iterate the shots , helping judge model to better understand the complex criteria.
A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder
A$^{2}$-MAE integrates an anchor-aware masking strategy and a geographic encoding module to comprehensively exploit the properties of RS images.
FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding
Although there have been advances in high-quality land cover datasets that reveal the physical features of urban landscapes, the lack of fine-grained land use datasets hinders a deeper understanding of how human activities are distributed across the landscape and the impact of these activities on the environment, thus constraining proper technique development.
UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model
Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information.
HaVTR: Improving Video-Text Retrieval Through Augmentation Using Large Foundation Models
While recent progress in video-text retrieval has been driven by the exploration of powerful model architectures and training strategies, the representation learning ability of video-text retrieval models is still limited due to low-quality and scarce training data annotations.
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model
Specifically, we inject the priors into the denoising model to improve the utilization of reference information in unchanged areas and regulate the reconstruction of semantically relevant content in changed areas.
A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond
Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence, uncovering new cross-domain opportunities and illustrating the substantial influence of code intelligence across various domains.
DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing Data
Nighttime light (NTL) remote sensing observation serves as a unique proxy for quantitatively assessing progress toward meeting a series of Sustainable Development Goals (SDGs), such as poverty estimation, urban sustainable development, and carbon emission.
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping
However, when applied to the infrared destriping task, it becomes challenging for the vanilla auxiliary generator to consistently produce vertical noise under unsupervised constraints.
Optimizing for ROC Curves on Class-Imbalanced Data by Training over a Family of Loss Functions
Although binary classification is a well-studied problem in computer vision, training reliable classifiers under severe class imbalance remains a challenging problem.
KS-Lottery: Finding Certified Lottery Tickets for Multilingual Language Models
We further theoretically prove that KS-Lottery can find the certified winning tickets in the embedding layer, fine-tuning on the found parameters is guaranteed to perform as well as full fine-tuning.
SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection
In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full scales.
ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping
In a real-world infrared imaging system, effectively learning a consistent stripe noise removal model is essential.
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language(e. g., chemical molecular formula).
How Vocabulary Sharing Facilitates Multilingualism in LLaMA?
Large Language Models (LLMs), often show strong performance on English tasks, while exhibiting limitations on other languages.
A review of individual tree crown detection and delineation from optical remote sensing images
); (2) traditional machine learning methods (such as random forest, decision tree, etc.
UFD-PRiME: Unsupervised Joint Learning of Optical Flow and Stereo Depth through Pixel-Level Rigid Motion Estimation
A second network, trained with optical flow from the first as pseudo-labels, takes disparities from the first network, estimates 3D rigid motion at every pixel, and reconstructs optical flow again.
Mitigating Test-Time Bias for Fair Image Retrieval
We address the challenge of generating fair and unbiased image retrieval results given neutral textual queries (with no explicit gender or race connotations), while maintaining the utility (performance) of the underlying vision-language (VL) model.
SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving
We show that additional information such as semantics and domain knowledge can help better constrain this problem.
Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation
Moreover, we propose the Class Center Contrast method to jointly utilize the labeled and unlabeled data.
Unsupervised Flow Refinement near Motion Boundaries
Unsupervised optical flow estimators based on deep learning have attracted increasing attention due to the cost and difficulty of annotating for ground truth.
Cross-Attention Transformer for Video Interpolation
We propose TAIN (Transformers and Attention for video INterpolation), a residual neural network for video interpolation, which aims to interpolate an intermediate frame given two consecutive image frames around it.
Optical Flow Training under Limited Label Budget via Active Learning
Supervised training of optical flow predictors generally yields better accuracy than unsupervised training.
RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments
Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely recognized as an enabling technology for improving radio spectrum efficiency.
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing
In this paper, a novel bandwidth negotiation mechanism is proposed for massive devices wireless spectrum sharing, in which individual device locally negotiates bandwidth usage with neighbor devices and globally optimal spectrum utilization is achieved through distributed decision-making.
Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands
In this paper, a noise attention method is proposed for unsupervised spectrum anomaly detection in unauthorized bands.
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
Solving this problem using a learning-to-learn approach -- using meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates -- was recently shown to be effective.
Spatially Constrained Spectral Clustering Algorithms for Region Delineation
To address these two challenges, first, we develop a spatially constrained spectral clustering framework for region delineation that incorporates the tradeoff between region homogeneity and spatial contiguity.
Learning Continuous User Representations through Hybrid Filtering with doc2vec
In order to maximize the predictive performance of our look-alike modeling algorithms, we propose two novel hybrid filtering techniques that utilize the recent neural probabilistic language model algorithm doc2vec.
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads.
Computer Science and Game Theory
Real-Time Bidding Benchmarking with iPinYou Dataset
This dataset directly supports the experiments of some important research problems such as bid optimisation and CTR estimation.
Computer Science and Game Theory Computers and Society
A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising
From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB.
Computer Science and Game Theory
Optimisation for job scheduling at automated container terminals using genetic algorithm
The GA-based approach has been implemented in the terminal scheduling system and the live testing results show that the GA-based approach can reduce the overall time-related cost of container transfers at the automated container terminal.
