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hello and welcome to our presentation on
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a groundbreaking approach in the field
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of video and painting our paper
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introduces a novel concept titled
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towards language driven video imp
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painting via multimodal large language
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models traditional video imp painting
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has relied heavily on binary masks to
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identify areas for restoration a process
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both timec consuming and labor intensive
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especially in applications like object
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removal recognizing this challenge our
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research pivots to a pioneering method
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language driven video and painting this
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new paradigm leverages natural language
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instructions significantly simplifying
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and accelerating the Imp painting
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process in the concept of language
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driven video imp painting our research
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introduces two subtasks referring video
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and painting this involves interpreting
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linguistic descriptions to identify and
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remove objects from
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videos like remove man in the left the
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model processes these referring
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Expressions to accurately Target and imp
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paint the specified areas in the video
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interactive video imp painting extends
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the concept to an interactive context
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where in painting requests are made
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through chat style implicit
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conversations here the model must
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understand and interpret the user's
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intent in a dynamic conversational
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format adapting the Imp painting process
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accordingly these subtasks collectively
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enhance the effic and intuitiveness of
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video and painting allowing for more
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natural and flexible user interactions
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in video
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editing
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however however there is no existing
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data set that can perform training and
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evaluation of the language driven task
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therefore we build a remove objects from
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videos by instructions roie data set
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which includes
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5,650 videos and
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9,091 in painting results come previous
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data sets the ROI data set contains a
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novel annotation type of chat style
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conversations it is also the first video
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data set that contains in painting
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annotations the the data construction
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pipeline involved two main phases
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incorporating and painting results into
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existing referring video segmentation
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data sets and the interactive annotation
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process the initial data sets used for
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this purpose are refer you to BOS and
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a2d
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sentences these data sets already
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contain masks and expressions which
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provides a foundation for further
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development The annotation pipeline
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utilized a state-of-art video imp
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painting model specifically E2 fgv to
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generate the Imp painting ground truth
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we also incorporate human annotation
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efforts to ensure the quality of the Imp
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painting results a key aspect of the
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data set's Innovation was its focus on
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interactive
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annotations unlike traditional methods
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that might use straightforward remove
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sentences the the interactive requests
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in the roie data set are designed to be
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implicit chat style conversations this
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design Choice necessitated the model's
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ability to discern the users under
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intent to achieve this the pipeline
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employed large language models llms and
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multimodal large language models mlms to
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simulate human users and generate
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potential requests and
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responses this approach enabled the roie
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data set to handle complex user requests
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significantly enhan util
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and relevance for language driven video
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and painting tasks after the data set
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is after the data set is constructed we
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build a diffusion-based language driven
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video and painting framework this
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framework represents the first endtoend
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Baseline for this task it incorporates
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video infl a visual conditioning
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temporal attention and mask supervision
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architecture it is also distinguished by
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its in with multimodal large language
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models for the interactive video and
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painting task these modules enable our
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model to comprehend and effectively
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process complex language based and
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painting
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requests in the quantitative results
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previous methods and LGV experienced
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different performance drops for
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evaluating interactive video and
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painting compared with referring video
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and painting this is intuitive because
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chat style requests are more complex and
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hard to understand for the novel
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interactive task the MLM at LGV model
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achieved the best performance
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demonstrating the efficiency of our
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proposed
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models qual qualitative results and
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comparisons also show our models
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capability of handling a wide range of
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imp painting scenarios driven by natural
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language
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instructions
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in this video we show more video
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examples of qualitative
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results note that despite the comparable
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stronger performance with previous
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methods the proposed model is designed
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as an end-to-end Baseline for the novel
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language driven video and painting task
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we expect future work to build more
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advanced models in this field please see
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the discussions in our main paper and
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the limitations in the
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supplementary
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the language driven video in painting
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faces many potential
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challenges first this task relies
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heavily on the accuracy and Clarity of
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language
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inputs ambiguities or vagueness in
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language descriptions can lead to
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inaccuracies and in painting
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results second video and painting in a
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real-time setting especially with
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complex language driven inputs is
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computationally demanding
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diffusion-based models also experience
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the slow inference problem due to the
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marof to noising process improving the
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speed and efficiency of these models
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without compromising accuracy is a
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crucial challenge third models might
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perform well on the debt they were
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trained on but struggle with new unseen
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data to better facilitate the proposed
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task and overcome the problems we expect
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future work in the following domains
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first employing deep learning techniques
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specifically focused on resolving
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ambiguities and language inputs possibly
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by using contextual clues video or
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previous language
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inputs second researching methods to
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optimize these models for real-time
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video and painting could be valuable for
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live broadcasting or interactive media
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third incorporating interactive user
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feedback mechanisms that allow the
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system to learn from Corrections or
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preferences indicated Byers thereby
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improving the accuracy and relevance of
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the inaine results over
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time thank you for your
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attention
