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Yuting Su

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6 papers

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AAAI Conference 2026 Conference Paper

SGoT-R1: Social Graph of Thought Reasoning-Enhanced Multimodal Large Language Model for Harmful Meme Detection

Xiuxian Wang
Yuting Su
Wenhui Li
Xiaowen Wang
Zhuojun Li
Anan Liu

Internet memes serve as widely distributed multimodal social content that conveys complex ideas through metaphorical expressions, often containing harmful implications that make accurate harmful meme detection an important problem. Reasoning knowledge extracted from large language models plays a crucial role in recent advances in harmful meme detection. However, these methods only perform reasoning analysis on memes from a single opinion, ignoring that memes are essentially products of group consensus, where their true meaning interpretation highly depends on the collision and aggregation process of diverse user viewpoints. To address this problem, we propose a Social Graph of Thought Reasoning Enhancement (SGoTRE) framework for harmful meme detection. The SGoTRE contains three key steps: First, through multi-agent simulation technology, we obtain diverse chains of thought that represent the parsing logic of users from different backgrounds toward memes, authentically restoring the diversity characteristics of group cognition. Second, we construct a Social Graph of Thought (SGoT) that effectively integrates multi-chain reasoning processes and structurally expresses the consensus and diversity of viewpoints among users. Finally, we utilize the SGoT for cognitive distillation, internalizing multi-opinion reasoning logic into a single multimodal large model SGoT-R1 to achieve efficient and interpretable harmful meme detection. Experimental results show that SGoT-R1 significantly improves detection performance on mainstream datasets. Particularly on the most challenging FHM dataset, SGoT-R1 achieves an 8.9% improvement over state-of-the-art models.

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AAAI Conference 2024 Conference Paper

Graph Disentangled Contrastive Learning with Personalized Transfer for Cross-Domain Recommendation

Jing Liu
Lele Sun
Weizhi Nie
Peiguang Jing
Yuting Su

Cross-Domain Recommendation (CDR) has been proven to effectively alleviate the data sparsity problem in Recommender System (RS). Recent CDR methods often disentangle user features into domain-invariant and domain-specific features for efficient cross-domain knowledge transfer. Despite showcasing robust performance, three crucial aspects remain unexplored for existing disentangled CDR approaches: i) The significance nuances of the interaction behaviors are ignored in generating disentangled features; ii) The user features are disentangled irrelevant to the individual items to be recommended; iii) The general knowledge transfer overlooks the user's personality when interacting with diverse items. To this end, we propose a Graph Disentangled Contrastive framework for CDR (GDCCDR) with personalized transfer by meta-networks. An adaptive parameter-free filter is proposed to gauge the significance of diverse interactions, thereby facilitating more refined disentangled representations. In sight of the success of Contrastive Learning (CL) in RS, we propose two CL-based constraints for item-aware disentanglement. Proximate CL ensures the coherence of domain-invariant features between domains, while eliminatory CL strives to disentangle features within each domains using mutual information between users and items. Finally, for domain-invariant features, we adopt meta-networks to achieve personalized transfer. Experimental results on four real-world datasets demonstrate the superiority of GDCCDR over state-of-the-art methods.

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IJCAI Conference 2020 Conference Paper

Consistent Domain Structure Learning and Domain Alignment for 2D Image-Based 3D Objects Retrieval

Yuting Su
Yuqian Li
Dan Song
Weizhi Nie
Wenhui Li
An-An Liu

2D image-based 3D objects retrieval is a new topic for 3D objects retrieval which can be used to manage 3D data with 2D images. The goal is to search some related 3D objects when given a 2D image. The task is challenging due to the large domain gap between 2D images and 3D objects. Therefore, it is essential to consider domain adaptation problems to reduce domain discrepancy. However, most of the existing domain adaptation methods only utilize the semantic information from the source domain to predict labels in the target domain and neglect the intrinsic structure of the target domain. In this paper, we propose a domain alignment framework with consistent domain structure learning to reduce the large gap between 2D images and 3D objects. The domain structure learning module makes use of both the semantic information from the source domain and the intrinsic structure of the target domain, which provides more reliable predicted labels to the domain alignment module to better align the conditional distribution. We conducted experiments on two public datasets, MI3DOR and MI3DOR-2, and the experimental results demonstrate the proposed method outperforms the state-of-the-art methods.

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IJCAI Conference 2018 Conference Paper

Cross-Domain 3D Model Retrieval via Visual Domain Adaption

Anan Liu
Shu Xiang
Wenhui Li
Weizhi Nie
Yuting Su

Recent advances in 3D capturing devices and 3D modeling software have led to extensive and diverse 3D datasets, which usually have different distributions. Cross-domain 3D model retrieval is becoming an important but challenging task. However, existing works mainly focus on 3D model retrieval in a closed dataset, which seriously constrain their implementation for real applications. To address this problem, we propose a novel crossdomain 3D model retrieval method by visual domain adaptation. This method can inherit the advantage of deep learning to learn multi-view visual features in the data-driven manner for 3D model representation. Moreover, it can reduce the domain divergence by exploiting both domainshared and domain-specific features of different domains. Consequently, it can augment the discrimination of visual descriptors for cross-domain similarity measure. Extensive experiments on two popular datasets, under three designed cross-domain scenarios, demonstrate the superiority and effectiveness of the proposed method by comparing against the state-of-the-art methods. Especially, the proposed method can significantly outperform the most recent method for cross-domain 3D model retrieval and the champion of Shrec’16 Large-Scale 3D Shape Retrieval from ShapeNet Core55.

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IJCAI Conference 2018 Conference Paper

Hierarchical Graph Structure Learning for Multi-View 3D Model Retrieval

Yuting Su
Wenhui Li
Anan Liu
Weizhi Nie

3D model retrieval has been widely utilized in numerous domains, such as computer-aided design, digital entertainment and virtual reality. Recently, many graph-based methods have been proposed to address this task by using multiple views of 3D models. However, these methods are always constrained by the many-to-many graph matching for similarity measure between pair-wise models. In this paper, we propose an hierarchical graph structure learning method (HGS) for 3D model retrieval. The proposed method can decompose the complicated multi-view graph-based similarity measure into multiple single-view graph-based similarity measures. In the bottom hierarchy, we present the method for single-view graph generation and further propose the novel method for similarity measure in single-view graph by leveraging both node-wise context and model-wise context. In the top hierarchy, we fuse the similarities in single-view graphs with respect to different viewpoints to get the multi-view similarity between pair-wise models. In this way, the proposed method can avoid the difficulty in definition and computation in the traditional high-order graph. Moreover, this method is unsupervised and is independent of large-scale 3D dataset for model learning. We conduct extensive evaluation on three popular and challenging datasets. The comparison demonstrates the superiority and effectiveness of the proposed method comparing with the state of the arts. Especially, this unsupervised method can achieve competing performance against the most recent supervised & deep learning method.

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IJCAI Conference 2018 Conference Paper

Multi-Level Policy and Reward Reinforcement Learning for Image Captioning

Anan Liu
Ning Xu
Hanwang Zhang
Weizhi Nie
Yuting Su
Yongdong Zhang

Image captioning is one of the most challenging hallmark of AI, due to its complexity in visual and natural language understanding. As it is essentially a sequential prediction task, recent advances in image captioning use Reinforcement Learning (RL) to better explore the dynamics of word-by-word generation. However, existing RL-based image captioning methods mainly rely on a single policy network and reward function that does not well fit the multi-level (word and sentence) and multi-modal (vision and language) nature of the task. To this end, we propose a novel multi-level policy and reward RL framework for image captioning. It contains two modules: 1) Multi-Level Policy Network that can adaptively fuse the word-level policy and the sentence-level policy for the word generation; and 2) Multi-Level Reward Function that collaboratively leverages both vision-language reward and language-language reward to guide the policy. Further, we propose a guidance term to bridge the policy and the reward for RL optimization. Extensive experiments and analysis on MSCOCO and Flickr30k show that the proposed framework can achieve competing performances with respect to different evaluation metrics.

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