Subgraph Graph Neural Networks (GNNs) are emerging as a promising approach to enhance GNN expressiveness, but their more complex graph structures with numerous independent and irregular subgraphs pose significant hardware deployment challenges. In this work, we propose 3D-SubG, a 3D stacked hybrid processing-near/in-memory accelerator for subgraph GNNs. With hybrid bonding packaging technology, a logic die is 3D stacked with a DRAM die for highly parallel memory accesses. The logic die employs digital SRAM-based processing-in-memory (PIM) macros to boost computation density and minimize data transfer. We further propose a bit-level non-zero gathering method to exploit graph sparsity for PIM, a workloadbalanced mapping strategy for subgraph allocation onto different logic-to-DRAM blocks, and a distributed global pooling approach to reduce inter-block data movements. Experimental results show that 3D-SubG achieves average improvements of $146.11 \times$ in performance, $934.18 \times$ in area efficiency, and $1171.80 \times$ in energy efficiency compared to RTX 3090Ti.

With the increasing popularity of online social applications, stickers have become common in online chats. Teaching a model to select the appropriate sticker from a set of candidate stickers based on dialogue context is important for optimizing the user experience. Existing methods have proposed leveraging emotional information to facilitate the selection of appropriate stickers. However, considering the frequent co-occurrence among sticker images, words with emotional preference in the dialogue and emotion labels, these methods tend to over-rely on such dataset bias, inducing spurious correlations during training. As a result, these methods may select inappropriate stickers that do not match users' intended expression. In this paper, we introduce a causal graph to explicitly identify the spurious correlations in the sticker selection task. Building upon the analysis, we propose a Causal Knowledge-Enhanced Sticker Selection model to mitigate spurious correlations. Specifically, we design a knowledge-enhanced emotional utterance extractor to identify emotional information within dialogues. Then an interventional visual feature extractor is employed to obtain unbiased visual features, aligning them with the emotional utterances representation. Finally, a standard transformer encoder fuses the multimodal information for emotion recognition and sticker selection. Extensive experiments on the MOD dataset show that our CKS model significantly outperforms the baseline models.

Visual question answering with natural language explanation (VQA-NLE) is a challenging task that requires models to not only generate accurate answers but also to provide explanations that justify the relevant decision-making processes. This task is accomplished by generating natural language sentences based on the given question-image pair. However, existing methods often struggle to ensure consistency between the answers and explanations due to their disregard of the crucial interactions between these factors. Moreover, existing methods overlook the potential benefits of incorporating additional knowledge, which hinders their ability to effectively bridge the semantic gap between questions and images, leading to less accurate explanations. In this paper, we present a novel approach denoted the knowledge-based iterative consensus VQA-NLE (KICNLE) model to address these limitations. To maintain consistency, our model incorporates an iterative consensus generator that adopts a multi-iteration generative method, enabling multiple iterations of the answer and explanation in each generation. In each iteration, the current answer is utilized to generate an explanation, which in turn guides the generation of a new answer. Additionally, a knowledge retrieval module is introduced to provide potentially valid candidate knowledge, guide the generation process, effectively bridge the gap between questions and images, and enable the production of high-quality answer-explanation pairs. Extensive experiments conducted on three different datasets demonstrate the superiority of our proposed KICNLE model over competing state-of-the-art approaches. Our code is available at https://github.com/Gary-code/KICNLE.