Search Results for author:
Found 6 papers, 3 papers with code
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision
With this unpaired mask-text supervision, we propose a new weakly-supervised open-vocabulary segmentation framework (Uni-OVSeg) that leverages confident pairs of mask predictions and entities in text descriptions.
An Efficient Transformer for Simultaneous Learning of BEV and Lane Representations in 3D Lane Detection
Our method obtains 2D and 3D lane predictions by applying the lane features to the image-view and BEV features, respectively.
Generating Dynamic Kernels via Transformers for Lane Detection
While such methods reduce the reliance on specific knowledge, the kernels computed from the key locations fail to capture the lane line's global structure due to its long and thin structure, leading to inaccurate detection of lane lines with complex topologies.
Ranked #1 on
Lane Detection
on CurveLanes
Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference
Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure.
Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search
Despite their efficiency, current graph-based predictors treat all operations equally, resulting in biased topological knowledge of cell architectures.
Neural Mixed Counting Models for Dispersed Topic Discovery
Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics.
