Search Results for author:
Found 11 papers, 5 papers with code
HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200, 000 edits.
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge
We identify that the Decoder-only 3D-TransUNet model should offer enhanced efficacy in the segmentation of brain metastases, as indicated by our 5-fold cross-validation on the training set.
AQA-Bench: An Interactive Benchmark for Evaluating LLMs' Sequential Reasoning Ability
This paper introduces AQA-Bench, a novel benchmark to assess the sequential reasoning capabilities of large language models (LLMs) in algorithmic contexts, such as depth-first search (DFS).
Large-scale Weakly Supervised Learning for Road Extraction from Satellite Imagery
Our extensive experimental results show that the prediction accuracy increases with the amount of the weakly labeled data, as well as the road density in the areas chosen for training.
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.
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
Enhancing the robustness of vision algorithms in real-world scenarios is challenging.
AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation
Through systematic analysis, we found that the commonly used pairwise affinity loss has two limitations: (1) it works with color affinity but leads to inferior performance with other modalities such as depth gradient, (2)the original affinity loss does not prevent trivial predictions as intended but actually accelerates this process due to the affinity loss term being symmetric.
XCon: Learning with Experts for Fine-grained Category Discovery
We address the problem of generalized category discovery (GCD) in this paper, i. e. clustering the unlabeled images leveraging the information from a set of seen classes, where the unlabeled images could contain both seen classes and unseen classes.
Rail-5k: a Real-World Dataset for Rail Surface Defects Detection
This paper presents the Rail-5k dataset for benchmarking the performance of visual algorithms in a real-world application scenario, namely the rail surface defects detection task.
Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization
Visual pattern recognition over agricultural areas is an important application of aerial image processing.
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
