Search Results for author:
Found 6 papers, 5 papers with code
AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation
This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval.
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity
While the first provides rich and implicit context for the search, the latter explicitly calls for new traits, or specifies how some elements of the example image should be changed to retrieve the desired target image.
Ranked #11 on
Image Retrieval
on CIRR
Learning with Label Noise for Image Retrieval by Selecting Interactions
Learning with noisy labels is an active research area for image classification.
Probabilistic Embeddings for Cross-Modal Retrieval
Instead, we propose to use Probabilistic Cross-Modal Embedding (PCME), where samples from the different modalities are represented as probabilistic distributions in the common embedding space.
StacMR: Scene-Text Aware Cross-Modal Retrieval
Then, armed with this dataset, we describe several approaches which leverage scene text, including a better scene-text aware cross-modal retrieval method which uses specialized representations for text from the captions and text from the visual scene, and reconcile them in a common embedding space.
Learning with Average Precision: Training Image Retrieval with a Listwise Loss
Recent deep models for image retrieval have outperformed traditional methods by leveraging ranking-tailored loss functions, but important theoretical and practical problems remain.
