Latest Research
A text-based, generative deep learning model for soil reflectance spectrum simulation in the VIS-NIR (400-2499 nm) bands
To address this, a fully data-driven soil optics generative model (SOGM) for simulation of soil reflectance spectra based on soil property inputs was developed.
Dynamic Online Ensembles of Basis Expansions
Practical Bayesian learning often requires (1) online inference, (2) dynamic models, and (3) ensembling over multiple different models.
RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation
liamheng/non-iid_medical_image_segmentation
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To tackle domain shifts in data-scarce medical scenarios, we propose a Random frequency filtering enabled Single-source Domain Generalization algorithm (RaffeSDG), which promises robust out-of-domain inference with segmentation models trained on a single-source domain.
Language Fairness in Multilingual Information Retrieval
We also evaluate real MLIR systems on two publicly available benchmarks and show that the PEER scores align with prior analytical findings on MLIR fairness.
StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation
This module converts the generated sequence of images into videos with smooth transitions and consistent subjects that are significantly more stable than the modules based on latent spaces only, especially in the context of long video generation.
Spider: A Unified Framework for Context-dependent Concept Understanding
Different from the context-independent (CI) concepts such as human, car, and airplane, context-dependent (CD) concepts require higher visual understanding ability, such as camouflaged object and medical lesion.
Technical Report of NICE Challenge at CVPR 2024: Caption Re-ranking Evaluation Using Ensembled CLIP and Consensus Scores
This report presents the ECO (Ensembled Clip score and cOnsensus score) pipeline from team DSBA LAB, which is a new framework used to evaluate and rank captions for a given image.
Multi-intent-aware Session-based Recommendation
Session-based recommendation (SBR) aims to predict the following item a user will interact with during an ongoing session.
SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources.
Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space
We demonstrate the adaptability of our agents to novel scenarios and assembly sequences while emphasizing the potential of leveraging advanced simulation techniques for robot learning in space.
