199,470 research outputs found
Learning Visual Question Answering by Bootstrapping Hard Attention
Attention mechanisms in biological perception are thought to select subsets
of perceptual information for more sophisticated processing which would be
prohibitive to perform on all sensory inputs. In computer vision, however,
there has been relatively little exploration of hard attention, where some
information is selectively ignored, in spite of the success of soft attention,
where information is re-weighted and aggregated, but never filtered out. Here,
we introduce a new approach for hard attention and find it achieves very
competitive performance on a recently-released visual question answering
datasets, equalling and in some cases surpassing similar soft attention
architectures while entirely ignoring some features. Even though the hard
attention mechanism is thought to be non-differentiable, we found that the
feature magnitudes correlate with semantic relevance, and provide a useful
signal for our mechanism's attentional selection criterion. Because hard
attention selects important features of the input information, it can also be
more efficient than analogous soft attention mechanisms. This is especially
important for recent approaches that use non-local pairwise operations, whereby
computational and memory costs are quadratic in the size of the set of
features.Comment: ECCV 201
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets
Visual question answering (Visual QA) has attracted a lot of attention
lately, seen essentially as a form of (visual) Turing test that artificial
intelligence should strive to achieve. In this paper, we study a crucial
component of this task: how can we design good datasets for the task? We focus
on the design of multiple-choice based datasets where the learner has to select
the right answer from a set of candidate ones including the target (\ie the
correct one) and the decoys (\ie the incorrect ones). Through careful analysis
of the results attained by state-of-the-art learning models and human
annotators on existing datasets, we show that the design of the decoy answers
has a significant impact on how and what the learning models learn from the
datasets. In particular, the resulting learner can ignore the visual
information, the question, or both while still doing well on the task. Inspired
by this, we propose automatic procedures to remedy such design deficiencies. We
apply the procedures to re-construct decoy answers for two popular Visual QA
datasets as well as to create a new Visual QA dataset from the Visual Genome
project, resulting in the largest dataset for this task. Extensive empirical
studies show that the design deficiencies have been alleviated in the remedied
datasets and the performance on them is likely a more faithful indicator of the
difference among learning models. The datasets are released and publicly
available via http://www.teds.usc.edu/website_vqa/.Comment: Accepted for Oral Presentation at NAACL-HLT 201
μ΄μΌκΈ°ν μ€λͺ λ¬Έμ νμ©ν λκ·λͺ¨ λΉλμ€ νμ΅ μ°κ΅¬
νμλ
Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ»΄ν¨ν°κ³΅νλΆ, 2021. 2. κΉκ±΄ν¬.Extensive contributions are being made to develop intelligent agents that can recognize and communicate with the world. In this sense, various video-language tasks have drawn a lot of interests in computer vision research, including image/video captioning, video retrieval and video question answering.
It can be applied to high-level computer vision tasks and various future industries such as search engines, social marketing, automated driving, and robotics support through QA / dialog generation for the surrounding environment.
However, despite these developments, video-language learning suffers from a higher degree of complexity.
This thesis investigates methodologies for learning the relationship between videos and free-formed languages, including explanations, conversations, and question-and-answers, so that the machine can easily adapt to target downstream tasks.
First, we introduce several methods to learn the relationship between long sentences and videos efficiently. We introduce the approaches for supervising human attention transfer for the video attention model, which shows the video attention mechanism can benefit from explicit human gaze labels. Next, we introduce the end-to-end semantic attention method, which further reduces the visual attention algorithm's complexity by using the representative visual concept word detected by the attention-based detector. As a follow-up study on previous methods, we introduce a JSFusion (Joint Sequence Fusion) method that enables efficient video search and QA by enabling many-to-many matching of attention model.
Next, we introduce the CiSIN(Character in Story Identification Network), which uses Attention to increase the performance of character grounding and character re-identification in the movie. Finally, we introduce Transitional Adaptation, which promotes the caption generation models to generates coherent narratives for long videos.
In summary, this thesis presents a novel approaches for automatic video description generation/retrieval and shows the benefits of extracting linguistic knowledge for object and motion in the video as well as the advantage of multimodal audio-visual learning for understanding videos. Since the proposed methods are easily adapted to any video-language tasks, it is expected to be applied to the latest models, bringing additional performance improvements.
Moving forward, we plan to design an unsupervised video learning framework that can solve many challenges in the industry by integrating an unlimited amount of video, audio, and free-formed language data from the web.μκ°-μΈμ΄ νμ΅μ μ΄λ―Έμ§/λΉλμ€ μΊ‘μ
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λ³Έ λ
Όλ¬Έμμλ λΉλμ€μ μ΄μ λμνλ μ€λͺ
, λν, μ§μ μλ΅ λ± λ λμκ° μμ ννμ μΈμ΄ (Free-formed language)κ°μ κ΄κ³λ₯Ό λμ± ν¨μ¨μ μΌλ‘ νμ΅νκ³ , λͺ©ν μ무μ μ λμν μ μλλ‘ κ°μ νλ κ²μ λͺ©νλ‘ νλ€.
λ¨Όμ , μκ°μ 볡μ‘λκ° μ΄λ―Έμ§λ³΄λ€ λμ λΉλμ€μ κΈ΄ λ¬Έμ₯ μ¬μ΄μ κ΄κ³λ₯Ό ν¨μ¨μ μΌλ‘ νμ΅νκΈ° μν μ¬λ¬ λ°©λ²λ€μ μκ°νλ€. μΈκ°μ μ£Όμ μΈμ(Attention) λͺ¨λΈμ λΉλμ€-μΈμ΄ λͺ¨λΈμ μ§λ νμ΅ νλ λ°©λ²μ μκ°νκ³ , μ΄μ΄μ λΉλμ€μμ μ°μ κ²μΆλ λν μκ° λ¨μ΄λ₯Ό 맀κ°λ‘ νμ¬ μ£Όμ μΈμ(Attention) μκ³ λ¦¬μ¦μ 볡μ‘λλ₯Ό λμ± μ€μ΄λ μλ―Έ μ€μ¬ μ£Όμ μΈμ (Semantic Attention) λ°©λ², μ΄ν
μ
λͺ¨λΈμ λ€λλ€ λ§€μΉμ κΈ°λ°μΌλ‘ ν¨μ¨μ μΈ λΉλμ€ κ²μ λ° μ§μμλ΅μ κ°λ₯μΌ νλ λΉλμ€-μΈμ΄κ° μ΅ν© (Joint Sequence Fusion) λ°©λ² λ± λΉλμ€ μ£Όμ μΈμμ ν¨μ¨μ μΌλ‘ νμ΅μν¬ μ μλ λ°©λ²λ€μ μ μνλ€.
λ€μμΌλ‘λ, μ£Όμ μΈμ(Attention) λͺ¨λΈμ΄ 물체-λ¨μ΄ κ° κ΄κ³λ₯Ό λμ΄ λΉλμ€ μμμ μΈλ¬Ό κ²μ (Person Searching) κ·Έλ¦¬κ³ μΈλ¬Ό μ¬ μλ³ (Person Re-Identification)μ λμμ μννλ©° μμΉμμ©μ μΌμΌν€λ μ€ν 리 μ μΊλ¦ν° μΈμ μ κ²½λ§ (Character in Story Identification Network) μ μκ°νλ©°, λ§μ§λ§μΌλ‘ μκΈ° μ§λ νμ΅(Self-supervised Learning)μ ν΅ν΄ μ£Όμ μΈμ(Attention) κΈ°λ° μΈμ΄ λͺ¨λΈμ΄ κΈ΄ λΉλμ€μ λν μ€λͺ
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(Video captioning), λΉλμ€ κ²μ(Video Retrieval), μκ° μ§μμλ΅(Video Question and Answering)λ±μ ν΄κ²°ν μ μλ κΈ°μ μ λλ€λμ΄ λλ©°, λΉλμ€ μΊ‘μ
νμ΅μ ν΅ν΄ νμ΅λ μ£Όμ μΈμ λͺ¨λμ κ²μ λ° μ§μμλ΅, μΈλ¬Ό κ²μ λ± κ° λ€νΈμν¬μ μ΄μλλ©΄μ μλ‘μ΄ λ¬Έμ λ€μ λν΄ λμμ μ΅κ³ μμ€(State-of-the-art)μ μ±λ₯μ λ¬μ±νμλ€. μ΄λ₯Ό ν΅ν΄ λΉλμ€-μΈμ΄ νμ΅μΌλ‘ μ»μ μΈμ΄ μ§μμ μ΄μ μ μκ°-μ²κ°μ μμ°λ₯΄λ λΉλμ€ λ©ν°λͺ¨λ¬ νμ΅μ ν° λμμ΄ λλ κ²μ μ€νμ μΌλ‘ 보μ¬μ€λ€. ν₯ν μμ
λ°©ν₯ (Future Work)μΌλ‘λ μμ μ°κ΅¬ν λ΄μ©λ€μ κΈ°λ°μΌλ‘ μΉ μμ μ‘΄μ¬νλ λκ·λͺ¨μ μΈμ΄, λΉλμ€, μ€λμ€ λ°μ΄ν°λ₯Ό ν΅ν©ν΄ νμ΅μ νμ©νμ¬ μ°μ
κ³μ λ§μ λμ λ₯Ό ν΄κ²°ν μ μλ λΉμ§λ νμ΅ λͺ¨λΈμ λ§λ€κ³ μ νλ€.Chapter 1
Introduction
1.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4
1.2 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . .8
Chapter 2
Related Work
2.1 Video Captioning . . . . . . . . . . . . . . . . . . . . . . . . . . .9
2.2 Video Retrieval with Natural Language . . . . . . . . . . . . . . 12
2.3 Video Question and Answering . . . . . . . . . . . . . . . . . . . 13
2.4 Cross-modal Representation Learning for Vision and LanguageTasks . . . . 15
Chapter 3 Human Attention Transfer for Video Captioning18
3.1 Introduction
3.2 Video Datasets for Caption and Gaze . . . . . . . . . . . . . . . 21
3.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.1 Video Pre-processing and Description . . . . . . . . . . . 22
3.3.2The Recurrent Gaze Prediction (RGP) Model . . . . . . . 23
3.3.3Construction of Visual Feature Pools . . . . . . . . . . . . 24
3.3.4The Decoder for Caption Generation . . . . . . . . . . . . 26
3.3.5Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.4.1Evaluation of Gaze Prediction . . . . . . . . . . . . . . . . 29
3.4.2Evaluation of Video Captioning . . . . . . . . . . . . . . . 32
3.4.3Human Evaluation via AMT . . . . . . . . . . . . . . . . 35
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Chapter 4 Semantic Word Attention for Video QA and VideoCaptioning
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.1.1Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.1.2Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2.1Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2.2An Attention Model for Concept Detection . . . . . . . . 42
4.2.3Video-to-Language Models . . . . . . . . . . . . . . . . . 45
4.2.4A Model for Description . . . . . . . . . . . . . . . . . . . 45
4.2.5A Model for Fill-in-the-Blank . . . . . . . . . . . . . . . . 48
4.2.6A Model for Multiple-Choice Test . . . . . . . . . . . . . 50
4.2.7A Model for Retrieval . . . . . . . . . . . . . . . . . . . . 51
4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.3.1The LSMDC Dataset and Tasks . . . . . . . . . . . . . . 52
4.3.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 54
4.3.3Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 56
4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Chapter 5 Joint Sequnece Fusion Attention for Multimodal Sequence Data
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.3.1Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.3.2The Joint Semantic Tensor . . . . . . . . . . . . . . . . . 65
5.3.3The Convolutional Hierarchical Decoder . . . . . . . . . . 66
5.3.4An Illustrative Example of How the JSFusion Model Works 68
5.3.5Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.3.6Implementation of Video-Language Models . . . . . . . . 69
5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.4.1LSMDC Dataset and Tasks . . . . . . . . . . . . . . . . . 71
5.4.2MSR-VTT-(RET/MC) Dataset and Tasks . . . . . . . . . 73
5.4.3Quantitative Results . . . . . . . . . . . . . . . . . . . . . 74
5.4.4Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 76
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
Chapter 6 Character Re-Identification and Character Ground-ing for Movie Understanding
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.3.1Video Preprocessing . . . . . . . . . . . . . . . . . . . . . 84
6.3.2Visual Track Embedding . . . . . . . . . . . . . . . . . . . 85
6.3.3Textual Character Embedding . . . . . . . . . . . . . . . 86
6.3.4Character Grounding . . . . . . . . . . . . . . . . . . . . 87
6.3.5Re-Identification . . . . . . . . . . . . . . . . . . . . . . . 88
6.3.6Joint Training . . . . . . . . . . . . . . . . . . . . . . . . 90
6.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.4.1Experimental Setup . . . . . . . . . . . . . . . . . . . . . 92
6.4.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 93
6.4.3Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 95
6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Chapter 7 Transitional Adaptation of Pretrained Models forVisual Storytelling
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
7.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
7.3.1The Visual Encoder . . . . . . . . . . . . . . . . . . . . . 104
7.3.2The Language Generator . . . . . . . . . . . . . . . . . . 104
7.3.3Adaptation training . . . . . . . . . . . . . . . . . . . . . 105
7.3.4The Sequential Coherence Loss . . . . . . . . . . . . . . . 105
7.3.5Training with the adaptation Loss . . . . . . . . . . . . . 107
7.3.6Fine-tuning and Inference . . . . . . . . . . . . . . . . . . 107
7.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.4.1Experimental Setup . . . . . . . . . . . . . . . . . . . . . 109
7.4.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 112
7.4.3Further Analyses . . . . . . . . . . . . . . . . . . . . . . . 112
7.4.4Human Evaluation Results . . . . . . . . . . . . . . . . . 115
7.4.5Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 116
7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Chapter 8 Conclusion
8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
8.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Bibliography ... 123
μμ½ ... 148
Acknowledgements ... 150Docto
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
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