919 research outputs found
Learning to Hash-tag Videos with Tag2Vec
User-given tags or labels are valuable resources for semantic understanding
of visual media such as images and videos. Recently, a new type of labeling
mechanism known as hash-tags have become increasingly popular on social media
sites. In this paper, we study the problem of generating relevant and useful
hash-tags for short video clips. Traditional data-driven approaches for tag
enrichment and recommendation use direct visual similarity for label transfer
and propagation. We attempt to learn a direct low-cost mapping from video to
hash-tags using a two step training process. We first employ a natural language
processing (NLP) technique, skip-gram models with neural network training to
learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a
corpus of 10 million hash-tags. We then train an embedding function to map
video features to the low-dimensional Tag2vec space. We learn this embedding
for 29 categories of short video clips with hash-tags. A query video without
any tag-information can then be directly mapped to the vector space of tags
using the learned embedding and relevant tags can be found by performing a
simple nearest-neighbor retrieval in the Tag2Vec space. We validate the
relevance of the tags suggested by our system qualitatively and quantitatively
with a user study
Clue: Cross-modal Coherence Modeling for Caption Generation
We use coherence relations inspired by computational models of discourse to
study the information needs and goals of image captioning. Using an annotation
protocol specifically devised for capturing image--caption coherence relations,
we annotate 10,000 instances from publicly-available image--caption pairs. We
introduce a new task for learning inferences in imagery and text, coherence
relation prediction, and show that these coherence annotations can be exploited
to learn relation classifiers as an intermediary step, and also train
coherence-aware, controllable image captioning models. The results show a
dramatic improvement in the consistency and quality of the generated captions
with respect to information needs specified via coherence relations.Comment: Accepted as a long paper to ACL 202
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
RS5M: A Large Scale Vision-Language Dataset for Remote Sensing Vision-Language Foundation Model
Pre-trained Vision-Language Foundation Models utilizing extensive image-text
paired data have demonstrated unprecedented image-text association
capabilities, achieving remarkable results across various downstream tasks. A
critical challenge is how to make use of existing large-scale pre-trained VLMs,
which are trained on common objects, to perform the domain-specific transfer
for accomplishing domain-related downstream tasks. In this paper, we propose a
new framework that includes the Domain Foundation Model (DFM), bridging the gap
between the General Foundation Model (GFM) and domain-specific downstream
tasks. Moreover, we present an image-text paired dataset in the field of remote
sensing (RS), RS5M, which has 5 million RS images with English descriptions.
The dataset is obtained from filtering publicly available image-text paired
datasets and captioning label-only RS datasets with pre-trained VLM. These
constitute the first large-scale RS image-text paired dataset. Additionally, we
tried several Parameter-Efficient Fine-Tuning methods on RS5M to implement the
DFM. Experimental results show that our proposed dataset are highly effective
for various tasks, improving upon the baseline by in
zero-shot classification tasks, and obtaining good results in both
Vision-Language Retrieval and Semantic Localization tasks.
\url{https://github.com/om-ai-lab/RS5M}Comment: RS5M dataset v
What Else Would I Like? A User Simulator using Alternatives for Improved Evaluation of Fashion Conversational Recommendation Systems
In Conversational Recommendation Systems (CRS), a user can provide feedback
on recommended items at each interaction turn, leading the CRS towards more
desirable recommendations. Currently, different types of CRS offer various
possibilities for feedback, i.e., natural language feedback, or answering
clarifying questions. In most cases, a user simulator is employed for training
as well as evaluating the CRS. Such user simulators typically critique the
current retrieved items based on knowledge of a single target item. Still,
evaluating systems in offline settings with simulators suffers from problems,
such as focusing entirely on a single target item (not addressing the
exploratory nature of a recommender system), and exhibiting extreme patience
(consistent feedback over a large number of turns). To overcome these
limitations, we obtain extra judgements for a selection of alternative items in
common CRS datasets, namely Shoes and Fashion IQ Dresses. Going further, we
propose improved user simulators that allow simulated users not only to express
their preferences about alternative items to their original target, but also to
change their mind and level of patience. In our experiments using the relative
image captioning CRS setting and different CRS models, we find that using the
knowledge of alternatives by the simulator can have a considerable impact on
the evaluation of existing CRS models, specifically that the existing
single-target evaluation underestimates their effectiveness, and when simulated
users are allowed to instead consider alternatives, the system can rapidly
respond to more quickly satisfy the user
Large Scale Retrieval and Generation of Image Descriptions
What is the story of an image? What is the relationship between pictures, language, and information we can extract using state of the art computational recognition systems? In an attempt to address both of these questions, we explore methods for retrieving and generating natural language descriptions for images. Ideally, we would like our generated textual descriptions (captions) to both sound like a person wrote them, and also remain true to the image content. To do this we develop data-driven approaches for image description generation, using retrieval-based techniques to gather either: (a) whole captions associated with a visually similar image, or (b) relevant bits of text (phrases) from a large collection of image + description pairs. In the case of (b), we develop optimization algorithms to merge the retrieved phrases into valid natural language sentences. The end result is two simple, but effective, methods for harnessing the power of big data to produce image captions that are altogether more general, relevant, and human-like than previous attempts
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations
Large Vision & Language models pretrained on web-scale data provide
representations that are invaluable for numerous V&L problems. However, it is
unclear how they can be used for reasoning about user-specific visual concepts
in unstructured language. This problem arises in multiple domains, from
personalized image retrieval to personalized interaction with smart devices. We
introduce a new learning setup called Personalized Vision & Language (PerVL)
with two new benchmark datasets for retrieving and segmenting user-specific
"personalized" concepts "in the wild". In PerVL, one should learn personalized
concepts (1) independently of the downstream task (2) allowing a pretrained
model to reason about them with free language, and (3) does not require
personalized negative examples. We propose an architecture for solving PerVL
that operates by extending the input vocabulary of a pretrained model with new
word embeddings for the new personalized concepts. The model can then reason
about them by simply using them in a sentence. We demonstrate that our approach
learns personalized visual concepts from a few examples and can effectively
apply them in image retrieval and semantic segmentation using rich textual
queries
- …