Instance Level Research Articles

HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction

Risk prediction, usually achieved by learning representations from patient’s physiological sequence or user’s behavioral sequence data, and has been widely applied in healthcare and finance. Despite that, some recent time-aware deep learning methods have led to superior performances in such sequence representation learning tasks, such improvement is limited due to a lack of guidance from hierarchical global view. To address this issue, we propose a novel end-to-end H ierarchical G lobal V iew-guided (HGV) sequence representation learning framework. Specifically, the Global Graph Embedding (GGE) module is proposed to learn sequential clip-aware representations from temporal correlation graph (TCG) at instance level. Furthermore, following the way of key-query attention, the harmonic β-attention (β-Attn) is also developed for making a global tradeoff between time-aware decay and observation significance at channel level adaptively. Moreover, the hierarchical representations at both instance level and channel level can be coordinated by the heterogeneous information aggregation under the guidance of global view. Experimental results on both healthcare risk prediction benchmark and SMEs credit overdue risk prediction task from the real-world industrial scenario in MYBank, Ant Group, have illustrated that the proposed model can achieve competitive prediction performance compared with other known baselines. The code has been released public available at: https://github.com/LiYouru0228/HGV.

Image emotion analysis based on semantic concepts

With the increasing number of users express their emotions via images on social media, image emotion analysis attracts much attention of researchers. For the ambiguity and subjectivity of emotion, image emotion analysis is more challenging than other computer vision tasks. Previous methods merely learn a direct mapping between image feature and emotion. However, in emotion perception theory of psychology, it is demonstrated that human beings perceive emotion in a stepwise way. Therefore, we propose a novel image emotion analysis framework that makes use of emotional concepts as middle-level feature to bridge image and emotion. Firstly, the relationship between the concept and the emotion is organized in the form of knowledge graph. The relation between the image and the emotion in the semantic embedding space is explored where the knowledge is encoded into. On the other hand, a multi-level deep metric learning method to optimize the model from both label level and instance level is proposed. Extensive experimental results on two image emotion datasets, demonstrate that the present approach performs favorably against the state-of-the-art methods on both affective image retrieval and classification tasks.

Intrinsic feature extraction for unsupervised domain adaptation

PurposeUnsupervised domain adaptation object detection not only mitigates model terrible performance resulting from domain gap, but also has the ability to apply knowledge trained on a definite domain to a distinct domain. However, aligning the whole feature may confuse the object and background information, making it challenging to extract discriminative features. This paper aims to propose an improved approach which is called intrinsic feature extraction domain adaptation (IFEDA) to extract discriminative features effectively.Design/methodology/approachIFEDA consists of the intrinsic feature extraction (IFE) module and object consistency constraint (OCC). The IFE module, designed on the instance level, mainly solves the issue of the difficult extraction of discriminative object features. Specifically, the discriminative region of the objects can be paid more attention to. Meanwhile, the OCC is deployed to determine whether category prediction in the target domain brings into correspondence with it in the source domain.FindingsExperimental results demonstrate the validity of our approach and achieve good outcomes on challenging data sets.Research limitations/implicationsLimitations to this research are that only one target domain is applied, and it may change the ability of model generalization when the problem of insufficient data sets or unseen domain appeared.Originality/valueThis paper solves the issue of critical information defects by tackling the difficulty of extracting discriminative features. And the categories in both domains are compelled to be consistent for better object detection.

Toward Deeper Understanding of Camouflaged Object Detection

Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to survival. To detect and segment the whole scope of a camouflaged object, camouflaged object detection (COD) is introduced as a binary segmentation task, with the binary ground truth camouflage map indicating the exact regions of the camouflaged objects. In this paper, we revisit this task and argue that the binary segmentation setting fails to fully understand the concept of camouflage. We find that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage, but also provide guidance to designing more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first triple-task learning framework to simultaneously <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localize, segment, and rank</i> camouflaged objects, indicating the conspicuousness level of camouflage. As no corresponding datasets exist for either the localization model or the ranking model, we generate localization maps with an eye tracker, which are then processed according to the instance level labels to generate our ranking-based training and testing dataset. We also contribute the largest COD testing set to comprehensively analyse performance of the COD models. Experimental results show that our triple-task learning framework achieves new state-of-the-art, leading to a more explainable COD network. Our code, data, and results are available at: https://github.com/JingZhang617/ COD-Rank-Localize-and-Segment.

Dual Mutual Information Constraints for Discriminative Clustering

Deep clustering is a fundamental task in machine learning and data mining that aims at learning clustering-oriented feature representations. In previous studies, most of deep clustering methods follow the idea of self-supervised representation learning by maximizing the consistency of all similar instance pairs while ignoring the effect of feature redundancy on clustering performance. In this paper, to address the above issue, we design a dual mutual information constrained clustering method named DMICC which is based on deep contrastive clustering architecture, in which the dual mutual information constraints are particularly employed with solid theoretical guarantees and experimental validations. Specifically, at the feature level, we reduce the redundancy among features by minimizing the mutual information across all the dimensionalities to encourage the neural network to extract more discriminative features. At the instance level, we maximize the mutual information of the similar instance pairs to obtain more unbiased and robust representations. The dual mutual information constraints happen simultaneously and thus complement each other to jointly optimize better features that are suitable for the clustering task. We also prove that our adopted mutual information constraints are superior in feature extraction, and the proposed dual mutual information constraints are clearly bounded and thus solvable. Extensive experiments on five benchmark datasets show that our proposed approach outperforms most other clustering algorithms. The code is available at https://github.com/Li-Hyn/DMICC.

Hierarchical Contrast for Unsupervised Skeleton-Based Action Representation Learning

This paper targets unsupervised skeleton-based action representation learning and proposes a new Hierarchical Contrast (HiCo) framework. Different from the existing contrastive-based solutions that typically represent an input skeleton sequence into instance-level features and perform contrast holistically, our proposed HiCo represents the input into multiple-level features and performs contrast in a hierarchical manner. Specifically, given a human skeleton sequence, we represent it into multiple feature vectors of different granularities from both temporal and spatial domains via sequence-to-sequence (S2S) encoders and unified downsampling modules. Besides, the hierarchical contrast is conducted in terms of four levels: instance level, domain level, clip level, and part level. Moreover, HiCo is orthogonal to the S2S encoder, which allows us to flexibly embrace state-of-the-art S2S encoders. Extensive experiments on four datasets, i.e., NTU-60, NTU-120, PKU-I and PKU-II, show that HiCo achieves a new state-of-the-art for unsupervised skeleton-based action representation learning in two downstream tasks including action recognition and retrieval, and its learned action representation is of good transferability. Besides, we also show that our framework is effective for semi-supervised skeleton-based action recognition. Our code is available at https://github.com/HuiGuanLab/HiCo.

Scene-Level Sketch-Based Image Retrieval with Minimal Pairwise Supervision

The sketch-based image retrieval (SBIR) task has long been researched at the instance level, where both query sketches and candidate images are assumed to contain only one dominant object. This strong assumption constrains its application, especially with the increasingly popular intelligent terminals and human-computer interaction technology. In this work, a more general scene-level SBIR task is explored, where sketches and images can both contain multiple object instances. The new general task is extremely challenging due to several factors: (i) scene-level SBIR inherently shares sketch-specific difficulties with instance-level SBIR (e.g., sparsity, abstractness, and diversity), (ii) the cross-modal similarity is measured between two partially aligned domains (i.e., not all objects in images are drawn in scene sketches), and (iii) besides instance-level visual similarity, a more complex multi-dimensional scene-level feature matching problem is imposed (including appearance, semantics, layout, etc.). Addressing these challenges, a novel Conditional Graph Autoencoder model is proposed to deal with scene-level sketch-images retrieval. More importantly, the model can be trained with only pairwise supervision, which distinguishes our study from others in that elaborate instance-level annotations (for example, bounding boxes) are no longer required. Extensive experiments confirm the ability of our model to robustly retrieve multiple related objects at the scene level and exhibit superior performance beyond strong competitors.

Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.

Robust semantic segmentation of tumour micro-environment is one of the major open challenges in machine learning enabled computational pathology. Though deep learning based systems have made significant progress, their task agnostic data driven approach often lacks the contextual grounding necessary in biomedical applications. We present a novel fuzzy water flow scheme that takes the coarse segmentation output of a base deep learning framework to then provide a more fine-grained and instance level robust segmentation output. Our two stage synergistic segmentation method, Deep-Fuzz, works especially well for overlapping objects, and achieves state-of-the-art performance in four public cell nuclei segmentation datasets. We also show through visual examples how our final output is better aligned with pathological insights, and thus more clinically interpretable.

Dual Distillation Discriminator Networks for Domain Adaptive Few-Shot Learning

Domain Adaptive Few-Shot Learning (DA-FSL) aims at accomplishing few-shot classification tasks on a novel domain with the aid of a large number of source-style samples and several target-style samples. It is essential for DA-FSL to transfer task knowledge from the source domain to the target domain and overcome the asymmetry amount of labeled data in both domains. To this end, we propose Dual Distillation Discriminator Networks (D3Net) from the perspective of the lack of labeled target domain style samples in DA-FSL. Specifically, we employ the idea of distillation discrimination to avoid the over-fitting caused by the unequal number of samples in the target and source domains, which trains the student discriminator by the soft labels from the teacher discriminator. Meanwhile, we design the task propagation stage and the mixed domain stage respectively from the level of feature space and instances to generate more target-style samples, which apply the task distributions and the sample diversity of the source domain to enhance the target domain. Our D3Net realizes the distribution alignment between the source domain and the target domain and constraints the FSL task distribution by prototype distributions on the mixed domain. Extensive experiments on three DA-FSL benchmark datasets, i.e., mini-ImageNet, tiered-ImageNet, and DomainNet, demonstrate that our D3Net achieves competitive performance.

Conformal Predictive Distribution Trees

AbstractBeing able to understand the logic behind predictions or recommendations on the instance level is at the heart of trustworthy machine learning models. Inherently interpretable models make this possible by allowing inspection and analysis of the model itself, thus exhibiting the logic behind each prediction, while providing an opportunity to gain insights about the underlying domain. Another important criterion for trustworthiness is the model’s ability to somehow communicate a measure of confidence in every specific prediction or recommendation. Indeed, the overall goal of this paper is to produce highly informative models that combine interpretability and algorithmic confidence. For this purpose, we introduce conformal predictive distribution trees, which is a novel form of regression trees where each leaf contains a conformal predictive distribution. Using this representation language, the proposed approach allows very versatile analyses of individual leaves in the regression trees. Specifically, depending on the chosen level of detail, the leaves, in addition to the normal point predictions, can provide either cumulative distributions or prediction intervals that are guaranteed to be well-calibrated. In the empirical evaluation, the suggested conformal predictive distribution trees are compared to the well-established conformal regressors, thus demonstrating the benefits of the enhanced representation.

Temporal Variations of Air Quality in Cage-Free Experimental Pullet Houses

The welfare of laying hens in conventional caged houses has become an increased public concern, leading primary food chains, restaurants, and grocers in the United States to pledge to source only cage-free (CF) eggs by 2025 or 2030. Cage-free housing systems have been considered as a more humane alternative; however, they still come with certain challenges. One of the primary challenges with CF housing is the poor indoor air quality due to the high levels of ammonia (NH3) and particulate matter (PM). Despite the importance of air quality in animal welfare, most studies have focused on the egg-laying stage, thereby leaving a significant knowledge gap in the pullet phase. Addressing this gap is essential to ensure the well-being of laying hens in CF housing and to help producers and researchers identify effective strategies to mitigate the impact of poor indoor air quality on the bird’s health and welfare. Therefore, the objective of this study was to (a) examine the effect of the pullets’ age on NH3 and PM levels, and (b) find the effect of housing, litter moisture content (LMC), and relative humidity (RH) on air pollutant concentrations. The results show that the PM levels of PM2.5, PM10, and total suspended particles (TSP) increased significantly with the growth of birds from 1 to 16 weeks of age (WOA) (p &lt; 0.01). For instance, PM2.5, PM10, and TSP levels were measured at 0.023 ± 0.003, 0.031 ± 0.004, and 0.058 ± 0.013 mg m−3 in the first week, and these levels increased to 1.44 ± 0.58, 2.723 ± 1.094, and 6.39 ± 2.96 mg m−3, respectively, by 16 WOA. In addition, PM levels measured near the perch were found to be three times higher than other locations inside the rooms (e.g., between the feeder and drinker or near the exhaust fan) (p &lt; 0.01), as perching is one of the primary reasons for dust generation. Furthermore, a significant interaction between the age of the pullets and PM levels was found (p &lt; 0.01), as the litter quality and the behaviors of birds were changing over time. For NH3 levels, average daily concentrations were lower than 1 ppm at 16 WOA for all rooms due to dry litter conditions (i.e., 9–10% LMC). Additionally, RH has been shown to have a significant effect on air pollutant concentration. Overall, the results indicate that the bird’s age significantly affects PM generation and PM variation within the rooms. The variation of PM was directly affected by RH inside the house. Therefore, this research will provide valuable information for both researchers and producers to control air pollutant emissions from the pullet stage in CF housing to ultimately improve the health and welfare of hens.

Trademark Dispute of Starbucks in Indonesia: Is There A Legal Protection?

In Starbucks Corporation Trademark Dispute against PT Sumatera Tobacco Trading Company, PT Sumatera Tobacco Trading Company won because it had previously registered the same Starbucks trademark as a cigarette. The ruling was made by the Commercial Court of Central Jakarta Decision No. 51/Pdt.Sus/Tra. However, the Starbucks Corporation won at the cassation (third instance) level in the Supreme Court by Decision No. 836 K/Rev.Sus-HKI/2022 since the owner of the coffee trademark was classified as a well-known trademark. In the wake of this, it is essential to research and evaluate how the judges’ processes differ between the instances and how is the protection system of companies and trademarks that are well-known in Indonesia. This study is carried out through normative legal research, using statutory and conceptual approaches, and the secondary legal materials were collected through literature studies and qualitative descriptive analysis methods.&#x0D; Keywords: Trademark Dispute, Starbucks, Legal Protection for Well-Known Trademark

An Efficient Data Analytics and Optimized Algorithm for Enhancing the Performance of Image Segmentation Using Deep Learning Model

Image segmentation is the key topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality among many others. There are numerous algorithms are developed in the literature for image segmentation. This paper provides a broad spectrum of pioneering works for instance and semantic level segmentation with mask Region based Convolution Neural Network with Monarch butterfly Optimization (RCNN-MBO) architecture. The system is initially constructed in a Python environment with images of people and animals being input. Remove the unnecessary data from the gathered datasets during the pre-processing stage. Next, use a stochastic threshold function to segment the image. Then update the segmented images into a designed model for detecting and classifying a group of images. The main goal of the designed approach is to attain accurate prediction results also improve the performance of the designed model by attaining better results. To enhance the performance, two activation functions were used and MBO fitness is updated in the classification layer. It improves the prediction results and takes less time to detect and classify images. Finally, the experimental outcomes show the reliability of the designed approach by other conventional techniques in terms of accuracy, precision, sensitivity, specificity, F-measure, error rate, and computation time.

Multilevel Feature Aggregated Network with Instance Contrastive Learning Constraint for Building Extraction

Building footprint extraction from remotely sensed imagery is a critical task in the field of illegal building discovery, urban dynamic monitoring, and disaster emergency response. Recent research has made significant progress in this area by utilizing deep learning techniques. However, it remains difficult to efficiently balance the spatial detail and rich semantic features. In particular, the extracted building edge is often inaccurate, especially in areas where the buildings are densely distributed, and the boundary of adjacent building instances is difficult to distinguish accurately. Additionally, identifying buildings with varying scales remains a challenging problem. To address the above problems, we designed a novel framework that aggregated multilevel contextual information extracted from multiple encoders. Furthermore, we introduced an instance constraint into contrastive learning to enhance the robustness of the feature representation. Experimental results demonstrated that our proposed method achieved 91.07% and 74.58% on the intersection over union metric on the WHU and Massachusetts datasets, respectively, outperforming the most recent related methods. Notably, our method significantly improved the accuracy of building boundaries, especially at the building instance level, and the integrity of multi-scale buildings.

Tanggung Jawab Notaris Terhadap Akta Kuasa Menjual Dalam Peralihan Hak Atas Tanah

This study aims to determine the responsibility of a notary for a power of attorney to sell in the transfer of land rights and differences in consideration of the decisions of judges of first instance, judges of appellate level and judges of final instance in judicial review based on Supreme Court decision Number 20 PK/Pid/2020. This study uses a normative juridical research method, with a conceptual and statutory approach. The technique of collecting data is by studying literature relevant to legal issues and then analyzing it qualitatively. This study concludes that the notary Ketut Neli Asih can only be subject to administrative sanctions that adhere to the principle of responsibility based on fault (based on fault of liability), so that the notary concerned cannot be held criminally responsible, because the notary performs his duties as a public official. The verdict of the Panel of Judges of the First Level and the Panel of Judges of the Appellate Level are of the opinion and believe that the actions committed by Ketut Neli Asih as a notary have fulfilled the elements of a crime Article 378 jo. Article 56 paragraph 2 of the Criminal Code, namely "Deliberately Providing Opportunities or Means in the Crime of Fraud". Meanwhile, the Panel of Judges of the Supreme Court who tried the review was of the opinion and believed that the charges charged by the Public Prosecutor were true and proven, but it was not a crime, but rather an administrative matter.

The establishment of transient expression systems and their application for gene function analysis of flavonoid biosynthesis in Carthamus tinctorius L

BackgroundSafflower (Carthamus tinctorius L.) is an important economic crop and a traditional medicinal material rich in flavonoids, which can alleviate cardiovascular and cerebrovascular pathologies. Thus, many candidate genes involved in safflower flavonoid biosynthesis have been cloned. However, owing to the lack of a homologous gene expression system, research on gene function is limited to model plants. Therefore, a gene function identification protocol for safflower must be established.ResultsIn the present study, using safflower callus as the experimental material, Agrobacterium and biolistic transient expression systems were established. In the Agrobacterium transient expression system, the highest transformation rate was obtained at the original Agrobacterium concentration of OD600 0.4, infiltration concentration of OD600 0.6, infection for 20 min, co-culture for 3 days, and acetosyringone concentration of 100 μmol·L−1. In the biolistic transient expression system, the highest transformation efficiency was observed at helium pressure of 1,350 psi, vacuum degree of -0.8 bar, flight distance of 6.5 cm, one round of bombardment, plasmid concentration of 3 μg·shot−1, and gold particle concentration of 100 μg·shot−1. Further, these two transient expression systems were used for the functional analysis of CtCHS1 as an example. After overexpression, relative CtCHS1 expression increased, particularly in Agrobacterium-transformed calli. Additionally, the contents of some flavonoids were altered; for instance, naringenin and genistein levels were significantly increased in Agrobacterium-transformed calli, whereas luteolin, luteolin-7-O-rutinoside, and apigenin derivative levels were significantly decreased in biolistic-transformed calli.ConclusionUsing safflower callus as the experimental material, highly efficient Agrobacterium and biolistic transient expression systems were successfully established, and the utility of both systems for investigating gene function was demonstrated. The proposed safflower callus transient expression systems will be useful for further functional analyses of flavonoid biosynthetic genes in safflower.

Ontology is what makes data interesting: Interestingness framework for COVID-19 corpora

The COVID-19 pandemic has already shown to be a worldwide threat, demonstrating how susceptible humans may be. It has also inspired experts from a range of aspects and countries to find the potential solution to control the widespread. In line with this, our research proposes a novel framework for finding interesting facts from COVID-19 corpora using domain ontology. Since data mining with domain knowledge provides semantically rich facts, we use ontology in our proposed approaches. Most of the state-of-the-art methods rely on instance level or user intervention. These methods do not entirely exploit the richness of ontology. In this work, we demonstrate how to extract exciting rules from data at ontology’s schema and instance levels. Our experiments were carried out on two COVID-19 corpora that depict COVID-19 patients’ symptoms and drug information. The proposed framework outperformed the traditional methods by reducing the number of rules by 70% and generating semantic-rich rules that are more user-readable and quickly adopted by decision-makers. Furthermore, to support our claims, we compared the outcomes of the proposed framework with the most recent approach in the field. Also, statistically significant tests and domain expert evaluations are conducted to validate our framework.

Design of Data Quality Control System Based on ETL

Aiming at the problems of incremental data extraction and task scheduling in ETL processing, an optimization strategy based on data quality control is proposed. We discuss the common mechanisms of data incremental extraction and quality control in the ET subsystem of data warehouse, and propose an incremental data extraction method based on key attribute comparison. The key functions of ETL data extraction is utilized, and the database incremental extraction method is adopted. During the process of data processing, the problem of data loss is solved by means of auxiliary tables, and the problem of data quality at the instance level is solved by data cleaning. Finally, the experimental results on real data sets show that the ETL design with quality control optimizes the overall workflow and ensures that data can be accurately extracted into the data warehouse.

DA-RDD: Toward Domain Adaptive Road Damage Detection Across Different Countries

Recent advances on road damage detection relies on a large amount of labeled data, whilst collecting pavement image is labor-intensive and time-consuming. Unsupervised Domain Adaptation (UDA) provides a promising solution to adapt a source domain to the target domain, however, cross-domain crack detection is still an open problem. In this paper, we propose domain adaptive road damage detection termed as DA-RDD, by incorporating image-level with instance-level feature alignment for domain-invariant representation learning in an adversarial manner. Specifically, importance weighting is introduced to evaluate the intermediate samples for image-level alignment between domains, and we aggregate RoI-wise feature with multi-scale contextual information to recover the crack details for progressive domain alignment at instance level. Additionally, a large-scale road damage dataset (based on Road Damage Dataset 2020 (RDD2020)) named as RDD2021 is constructed with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100k$ </tex-math></inline-formula> synthetic labeled distress images. Extensive experimental results on damage detection across different countries demonstrate the universality and superiority of DA-RDD, and empirical studies on RDD2021 further claim its effectiveness and advancement. To our best knowledge, it is the first time to investigate domain adaptative pavement crack detection, and we expect the contributions in this work would facilitate the development of generalized road damage detection in the future.

A Computational Meta-Learning Inspired Model for Sketch-based Video Retrieval

Objectives: To design and develop an efficient computing framework for sketch-based video retrieval using fine-grained intrinsic computational approach. Methods: The primary method of sketch-based video retrieval adopts multi-stream multi-modality of joint embedding method for improved P-SBVR from improved fine-grained KTH and TSF related dataset. It considers the potential aspects of the computation of significant visual intrinsic appearance details for sketch objects. The extracted appearance and motion-based features are used to train three different CNN baselines under strong and weak supervision. The system also implements a meta-learning model for different supervised settings to attain better performance of sketch-based video retrieval along with a relational module to overcome the problem of overfitting. Findings: The study derives specific sketch sequences from its formulated dataset to compute instance-level query processing for video retrieval. Further, it also addresses the limitations arising in the context of coarse-grained video retrieval models and sketch-based still image retrieval. The aggregated dataset for rich annotation assisted in the experimental simulation. The experimental evaluation with respect to the performance metric evaluates the 3D CNN baselines under strong supervision and weak-supervision where CNN BL-Type-2 attains maximum video retrieval accuracy of 99.96% for triplet grading feature under relational schema. CNN BL-Type-1 attains maximum retrieval accuracy of 97.40% considering the triplet grading features from the improved SBVR. The evaluation metric for the instance level retrieval process also considers true matching of sketches with the videos, it clearly shows that the appropriate appearance and motion based feature selection has enhanced the video retrieval accuracy up to 96.90% with 99.28% accuracy in action identification considering motion stream, 98.17% for appearance module and 98.45% for fusion module. Another important aspect of the proposed research context is that it addresses the problem of cross-modality while executing the simultaneous matching paradigm for visual appearances of the object with its movement appearing on particular video scenes. The experimental outcome showsits comparable effectiveness relative to the existing system of CNN. Novelty: Unlike the conventional system of sketch analysis, which is more focused on static objects or scenes, the presented approach can efficiently compute the important visual intrinsic appearance details of the object of interest from the sketch and then activate the operations for video retrieval. The proposed CNN based learning model with improved P-SBVR dataset attains better computing time for retrieval with are approximately (200, 210 and 214) milliseconds for CNN BL-Type-1, CNN BL-Type-2, CNN BL-Type-3 and comparable with the existing deep learning based SBVR models. Keywords: Sketch Based Video Retrieval; Intrinsic Appearance Details; Meta Learning; Sketch Dataset; Cross Modality Problem