## 1 Introduction  
Recent advancements related to convolutional neural networks (CNNs) have led to significant advancements in image-to-image translation problems. Some popular examples include edge region-based segmentation [1] and dehazing with dark channel priors [2], among others. This success can be attributed to the ability of CNNs to extract deep and complex features from images without needing any hand-crafted features. The underlying mechanism of CNNs is that they reduce the spatial information contained in images by gradually down-sampling to a set of feature maps. For this purpose, CNNs employ convolutional kernels that convolve pixels only locally, without taking the whole global context into consideration. This works well for conventional downstream tasks such as classification. However, the lack of global context limits their performance on challenging image-to-image translation problems such as the segmentation of transparent objects, dehazing, and optical flow estimation. In transparent objects, although the edges may bear a clear signature of the object, there are strong features of other background objects that adversely affect the segmentation process. To tackle this issue, it is desirable that the information from the edges be transferred to other parts of the object as well. In dehazing, shapes and edges of the object need to be restored from original images where hazing fuzzifies these features; information about the non-local context should help in this task. Several previous works have been reported on the incorporation of the global spatial context into the learning process. Some examples include layer-wise average feature augmentation [3]; the use of mixtures of conditional random fields and CNNs [4]; and the construction of encoder-decoder-style, fully convolutional networks that use deconvolutions to create output, building upon U-Net [5]. Two popular approaches for introducing global context without compromising localization accuracy are the use of atrous or dilated convolutions [6] and transformer networks [7]. Although both approaches have been successful, they are accompanied by significant increases in the number of parameters and the associated FLOPs. Alternatively, criss-cross attention (referred to as CCNet) [8] represents a light-weight solution to model the global context in the learning process. It collects contextual information from vertical and horizontal criss-cross channels and applies an affinity operation in the latent space. Compared to other approaches, this method is computationally efficient, with a time complexity of \(\mathcal{O}(N^{1.5})\), where \(N\) denotes the size of the feature map. Figure 1 presents the segmentation results obtained using CCNet vs. LGA. Interestingly, the ability to capture the global context plays an adverse role in this case. CCNet relies on the semantic relation between any two distant points; however, it does not take into account the characteristics of the intermediate points between them. Clearly, as seen in Figure 1, parts of other objects being segmented falsely is our object of interest. To circumvent this issue, we propose an alternate scheme that spatially constructs _chained attention_, thereby taking into account the information of the entire route to compute the relation between any two distant pixels. This is achieved through the use of locally connected graph networks constructed over the latent feature maps, and these gradually propagate information to the distant neighbors of any point through a stacked set of layers used in the graph network. Our approach is adaptive and provides the flexibility of choosing the desired spatial extent to be captured. Furthermore, our approach is computationally faster and provides a speedup in \(\sqrt{N}\) over CCNet. **Contributions. ** The main contributions of this paper can be summarized as follows:  
* We present Latent Graph Attention (LGA), a graph network-based module, to incorporate the global context in existing CNN architectures.