Search Results for author:
Found 19 papers, 11 papers with code
Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning
Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people's lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models.
Ranked #6 on
Cross-Modal Retrieval
on Recipe1M
Mask-guided sample selection for Semi-Supervised Instance Segmentation
Our method consists in first predicting pseudo-masks for the unlabeled pool of samples, together with a score predicting the quality of the mask.
WiCV 2019: The Sixth Women In Computer Vision Workshop
In this paper we present the Women in Computer Vision Workshop - WiCV 2019, organized in conjunction with CVPR 2019.
Budget-aware Semi-Supervised Semantic and Instance Segmentation
Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention.
Elucidating image-to-set prediction: An analysis of models, losses and datasets
In this paper, we identify an important reproducibility challenge in the image-to-set prediction literature that impedes proper comparisons among published methods, namely, researchers use different evaluation protocols to assess their contributions.
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker.
RVOS: End-to-End Recurrent Network for Video Object Segmentation
Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence.
Ranked #1 on
One-shot visual object segmentation
on YouTube-VOS
One-shot visual object segmentation
Unsupervised Video Object Segmentation
+1
Inverse Cooking: Recipe Generation from Food Images
Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously.
Ranked #1 on
Recipe Generation
on Recipe1M
Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
In this paper, we introduce Recipe1M+, a new large-scale, structured corpus of over one million cooking recipes and 13 million food images.
Ranked #2 on
Cross-Modal Retrieval
on Recipe1M+
Cross-modal Embeddings for Video and Audio Retrieval
The increasing amount of online videos brings several opportunities for training self-supervised neural networks.
Recurrent Neural Networks for Semantic Instance Segmentation
We present a recurrent model for semantic instance segmentation that sequentially generates binary masks and their associated class probabilities for every object in an image.
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images.
Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks
This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed.
Faster R-CNN Features for Instance Search
This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN.
Bags of Local Convolutional Features for Scalable Instance Search
This work proposes a simple instance retrieval pipeline based on encoding the convolutional features of CNN using the bag of words aggregation scheme (BoW).
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Visual media are powerful means of expressing emotions and sentiments.
Quality Control in Crowdsourced Object Segmentation
This paper explores processing techniques to deal with noisy data in crowdsourced object segmentation tasks.
Cultural Event Recognition with Visual ConvNets and Temporal Models
Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme.
Exploring EEG for Object Detection and Retrieval
We show that it is indeed possible to detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others.
