11,395 research outputs found
A closer look at referring expressions for video object segmentation
The task of Language-guided Video Object Segmentation (LVOS) aims at generating binary masks for an object referred by a linguistic expression. When this expression unambiguously describes an object in the scene, it is named referring expression (RE). Our work argues that existing benchmarks used for LVOS are mainly composed of trivial cases, in which referents can be identified with simple phrases. Our analysis relies on a new categorization of the referring expressions in the DAVIS-2017 and Actor-Action datasets into trivial and non-trivial REs, where the non-trivial REs are further annotated with seven RE semantic categories. We leverage these data to analyze the performance of RefVOS, a novel neural network that obtains competitive results for the task of language-guided image segmentation and state of the art results for LVOS. Our study indicates that the major challenges for the task are related to understanding motion and static actions.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by the projects PID2019-107255GB-C22 and PID2020-117142GB-I00 funded by MCIN/ AEI /10.13039/501100011033 Spanish Ministry of Science, and the grant 2017-SGR-1414 of the Government of Catalonia. This work was also partially supported by the project RTI2018-095232-B-C22 funded by the Spanish Ministry of Science, Innovation and Universities.Peer ReviewedPostprint (published version
Object Referring in Videos with Language and Human Gaze
We investigate the problem of object referring (OR) i.e. to localize a target
object in a visual scene coming with a language description. Humans perceive
the world more as continued video snippets than as static images, and describe
objects not only by their appearance, but also by their spatio-temporal context
and motion features. Humans also gaze at the object when they issue a referring
expression. Existing works for OR mostly focus on static images only, which
fall short in providing many such cues. This paper addresses OR in videos with
language and human gaze. To that end, we present a new video dataset for OR,
with 30, 000 objects over 5, 000 stereo video sequences annotated for their
descriptions and gaze. We further propose a novel network model for OR in
videos, by integrating appearance, motion, gaze, and spatio-temporal context
into one network. Experimental results show that our method effectively
utilizes motion cues, human gaze, and spatio-temporal context. Our method
outperforms previousOR methods. For dataset and code, please refer
https://people.ee.ethz.ch/~arunv/ORGaze.html.Comment: Accepted to CVPR 2018, 10 pages, 6 figure
Recurrent Multimodal Interaction for Referring Image Segmentation
In this paper we are interested in the problem of image segmentation given
natural language descriptions, i.e. referring expressions. Existing works
tackle this problem by first modeling images and sentences independently and
then segment images by combining these two types of representations. We argue
that learning word-to-image interaction is more native in the sense of jointly
modeling two modalities for the image segmentation task, and we propose
convolutional multimodal LSTM to encode the sequential interactions between
individual words, visual information, and spatial information. We show that our
proposed model outperforms the baseline model on benchmark datasets. In
addition, we analyze the intermediate output of the proposed multimodal LSTM
approach and empirically explain how this approach enforces a more effective
word-to-image interaction.Comment: To appear in ICCV 2017. See http://www.cs.jhu.edu/~cxliu/ for code
and supplementary materia
Object Referring in Visual Scene with Spoken Language
Object referring has important applications, especially for human-machine
interaction. While having received great attention, the task is mainly attacked
with written language (text) as input rather than spoken language (speech),
which is more natural. This paper investigates Object Referring with Spoken
Language (ORSpoken) by presenting two datasets and one novel approach. Objects
are annotated with their locations in images, text descriptions and speech
descriptions. This makes the datasets ideal for multi-modality learning. The
approach is developed by carefully taking down ORSpoken problem into three
sub-problems and introducing task-specific vision-language interactions at the
corresponding levels. Experiments show that our method outperforms competing
methods consistently and significantly. The approach is also evaluated in the
presence of audio noise, showing the efficacy of the proposed vision-language
interaction methods in counteracting background noise.Comment: 10 pages, Submitted to WACV 201
MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions
This paper strives for motion expressions guided video segmentation, which
focuses on segmenting objects in video content based on a sentence describing
the motion of the objects. Existing referring video object datasets typically
focus on salient objects and use language expressions that contain excessive
static attributes that could potentially enable the target object to be
identified in a single frame. These datasets downplay the importance of motion
in video content for language-guided video object segmentation. To investigate
the feasibility of using motion expressions to ground and segment objects in
videos, we propose a large-scale dataset called MeViS, which contains numerous
motion expressions to indicate target objects in complex environments. We
benchmarked 5 existing referring video object segmentation (RVOS) methods and
conducted a comprehensive comparison on the MeViS dataset. The results show
that current RVOS methods cannot effectively address motion expression-guided
video segmentation. We further analyze the challenges and propose a baseline
approach for the proposed MeViS dataset. The goal of our benchmark is to
provide a platform that enables the development of effective language-guided
video segmentation algorithms that leverage motion expressions as a primary cue
for object segmentation in complex video scenes. The proposed MeViS dataset has
been released at https://henghuiding.github.io/MeViS.Comment: ICCV 2023, Project Page: https://henghuiding.github.io/MeViS
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Rich and dense human labeled datasets are among the main enabling factors for
the recent advance on vision-language understanding. Many seemingly distant
annotations (e.g., semantic segmentation and visual question answering (VQA))
are inherently connected in that they reveal different levels and perspectives
of human understandings about the same visual scenes --- and even the same set
of images (e.g., of COCO). The popularity of COCO correlates those annotations
and tasks. Explicitly linking them up may significantly benefit both individual
tasks and the unified vision and language modeling. We present the preliminary
work of linking the instance segmentations provided by COCO to the questions
and answers (QAs) in the VQA dataset, and name the collected links visual
questions and segmentation answers (VQS). They transfer human supervision
between the previously separate tasks, offer more effective leverage to
existing problems, and also open the door for new research problems and models.
We study two applications of the VQS data in this paper: supervised attention
for VQA and a novel question-focused semantic segmentation task. For the
former, we obtain state-of-the-art results on the VQA real multiple-choice task
by simply augmenting the multilayer perceptrons with some attention features
that are learned using the segmentation-QA links as explicit supervision. To
put the latter in perspective, we study two plausible methods and compare them
to an oracle method assuming that the instance segmentations are given at the
test stage.Comment: To appear on ICCV 201
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