Cross-modal Task Research Articles

Improving Scene Text Retrieval via Stylized Middle Modality

Scene text retrieval addresses the challenge of localizing and searching for all text instances within scene images based on a query text. This cross-modal task has significant applications in various domains, such as intelligent transportation systems and social media analysis. In practice, ensuring consistency of the same content between two modalities is crucial in improving retrieval accuracy. This paper addresses the issue by introducing a stylized middle modality, which fuses the graphical query text with the style of the extracted text proposal. To this end, we propose a stylized middle modality learning (SM \({}^{2}\) L) framework. The proposed stylized middle modality enables the network to jointly enforce constraints on visual feature coherence and text semantic feature consistency in the optimization phase, thereby minimizing the modality gap in the retrieval space. This brings in two major advantages: 1) SM \({}^{2}\) L will pave the way to seamlessly benefit the scene text retrieval and 2) the proposed learning paradigm enables the machine to avoid adding redundant computing resources in the inference phase. Substantial experiments demonstrate that the proposed method outperforms the state-of-the-art retrieval performance considerably.

Walking while performing visual, auditory and crossmodal tasks produces oscillations entrained to the gait cycle

Walking while performing visual, auditory and crossmodal tasks produces oscillations entrained to the gait cycle

Causality-Invariant Interactive Mining for Cross-Modal Similarity Learning.

In the real world, how to effectively learn consistent similarity measurement across different modalities is essential. Most of the existing similarity learning methods cannot deal well with cross-modal data due to the modality gap and have obvious performance degeneration when applied to cross-modal data. To tackle this problem, we propose a novel cross-modal similarity learning method, called Causality-Invariant Interactive Mining (CIIM), that can effectively capture informative relationships among different samples and modalities to derive the modality-consistent feature embeddings in the unified metric space. Our CIIM tackles the modality gap from two aspects, i.e., sample-wise and feature-wise. Specifically, we start from the sample-wise view and learn the single-modality and hybrid-modality proxies for exploring the cross-modal similarity with the elaborate metric losses. In this way, sample-to-sample and sample-to-proxy correlations are both taken into consideration. Furthermore, we conduct the causal intervention to eliminate the modality bias and reconstruct the invariant causal embedding in the feature-wise aspect. To this end, we force the learned embeddings to satisfy the specific properties of our causal mechanism and derive the causality-invariant feature embeddings in the unified metric space. Extensive experiments on two cross-modality tasks demonstrate the superiority of our proposed method over the state-of-the-art methods.

Adaptive multi-task learning for speech to text translation

End-to-end speech to text translation aims to directly translate speech from one language into text in another, posing a challenging cross-modal task particularly in scenarios of limited data. Multi-task learning serves as an effective strategy for knowledge sharing between speech translation and machine translation, which allows models to leverage extensive machine translation data to learn the mapping between source and target languages, thereby improving the performance of speech translation. However, in multi-task learning, finding a set of weights that balances various tasks is challenging and computationally expensive. We proposed an adaptive multi-task learning method to dynamically adjust multi-task weights based on the proportional losses incurred during training, enabling adaptive balance in multi-task learning for speech to text translation. Moreover, inherent representation disparities across different modalities impede speech translation models from harnessing textual data effectively. To bridge the gap across different modalities, we proposed to apply optimal transport in the input of end-to-end model to find the alignment between speech and text sequences and learn the shared representations between them. Experimental results show that our method effectively improved the performance on the Tibetan-Chinese, English-German, and English-French speech translation datasets.

Evaluating frontoparietal network topography for diagnostic markers of Alzheimer’s disease

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer’s disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. Event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (n = 63) mild untreated AD patients and a control group of (n = 73) healthy age-matched adults. Based on the literature presented, it was expected that healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks, thus demonstrating healthy cognitive functioning consistent with older healthy adults. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity, our findings demonstrated statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude through topographic mapping that FPN integration occurs in healthy controls but is absent in AD patients during cross-modal memory tasks.

SEMScene: Semantic-Consistency Enhanced Multi-Level Scene Graph Matching for Image-Text Retrieval

Image-text retrieval, a fundamental cross-modal task, performs similarity reasoning for images and texts. The primary challenge for image-text retrieval is cross-modal semantic heterogeneity, where the semantic features of visual and textual modalities are rich but distinct. Scene graph is an effective representation for images and texts as it explicitly models objects and their relations. Existing scene graph based methods have not fully taken the features regarding various granularities implicit in scene graph into consideration (e.g. triplets), the inadequate feature matching incurs the absence of non-trivial semantic information (e.g. inner relations among triplets). Therefore, we propose a S emantic-Consistency E nhanced M ulti-Level Scene Graph Matching (SEMScene) network, which exploits the semantic relevance between visual and textual scene graphs from fine-grained to coarse-grained. Firstly, under the scene graph representation, we perform feature matching including low-level node matching, mid-level semantic triplet matching, and high-level holistic scene graph matching. Secondly, to enhance the semantic-consistency for object-fused triplets carrying key correlation information, we propose a dual-step constraint mechanism in mid-level matching. Thirdly, to guide the model to learn the semantic-consistency of matched image-text pairs, we devise effective loss functions for each stage of the dual-step constraint. Comprehensive experiments on Flickr30K and MS-COCO datasets demonstrate that SEMScene achieves state-of-the-art performances with significant improvements.

CDPNet: Cross-Modal Dual Phases Network for Point Cloud Completion

Point cloud completion aims at completing shapes from their partial. Most existing methods utilized shape’s priors information for point cloud completion, such as inputting the partial and getting the complete one through an encoder-decoder deep learning structure. However, it is very often to easily cause the loss of information in the generation process because of the invisibility of missing areas. Unlike most existing methods directly inferring the missing points using shape priors, we address it as a cross-modality task. We propose a new Cross-modal Dual Phases Network (CDPNet) for shape completion. Our key idea is that the global information of the shape is obtained from the extra single-view image, and the partial point clouds provide the geometric information. After that, the multi-modal features jointly guide the specific structural information. To learn the geometric details of the shape, we chose to use patches to preserve the local geometric feature. In this way, we can generate shapes with enough geometric details. Experimental results show that our method achieves state-of-the-art performance on point cloud completion.

Cross-Modal Feature Distribution Calibration for Few-Shot Visual Question Answering

Few-shot Visual Question Answering (VQA) realizes few-shot cross-modal learning, which is an emerging and challenging task in computer vision. Currently, most of the few-shot VQA methods are confined to simply extending few-shot classification methods to cross-modal tasks while ignoring the spatial distribution properties of multimodal features and cross-modal information interaction. To address this problem, we propose a novel Cross-modal feature Distribution Calibration Inference Network (CDCIN) in this paper, where a new concept named visual information entropy is proposed to realize multimodal features distribution calibration by cross-modal information interaction for more effective few-shot VQA. Visual information entropy is a statistical variable that represents the spatial distribution of visual features guided by the question, which is aligned before and after the reasoning process to mitigate redundant information and improve multi-modal features by our proposed visual information entropy calibration module. To further enhance the inference ability of cross-modal features, we additionally propose a novel pre-training method, where the reasoning sub-network of CDCIN is pretrained on the base class in a VQA classification paradigm and fine-tuned on the few-shot VQA datasets. Extensive experiments demonstrate that our proposed CDCIN achieves excellent performance on few-shot VQA and outperforms state-of-the-art methods on three widely used benchmark datasets.

Semantic Distance Adversarial Learning for Text-to-Image Synthesis

Text-to-Image (T2I) synthesis is a cross-modality task that requires a text description as input to generate a realistic and semantically consistent image. To guarantee semantic consis- tency, previous studies regenerate text descriptions from synthetic images and align them with the given descriptions. However, the existing redescription modules lack explicit modeling of their training objectives, which is crucial for reliable measurement of semantic distance between redescriptions and given text inputs. Consequently, the aligned text redescriptions suffer from training bias caused by the emergence of adversarial image samples, unseen semantics, and mistaken contents from low- quality synthesized images. To this end, we propose a SEMantic distance Adversarial learning (SEMA) framework for Text-to- Image synthesis which strengthens semantic consistency from two aspects: 1) We introduce adversarial learning between the image generator and the text redescription module to mutually promote or demote the quality of generated image or text instances. This learning model ensures accurate redescription of image contents, thus diminishing the generation of adversarial image samples. 2) We introduce two-fold semantic distance discrimination (SEM distance) to characterize semantic relevance between matching text or image pairs. The unseen semantics and mistaken contents will be penalized with a large SEM distance. The proposed discrimination method also simplifies the model training process with no need to optimize multiple discriminators. Experimental results on CUB Birds 200 and MS-COCO datasets show that the proposed model outperforms the state-of-the-art methods. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NJUPT-MCC/SEMA</uri> .

SATR: Semantics-Aware Triadic Refinement network for referring image segmentation

Referring image segmentation (RIS) is a fundamental cross-modal task that aims to predict the pixel-level segmentation mask of the target referred by a natural language expression. Existing methods usually focus on leveraging word-to-pixel interaction mechanisms to directly generate final masks, while they ignore semantic alignment between the query and visual context, as well as rich fine-grained spatial details of the referred object, resulting in inaccurate identification, blurry boundary, and missing small objects.To address these issues, we propose a Semantics-Aware Triadic Refinement (SATR) network for referring image segmentation. Specifically, to bridge the gap between visual and linguistic modalities, we propose a Language-Guided Pixel Modulation (LGPM), which utilizes word- and sentence-level features to facilitate word-to-pixel interaction and sentence-to-object alignment, respectively. Meanwhile, the LGPM is plugged into an off-the-shelf pre-trained visual backbone to jointly learn and extract multi-modal features, which avoids the tedious learning phase of low-level features from scratch. In addition, without any post-processing for refining the final mask, we design a triadic refinement decoder to selectively extract and aggregate salient object features, pixel-level details, and boundary information, which preserves the rich spatial features to generate a high-quality mask. Further, we use a multi-task strategy to capture the target-specific context during training. Experimental results demonstrate the proposed method performs favorably against the previous approaches on the challenging RefCOCO, RefCOCO+, and RefCOCOg datasets.

Fully Transformer-Equipped Architecture for end-to-end Referring Video Object Segmentation

Referring Video Object Segmentation (RVOS) requires segmenting the object in video referred by a natural language query. Existing methods mainly rely on sophisticated pipelines to tackle such cross-modal task, and do not explicitly model the object-level spatial context which plays an important role in locating the referred object. Therefore, we propose an end-to-end RVOS framework completely built upon transformers, termed Fully Transformer-Equipped Architecture (FTEA), which treats the RVOS task as a mask sequence learning problem and regards all the objects in video as candidate objects. Given a video clip with a text query, the visual–textual features are yielded by encoder, while the corresponding pixel-level and word-level features are aligned in terms of semantic similarity. To capture the object-level spatial context, we have developed the Stacked Transformer, which individually characterizes the visual appearance of each candidate object, whose feature map is decoded to the binary mask sequence in order directly. Finally, the model finds the best matching between mask sequence and text query. In addition, to diversify the generated masks for candidate objects, we impose a diversity loss on the model for capturing more accurate mask of the referred object. Empirical studies have shown the superiority of the proposed method on three benchmarks, e.g., FETA achieves 45.1% and 38.7% in terms of mAP on A2D Sentences (3782 videos) and J-HMDB Sentences (928 videos), respectively; it achieves 56.6% in terms of J&F on Ref-YouTube-VOS (3975 videos and 7451 objects). Particularly, compared to the best candidate method, it has a gain of 2.1% and 3.2% in terms of P@0.5 on the former two, respectively, while it has a gain of 2.9% in terms of J on the latter one.

Correlation of motor-auditory cross-modal and auditory unimodal N1 and mismatch responses of schizophrenic patients and normal subjects: an MEG study.

It has been suggested that the positive symptoms of schizophrenic patients (hallucinations, delusions, and passivity experience) are caused by dysfunction of their internal and external sensory prediction errors. This is often discussed as related to dysfunction of the forward model that executes self-monitoring. Several reports have suggested that dysfunction of the forward model in schizophrenia causes misattributions of self-generated thoughts and actions to external sources. There is some evidence that the forward model can be measured using the electroencephalography (EEG) and magnetoencephalography (MEG) components such as N1 (m) and mismatch negativity (MMN) (m). The objective in this MEG study is to investigate differences in the N1m and MMNm-like activity generated in motor-auditory cross-modal tasks in normal control (NC) subjects and schizophrenic (SC) patients, and compared that activity with N1m and MMNm in the auditory unimodal task. The N1m and MMNm/MMNm-like activity were recorded in 15 SC patients and 12 matched NC subjects. The N1m-attenuation effects and peak amplitude of MMNm/MMNm-like activity of the NC and SC groups were compared. Additionally, correlations between MEG measures (N1m suppression rate, MMNm, and MMNm-like activity) and clinical variables (Positive and Negative Syndrome Scale (PANSS) scores and antipsychotic drug (APD) dosages) in SC patients were investigated. It was found that (i) there was no significant difference in N1m-attenuation for the NC and SC groups, and that (ii) MMNm in the unimodal task in the SC group was significantly smaller than that in the NC group. Further, the MMNm-like activity in the cross-modal task was smaller than that of the MMNm in the unimodal task in the NC group, but there was no significant difference in the SC group. The PANSS positive symptoms and general psychopathology score were moderately negatively correlated with the amplitudes of the MMNm-like activity, and the APD dosage was moderately negatively correlated with the N1m suppression rate. However, none of these correlations reached statistical significance. The findings suggest that schizophrenic patients perform altered predictive processes differently from healthy subjects in latencies reflecting MMNm, depending on whether they are under forward model generation or not. This may support the hypothesis that schizophrenic patients tend to misattribute their inner experience to external agents, thus leading to the characteristic schizophrenia symptoms.

MITER: Medical Image–TExt joint adaptive pretRaining with multi-level contrastive learning

Recently multimodal medical pretraining models play a significant role in automatic medical image and text analysis that has wide social and economical impact in healthcare. Despite being able to be quickly transferred to downstream tasks, the models are greatly limited due to the fact that these models can only be pretrained with professional medical image–text datasets, which usually contain a very small number of samples. In this work we propose MITER (Medical Image–Text Joint adaptive Pretraining), a joint adaptive pretraining framework via multi-level contrastive learning to overcome this limitation by pretraining image and text models for medical domain and utilizing existing models pretrained on generic data, which contain enormous number of samples. MITER features two types of objectives to solve the problem. The first type is uni-modal objectives that pretrain the models with medical images and text separately on uni-modal tasks. The other type is a cross-modal objective that pretrains jointly, allowing the models to influence each other on cross-modal tasks. We also introduce a strategy to dynamically select hard negative samples during the training process for better performance. Experimental results over four medical tasks, image-report retrieval, multi-label image classification, visual question answering, and report generation, show that our MITER framework solves the limitation problem by greatly outperforming existing benchmark models on all the tasks. The source code of our framework is available online.22https://github.com/ZhuYi98/MITER.

Less is Better: Exponential Loss for Cross-Modal Matching

Deep metric learning has become a key component of cross-modal retrieval. By learning to pull the features of matched instances closer while pushing the features of mismatched instances farther away, one can learn highly robust multi-modal representations. Most existing cross-modal retrieval methods leverage vanilla triplet loss to train the network, which cannot adaptively penalize pairs with different hardness. Although various weighting strategies have been designed for unimodal matching tasks, few weighting strategies have been applied to cross-modal tasks due to the specificity of cross-modal tasks. While few weighting strategies are designed for cross-modal scenarios, they usually involve a lot of hyper-parameters, which require a lot of computational resources to fine-tune. In this paper, we introduce a new exponential loss, which can assign appropriate weights to individual positive and negative pairs according to their similarity so that it can adaptively penalize pairs with different hardness. Furthermore, the exponential loss has only two hyper-parameters, making it easier to find the optimal parameters to suit various data distributions in practice. Exponential loss can be universally applied to well-established cross-modal models and further boost their retrieval performance. We exhaustively ablate our method on Image-Text matching, Video-Text matching, as well as unimodal Image matching. Experimental results show that a standard model trained with exponential loss can achieve noticeable performance gains.

Complementarity-Aware Space Learning for Video-Text Retrieval

In general, videos are powerful at recording physical patterns ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., spatial layout) while texts are great at describing abstract symbols ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., emotion). When video and text are used in multi-modal tasks, they are claimed to be complementary and their distinct information is crucial. However, when it comes to cross-modal tasks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., retrieval), existing works usually use their common part in the form of common space learning while their distinct information is abandoned. In this paper, we argue that distinct information is also beneficial for cross-modal retrieval. To address this problem, we propose a divide-and-conquer learning approach, namely Complementarity-aware Space Learning (CSL), by recasting this challenge into learning of two spaces ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., latent and symbolic spaces) to simultaneously explore their common and distinct information by considering multi-modal complementary character. Specifically, we first propose to learn a symbolic space from video with a memory-based video encoder and a symbolic generator. In contrast, we also introduce learning a latent space from text with a text encoder and a memory-based latent feature selector. Finally, we propose a complementarity-aware loss by integrating two spaces to facilitate video-text retrieval tasks. Extensive experiments show that our approach outperforms existing state-of-the-art methods by 5.1%, 2.1% and 0.9% of R@10 for text-to-video retrieval on three benchmarks, respectively. Ablation study also verifies that the distinct information from video and text improves the retrieval performance. Trained models and source code have been released at https://github.com/NovaMind-Z/CSL.

Lesion-behavior mapping indicates a strategic role for parietal substrates of associative memory

Numerous neuroimaging studies indicate that ventral parietal cortex (VPC), especially angular gyrus, plays an important role in episodic memory. However, the nature of the mnemonic processes supported by this region is far from clear. We previously found that stroke lesions in VPC and lateral temporal cortex caused deficits in cued recall of unimodal word pairs and picture pairs, and cross-modal picture-sound pairs, with larger deficits in the cross-modal task. However, those findings leave open the question whether those regions' integrity is necessary for maintenance of associative representations, or for strategic processes required for their recall. We addressed this question using associative recognition versions of those tasks. We additionally manipulated semantic relatedness of the associated memoranda, to assess VPC’s involvement in semantic processing in the context of episodic memory. We analyzed performance of 62 first-event, sub-acute phase stroke patients (31 right- and 31 left-hemisphere damage) relative to 65 healthy participants, and employed voxel-based lesion-behavior mapping (VLBM) to identify task-relevant structures. Patients displayed greater false associative recognition of semantically related compared to unrelated recombined pairs. VLBM analysis implicated right lateral temporo-parietal regions in associative recognition deficits in the cross-modal pairs task, specifically for related recombined and new pairs, seemingly because of difficulty overcoming semantic relatedness bias effects on episodic discrimination. In contrast, damage to ventral parietal and lateral temporal cortex was not implicated in memory for unrelated memoranda. We interpret this pattern of lesion-behavior effects as indicating lateral temporo-parietal cortex involvement in strategic, rather than representational, roles in episodic associative memory.

A Video Question Answering Model Based on Knowledge Distillation

Video question answering (QA) is a cross-modal task that requires understanding the video content to answer questions. Current techniques address this challenge by employing stacked modules, such as attention mechanisms and graph convolutional networks. These methods reason about the semantics of video features and their interaction with text-based questions, yielding excellent results. However, these approaches often learn and fuse features representing different aspects of the video separately, neglecting the intra-interaction and overlooking the latent complex correlations between the extracted features. Additionally, the stacking of modules introduces a large number of parameters, making model training more challenging. To address these issues, we propose a novel multimodal knowledge distillation method that leverages the strengths of knowledge distillation for model compression and feature enhancement. Specifically, the fused features in the larger teacher model are distilled into knowledge, which guides the learning of appearance and motion features in the smaller student model. By incorporating cross-modal information in the early stages, the appearance and motion features can discover their related and complementary potential relationships, thus improving the overall model performance. Despite its simplicity, our extensive experiments on the widely used video QA datasets, MSVD-QA and MSRVTT-QA, demonstrate clear performance improvements over prior methods. These results validate the effectiveness of the proposed knowledge distillation approach.

Dual adversarial models with cross-coordination consistency constraint for domain adaption in brain tumor segmentation.

The brain tumor segmentation task with different domains remains a major challenge because tumors of different grades and severities may show different distributions, limiting the ability of a single segmentation model to label such tumors. Semi-supervised models (e.g., mean teacher) are strong unsupervised domain-adaptation learners. However, one of the main drawbacks of using a mean teacher is that given a large number of iterations, the teacher model weights converge to those of the student model, and any biased and unstable predictions are carried over to the student. In this article, we proposed a novel unsupervised domain-adaptation framework for the brain tumor segmentation task, which uses dual student and adversarial training techniques to effectively tackle domain shift with MR images. In this study, the adversarial strategy and consistency constraint for each student can align the feature representation on the source and target domains. Furthermore, we introduced the cross-coordination constraint for the target domain data to constrain the models to produce more confident predictions. We validated our framework on the cross-subtype and cross-modality tasks in brain tumor segmentation and achieved better performance than the current unsupervised domain-adaptation and semi-supervised frameworks.

ReGR: Relation-aware graph reasoning framework for video question answering

As one of the challenging cross-modal tasks, video question answering (VideoQA) aims to fully understand video content and answer relevant questions. The mainstream approach in current work involves extracting appearance and motion features to characterize videos separately, ignoring the interactions between them and with the question. Furthermore, some crucial semantic interaction details between visual objects are overlooked. In this paper, we propose a novel Relation-aware Graph Reasoning (ReGR) framework for video question answering, which first combines appearance–motion and location–semantic multiple interaction relations between visual objects. For the interaction between appearance and motion, we design the Appearance–Motion Block, which is question-guided to capture the interdependence between appearance and motion. For the interaction between location and semantics, we design the Location–Semantic Block, which utilizes the constructed Multi-Relation Graph Attention Network to capture the geometric position and semantic interaction between objects. Finally, the question-driven Multi-Visual Fusion captures more accurate multimodal representations. Extensive experiments on three benchmark datasets, TGIF-QA, MSVD-QA, and MSRVTT-QA, demonstrate the superiority of our proposed ReGR compared to the state-of-the-art methods.

Deep Learning Methods of Cross-Modal Tasks for Conceptual Design of Product Shapes: A Review

Abstract Conceptual design is the foundational stage of a design process that translates ill-defined design problems into low-fidelity design concepts and prototypes through design search, creation, and integration. In this stage, product shape design is one of the most paramount aspects. When applying deep learning-based methods to product shape design, two major challenges exist: (1) design data exhibit in multiple modalities and (2) an increasing demand for creativity. With recent advances in deep learning of cross-modal tasks (DLCMTs), which can transfer one design modality to another, we see opportunities to develop artificial intelligence (AI) to assist the design of product shapes in a new paradigm. In this paper, we conduct a systematic review of the retrieval, generation, and manipulation methods for DLCMT that involve three cross-modal types: text-to-3D shape, text-to-sketch, and sketch-to-3D shape. The review identifies 50 articles from a pool of 1341 papers in the fields of computer graphics, computer vision, and engineering design. We review (1) state-of-the-art DLCMT methods that can be applied to product shape design and (2) identify the key challenges, such as lack of consideration of engineering performance in the early design phase that need to be addressed when applying DLCMT methods. In the end, we discuss the potential solutions to these challenges and propose a list of research questions that point to future directions of data-driven conceptual design.