Search Results for author:
Found 27 papers, 11 papers with code
Multimodal Prompting with Missing Modalities for Visual Recognition
In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations; and 2) when the computation resources are not available to finetune on heavy transformer models.
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image.
Neural Spline Search for Quantile Probabilistic Modeling
Accurate estimation of output quantiles is crucial in many use cases, where it is desired to model the range of possibility.
A Benchmark for Structured Extractions from Complex Documents
Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry.
QueryForm: A Simple Zero-shot Form Entity Query Framework
Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.
Prefix Conditioning Unifies Language and Label Supervision
However, a naive unification of the real caption and the prompt sentences could lead to a complication in learning, as the distribution shift in text may not be handled properly in the language encoder.
DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning
Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting.
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction
Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks.
Towards Group Robustness in the presence of Partial Group Labels
Such a requirement is impractical in situations where the data labeling efforts for minority or rare groups are significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings.
Learning to Prompt for Continual Learning
The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.
Invariant Learning with Partial Group Labels
Such a requirement is impractical in situations where the data labelling efforts for minority or rare groups is significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning
While remarkable progress in imbalanced supervised learning has been made recently, less attention has been given to the setting of imbalanced semi-supervised learning (SSL) where not only is a few labeled data provided, but the underlying data distribution can be severely imbalanced.
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Natural reading orders of words are crucial for information extraction from form-like documents.
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
We demonstrate our method on various unsupervised AD tasks with image and tabular data.
Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts
To address this issue, we introduce a new method for pre-training video action recognition models using queried web videos.
Exploring Sub-Pseudo Labels for Learning from Weakly-Labeled Web Videos
To address this issue, we introduce a new method for pre-training video action recognition models using queried web videos.
Learning to Branch for Multi-Task Learning
Training multiple tasks jointly in one deep network yields reduced latency during inference and better performance over the single-task counterpart by sharing certain layers of a network.
A Simple Semi-Supervised Learning Framework for Object Detection
Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data.
Ranked #10 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
In this work, we connect two distinct concepts for unsupervised domain adaptation: feature distribution alignment between domains by utilizing the task-specific decision boundary and the Wasserstein metric.
Ranked #17 on
Domain Adaptation
on VisDA2017
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly.
RoomNet: End-to-End Room Layout Estimation
This paper focuses on the task of room layout estimation from a monocular RGB image.
Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
We present recursive recurrent neural networks with attention modeling (R$^2$AM) for lexicon-free optical character recognition in natural scene images.
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures.
Ranked #19 on
Image Classification
on SVHN
Training Deeper Convolutional Networks with Deep Supervision
One of the most promising ways of improving the performance of deep convolutional neural networks is by increasing the number of convolutional layers.
Deeply-Supervised Nets
Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent.
Ranked #25 on
Image Classification
on SVHN
Region-based Discriminative Feature Pooling for Scene Text Recognition
We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition.
