## 2 Related Work  
Context is a useful prior for understanding object-part relations with respect to each other and the background. We divide the literature review into three subsections based on the context aggregation technique, light-weight CNN architecture, and graph-based method for attention in CNNs. **Context aggregation. ** Spatial context provides valuable information for many image-to-image translation tasks [9]. Context aggregation is a recurrent theme that appears in different types of convolution strategies [6], such as pooling variations, attention mechanisms [8], channel operations, and architectural search [10]. Dilated convolution, multi-scale feature generation, and large-kernel approaches leverage contextual information based on modulation of the receptive field to extract information from non-local regions [6]. [PERSON] et al. proposed a generalized model based on a physics-inspired network [11]. Architecture search approaches such as Auto-DeepLab, Global2 Local, and DCNAS automate context modeling by searching multiple paths and channels at the cell or network level [12]. Unlike these methods, attention has served as a very versatile context aggregation mechanism, working at various scales, including local, non-local [9], global, and cross or self-attention [8] scales. Recent trends have shifted from variants of attention in CNNs to full attention in transformers [13]. However, this push towards the use of heavier networks for context aggregation has come at the cost of network size and resources [8]. These challenges demand the confluence of high-performing attention traits in small-size networks. **Light-weight architectures. ** Architectural buoyancy in CNNs is generally achieved through adaptations in convolutional operations such as grouping or reduction in memory access cost in small-scale architectures [14]. [PERSON] et al. used point-wise group convolutions and channel shuffling for cross-channel information exchange to greatly reduce computation cost while maintaining accuracy [15]. [PERSON] et al. proposed light-weight attention in CNNs by aggregating the contextual information of pixels on orthogonal paths, as well as for the full image for every pixel [8]. [PERSON] et al. proposed mutual knowledge distillation strategies for information flow between teacher-student networks for optical flow estimation [16]. Of these three method, an attention approach like the criss-cross method [8] is easily adaptable, highly modular and task-agnostic [8]. Criss-cross attention (or CCNet) is one of the best light-weight attention modules; however, it employs spatial position-agnostic correlation, which does not work well when sequential context propagation is required. Graph-based methods provide control over these pathways in devising chained attention mechanisms [17]. **Graph-based networks. ** Despite developments in graph-based solutions like the generalization of CNNs as graphs [17], the use of graph attention networks for molecular substructures in drug discovery using feed-forward layer-based simple graph attention [18], and the use of sparse graph attention in scene graph generation [19], graph attention has not been explored from a chained attention perspective. Previously, [PERSON] et al. presented an rgb-d-based dataset for transparent object segmentation [20]. [PERSON] et al. presented polarization-based transparent object segmentation [21]. [PERSON] et al. [22], proposed an end-to-end graph CNN method for transparent object segmentation by forming undirected edges between any two adjacent super-pixels. However, unlike directed graphs, undirected graphs are unable to associate two far-away nodes to the same structural context. Moreover, the adjacency matrix generation reported in [22] for an image resulting in \(N\) nodes bears a time complexity of \(N^{2}\). This shows that achieving global context through the use of graphsis associated with high time complexity. To provide a more local context, [PERSON] et al.