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Yibo Zhao

14 accepted papers

2026

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

AAAI 2026technical

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

Cited by 0SourcePDFScholar
2026

Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving

AAAI 2026technical

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 prom

Cited by 0SourcePDFScholar
2026

RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing

ICML 2026poster

Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply–demand conditions. Adaptive delayed matching, which controls the holding intervals for batched sets of requests and vehicles, reveals an inherent trade-off betw…

Cited by 0SourceScholar
2026

SmokeSVD: Smoke Reconstruction from A Single View via Progressive Novel View Synthesis and Refinement with Diffusion Models

CVPR 2026

Reconstructing dynamic fluids from sparse views is a long-standing and challenging problem, due to the severe lack of 3D information from insufficient view coverage. While several pioneering approaches have attempted to address this issue using differentiable rendering or novel view synthesis, they

Cited by 0SourcecodeScholar
2025

Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph

ACL 2025finding

The rapid growth of social media platforms has raised significant concerns regarding online content toxicity. When Large Language Models (LLMs) are used for toxicity detection, two key challenges emerge: 1) the absence of domain-specific toxicity knowledge leads to false negatives; 2) the excessive…

2025

Local Conditional Controlling for Text-to-Image Diffusion Models

AAAI 2025technical

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 e…

2025

Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning

NeurIPS 2025spotlight

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 influe…

Cited by 0SourcecodeScholar
2025

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

NeurIPS 2025poster

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…

Cited by 0SourceScholar
2025

Text Detoxification: Data Efficiency, Semantic Preservation and Model Generalization

EMNLP 2025

The widespread dissemination of toxic content on social media poses a serious threat to both online environments and public discourse, highlighting the urgent need for detoxification methods that effectively remove toxicity while preserving the original semantics.However, existing approaches often s

2024

CHECKWHY: Causal Fact Verification via Argument Structure

ACL 2024long

With the growing complexity of fact verification tasks, the concern with “thoughtful” reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focus on checking a narrow scope of semantic factoids within claims and lack an explicit logical reasoning process. In this…

2023

EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification

EMNLP 2023long main

Automatic multi-hop fact verification task has gained significant attention in recent years. Despite impressive results, these well-designed models perform poorly on out-of-domain data. One possible solution is to augment the training data with counterfactuals, which are generated by minimally alter…

Cited by 0SourcecodeScholar