Object Referring in Visual Scene with Spoken Language

Vasudevan, Arun Balajee; Dai, Dengxin

doi:10.1109/wacv.2018.00206

Cited by 15 publications

(16 citation statements)

References 43 publications

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“…Using speech as an input modality has a long history (Bolt 1980) and is recently emerging as a research direction in Computer Vision (Dai 2016;Vasudevan et al 2017;Vaidyanathan et al 2018;Harwath et al 2018). To the best of our knowledge, however, our paper is the first to show that speech allows for more efficient object class labelling than (Lin et al 2014;Deng et al 2014) and enables simultaneous class and box labelling.…”

Section: Related Workmentioning

confidence: 80%

Efficient Object Annotation via Speaking and Pointing

Gygli

Ferrari

2019

Int J Comput Vis

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Deep neural networks deliver state-of-theart visual recognition, but they rely on large datasets, which are time-consuming to annotate. These datasets are typically annotated in two stages: (1) determining the presence of object classes at the image level and (2) marking the spatial extent for all objects of these classes. In this work we use speech, together with mouse inputs, to speed up this process. We first improve stage one, by letting annotators indicate object class presence via speech. We then combine the two stages: annotators draw an object bounding box via the mouse and simultaneously provide its class label via speech. Using speech has distinct advantages over relying on mouse inputs alone. First, it is fast and allows for direct access to the class name, by simply saying it. Second, annotators can simultaneously speak and mark an object location. Finally, speech-based interfaces can be kept extremely simple, hence using them requires less mouse movement compared to existing approaches. Through extensive experiments on the COCO and ILSVRC datasets we show that our approach yields high-quality annotations at significant speed gains. Stage one takes 2.3× − 14.9× less annotation time than existing methods based on a hierarchical organization of the classes to be annotated. Moreover, when combining the two stages, we find that object class labels come for free: annotating them at the same time as bounding boxes has zero additional cost. On COCO, this makes the overall process 1.9× faster than the two-stage approach.

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Section: Related Workmentioning

confidence: 80%

Efficient Object Annotation via Speaking and Pointing

Gygli

Ferrari

2019

Int J Comput Vis

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“…[52] shows that LOP performs significantly better than other techniques when we propose expression-aware object candidates. For the same reason, we use LOP [52] for the object proposals.…”

Section: Object Proposalsmentioning

confidence: 92%

“…For instance, [23] uses EdgeBox [59] for the object proposals; [34] and [25] use the faster RCNN (FRCNN) object detector [43], Mask-RCNN [18] and Language based Object Proposals (LOP) [52] and others to propose the candidates. [52] shows that LOP performs significantly better than other techniques when we propose expression-aware object candidates. For the same reason, we use LOP [52] for the object proposals.…”

Section: Object Proposalsmentioning

confidence: 99%

Object Referring in Videos with Language and Human Gaze

Vasudevan

Dai

Gool

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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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 previous OR methods. For dataset and code, please refer https: //people.ee.ethz.ch/˜arunv/ORGaze.html.

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“…In Computer Vision, Vasudevan et al [32] detect objects given spoken referring expressions, while Harwath et al [9] learn an embedding from spoken image-caption pairs. Their approach obtains promising first results, but still performs inferior to learning on top of textual captions obtained from Google's automatic speech recognition.…”

Section: Related Workmentioning

confidence: 99%

Fast Object Class Labelling via Speech

Gygli

Ferrari

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Object class labelling is the task of annotating images with labels on the presence or absence of objects from a given class vocabulary. Simply asking one yes/no question per class, however, has a cost that is linear in the vocabulary size and is thus inefficient for large vocabularies. Modern approaches rely on a hierarchical organization of the vocabulary to reduce annotation time, but remain expensive (several minutes per image for the 200 classes in ILSVRC). Instead, we propose a new interface where classes are annotated via speech. Speaking is fast and allows for direct access to the class name, without searching through a list or hierarchy. As additional advantages, annotators can simultaneously speak and scan the image for objects, the interface can be kept extremely simple, and using it requires less mouse movement. As annotators using our interface should only say words from a given class vocabulary, we propose a dedicated task that trains them to do so. Through experiments on COCO and ILSVRC, we show our method yields high-quality annotations at 2.3× − 14.9× less annotation time than existing methods.

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Object Referring in Visual Scene with Spoken Language

Cited by 15 publications

References 43 publications

Efficient Object Annotation via Speaking and Pointing

Efficient Object Annotation via Speaking and Pointing

Object Referring in Videos with Language and Human Gaze

Fast Object Class Labelling via Speech

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