Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMiniVQA - A resource to build your tailored VQA competition
Minivqa - مورد لبناء مسابقة VQA المصممة
369
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
Minivqa هو دفتر ملاحظات Jupiter لبناء مسابقة VQA مصممة خصيصا لطلابك.ينشئ المورد جميع الموارد اللازمة لإنشاء مسابقة الفصل الدراسي التي تشارك وتلهم طلابك على منصة Kaggle المجانية والخدمة الذاتية.مسابقات inclass تجعل آلة التعلم المتعة!
MiniVQA is a Jupyter notebook to build a tailored VQA competition for your students. The resource creates all the needed resources to create a classroom competition that engages and inspires your students on the free, self-service Kaggle platform. InClass competitions make machine learning fun!.
References used
https://aclanthology.org/
rate research
Read More
We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample. EASE is based on the pattern of answers provided by multiple annotators to a given question. In particular, it
considers two aspects of the answers: (i) their Entropy; (ii) their Semantic content. First, we prove the validity of our diagnostic to identify samples that are easy/hard for state-of-art VQA models. Second, we show that EASE can be successfully used to select the most-informative samples for training/fine-tuning. Crucially, only information that is readily available in any VQA dataset is used to compute its scores.
One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a
key role. In this paper, we propose a semi-automatic framework for generating disentangled shifts by introducing a controllable visual question-answer generation (VQAG) module that is capable of generating highly-relevant and diverse question-answer pairs with the desired dataset style. We use it to create CrossVQA, a collection of test splits for assessing VQA generalization based on the VQA2, VizWiz, and Open Images datasets. We provide an analysis of our generated datasets and demonstrate its utility by using them to evaluate several state-of-the-art VQA systems. One important finding is that the visual shifts in cross-dataset VQA matter more than the language shifts. More broadly, we present a scalable framework for systematically evaluating the machine with little human intervention.
Large language models have shown promising results in zero-shot settings. For example, they can perform multiple choice tasks simply by conditioning on a question and selecting the answer with the highest probability. However, ranking by string proba
bility can be problematic due to surface form competition---wherein different surface forms compete for probability mass, even if they represent the same underlying concept in a given context, e.g. computer'' and PC.'' Since probability mass is finite, this lowers the probability of the correct answer, due to competition from other strings that are valid answers (but not one of the multiple choice options). We introduce Domain Conditional Pointwise Mutual Information, an alternative scoring function that directly compensates for surface form competition by simply reweighing each option according to its a priori likelihood within the context of a specific task. It achieves consistent gains in zero-shot performance over both calibrated and uncalibrated scoring functions on all GPT-2 and GPT-3 models on a variety of multiple choice datasets.
Word meaning is notoriously difficult to capture, both synchronically and diachronically. In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based
on 100,000 human semantic proximity judgments. We describe in detail the multi-round incremental annotation process, the choice for a clustering algorithm to group usages into senses, and possible -- diachronic and synchronic -- uses for this dataset.
This paper presents the methods of designing a model to determine the appropriate points to build fire towers extended on an area of Syrian Arab Republic, in order to monitor and early warning the forest fire. This helps the authorities' process of
extinguishing the fire as it arises and thereby protect our forest from fire that destroys thousands of hectares every year.
The model has been designed using the tools of Spatial Analysis available in ArcGIS program. These tools have been applied to evaluate the performance of the fire towers in the studied area, and to suggest new sites for fire towers in this region in order
to increase the vision of the area. We calculate the area of visible areas covered by the suggested fire towers. We finally customize the parameters of the model to be applied for different areas using different input data, so the users of the model simply can enter the
parameters of their own area and apply the model.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
