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المنطقة قيد المناقشة للحوار البصري
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يفترض أن الحوار المرئي يطلب من محفوظات الحوار إنشاء ردود صحيحة أثناء مربع حوار.ومع ذلك، ليس من الواضح من العمل السابق كيفية حاجة تاريخ حوار الحوار إلى مربع الحوار المرئي.في هذه الورقة، نحدد ما يعنيه سؤال مرئي يحتاج إلى سجل حوار ونصدر مجموعة فرعية من التخمين؟!الأسئلة التي تغير تاريخ حوارهم تماما ردودهم.نقترح تمثيل رواية مفسدية تاريخ حوار حوار بصريا: المنطقة قيد المناقشة.وهو يقيد ميزات الصورة المكانية وفقا لتمثيل الدلالي للتاريخ المستوحى من مفهوم هيكل المعلومات حول السؤال قيد المناقشة. نحن نقيم الهندسة المعمارية على النماذج متعددة الوسائط الخاصة بمهام المهام ونموذج محول البصر lxmert.
Visual Dialog is assumed to require the dialog history to generate correct responses during a dialog. However, it is not clear from previous work how dialog history is needed for visual dialog. In this paper we define what it means for a visual question to require dialog history and we release a subset of the Guesswhat?! questions for which their dialog history completely changes their responses. We propose a novel interpretable representation that visually grounds dialog history: the Region under Discussion. It constrains the image's spatial features according to a semantic representation of the history inspired by the information structure notion of Question under Discussion.We evaluate the architecture on task-specific multimodal models and the visual transformer model LXMERT.
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Visual dialog is challenging since it needs to answer a series of coherent questions based on understanding the visual environment. How to ground related visual objects is one of the key problems. Previous studies utilize the question and history to
attend to the image and achieve satisfactory performance, while these methods are not sufficient to locate related visual objects without any guidance. The inappropriate grounding of visual objects prohibits the performance of visual dialog models. In this paper, we propose a novel approach to Learn to Ground visual objects for visual dialog, which employs a novel visual objects grounding mechanism where both prior and posterior distributions over visual objects are used to facilitate visual objects grounding. Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process. Meanwhile, a prior distribution, which is inferred from context only, is used to approximate the posterior distribution so that appropriate visual objects can be grounding even without answers during the inference process. Experimental results on the VisDial v0.9 and v1.0 datasets demonstrate that our approach improves the previous strong models in both generative and discriminative settings by a significant margin.
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context. In this paper, we study how to address two fundamental challenges for this task: (1) reasoning over underlying semantic st
ructures among dialog rounds and (2) identifying several appropriate answers to the given question. To address these challenges, we propose a Sparse Graph Learning (SGL) method to formulate visual dialog as a graph structure learning task. SGL infers inherently sparse dialog structures by incorporating binary and score edges and leveraging a new structural loss function. Next, we introduce a Knowledge Transfer (KT) method that extracts the answer predictions from the teacher model and uses them as pseudo labels. We propose KT to remedy the shortcomings of single ground-truth labels, which severely limit the ability of a model to obtain multiple reasonable answers. As a result, our proposed model significantly improves reasoning capability compared to baseline methods and outperforms the state-of-the-art approaches on the VisDial v1.0 dataset. The source code is available at https://github.com/gicheonkang/SGLKT-VisDial.
Conversations are often held in laboratories and companies. A summary is vital to grasp the content of a discussion for people who did not attend the discussion. If the summary is illustrated as an argument structure, it is helpful to grasp the discu
ssion's essentials immediately. Our purpose in this paper is to predict a link structure between nodes that consist of utterances in a conversation: classification of each node pair into linked'' or not-linked.'' One approach to predict the structure is to utilize machine learning models. However, the result tends to over-generate links of nodes. To solve this problem, we introduce a two-step method to the structure prediction task. We utilize a machine learning-based approach as the first step: a link prediction task. Then, we apply a score-based approach as the second step: a link selection task. Our two-step methods dramatically improved the accuracy as compared with one-step methods based on SVM and BERT.
Various machine learning tasks can benefit from access to external information of different modalities, such as text and images. Recent work has focused on learning architectures with large memories capable of storing this knowledge. We propose augme
nting generative Transformer neural networks with KNN-based Information Fetching (KIF) modules. Each KIF module learns a read operation to access fixed external knowledge. We apply these modules to generative dialog modeling, a challenging task where information must be flexibly retrieved and incorporated to maintain the topic and flow of conversation. We demonstrate the effectiveness of our approach by identifying relevant knowledge required for knowledgeable but engaging dialog from Wikipedia, images, and human-written dialog utterances, and show that leveraging this retrieved information improves model performance, measured by automatic and human evaluation.
An assessment of nine vicia faba genotypes (flip84-59fb, AGUADOLCE LB 1266
SML, FLIP84-14FB, GIZE.461, REINA BLANCA, autochthon, Spanish, and Cypriotes)
was achieved, during 2010-2011 and 2011-2012 seasons, in Al_Bassa farm, near Lattakia
city. Su
perior genotypes will be adopted as a high yield improved varieties in that area,
however, the other genotypes (possessing genetic characteristics, superior of local
genotypes), will be used in future breeding programs.
The results indicated a significant differences between studied characteristics of the
genotypes, as Spanish genotype recorded the best pod length (17.16cm), having high
degree of inheritance (68.24), followed by filp84-59fb genotype (15.1 cm), with weight
seeds per pod (33.6 g), having high degree of inheritance (68.45), followed by the Cypriot
genotype, by seed weight (14.66 g), number of pod (4.6), having low degree of inheritance
(23.53), followed by Cyprian autochtone genotype, and Aguadolce.lb1266,and filip84 -
14fb number of pod (3.6).
The Cypriot genotype was the best, in terms of pod weight (23:43 g), having high
degree of inheritance (76.45) followed by Spanish (20.63g), and seed weight (3.93g),
having medium degree of inheritance (54.82), followed by style filip84-59fb (3.73 g), and
100-seed weight (4.1g), having high degree of inheritance (97.49), followed by Aguadolce
genotypes (285 g).
The SML genotype is the best among premature genotypes in terms of flowering (46 days)
and maturity (148 days), followed by Cypriot in terms of flowering (51 days) and flip84-
59fb in terms of maturity (155 days)
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