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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
TLDR
The Binary-Weight-Network version of AlexNet is compared with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than \(16\,\%\) in top-1 accuracy. Expand
ReferItGame: Referring to Objects in Photographs of Natural Scenes
TLDR
A new game to crowd-source natural language referring expressions by designing a two player game that can both collect and verify referring expressions directly within the game and provides an in depth analysis of the resulting dataset. Expand
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
TLDR
A data-augmentation approach is demonstrated that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by rule-based, feature-rich, and neural coreference systems in WinoBias without significantly affecting their performance on existing datasets. Expand
Im2Text: Describing Images Using 1 Million Captioned Photographs
TLDR
A new objective performance measure for image captioning is introduced and methods incorporating many state of the art, but fairly noisy, estimates of image content are developed to produce even more pleasing results. Expand
High level describable attributes for predicting aesthetics and interestingness
TLDR
This paper demonstrates a simple, yet powerful method to automatically select high aesthetic quality images from large image collections and demonstrates that an aesthetics classifier trained on describable attributes can provide a significant improvement over baseline methods for predicting human quality judgments. Expand
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
TLDR
This work proposes to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference to reduce the magnitude of bias amplification in multilabel object classification and visual semantic role labeling. Expand
Collective Generation of Natural Image Descriptions
TLDR
A holistic data-driven approach to image description generation, exploiting the vast amount of (noisy) parallel image data and associated natural language descriptions available on the web to generate novel descriptions for query images. Expand
BabyTalk: Understanding and Generating Simple Image Descriptions
TLDR
The proposed system to automatically generate natural language descriptions from images is very effective at producing relevant sentences for images and generates descriptions that are notably more true to the specific image content than previous work. Expand
Gender Bias in Contextualized Word Embeddings
TLDR
It is shown that a state-of-the-art coreference system that depends on ELMo inherits its bias and demonstrates significant bias on the WinoBias probing corpus and two methods to mitigate such gender bias are explored. Expand
Learning High-Level Judgments of Urban Perception
TLDR
This paper applies computational vision techniques to the task of predicting the perceptual characteristics of places by leveraging recent work on visual features along with a geo-tagged dataset of images associated with crowd-sourced urban perception judgments for wealth, uniqueness, and safety. Expand
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