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Yibo Zhao

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Amplifying Discrepancies: Exploiting Macro and Micro Inconsistencies for Image Manipulation Localization

Shenghao Chen
Yibo Zhao
Tianyi Wang
Chunjie Ma
Weili Guan
Ming Li
Zan Gao

The rapid development of image manipulation technologies poses significant challenges to multimedia forensics, especially in accurate localization of manipulated regions. Existing methods often fail to fully explore the intrinsic discrepancies between manipulated and authentic regions, resulting in sub-optimal performance. To address this limitation, we propose the Focus Region Discrepancy Network (FRD-Net), a novel and efficient framework that significantly enhances manipulation localization by amplifying discrepancies at both macro- and micro-levels. Specifically, our proposed Iterative Clustering Module (ICM) groups features into two discriminative clusters and refines representations via backward propagation from cluster centers, improving the distinction between tampered and authentic regions at the macro level. Thereafter, our Differential Progressive Module (DPM) is constructed to capture fine-grained structural inconsistencies within local neighborhoods and integrate them into a Central Difference Convolution, increasing sensitivity to subtle manipulation details at the micro level. Finally, these complementary modules are seamlessly integrated into a compact architecture that achieves a favorable balance between accuracy and efficiency. Extensive experiments on multiple benchmarks demonstrate that FRD-Net consistently surpasses state-of-the-art methods in terms of manipulation localization performance while maintaining a lower computational cost.

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AAAI Conference 2026 Conference Paper

Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving

Yao Cheng
Yibo Zhao
Jiapeng Zhu
Yao Liu
Xing Sun
Xiang Li

Large Language Models (LLMs) have demonstrated significant potential across various domains. However, they often struggle with integrating external knowledge and performing complex reasoning, leading to hallucinations and unreliable outputs. Retrieval Augmented Generation (RAG) has emerged as a promising paradigm to mitigate these issues by incorporating external knowledge. Yet, conventional RAG approaches, especially those based on vector similarity, fail to effectively capture relational dependencies and support multi-step reasoning. In this work, we propose CogGRAG, a human cognition-inspired, graph-based RAG framework designed for Knowledge Graph Question Answering (KGQA). CogGRAG models the reasoning process as a tree-structured mind map that decomposes the original problem into interrelated subproblems and explicitly encodes their semantic relationships. This structure not only provides a global view to guide subsequent retrieval and reasoning but also enables self-consistent verification across reasoning paths. The framework operates in three stages: (1) top-down problem decomposition via mind map construction, (2) structured retrieval of both local and global knowledge from external Knowledge Graphs (KGs), and (3) bottom-up reasoning with dual-process self-verification. Unlike previous tree-based decomposition methods such as MindMap or Graph-CoT, CogGRAG unifies problem decomposition, knowledge retrieval, and reasoning under a single graph-structured cognitive framework, allowing early integration of relational knowledge and adaptive verification. Extensive experiments demonstrate that CogGRAG achieves superior accuracy and reliability compared to existing methods.

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AAAI Conference 2025 Conference Paper

Local Conditional Controlling for Text-to-Image Diffusion Models

Yibo Zhao
Liang Peng
Yang Yang
Zekai Luo
Hengjia Li
Yao Chen
Zheng Yang
Xiaofei He

Diffusion models have exhibited impressive prowess in the text-to-image task. Recent methods add image-level structure controls, e.g., edge and depth maps, to manipulate the generation process together with text prompts to obtain desired images. This controlling process is globally operated on the entire image, which limits the flexibility of control regions. In this paper, we explore a novel and practical task setting: local control. It focuses on controlling specific local region according to user-defined image conditions, while the remaining regions are only conditioned by the original text prompt. However, it is non-trivial to achieve it. The naive manner of directly adding local conditions may lead to the local control dominance problem, which forces the model to focus on the controlled region and neglect object generation in other regions. To mitigate this problem, we propose Regional Discriminate Loss to update the noised latents, aiming at enhanced object generation in non-control regions. Furthermore, the proposed Focused Token Response suppresses weaker attention scores which lack the strongest response to enhance object distinction and reduce duplication. Lastly, we adopt Feature Mask Constraint to reduce quality degradation in images caused by information differences across the local control region. All proposed strategies are operated at the inference stage. Extensive experiments demonstrate that our method can synthesize high-quality images aligned with the text prompt under local control conditions.

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NeurIPS Conference 2025 Conference Paper

Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning

Yibo Zhao
Yang Zhao
Hongru Du
Hao Frank Yang

Decision-making models for individuals, particularly in high-stakes scenarios like vaccine uptake, often diverge from population optimal predictions. This gap arises from the uniqueness of the individual decision-making process, shaped by numerical attributes (e. g. , cost, time) and linguistic influences (e. g. , personal preferences and constraints). Developing upon Utility Theory and leveraging the textual-reasoning capabilities of Large Language Models (LLMs), this paper proposes an Adaptive Textual-symbolic Human-centric Reasoning framework (ATHENA) to address the optimal information integration. ATHENA uniquely integrates two stages: First, it discovers robust, group-level symbolic utility functions via LLM-augmented symbolic discovery; Second, it implements individual-level semantic adaptation, creating personalized semantic templates guided by the optimal utility to model personalized choices. Validated on real-world travel mode and vaccine choice tasks, ATHENA consistently outperforms utility-based, machine learning, and other LLM-based models, lifting F1 score by at least 6. 5\% over the strongest cutting-edge models. Further, ablation studies confirm that both stages of ATHENA are critical and complementary, as removing either clearly degrades overall predictive performance. By organically integrating symbolic utility modeling and semantic adaptation, ATHENA provides a new scheme for modeling human-centric decisions. The project page can be found at https: //yibozh. github. io/Athena.

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NeurIPS Conference 2025 Conference Paper

Self-Supervised Direct Preference Optimization for Text-to-Image Diffusion Models

Liang Peng
Boxi Wu
Haoran Cheng
Yibo Zhao
Xiaofei He

Direct preference optimization (DPO) is an effective method for aligning generative models with human preferences and has been successfully applied to fine‑tune text‑to‑image diffusion models. Its practical adoption, however, is hindered by a labor‑intensive pipeline that first produces a large set of candidate images and then requires humans to rank them pairwise. We address this bottleneck with self‑supervised direct preference optimization, a new paradigm that removes the need for any pre‑generated images or manual ranking. During training, we create preference pairs on the fly through self‑supervised image transformations, allowing the model to learn from fresh and diverse comparisons at every iteration. This online strategy eliminates costly data collection and annotation while remaining plug‑and‑play for any text‑to‑image diffusion method. Surprisingly, the on‑the‑fly pairs produced by the proposed method not only match but exceed the effectiveness of conventional DPO, which we attribute to the greater diversity of preferences sampled during training. Extensive experiments with Stable Diffusion 1. 5 and Stable Diffusion XL confirm that our method delivers substantial gains.

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ECAI Conference 2024 Conference Paper

Differentiable Neural Network for Assembling Blocks

Zhiwei Liu
Qiang Lu 0005
Yibo Zhao
Yanhong Zhao
Jake Luo

The goal of assembly blocks is to select blocks from pre-trained neural network (NN) models and combine them into a new NN for a different dataset. By reusing the weights of these blocks, training the NN with the new dataset becomes cost-effective. To achieve this goal, we propose an end-to-end differentiable neural network called PA-DNN. PA-DNN consists of two modules: a partition NN module and an assembly NN module. For the new dataset, the partition NN module divides existing pre-trained NN models into blocks. The assembly NN module then selects some of these blocks and combines them into a new NN using a stitching component. To train PA-DNN, we design a score function that evaluates the performance of each new NN generated by PA-DNN. The evaluated value is used to train the partition NN module. Additionally, two loss functions are created to train the assembly NN module and the stitching component in the new NN, respectively. After the training process, PA-DNN infers a new NN, and only the stitching component of the NN is fine-tuned with the new dataset. Experiments show that, compared to manual models, neural architecture search, and the assembly model DeRy, PA-DNN can generate a more accurate and lightweight NN with lower training costs.

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IROS Conference 2022 Conference Paper

IMU Dead-Reckoning Localization with RNN-IEKF Algorithm

Hang Zhou
Yibo Zhao
Xiaogang Xiong
Yunjiang Lou
Shyam Kamal

In complex urban environments, the Inertial Navigation System (INS) is important for navigating unmanned ground vehicles (UAVs) for its environment-independency and reliability of real-time localization. It is usually employed as the baseline in the case of other sensors failures, such as the GPS, Lidar, or Cameras. However, one problem for the INS is that its estimation error of localization accumulates over time, and thus the estimated trajectories of the UAVs continue to drift away from their ground truths. To solve this problem, this paper proposes an improved algorithm based on the Invariant Extended Kalman Filter (IEKF) for dead-reckoning of autonomous vehicles, which dynamically adjusts the process noise and the observation noise covariance matrixes through Attention mechanism and Recurrent Neural Network (RNN). The algorithm achieves more robust and accurate dead-reckoning localization in the experiments conducted on the KITTI dataset, reducing the translational error by about 45%compared to the baseline.

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AAMAS Conference 2022 Conference Paper

Learning Generalizable Multi-Lane Mixed-Autonomy Behaviors in Single Lane Representations of Traffic

Abdul Rahman Kreidieh
Yibo Zhao
Samyak Parajuli
Alexandre M. Bayen

This paper tackles the problem of learning generalizable congestionmitigation strategies in simple representations of traffic. In particular, we look to mixed-autonomy ring roads as depictions of instabilities common to many generic settings, and ask the question: What features are needed to ensure that policies here can be adapted to typical multi-lane highways? To answer this, we study the implications of the scale of the source task and the modeling of pseudo-lane change events within it on the transferability of policies learned to complex networks. Our findings suggest that negating the effects of boundary conditions and introducing lane changes that approximately match trends in more complex systems can significantly improve the generalizability of learned behaviors.

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