Search Results for author:
Found 9 papers, 6 papers with code
Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks.
Instance Segmentation With Mask-Supervised Polygonal Boundary Transformers
In this paper, we present an end-to-end instance segmentation method that regresses a polygonal boundary for each object instance.
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models
In this paper, we detail the relationship between convolutions and self-attention in natural language tasks.
Pose Recognition with Cascade Transformers
Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches.
Co-Scale Conv-Attentional Image Transformers
In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms.
Line Segment Detection Using Transformers without Edges
In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free.
Ranked #1 on
Line Segment Detection
on York Urban Dataset
(FH metric)
Constellation Nets for Few-Shot Learning
The success of deep convolutional neural networks builds on top of the learning of effective convolution operations, capturing a hierarchy of structured features via filtering, activation, and pooling.
Ranked #15 on
Few-Shot Image Classification
on FC100 5-way (5-shot)
Guided Variational Autoencoder for Disentanglement Learning
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning.
Wasserstein Introspective Neural Networks
We present Wasserstein introspective neural networks (WINN) that are both a generator and a discriminator within a single model.
