39 research outputs found
Where are the objects? : weakly supervised methods for counting, localization and segmentation
No full textIn 2012, deep learning made a major comeback. Deep learning started breaking records in many machine learning benchmarks, especially those in the field of computer vision. By integrating deep learning, machine learning methods have became more practical for many applications like object counting, detection, or segmentation. Unfortunately, in the typical supervised learning setting, deep learning methods might require a lot of labeled data that are costly to acquire. For instance, in the case of acquiring segmentation labels, the annotator has to label each pixel in order to draw a mask over each object and get the background regions. In fact, each image in the CityScapes dataset took around 1.5 hours to label. Further, to achieve high accuracy, we might need millions of such images.
In this work, we propose four weakly supervised methods. They only require labels that are cheap to collect, yet they perform well in practice. We devised an experimental setup for each proposed method. In the first setup, the model needs to learn to count objects from point annotations. In the second setup, the model needs to learn to segment objects from point annotations. In the third setup, the model needs to segment objects from image level annotations. In the final setup, the model needs to learn to detect objects using counts only. For each of these setups the proposed method achieves state-of-the-art results in its respective benchmark. Interestingly, our methods are not much worse than fully supervised methods. This is despite their training labels being significantly cheaper to acquire than for the fully supervised case. In fact, in fixing the time budget for collecting annotations, our models performed much better than fully supervised methods. This suggests that carefully designed models can effectively learn from data labeled with low human effort.Science, Faculty ofComputer Science, Department ofGraduat
Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
No full textBlock coordinate descent (BCD) methods are widely used for large-scale
numerical optimization because of their cheap iteration costs, low memory
requirements, amenability to parallelization, and ability to exploit problem
structure. Three main algorithmic choices influence the performance of BCD
methods: the block partitioning strategy, the block selection rule, and the
block update rule. In this paper we explore all three of these building blocks
and propose variations for each that can significantly improve the progress
made by each BCD iteration. We (i) propose new greedy block-selection
strategies that guarantee more progress per iteration than the Gauss-Southwell
rule; (ii) explore practical issues like how to implement the new rules when
using "variable" blocks; (iii) explore the use of message-passing to compute
matrix or Newton updates efficiently on huge blocks for problems with sparse
dependencies between variables; and (iv) consider optimal active manifold
identification, which leads to bounds on the "active-set complexity" of BCD
methods and leads to superlinear convergence for certain problems with sparse
solutions (and in some cases finite termination at an optimal solution). We
support all of our findings with numerical results for the classic machine
learning problems of least squares, logistic regression, multi-class logistic
regression, label propagation, and L1-regularization.Comment: Updated author affiliations and contact informatio
MixSumm: Topic-based Data Augmentation using LLMs for Low-resource Extractive Text Summarization
No full textLow-resource extractive text summarization is a vital but heavily
underexplored area of research. Prior literature either focuses on abstractive
text summarization or prompts a large language model (LLM) like GPT-3 directly
to generate summaries. In this work, we propose MixSumm for low-resource
extractive text summarization. Specifically, MixSumm prompts an open-source
LLM, LLaMA-3-70b, to generate documents that mix information from multiple
topics as opposed to generating documents without mixup, and then trains a
summarization model on the generated dataset. We use ROUGE scores and L-Eval, a
reference-free LLaMA-3-based evaluation method to measure the quality of
generated summaries. We conduct extensive experiments on a challenging text
summarization benchmark comprising the TweetSumm, WikiHow, and ArXiv/PubMed
datasets and show that our LLM-based data augmentation framework outperforms
recent prompt-based approaches for low-resource extractive summarization.
Additionally, our results also demonstrate effective knowledge distillation
from LLaMA-3-70b to a small BERT-based extractive summarizer
CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions
No full textIn the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous. However, despite the significant and undoubted progress of the field in addressing the issue of catastrophic forgetting, benchmarking different continual learning approaches is a difficult task by itself. In fact, given the proliferation of different settings, training and evaluation protocols, metrics and nomenclature, it is often tricky to properly characterize a continual learning algorithm, relate it to other solutions and gauge its real-world applicability. The first Continual Learning in Computer Vision challenge held at CVPR in 2020 has been one of the first opportunities to evaluate different continual learning algorithms on a common hardware with a large set of shared evaluation metrics and 3 different settings based on the realistic CORe50 video benchmark. In this paper, we report the main results of the competition, which counted more than 79 teams registered and 11 finalists. We also summarize the winning approaches, current challenges and future research directions
Prompt-based Pseudo-labeling Strategy for Sample-Efficient Semi-Supervised Extractive Summarization
No full textSemi-supervised learning (SSL) is a widely used technique in scenarios where labeled data is scarce and unlabeled data is abundant. While SSL is popular for image and text classification, it is relatively underexplored for the task of extractive text summarization. Standard SSL methods follow a teacher-student paradigm to first train a classification model and then use the classifier\u27s confidence values to select pseudo-labels for the subsequent training cycle; however, such classifiers are not suitable to measure the accuracy of pseudo-labels as they lack specific tuning for evaluation, which leads to confidence values that fail to capture the semantics and correctness of the generated summary. To address this problem, we propose a prompt-based pseudo-labeling strategy with LLMs that picks unlabeled examples with more accurate pseudo-labels than using just the classifier\u27s probability outputs. Our approach also includes a relabeling mechanism that improves the quality of pseudo-labels. We evaluate our method on three text summarization datasets: TweetSumm, WikiHow, and ArXiv/PubMed. We empirically show that a prompting-based LLM that scores and generates pseudo-labels outperforms existing SSL methods on ROUGE-1, ROUGE-2, and ROUGE-L scores on all the datasets. Furthermore, our method achieves competitive L-Eval scores (evaluation with LLaMa-3) as a fully supervised method in a data-scarce setting and outperforms fully supervised method in a data-abundant setting.8 pages, 6 figures, 3 table
Sparse Single-Hidden Layer Feedforward Network for Mapping Natural Language Questions to SQL Queries
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