이야기형 설명문을 활용한 대규모 비디오 학습 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 김건희.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.시각-언어 학습은 이미지/비디오 캡션(Image/Video captioning), 시각 질의응답(Visual Question and Answering), 비디오 검색(Video Retrieval), 장면 이해(scene understanding), 이벤트 인식(event detection) 등 고차원의 컴퓨터 비전 태스크(task)뿐만 아니라 주변 환경에 대한 질의 응답 및 대화 생성(Dialogue Generation)으로 인터넷 검색 뿐만 아니라 최근 활발한 소셜 마케팅(Social Marketing) 자율 주행(Automated Driving), 로보틱스(Robotics)을 보조하는 등 여러 미래 산업에 적용될 수 있어 활발히 연구되고 있는 중요한 분야이다. 컴퓨터 비젼과 자연어 처리는 이러한 중요성을 바탕으로 각자 고유한 영역에서 발전을 거듭해 왔으나, 최근 딥러닝의 등장과 함께 눈부시게 발전하면서 서로를 보완하며 학습 결과를 향상시키는 등 큰 시너지 효과를 발휘하게 되었다. 하지만 이런 발전에도 불구하고, 비디오-언어간 학습은 문제의 복잡도가 한층 높아 어려움을 겪게 되는 경우가 많다. 본 논문에서는 비디오와 이에 대응하는 설명, 대화, 질의 응답 등 더 나아가 자유 형태의 언어 (Free-formed language)간의 관계를 더욱 효율적으로 학습하고, 목표 임무에 잘 대응할 수 있도록 개선하는 것을 목표로 한다. 먼저, 시각적 복잡도가 이미지보다 높은 비디오와 긴 문장 사이의 관계를 효율적으로 학습하기 위한 여러 방법들을 소개한다. 인간의 주의 인식(Attention) 모델을 비디오-언어 모델에 지도 학습 하는 방법을 소개하고, 이어서 비디오에서 우선 검출된 대표 시각 단어를 매개로 하여 주의 인식(Attention) 알고리즘의 복잡도를 더욱 줄이는 의미 중심 주의 인식 (Semantic Attention) 방법, 어텐션 모델의 다대다 매칭을 기반으로 효율적인 비디오 검색 및 질의응답을 가능케 하는 비디오-언어간 융합 (Joint Sequence Fusion) 방법 등 비디오 주의 인식을 효율적으로 학습시킬 수 있는 방법들을 제시한다. 다음으로는, 주의 인식(Attention) 모델이 물체-단어 간 관계를 넘어 비디오 상에서 인물 검색 (Person Searching) 그리고 인물 재 식별 (Person Re-Identification)을 동시에 수행하며 상승작용을 일으키는 스토리 속 캐릭터 인식 신경망 (Character in Story Identification Network) 을 소개하며, 마지막으로 자기 지도 학습(Self-supervised Learning)을 통해 주의 인식(Attention) 기반 언어 모델이 긴 비디오에 대한 설명을 연관성 있게 잘 생성할 수 있도록 유도하는 방법을 소개한다. 요약하자면, 이 학위 논문에서 제안한 새로운 방법론들은 비디오-언어 학습에 해당하는 비디오 캡션(Video captioning), 비디오 검색(Video Retrieval), 시각 질의응답(Video Question and Answering)등을 해결할 수 있는 기술적 디딤돌이 되며, 비디오 캡션 학습을 통해 학습된 주의 인식 모듈은 검색 및 질의응답, 인물 검색 등 각 네트워크에 이식되면서 새로운 문제들에 대해 동시에 최고 수준(State-of-the-art)의 성능을 달성하였다. 이를 통해 비디오-언어 학습으로 얻은 언어 지식의 이전은 시각-청각을 아우르는 비디오 멀티모달 학습에 큰 도움이 되는 것을 실험적으로 보여준다. 향후 작업 방향 (Future Work)으로는 앞서 연구한 내용들을 기반으로 웹 속에 존재하는 대규모의 언어, 비디오, 오디오 데이터를 통합해 학습에 활용하여 산업계의 많은 난제를 해결할 수 있는 비지도 학습 모델을 만들고자 한다.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 요약 ... 148 Acknowledgements ... 150Docto

Short- and Long-Term Changes in Attention, Memory and Brain Activity Following Exercise, Motor Learning, and Expertise

How humans perceive, embody, and execute actions has been an area of intense study in cognitive neuroscience, and these investigations shed light on how we adaptively learn from and interact with an ever-changing world. All of the knowledge associated with action, including sensorimotor representations, the words we use to describe them, and the memories that store this information, are represented in distributed brain regions that comprise knowledge schemas. With repeated practice or training, one can acquire a highly specialized motor repertoire that fosters even more efficient and adaptable behaviour to achieve peak performance. Using behavioural, EEG and fMRI approaches, I will present a series of investigations that evaluate the impact of short-term exercise and long-term dance practice on the development of expert knowledge schemas. In Chapters 2 and 3, I will demonstrate that activating one domain in the schema (e.g., action processing) will prime other domains (e.g., verbal attention and working memory) to induce translational performance improvements. Subsequent chapters will reveal how familiarity with a specific genre of dance influences behavioural (Chapter 3) and neurophysiological signatures of action perception, how these motor representations are coded in sensorimotor association areas (Chapters 4), and how they change with repeated practice and performance (Chapter 5). How these findings contribute to our model of expert knowledge schemas will be discussed in Chapter 6. These findings bear efficacy for the therapeutic application of exercise and dance programs to alleviate motor, cognitive and neurophysiological impairments in several clinical populations, including people with Parkinsons disease

A Dance with Protein Assemblies : Analysis, Structure Prediction, and Design

Protein assemblies are some of the most complex molecular machines in nature. They facilitate many cellular functions, from DNA replication to molecular motion, energy production, and even the production of other proteins. In a series of 3 papers, we analyzed the structure, developed structure prediction tools, and design tools, for different protein assemblies. Many of the studies were centered around viral protein capsids. Viral capsids are protein coats found inside viruses that contain and protect the viral genome. In one paper, we studied the interfaces of these capids and their energy landscapes. We found that they differ from regular homomers in terms of the amino acid composition and size, but not in the quality of interactions. This contradicts existing experimental and theoretical studies that suggest that the interactions are weak. We hypothesise that the occlusion by our models of electrostatic and entropic contributions might be at play. In another paper, we developed methods to predict large cubic symmetrical protein assemblies, such as viral capsids, from sequence. This method is based upon AlphaFold, a new AI tool that has revolutionized protein structure prediction. We found that we can predict up to 50% of the structures of these assemblies. The method can quickly elucidate the structure of many relevant proteins for humans, and for understanding structures relevant to disease, such as the structures of viral capsids. In the final paper, we developed tools to design capsid-like proteins called cages – structures that can be used for drug delivery and vaccine design. A fundamental problem in designing cage structures is achieving different architectures and low porosity, goals that are important for vaccine design and the delivery of small drug molecules. By explicitly modelling the shapes of the subunits in the cage and matching the shapes with proteins from structural databases, we find that we can create structures with many different sizes, shapes, and porosities - including low porosities. While waiting for experimental validation, the design strategy described in the paper must be extended, and more designs must be tested

Expressive movement generation with machine learning

Movement is an essential aspect of our lives. Not only do we move to interact with our physical environment, but we also express ourselves and communicate with others through our movements. In an increasingly computerized world where various technologies and devices surround us, our movements are essential parts of our interaction with and consumption of computational devices and artifacts. In this context, incorporating an understanding of our movements within the design of the technologies surrounding us can significantly improve our daily experiences. This need has given rise to the field of movement computing – developing computational models of movement that can perceive, manipulate, and generate movements. In this thesis, we contribute to the field of movement computing by building machine-learning-based solutions for automatic movement generation. In particular, we focus on using machine learning techniques and motion capture data to create controllable, generative movement models. We also contribute to the field by creating datasets, tools, and libraries that we have developed during our research. We start our research by reviewing the works on building automatic movement generation systems using machine learning techniques and motion capture data. Our review covers background topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. Building on our literature review, we present WalkNet, an interactive agent walking movement controller based on neural networks. The expressivity of virtual, animated agents plays an essential role in their believability. Therefore, WalkNet integrates controlling the expressive qualities of movement with the goal-oriented behaviour of an animated virtual agent. It allows us to control the generation based on the valence and arousal levels of affect, the movement’s walking direction, and the mover’s movement signature in real-time. Following WalkNet, we look at controlling movement generation using more complex stimuli such as music represented by audio signals (i.e., non-symbolic music). Music-driven dance generation involves a highly non-linear mapping between temporally dense stimuli (i.e., the audio signal) and movements, which renders a more challenging modelling movement problem. To this end, we present GrooveNet, a real-time machine learning model for music-driven dance generation

Scene Understanding for Autonomous Manipulation with Deep Learning

Over the past few years, deep learning techniques have achieved tremendous success in many visual understanding tasks such as object detection, image segmentation, and caption generation. Despite this thriving in computer vision and natural language processing, deep learning has not yet shown signicant impact in robotics. Due to the gap between theory and application, there are many challenges when applying the results of deep learning to the real robotic systems. In this study, our long-term goal is to bridge the gap between computer vision and robotics by developing visual methods that can be used in real robots. In particular, this work tackles two fundamental visual problems for autonomous robotic manipulation: affordance detection and ne-grained action understanding. Theoretically, we propose dierent deep architectures to further improves the state of the art in each problem. Empirically, we show that the outcomes of our proposed methods can be applied in real robots and allow them to perform useful manipulation tasks

Adversarial content manipulation for analyzing and improving model robustness

The recent rapid progress in machine learning systems has opened up many real-world applications --- from recommendation engines on web platforms to safety critical systems like autonomous vehicles. A model deployed in the real-world will often encounter inputs far from its training distribution. For example, a self-driving car might come across a black stop sign in the wild. To ensure safe operation, it is vital to quantify the robustness of machine learning models to such out-of-distribution data before releasing them into the real-world. However, the standard paradigm of benchmarking machine learning models with fixed size test sets drawn from the same distribution as the training data is insufficient to identify these corner cases efficiently. In principle, if we could generate all valid variations of an input and measure the model response, we could quantify and guarantee model robustness locally. Yet, doing this with real world data is not scalable. In this thesis, we propose an alternative, using generative models to create synthetic data variations at scale and test robustness of target models to these variations. We explore methods to generate semantic data variations in a controlled fashion across visual and text modalities. We build generative models capable of performing controlled manipulation of data like changing visual context, editing appearance of an object in images or changing writing style of text. Leveraging these generative models we propose tools to study robustness of computer vision systems to input variations and systematically identify failure modes. In the text domain, we deploy these generative models to improve diversity of image captioning systems and perform writing style manipulation to obfuscate private attributes of the user. Our studies quantifying model robustness explore two kinds of input manipulations, model-agnostic and model-targeted. The model-agnostic manipulations leverage human knowledge to choose the kinds of changes without considering the target model being tested. This includes automatically editing images to remove objects not directly relevant to the task and create variations in visual context. Alternatively, in the model-targeted approach the input variations performed are directly adversarially guided by the target model. For example, we adversarially manipulate the appearance of an object in the image to fool an object detector, guided by the gradients of the detector. Using these methods, we measure and improve the robustness of various computer vision systems -- specifically image classification, segmentation, object detection and visual question answering systems -- to semantic input variations.Der schnelle Fortschritt von Methoden des maschinellen Lernens hat viele neue Anwendungen ermöglicht – von Recommender-Systemen bis hin zu sicherheitskritischen Systemen wie autonomen Fahrzeugen. In der realen Welt werden diese Systeme oft mit Eingaben außerhalb der Verteilung der Trainingsdaten konfrontiert. Zum Beispiel könnte ein autonomes Fahrzeug einem schwarzen Stoppschild begegnen. Um sicheren Betrieb zu gewährleisten, ist es entscheidend, die Robustheit dieser Systeme zu quantifizieren, bevor sie in der Praxis eingesetzt werden. Aktuell werden diese Modelle auf festen Eingaben von derselben Verteilung wie die Trainingsdaten evaluiert. Allerdings ist diese Strategie unzureichend, um solche Ausnahmefälle zu identifizieren. Prinzipiell könnte die Robustheit “lokal” bestimmt werden, indem wir alle zulässigen Variationen einer Eingabe generieren und die Ausgabe des Systems überprüfen. Jedoch skaliert dieser Ansatz schlecht zu echten Daten. In dieser Arbeit benutzen wir generative Modelle, um synthetische Variationen von Eingaben zu erstellen und so die Robustheit eines Modells zu überprüfen. Wir erforschen Methoden, die es uns erlauben, kontrolliert semantische Änderungen an Bild- und Textdaten vorzunehmen. Wir lernen generative Modelle, die kontrollierte Manipulation von Daten ermöglichen, zum Beispiel den visuellen Kontext zu ändern, die Erscheinung eines Objekts zu bearbeiten oder den Schreibstil von Text zu ändern. Basierend auf diesen Modellen entwickeln wir neue Methoden, um die Robustheit von Bilderkennungssystemen bezüglich Variationen in den Eingaben zu untersuchen und Fehlverhalten zu identifizieren. Im Gebiet von Textdaten verwenden wir diese Modelle, um die Diversität von sogenannten Automatische Bildbeschriftung-Modellen zu verbessern und Schreibtstil-Manipulation zu erlauben, um private Attribute des Benutzers zu verschleiern. Um die Robustheit von Modellen zu quantifizieren, werden zwei Arten von Eingabemanipulationen untersucht: Modell-agnostische und Modell-spezifische Manipulationen. Modell-agnostische Manipulationen basieren auf menschlichem Wissen, um bestimmte Änderungen auszuwählen, ohne das entsprechende Modell miteinzubeziehen. Dies beinhaltet das Entfernen von für die Aufgabe irrelevanten Objekten aus Bildern oder Variationen des visuellen Kontextes. In dem alternativen Modell-spezifischen Ansatz werden Änderungen vorgenommen, die für das Modell möglichst ungünstig sind. Zum Beispiel ändern wir die Erscheinung eines Objekts um ein Modell der Objekterkennung täuschen. Dies ist durch den Gradienten des Modells möglich. Mithilfe dieser Werkzeuge können wir die Robustheit von Systemen zur Bildklassifizierung oder -segmentierung, Objekterkennung und Visuelle Fragenbeantwortung quantifizieren und verbessern

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task