Search Results for author:
Found 12 papers, 4 papers with code
Learning Joint Visual Semantic Matching Embeddings for Language-guided Retrieval
Interactive image retrieval is an emerging research topic with the objective of integrating inputs from multiple modalities as query for retrieval, e. g., textual feedback from users to guide, modify or refine image retrieval.
ViewFusion: Towards Multi-View Consistency via Interpolated Denoising
Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images.
iEdit: Localised Text-guided Image Editing with Weak Supervision
We propose a novel learning method for text-guided image editing, namely \texttt{iEdit}, that generates images conditioned on a source image and a textual edit prompt.
Contrastive Language-Action Pre-training for Temporal Localization
Long-form video understanding requires designing approaches that are able to temporally localize activities or language.
Contrastive Learning
Few Shot Temporal Action Localization
+3
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
Learning Attribute-Driven Disentangled Representations for Interactive Fashion Retrieval
Interactive retrieval for online fashion shopping provides the ability of changing image retrieval results according to the user feedback.
Image Captioning as Neural Machine Translation Task in SOCKEYE
Image captioning is an interdisciplinary research problem that stands between computer vision and natural language processing.
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification
This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds.
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification
This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features, in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds.
Recurrent Mixture Density Network for Spatiotemporal Visual Attention
In this work, we propose a spatiotemporal attentional model that learns where to look in a video directly from human fixation data.
Self-taught Object Localization with Deep Networks
This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i. e., without using any ground-truth bounding boxes for training.
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space.
