Search Results for author:
Found 12 papers, 8 papers with code
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data
It designs a joint diffusion and denoising paradigm to model a potential noisy distribution in the task prediction or feature maps and generate rectified outputs for different tasks.
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis
On the highly competitive ADE20K and COCO benchmarks, our data generation method markedly improves the performance of state-of-the-art segmentation models in semantic segmentation, panoptic segmentation, and instance segmentation.
TaskExpert: Dynamically Assembling Multi-Task Representations with Memorial Mixture-of-Experts
Furthermore, to establish long-range modeling of the task-specific representations from different layers of TaskExpert, we design a multi-task feature memory that updates at each layer and acts as an additional feature expert for dynamic task-specific feature decoding.
Contrastive Multi-Task Dense Prediction
A core objective in design is how to effectively model cross-task interactions to achieve a comprehensive improvement on different tasks based on their inherent complementarity and consistency.
InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding
And then, we design a transformer decoder to establish spatial and cross-task interaction globally, and a novel UP-Transformer block is devised to increase the resolutions of multi-task features gradually and establish cross-task interaction at different scales.
Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation
TaskPrompter introduces a new multi-task benchmark based on Cityscapes-3D dataset, which requires the multi-task model to concurrently generate predictions for monocular 3D vehicle detection, semantic segmentation, and monocular depth estimation.
Ranked #1 on
Monocular Depth Estimation
on Cityscapes 3D
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding
Multi-task dense scene understanding is a thriving research domain that requires simultaneous perception and reasoning on a series of correlated tasks with pixel-wise prediction.
Ranked #1 on
Boundary Detection
on NYU-Depth V2
MOFA: Modular Factorial Design for Hyperparameter Optimization
MOFA pursues several rounds of HPO, where each round alternates between exploration of hyperparameter space by factorial design and exploitation of evaluation results by factorial analysis.
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization
The evaluation of hyperparameters, neural architectures, or data augmentation policies becomes a critical model selection problem in advanced deep learning with a large hyperparameter search space.
Bi-directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification
As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors.
Self-Refining Deep Symmetry Enhanced Network for Rain Removal
Rain removal aims to extract and remove rain streaks from images.
Video Logo Retrieval based on local Features
Estimation of the frequency and duration of logos in videos is important and challenging in the advertisement industry as a way of estimating the impact of ad purchases.
