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Fang, Xianyong
R$^2$: A LLM Based Novel-to-Screenplay Generation Framework with Causal Plot Graphs
Lin, Zefeng, Xiao, Yi, Mo, Zhiqiang, Zhang, Qifan, Wang, Jie, Chen, Jiayang, Zhang, Jiajing, Zhang, Hui, Liu, Zhengyi, Fang, Xianyong, Xu, Xiaohua
Published as a conference paper at ICLR 2025R 2: A LLM B ASED N OVEL-TO-S CREENPLAYG ENER-ATIONF RAMEWORK WITH C AUSALP LOT G RAPHS Zefeng Lin 1, Yi Xiao 1, Zhiqiang Mo 1, Qifan Zhang 1, Jie Wang 2, Jiayang Chen 2, Jiajing Zhang 2, Hui Zhang 1, Zhengyi Liu 3, Xianyong Fang 3, Xiaohua Xu 1 1 University of Science and Technology of China 2 Anhui Jianzhu University 3 Anhui University A BSTRACT Automatically adapting novels into screenplays is important for the TV, film, or opera industries to promote products with low costs. The strong performances of large language models (LLMs) in long-text generation call us to propose a LLM based framework Reader-Rewriter (R 2) for this task. However, there are two fundamental challenges here. First, the LLM hallucinations may cause inconsistent plot extraction and screenplay generation. Second, the causality-embedded plot lines should be effectively extracted for coherent rewriting. Therefore, two corresponding tactics are proposed: 1) A hallucination-aware refinement method (HAR) to iteratively discover and eliminate the affections of hallucinations; and 2) a causal plot-graph construction method (CPC) based on a greedy cycle-breaking algorithm to efficiently construct plot lines with event causalities. Recruiting those efficient techniques, R 2 utilizes two modules to mimic the human screenplay rewriting process: The Reader module adopts a sliding window and CPC to build the causal plot graphs, while the Rewriter module generates first the scene outlines based on the graphs and then the screenplays. HAR is integrated into both modules for accurate inferences of LLMs.
Local Consensus Enhanced Siamese Network with Reciprocal Loss for Two-view Correspondence Learning
Wang, Linbo, Wu, Jing, Fang, Xianyong, Liu, Zhengyi, Cao, Chenjie, Fu, Yanwei
Recent studies of two-view correspondence learning usually establish an end-to-end network to jointly predict correspondence reliability and relative pose. We improve such a framework from two aspects. First, we propose a Local Feature Consensus (LFC) plugin block to augment the features of existing models. Given a correspondence feature, the block augments its neighboring features with mutual neighborhood consensus and aggregates them to produce an enhanced feature. As inliers obey a uniform cross-view transformation and share more consistent learned features than outliers, feature consensus strengthens inlier correlation and suppresses outlier distraction, which makes output features more discriminative for classifying inliers/outliers. Second, existing approaches supervise network training with the ground truth correspondences and essential matrix projecting one image to the other for an input image pair, without considering the information from the reverse mapping. We extend existing models to a Siamese network with a reciprocal loss that exploits the supervision of mutual projection, which considerably promotes the matching performance without introducing additional model parameters. Building upon MSA-Net, we implement the two proposals and experimentally achieve state-of-the-art performance on benchmark datasets.