GenAI4ELab/papercli-papers huggingface dataset:
Retrieval-driven Reasoning for Deliberative Visual Classification
Authors: Jianye Xie; Lianyong Qi; Fan Wang; Anqi Wang; Wenjuan Gong; Danxin Wang; Wanchun Dou; Yang Cao; Shichao Pei; Xiaokang Zhou
Venue: AAAI 2026 (main)

Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities in visual classification tasks. Existing methods for enhancing VLMs on this task often rely heavily on direct category-to-image matching, which limits generalization and results in suboptimal performance. In addition, these methods provide no understanding of why a specific category is chosen. To address these limitations, we introduce a new deliberative visual classification task that decomposes the classification process into multiple deliberative steps and leverages Large Language Models (LLMs) to perform explicit reasoning before the final decision. Specifically, we propose a Retrieval-driven Reasoning model (RdR) with two components, i.e., retrieval database construction and deliberative category prediction. The first component leverages LLMs to extract category-relevant descriptors and constructs a retrieval database for effective image–descriptor matching. The second component facilitates multiple deliberative steps and performs explicit reasoning based on the retrieved descriptors to augment the category prediction. Extensive experiments on multiple datasets demonstrate that RdR consistently outperforms strong baselines, highlighting its robustness and generalization ability.
Unnoticed Yet Effective: A Hybrid Physical Camouflage Framework Against DNNs and Human Perception
Authors: Mingye Xie; Jiacheng Ruan; Xian Gao; Ting Liu; Yuzhuo Fu
Venue: AAAI 2026 (main)

Abstract: While adversarial attacks can effectively deceive deep neural networks, their real-world applicability is often limited by complex and conspicuous patterns that reveal their attack intent to human observers. To overcome this limitation, we propose UYE, a novel camouflage framework designed to simultaneously mislead DNNs and evade human perception. UYE incorporates two key components: an attention refiner leveraging a pre-trained vision encoder to optimize adversarial patterns for robust attacks across diverse environments, and a perception evaluator trained on a preference dataset curated using tailored prompts from human-aligned large multimodal models to ensure natural and unobtrusive camouflage generation. Extensive experiments demonstrate that UYE outperforms state-of-the-art methods in achieving an optimal balance between human stealth and model deception while maintaining effectiveness in real-world scenarios.
Human2Robot: Learning Robot Actions from Paired Human-Robot Videos
Authors: Sicheng Xie; Haidong Cao; Zejia Weng; Zhen Xing; Haoran Chen; Shiwei Shen; Jiaqi Leng; Zuxuan Wu; Yu-Gang Jiang
Venue: AAAI 2026 (main)

Abstract: Distilling knowledge from human demonstrations is a promising way for robots to learn and act. Existing methods, which often rely on coarsely-aligned video pairs, are typically constrained to learning global or task-level features. As a result, they tend to neglect the fine-grained frame-level dynamics required for complex manipulation and generalization to novel tasks. We posit that this limitation stems from a vicious circle of inadequate datasets and the methods they inspire. To break this cycle, we propose a paradigm shift that treats fine-grained human-robot alignment as a conditional video generation problem. To this end, we first introduce H&R, a novel third-person dataset containing 2,600 episodes of precisely synchronized human and robot motions, collected using a VR teleoperation system. We then present Human2Robot, a framework designed to leverage this data. Human2Robot employs a Video Prediction Model to learn a rich and implicit representation of robot dynamics by generating robot videos from human input, which in turn guides a decoupled action decoder. Our real-world experiments demonstrate that this approach not only achieves high performance on seen tasks but also exhibits significant one-shot generalization to novel positions, objects, instances, and even new task categories.
Sonic4D: Spatial Audio Generation for Immersive 4D Scene Exploration
Authors: Siyi Xie; Hanxin Zhu; Xinyi Chen; Tianyu He; Xin Li; Zhibo Chen
Venue: AAAI 2026 (main)

Abstract: Recent advancements in 4D generation have demonstrated its remarkable capability in synthesizing photorealistic renderings of dynamic 3D scenes. However, despite achieving impressive visual performance, almost all existing methods overlook the generation of spatial audio aligned with the corresponding 4D scenes, posing a significant limitation to truly immersive audiovisual experiences. To mitigate this issue, we propose Sonic4D, a novel framework that enables spatial audio generation for immersive exploration of 4D scenes. Specifically, our method is composed of three stages: 1) To capture both the dynamic visual content and raw auditory information from a monocular video, we first employ pre-trained expert models to generate the 4D scene and its corresponding monaural audio. 2) Subsequently, to transform the monaural audio into spatial audio, we localize and track the sound sources within the 4D scene, where their 3D spatial coordinates at different timestamps are estimated via a pixel-level visual grounding strategy. 3) Based on the estimated sound source locations, we further synthesize plausible spatial audio that varies across different viewpoints and timestamps using physics-based simulation. Extensive experiments have demonstrated that our proposed method generates realistic spatial audio consistent with the synthesized 4D scene in a training-free manner, significantly enhancing the immersive experience for users.
MGD:Mesh-guided Gaussians with Diffusion Priors for Dynamic Objects Reconstruction from Monocular RGB-D Video
Authors: Weixing Xie; Ying Ye; Xian Wu; Jintian Li; Bingchuan Li; Yanchen Lin; Junfeng Yao
Venue: AAAI 2026 (main)

Abstract: Reconstructing dynamic objects from monocular RGB-D video is critical for advancing 3D vision applications and enhancing user experience. 
However, monocular RGB-D video provides limited 3D observations, making the reconstruction of unobserved regions highly under-constrained. 
Despite recent advances that combine neural implicit surfaces with diffusion models, the inherent limitations of implicit representations and the lack of effective guidance in diffusion priors lead to blurry appearance and inaccurate geometry in dynamic object reconstruction.
To address the issue, we present MGD, which leverages scene-adaptive diffusion priors and Mesh-guided Gaussians for realistic rendering and geometrically accurate reconstruction of dynamic objects, including unobserved regions.
The dynamic 3D objects reconstructed by MGD are represented using our proposed Mesh-guided Gaussians, which leverage global and local Gaussians to capture large-scale deformations and fine-grained appearance details, respectively.
Additionally, in order to utilize depth information, we integrate a depth ControlNet into the diffusion model and conduct scene-adaptive fine-tuning. We design a self-generated image-pair strategy to produce image pairs used for fine-tuning. 
Extensive experiments demonstrate that MGD achieves state-of-the-art performance in both high-fidelity reconstruction and structural completeness, while maintaining real-time efficiency during training and rendering.
Unleashing the Potential of Large Language Models for Text-to-Image Generation Through Autoregressive Representation Alignment
Authors: Xing Xie; Jiawei Liu; Ziyue Lin; Huijie Fan; Zhi Han; Yandong Tang; Liangqiong Qu
Venue: AAAI 2026 (main)

Abstract: We present Autoregressive Representation Alignment (ARRA), a new training framework that unlocks global-coherent text-to-image generation in autoregressive LLMs without architectural modifications. Different from prior works that require complex architectural redesigns, ARRA aligns LLM's hidden states with visual representations from external visual foundational models via a global visual alignment loss and a hybrid token, . This token enforces dual constraints: local next-token prediction and global semantic distillation, enabling LLMs to implicitly learn spatial and contextual coherence while retaining their original autoregressive paradigm. Extensive experiments validate ARRA's plug-and-play versatility. When training T2I LLMs from scratch, ARRA reduces FID by 16.6% (ImageNet), 12.0% (LAION-COCO) for autoregressive LLMs like LlamaGen, without modifying original architecture and inference mechanism. For training from text-generation-only LLMs, ARRA reduces FID by 25.5% (MIMIC-CXR), 8.8% (DeepEyeNet) for advanced LLMs like Chameleon. For domain adaptation, ARRA aligns general-purpose LLMs with specialized models (e.g., BioMedCLIP), achieving an 18.6% FID reduction over direct fine-tuning on medical imaging (MIMIC-CXR). These results demonstrate that training objective redesign, rather than architectural modifications, can resolve cross-modal global coherence challenges. ARRA offers a complementary paradigm for advancing autoregressive models.
FCMO: A Flow-Curv Mamba Operator for Large-Scale 3D Vehicle Aerodynamics
Authors: Yuchen Xie; Yufeng Xie; Hanyu He; Yue Huang; Lijuan Sun; Hengyi Ren
Venue: AAAI 2026 (main)

Abstract: Large-scale three dimensional vehicle aerodynamics prediction poses critical computational challenges in modern automotive design, where traditional CFD methods require prohibitive simulation times that conflict with rapid design iteration demands. While recent neural operator approaches show promise, existing methods struggle with computational complexity in dense meshes and fail to preserve essential topological information when processing large-scale point clouds. We propose FCMO, a physics-aware neural operator that integrates fluid mechanics principles with selective state space modeling for efficient large-scale vehicle aerodynamics. FCMO introduces four synergistic components: FlowCurv Anchor Sampling that intelligently selects mesh nodes based on normalized local curvature and windward sensitivity. Additionally, dual-scale physics-aware position encoding with adaptive k-NN construction transforms 3D irregular meshes into causality-preserving sequences through feature-guided serpentine scanning. The model integrates a flow-aware Mamba processor incorporating selective mechanisms that dynamically modulate state transitions based on wall distance and flow characteristics. Finally, a physics-constrained decoder enforces conservation laws through mixed weighted interpolation. Extensive experiments on Ahmed-Body and DrivAerNet benchmarks demonstrate that FCMO achieves consistent state-of-the-art performance with 5.2% improvement in surface pressure prediction, 9.3% enhancement in wall shear stress estimation, and 11.4% boost in drag coefficient accuracy, while maintaining superior computational efficiency with 9.4% fewer FLOPs and 9.9% reduced memory usage compared to existing methods.
FilmSceneDesigner: Chaining Set Design for Procedural Film Scene Generation
Authors: Zhifeng Xie; Keyi Zhang; Yiye Yan; Yuling Guo; Fan Yang; Jiting Zhou; Mengtian Li
Venue: AAAI 2026 (main)

Abstract: Film set design plays a pivotal role in cinematic storytelling and shaping the visual atmosphere. However, the traditional process depends on expert-driven manual modeling, which is labor-intensive and time-consuming. To address this issue, we introduce FilmSceneDesigner, an automated scene generation system that emulates professional film set design workflow. Given a natural language description, including scene type, historical period, and style, we design an agent-based chaining framework to generate structured parameters aligned with film set design workflow, guided by prompt strategies that ensure parameter accuracy and coherence. On the other hand, we propose a procedural generation pipeline which executes a series of dedicated functions with the structured parameters for floorplan and structure generation, material assignment, door and window placement, and object retrieval and layout, ultimately constructing a complete film scene from scratch. Moreover, to enhance cinematic realism and asset diversity, we construct SetDepot-Pro, a curated dataset of 6,862 film-specific 3D assets and 733 materials. Experimental results and human evaluations demonstrate that our system produces structurally sound scenes with strong cinematic fidelity, supporting downstream tasks such as virtual previs, construction drawing and mood board creation.
Revealing the Invisible: Latent Structure Modeling for Semantically Consistent Cloud Removal
Authors: Jingwei Xin; Kai Guo; Jie Li; Nannan Wang
Venue: AAAI 2026 (main)

Abstract: Cloud removal (CR) in remote sensing imagery is a critical yet challenging task due to complex cloud patterns and diverse underlying ground structures. Despite recent progress in generative models such as diffusion models, CR remains limited by their inadequate capability to perceive and reconstruct structured information beneath cloud-covered areas. In this work, we propose a Visibility-guided Semantic Estimation and Reconstruction network for cloud removal (VISER-CR), which reformulates CR as a structure-guided completion problem. Specifically, VISER-CR explicitly models cloud interference via spatial masking, encouraging the model to reason beyond pixel-level appearance and enhance scene-level structural understanding. Moreover, to further improve the representation of structural information, we introduce Patch Saliency Encoding, a self-guided mechanism that implicitly models structural alignment among patches, significantly enhancing clustering consistency and semantic separability in the latent space. This adaptive mechanism guides the network to focus on learning and reconstructing structurally important regions, thereby reducing redundancy and improving overall cloud removal performance. Extensive experiments on multiple benchmark datasets demonstrate the superior effectiveness of our method.
Training and Inference Within 1 Second – Tackle Cross-Sensor Degradation of Real-World Pansharpening with Efficient Residual Feature Tailoring
Authors: Tianyu Xin; Jin-Liang Xiao; Zeyu Xia; Shan Yin; Liang-Jian Deng
Venue: AAAI 2026 (main)

Abstract: Deep learning methods for pansharpening have advanced rapidly, yet models pretrained on data from a specific sensor often generalize poorly to data from other sensors.
Existing methods to tackle such cross-sensor degradation include retraining model or zero-shot methods, but they are highly time-consuming or even need extra training data. 
To address these challenges, our method first performs modular decomposition on deep learning-based pansharpening models, revealing a general yet critical interface where high-dimensional fused features begin mapping to the channel space of the final image. % may need revisement
A Feature Tailor is then integrated at this interface to address cross-sensor degradation at the feature level, and is trained efficiently with physics-aware unsupervised losses. Moreover, our method operates in a patch-wise manner, training on partial patches and performing parallel inference on all patches to boost efficiency.
Our method offer two key advantages: (1) Improved Generalization Ability: it significantly enhance performance in cross-sensor cases. (2) Low Generalization Cost: it achieves sub-second training and inference, requiring only partial test inputs and no external data, whereas prior methods often take minutes or even hours.
Experiments on the real-world data from multiple datasets demonstrate that our method achieves state-of-the-art quality and efficiency in tackling cross-sensor degradation. For example, training and inference of 512 times 512 times 8 image within 0.2 seconds and 4000 times 4000 times 8 image within 3 seconds at the fastest setting on a commonly used RTX 3090 GPU, which is over 100 times faster than zero-shot methods.
OptMark: Robust Multi-bit Diffusion Watermarking via Inference Time Optimization
Authors: Jiazheng Xing; Hai Ci; Hongbin Xu; Hangjie Yuan; Yong Liu; Mike Zheng Shou
Venue: AAAI 2026 (main)

Abstract: Watermarking diffusion-generated images is crucial for copyright protection and user tracking. However, current diffusion watermarking methods face significant limitations: zero-bit watermarking systems lack the capacity for large-scale user tracking, while multi-bit methods are highly sensitive to certain image transformations or generative attacks, resulting in a lack of comprehensive robustness. In this paper, we propose OptMark, an optimization-based approach that embeds a robust multi-bit watermark into the intermediate latents of the diffusion denoising process. OptMark strategically inserts a structural watermark early to resist generative attacks and a detail watermark late to withstand image transformations, with tailored regularization terms to preserve image quality and ensure imperceptibility. To address the challenge of memory consumption growing linearly with the number of denoising steps during optimization, OptMark incorporates adjoint gradient methods, reducing memory usage from O(N) to O(1). Experimental results demonstrate that OptMark achieves invisible multi-bit watermarking while ensuring robust resilience against valuemetric transformations, geometric transformations, editing, and regeneration attacks.
MAGIC: Mastering Physical Adversarial Generation in Context Through Collaborative LLM Agents
Authors: Yun Xing; Nhat Chung; Jie Zhang; Yue Cao; Ivor Tsang; Yang Liu; Lei Ma; Qing Guo
Venue: AAAI 2026 (main)

Abstract: Physical adversarial attacks in driving scenarios can expose critical vulnerabilities in visual perception models. However, developing such attacks remains non-trivial due to diverse real-world environmental influences. Existing approaches either struggle to generalize to dynamic environments or fail to achieve consistent physical attack performance. To address these challenges, we propose MAGIC (Mastering Physical Adversarial Generation In Context), a novel framework powered by multi-modal LLM agents to automatically understand the scene context during testing time and generate adversarial patches through synergistic interaction of language and vision understanding. Specifically, MAGIC orchestrates three specialized LLM agents: the adv-patch generation agent masters the creation of deceptive patches via strategic prompt manipulation for text-to-image models; the adv-patch deployment agent ensures contextual coherence by determining optimal deployment strategies based on scene understanding; and the self-examination agent completes this trilogy by providing critical oversight and iterative refinement of both processes. We validate our approach with both digital and physical scenarios, i.e., nuImage and real-world scenes, where both statistical and visual results demonstrate that our MAGIC is powerful and effective for attacking widely applied object detection systems, such as YOLO and DETR series.
Gait Recognition via Collaborating Discriminative and Generative Diffusion Models
Authors: Haijun Xiong; Bin Feng; Bang Wang; Xinggang Wang; Wenyu Liu
Venue: AAAI 2026 (main)

Abstract: Gait recognition offers a non-intrusive biometric solution by identifying individuals through their walking patterns. Although discriminative models have achieved notable success in this domain, the full potential of generative models remains largely unexplored. In this paper, we introduce CoD², a novel framework that combines the data distribution modeling capabilities of diffusion models with the semantic representation learning strengths of discriminative models to extract robust gait features. We propose a Multi-level Conditional Control strategy that integrates both high-level identity-aware semantic conditions and low-level visual details. Specifically, the high-level condition, extracted by the discriminative extractor, guides the generation of identity-consistent gait sequences, while low-level visual details, such as appearance and motion, are preserved to enhance consistency. Moreover, the generated sequences facilitate the discriminative extractor's learning, enabling it to capture more comprehensive high-level semantic features. Extensive experiments on four datasets (SUSTech1K, CCPG, GREW, and Gait3D) demonstrate that CoD² achieves state-of-the-art performance and can be seamlessly integrated with existing discriminative methods, yielding consistent improvements.
DocR1: Evidence Page-Guided GRPO for Multi-Page Document Understanding
Authors: Junyu Xiong; Yonghui Wang; Weichao Zhao; Chenyu Liu; Bing Yin; Wengang Zhou; Houqiang Li
Venue: AAAI 2026 (main)

Abstract: Understanding multi-page documents poses a significant challenge for multimodal large language models (MLLMs), as it requires fine-grained visual comprehension and multi-hop reasoning across pages. While prior work has explored reinforcement learning (RL) for enhancing advanced reasoning in MLLMs, its application to multi-page document understanding remains underexplored. In this paper, we introduce DocR1, an MLLM trained with a novel RL framework, Evidence Page-Guided GRPO (EviGRPO). EviGRPO incorporates an evidence-aware reward mechanism that promotes a coarse-to-fine reasoning strategy, guiding the model to first retrieve relevant pages before generating answers. To support this, we design a rigorous two-stage annotation pipeline and a curriculum learning strategy that enables effective training with limited supervision. Using this pipeline, we construct two datasets: EviBench, a high-quality training set with 4.8k examples, and ArxivFullQA, a benchmark with 8.6k QA examples over full scientific papers. Extensive experiments across a wide range of benchmarks demonstrate that DocR1 achieves state-of-the-art performance on multi-page tasks while maintaining strong results on single-page benchmarks.
Disentangled Hypergraph-Guided Mamba Scanning for Fine-Grained Visual Recognition
Authors: Zhongwei Xiong; Hao Wang; Xiaoyan Yu; Lingling Li; Xuezhuan Zhao; Taisong Jin
Venue: AAAI 2026 (main)

Abstract: Fine-grained Visual Recognition (FGVR) aims to distinguish between categories with subtle inter-class differences and large intra-class variations. While Vision Transformers with attention mechanisms have been widely adopted for FGVR, they usually suffer from high computational complexity and entangled global representations. Recent advancements in state-space models, exemplified by Mamba, have showcased substantial potential in vision-related tasks due to their linear scalability and rich sequence modeling capacity. To this end, we propose DHMamba, a novel Mamba based FGVR method. The proposed method leverages hypergraph to guide selective scanning and strengthen Mamba’s capability in modeling fine-grained semantics. Furthermore, a Disentangled Local Scanning (DLS) module is introduced to utilize hyperedges to allocate distinct informative patches into independent channels for mitigating the representational entanglement. Extensive experiments conducted on multiple FGVR benchmarks demonstrate that the proposed DHMamba outperforms the state-of-the-art methods, validating the efficacy of combining state-space modeling with hypergraph-based feature structuring.