Search Results for author:
Found 10 papers, 4 papers with code
DocTr: Document Transformer for Structured Information Extraction in Documents
We present a new formulation for structured information extraction (SIE) from visually rich documents.
Ranked #2 on
Entity Linking
on FUNSD
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation
We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks.
Improving Face Recognition by Clustering Unlabeled Faces in the Wild
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation.
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years.
Automatic adaptation of object detectors to new domains using self-training
Our results demonstrate the usefulness of incorporating hard examples obtained from tracking, the advantage of using soft-labels via distillation loss versus hard-labels, and show promising performance as a simple method for unsupervised domain adaptation of object detectors, with minimal dependence on hyper-parameters.
Unsupervised Hard Example Mining from Videos for Improved Object Detection
In this work, we show how large numbers of hard negatives can be obtained {\em automatically} by analyzing the output of a trained detector on video sequences.
The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation
Traditional methods of motion segmentation use powerful geometric constraints to understand motion, but fail to leverage the semantics of high-level image understanding.
Bilinear CNN Models for Fine-Grained Visual Recognition
We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an image descriptor.
Ranked #61 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Visual Recognition
One-to-many face recognition with bilinear CNNs
We demonstrate the performance of the B-CNN model beginning from an AlexNet-style network pre-trained on ImageNet.
Bilinear CNNs for Fine-grained Visual Recognition
We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture.
Ranked #22 on
Fine-Grained Image Classification
on NABirds
Fine-Grained Image Classification
Fine-Grained Visual Recognition
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