Search Results for author:
Found 7 papers, 4 papers with code
VLPose: Bridging the Domain Gap in Pose Estimation with Language-Vision Tuning
Our research aims to bridge the domain gap between natural and artificial scenarios with efficient tuning strategies.
MOODv2: Masked Image Modeling for Out-of-Distribution Detection
The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-distribution (ID) representation, distinct from OOD samples.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
BAL: Balancing Diversity and Novelty for Active Learning
Experimental results demonstrate that, when labeling 80% of the samples, the performance of the current SOTA method declines by 0. 74%, whereas our proposed BAL achieves performance comparable to the full dataset.
MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks
Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks.
Ranked #1 on
Code Generation
on CodeContests
(Test Set pass@1 metric)
TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation
However, existing models easily misidentify input pixels from unseen classes, thus confusing novel classes with semantically-similar ones.
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples.
Ranked #12 on
Out-of-Distribution Detection
on ImageNet-1k vs Places
(AUROC metric)
IE-GAN: An Improved Evolutionary Generative Adversarial Network Using a New Fitness Function and a Generic Crossover Operator
The training of generative adversarial networks (GANs) is usually vulnerable to mode collapse and vanishing gradients.
