Multi-View Sequential Data (MvSD) is increasingly prevalent across diverse fields, ranging from climate science and urban informatics to multimedia analysis and healthcare. MvSD encompasses data from multiple sources or perspectives (views), which are inherently sequential and often heterogeneous in nature. These views can include various data types such as video clips, text descriptions, sensor readings, and trajectory data, each with unique statistical characteristics and temporal properties. Effectively processing and learning from MvSD presents significant challenges, particularly in scenarios where data is unbalanced, a common issue in real-world applications. This article delves into the key challenges of unbalanced multi-view deep learning, drawing upon insights from recent research and providing a comprehensive overview for researchers and practitioners in the field.
Fig. 2: Paradigm of Multi-View Sequential Data (MvSD) showcasing diverse view data types like video, text, and mobile data, each arranged in a specific order.
Data Types in Multi-View Sequential Learning
To understand the complexities of unbalanced MvSD, it’s crucial to first categorize the data types involved. While sequential data manifests in numerous forms like meteorological records, time-series sensor data, and genetic sequences, we can broadly classify them into four fundamental types for clarity and subsequent analysis: point, sequence, graph, and raster data. Each of these types can be directly or indirectly transformed into sequential data, forming the building blocks of MvSD.
Point Data
Point data represents discrete locations in space, defined by specific coordinates (e.g., latitude and longitude) and often enriched with additional attributes. A point is typically represented as a tuple ((p{i}, e{i}, t{i})), where (p{i}) denotes position, (t{i}) time of occurrence, and (e{i}) supplementary features (like temperature or color). We can distinguish between event-type data, where individual occurrences are treated as points (e.g., traffic incidents), and instance-based point sets, such as LiDAR data from sensor scans. Event data, as depicted in Figure 3a, signifies occurrences at specific locations and times with associated categories. Conversely, point cloud data, illustrated in Figure 3b, represents sets of 3D coordinates with attributes like reflectivity and color, commonly used in autonomous systems and 3D mapping. Point data finds applications across transportation (traffic accidents), criminology (crime incidents), social media (social events), and autonomous systems (point cloud data).
Fig. 3: Examples of Point Data: (a) Event Data illustrating discrete events in space and time; (b) Laser Data representing 3D point cloud data acquired by sensors.
Sequence Data
Sequence data is characterized by ordered observations over time or based on a logical progression. Time series data, a prominent type, comprises measurements taken at consecutive, uniform intervals. Examples include audio signals in mechanical fault diagnosis, where equipment frequencies are sampled regularly (Figure 4a). Video data, as shown in Figure 4b, is inherently sequential, consisting of frames ordered chronologically. Trajectory data, tracking the position of a moving object over time, is also categorized as sequence data (Figure 4c). Beyond time series, text data is another form of sequence data, governed by linguistic rules and logical flow. Our classification groups trajectory data, audio, video, time series, and text under the umbrella of sequence data.
Fig. 4: Examples of Sequence Data: (a) Audio Signal as a time series; (b) Video Data as a sequence of images; (c) Trajectory Data representing movement over time.
Graph Data
Graph data represents relationships between entities as vertices connected by edges, each potentially weighted. It’s widely used in modeling networks, such as traffic systems, social networks, and recommendation engines. In social networks, individuals are vertices, and their connections form edges, possibly directed. In traffic forecasting, road networks are naturally modeled as graphs, where road segments are edges and intersections are nodes within a spatial map.
Raster Data
Raster data is structured as a grid of pixels, each holding a value representing information at a specific location, like color or statistical measures. Image data (Figure 5a) is a prime example, where each pixel’s position is fixed, and its value is an observation. Functional magnetic resonance imaging (fMRI) in neuroscience utilizes raster data to analyze brain activity by measuring hemodynamic changes. Urban big data also leverages raster data, with fixed-position sensors collecting air quality and weather data to form spatial maps (Figure 5b).
Fig. 5: Examples of Raster Data: (a) Image Data composed of pixels in a grid; (b) Raster Traffic Data representing spatial information in a grid format.
Data Format Conversion
These data formats are often inter-convertible, adapting to specific tasks and models. Point data can become raster data through quantization within grid cells. For instance, traffic accidents within grid cells transform into event raster data, which can revert to point data. In autonomous driving, point clouds convert to 3D voxel grids or 2D bird’s-eye views (BEV). Point data can also be nodes in graph data, where spatial maps use sensors as graph nodes, with distances defining adjacency. Sequence data, like sensor readings, can become point data via interval sampling. Trajectory data can convert to raster data, with time-instant positions mapping to grid coordinates. Conversely, raster data like meteorological data can yield sequence data through time-series statistics at each location.
Challenges in Multi-View Sequential Deep Learning
Despite the increasing power of deep learning, MvSD presents unique challenges that demand specialized approaches. These challenges can be broadly categorized into five key areas, which often contribute to the “unbalanced” nature of multi-view data and hinder effective learning.
Temporal Dynamics
Sequential data inherently possesses temporal dynamics, where changes unfold chronologically. Data points at different time steps are interdependent, and neglecting this temporal granularity can obscure patterns and reduce predictive accuracy. Consider sentiment analysis: phrases like “I think it’s…but…” illustrate how sentiment evolves over time. Similarly, traffic flow exhibits daily, weekly, and seasonal patterns. Crime rates fluctuate, and air quality changes dynamically. These temporal dynamics, often termed intra-modality dynamics, are crucial for accurate modeling.
Early methods like Prophet, random forests, and autoregressive (AR) models addressed specific time-series tasks. AR models and their variants (ARMA, ARIMA) found use in stock forecasting, climate change analysis, and prognostics. However, modern approaches for MvSD leverage Recurrent Neural Networks (RNNs) and their variants like LSTMs and GRUs to capture temporal dependencies. For sentiment analysis, independent LSTMs model intra-modality dynamics for each view. Bi-directional LSTMs capture contextual sequence information. For weather forecasting, bi-directional LSTMs learn long-term temporal characteristics from multivariate time series. Attention mechanisms are also integrated to handle long-term dependencies and focus on relevant temporal segments.
Heterogeneity
MvSD inherently involves heterogeneity, as it combines views from different domains with varying distributions. Image and text data, for example, are fundamentally different. Images are raster-based and visually intuitive, while text is symbolic, rule-based, and semantically complex. This heterogeneity is a core aspect of unbalanced multi-view learning, as models must effectively bridge these disparate data types.
Fig. 6: Heterogeneity in MvSD, illustrating the differing distributions of various view data types, posing a challenge for unified learning.
To address heterogeneity, domain-specific models are often employed for feature extraction from each view. For instance, separate LSTMs extract features from language, audio, and visual modalities before exploring inter-modality relationships. Encoder-decoder structures facilitate modality transitions. Seq2Seq models translate modalities into joint representations, while cyclic translation methods convert between modalities to learn robust joint embeddings. Adversarial and domain adaptation techniques minimize the heterogeneity gap by mapping different views into a common feature space, promoting modality-invariant representations.
Cross-View Dynamics
Beyond intra-view temporal dynamics, MvSD exhibits dynamic interactions between different views, termed cross-view dynamics. These interactions can be spatio-temporal or semantic.
Spatio-temporal correlations arise from the simultaneous variation of MvSD in both space and time. Traffic sensor data, for example, is spatially correlated with neighboring sensors and temporally correlated with past readings. A common approach is to first model local spatial relationships using convolutional networks and then capture temporal dynamics with RNNs. Combining Graph Convolutional Networks (GCNs) with LSTMs allows for capturing both spatial and temporal dependencies. Attention mechanisms and encoder-decoder architectures further enhance spatio-temporal dynamic modeling.
Semantic interactions occur when different views provide complementary information. In video sentiment analysis, language is often the primary modality, with visual and audio cues acting as auxiliary modalities. Memory-based methods, encoder-based transformations, and contrastive learning techniques are used to model semantic interactions across views. These methods aim to fuse information from different views to enhance the understanding of the primary modality and capture richer semantic relationships.
Data Missing
Data missing is a pervasive issue in MvSD due to sensor failures, communication delays, or human factors. Complete MvSD datasets are rare, making robustness to missing data critical. Data can be missing during training, testing, or both, leading to various scenarios of unbalanced data availability.
Fig. 7: Missing Data Types in MvSD: (a) Complete Multi-View Sequence; (b) Missing Data during Training; (c) Missing Data during Testing; (d) Missing Data in both Training and Testing Phases.
Autoencoders are used to reconstruct missing data by encoding available data into latent features and decoding them to impute missing views. Deep Canonical Correlation Analysis (DCCA) combined with cross-modal autoencoders reconstructs missing modalities. Cyclic consistency losses in modality translation and factorized multimodal representations also aid in handling missing data. Meta-learning techniques, particularly Bayesian meta-learning, address data scarcity by learning from multiple tasks and generalizing to new tasks with limited data, enhancing robustness in data-missing scenarios.
Misalignment of Asynchronous Multi-View Data
MvSD views are often asynchronous and misaligned, both in sequence length and semantic correspondence. View sequences may have unequal lengths due to varying sampling rates, and semantic misalignment arises when there’s no direct one-to-one mapping between elements of different views. For instance, in video data, each image frame doesn’t necessarily correspond to a single word.
Fig. 8: Misalignment Scenarios in Asynchronous MvSD: (a) Ideal Alignment; (b) Length-Aligned but Semantically Unaligned; (c) Both Length and Semantically Misaligned.
While many early works assume multi-view sequence alignment, recent research addresses misalignment using attention-based mechanisms. Multimodal Transformers use cross-modal attention to interact between sequences at different time steps without explicit alignment. Pre-trained networks trained on large aligned datasets also facilitate aligned cross-modal representation. Multi-instance learning offers another approach, enabling learning without explicit data alignment by aggregating information from multiple instances within each view.
Conclusion
Unbalanced multi-view deep learning presents a complex landscape of challenges stemming from temporal dynamics, heterogeneity, cross-view interactions, data missingness, and misalignment. Addressing these challenges is crucial for realizing the full potential of MvSD in various applications. Current research leverages advanced deep learning techniques, including RNNs, attention mechanisms, autoencoders, and meta-learning, to tackle these issues. Future research should focus on developing more robust and efficient methods that can effectively handle the inherent complexities and imbalances in multi-view sequential data, paving the way for more accurate and reliable multi-view learning systems.
