GenAI4ELab/papercli-papers huggingface dataset:
Sim4Seg: Boosting Multimodal Multi-disease Medical Diagnosis Segmentation with Region-Aware Vision-Language Similarity Masks
Authors: Lingran Song; Yucheng Zhou; Jianbing Shen
Venue: AAAI 2026 (main)

Abstract: Despite significant progress in pixel-level medical image analysis, existing medical image segmentation models rarely explore medical segmentation and diagnosis tasks jointly. However, it is crucial for patients that models can provide explainable diagnoses along with medical segmentation results. In this paper, we introduce a medical vision-language task named Medical Diagnosis Segmentation (MDS), which aims to understand clinical queries for medical images and generate the corresponding segmentation masks as well as diagnostic results. To facilitate this task, we first present the Multimodal Multi-disease Medical Diagnosis Segmentation (M3DS) dataset, containing diverse multimodal multi-disease medical images paired with their corresponding segmentation masks and diagnosis chain-of-thought, created via an automated diagnosis chain-of-thought generation pipeline. Moreover, we propose Sim4Seg, a novel framework that improves the performance of diagnosis segmentation by taking advantage of the Region-Aware Vision-Language Similarity to Mask (RVLS2M) module. To improve overall performance, we investigate a test-time scaling strategy for MDS tasks. Experimental results demonstrate that our method outperforms baselines in both segmentation and diagnosis.
Creating Blank Canvas Against AI-enabled Image Forgery
Authors: Qi Song; Ziyuan Luo; Renjie Wan
Venue: AAAI 2026 (main)

Abstract: AIGC-based image editing technology has greatly simplified the realistic-level image modification, causing serious potential risks of image forgery. This paper introduces a new approach to tampering detection using the Segment Anything Model (SAM). Instead of training SAM to identify tampered areas, we propose a novel strategy.  The entire image is transformed into a blank canvas from the perspective of neural models.  Any modifications to this blank canvas would be noticeable to the models. To achieve this idea, we introduce adversarial perturbations to prevent SAM from seeing anything, allowing it to identify forged regions when the image is tampered with. Due to SAM's powerful perceiving capabilities, naive adversarial attacks cannot completely tame SAM. To thoroughly deceive SAM and make it blind to the image, we introduce a frequency-aware optimization strategy, which further enhances the capability of tamper localization. Extensive experimental results demonstrate the effectiveness of our method.
Insert Anything: Image Insertion via In-Context Editing in DiT
Authors: Wensong Song; Hong Jiang; Zongxin Yang; Zheqiao Cheng; Ruijie Quan; Yi Yang
Venue: AAAI 2026 (main)

Abstract: This work presents Insert Anything, a unified framework for reference-based image insertion that seamlessly integrates objects from reference images into target scenes under flexible, user-specified control guidance. Instead of training separate models for individual tasks, our approach is trained once on our new AnyInsertion dataset, the first open-source large-scale dataset specifically designed for reference image–based image editing, comprising 136K prompt-image pairs covering diverse tasks such as person, object, and garment insertion--and effortlessly generalizes to a wide range of insertion scenarios. Such a challenging setting requires capturing both identity features and fine-grained details, while allowing versatile local adaptations in style, color, and texture. To this end, we propose to leverage the multimodal attention of the Diffusion Transformer (DiT) to support both mask- and text-guided editing. Furthermore, we introduce an in-context editing mechanism that treats the reference image as contextual information, employing two prompting strategies to harmonize the inserted elements with the target scene while faithfully preserving their distinctive features. Extensive experiments on AnyInsertion, DreamBooth, and VTON-HD benchmarks demonstrate that our method consistently outperforms existing alternatives, underscoring its great potential in real-world applications such as creative content generation, virtual try-on, and scene composition.
CLUENet: Cluster Attention Makes Neural Networks Have Eyes
Authors: Xiangshuai Song; Jun-Jie Huang; Tianrui Liu; Ke Liang; Chang Tang
Venue: AAAI 2026 (main)

Abstract: Despite the success of convolution- and attention-based models in vision tasks, their rigid receptive fields and complex architectures limit their ability to model irregular spatial patterns and hinder interpretability, thereby posing challenges for tasks requiring high model transparency. Clustering paradigms offer promising interpretability and flexible semantic modeling, but suffer from limited accuracy, low efficiency, and gradient vanishing during training. To address these issues, we propose the CLUster attEntion Network (CLUENet), a transparent deep architecture for visual semantic understanding. Specifically, we introduce three key innovations, including (i) a Global and Soft Feature Aggregation with a Temperature-Scaled Cosine Attention for capturing long-range dependencies and a Gated Fusion Mechanism for enhanced local modeling, (ii) Hard and Shared Feature Dispatching, and (iii) an Improved Cluster Pooling Block. These enhancements significantly improve both classification performance and visual interpretability. Experiments on CIFAR-100 and Mini-ImageNet demonstrate that CLUENet outperforms existing clustering methods and mainstream visual models, offering a compelling balance of accuracy, efficiency, and transparency.
UniAlignment: Semantic Alignment for Unified Image Generation, Understanding, Manipulation and Perception
Authors: Xinyang Song; Libin Wang; Weining Wang; Shaozhen Liu; Dandan Zheng; Jingdong Chen; Qi Li; Zhenan Sun
Venue: AAAI 2026 (main)

Abstract: The remarkable success of diffusion models in text-to-image generation has sparked growing interest in expanding their capabilities to a variety of multi-modal tasks, including image understanding, manipulation, and perception. These tasks require advanced semantic comprehension across both visual and textual modalities, especially in scenarios involving complex semantic instructions. However, existing approaches often rely heavily on vision-language models (VLMs) or modular designs for semantic guidance, leading to fragmented architectures and computational inefficiency. To address these challenges, we propose UniAlignment, a unified multimodal generation framework within a single diffusion transformer. UniAlignment introduces a dual-stream diffusion training strategy that incorporates both intrinsic-modal semantic alignment and cross-modal semantic alignment, thereby enhancing the model's cross-modal consistency and instruction-following robustness. Additionally, we present SemGen-Bench, a new benchmark specifically designed to evaluate multimodal semantic consistency under complex textual instructions. Extensive experiments across multiple tasks and benchmarks demonstrate that UniAlignment outperforms existing baselines, underscoring the significant potential of diffusion models in unified multimodal generation.
Object Fusion via Diffusion Time-step for Customized Image Editing with Single Example
Authors: Xue Song; Zhongqi Yue; Jiequan Cui; Hanwang Zhang; Jingjing Chen
Venue: AAAI 2026 (main)

Abstract: We tackle the task of customized image editing using a text-conditioned Diffusion Model (DM). The goal is to fuse the subject in a reference image (e.g., sunglasses) with a source one (e.g., a boy), while retaining the fidelity of them both (e.g., the boy wearing the sunglasses). An intuitive approach, called LoRA fusion, first separately trains a DM LoRA for each image to encode its details. Then the two LoRAs are linearly combined by a weight to generate a fused image. Unfortunately, even through careful grid search or learning the weight, this approach still trades off the fidelity of one image against the other. We point out that the evil lies in the overlooked role of diffusion time-step in the generation process, i.e., a smaller time-step controls the generation of a more fine-grained attribute. For example, a large LoRA weight for the source may help preserve its fine-grained details (e.g., face attributes) at a small time-step, but could overpower the reference subject LoRA and lose the fidelity of its overall shape at a larger time-step. To address this deficiency, we propose TimeFusion, which learns a time-step-specific LoRA fusion weight that resolves the trade-off, i.e., generating the source and reference subject in high fidelity given their respective prompt. Then we can customize image editing using this weight and a target prompt.
UniMM-V2X: MoE-Enhanced Multi-Level Fusion for End-to-End Cooperative Autonomous Driving
Authors: Ziyi Song; Chen Xia; Chenbing Wang; Haibao Yu; Sheng Zhou; Zhisheng Niu
Venue: AAAI 2026 (main)

Abstract: Autonomous driving holds transformative potential but remains fundamentally constrained by the limited perception and isolated decision-making with standalone intelligence. While recent multi-agent approaches introduce cooperation, they often focus merely on perception-level tasks, overlooking the alignment with downstream planning and control, or fall short in leveraging the full capacity of the recent emerging end-to-end autonomous driving. In this paper, we present UniMM-V2X, a novel end-to-end multi-agent framework that enables hierarchical cooperation across perception, prediction, and planning. At the core of our framework is a multi-level fusion strategy that unifies perception and prediction cooperation, allowing agents to share queries and reason cooperatively for consistent and safe decision-making. To adapt to diverse downstream tasks and further enhance the quality of multi-level fusion, we incorporate a Mixture-of-Experts (MoE) architecture to dynamically enhance the BEV representations. We further extend MoE into the decoder to better capture diverse motion patterns. Extensive experiments on the DAIR-V2X dataset demonstrate our approach achieves state-of-the-art (SOTA) performance with a 39.7% improvement in perception accuracy, a 7.2% reduction in prediction error, and a 33.2% improvement in planning performance compared with UniV2X, showcasing the strength of our MoE-enhanced multi-level cooperative paradigm.
DiffNR: Diffusion-Enhanced Neural Representation Optimization for Sparse-View 3D Tomographic Reconstruction
Authors: Shiyan Su; Ruyi Zha; Danli Shi; Hongdong Li; Xuelian Cheng
Venue: AAAI 2026 (main)

Abstract: Neural representations (NRs), such as neural fields and 3D Gaussians, effectively model volumetric data in computed tomography (CT) but suffer from severe artifacts under sparse-view settings. To address this, we propose DiffNR, a novel framework that enhances NR optimization with diffusion priors. At its core is SliceFixer, a single-step diffusion model designed to correct artifacts in degraded slices. We integrate specialized conditioning layers into the network and develop tailored data curation strategies to support model finetuning. During reconstruction, SliceFixer periodically generates pseudo-reference volumes, providing auxiliary 3D perceptual supervision to fix underconstrained regions. Compared to prior methods that embed CT solvers into time-consuming iterative denoising, our repair-and-augment strategy avoids frequent diffusion model queries, leading to better runtime performance. Extensive experiments show that DiffNR improves PSNR by 3.99 dB on average, generalizes well across domains, and maintains efficient optimization.
Zero-to-Hero: Empowering Video Appearance Transfer with Zero-Shot Initialization and Holistic Restoration
Authors: Tongtong Su; Chengyu Wang; Haipeng Liao; Jun Huang; Dongming Lu
Venue: AAAI 2026 (main)

Abstract: Appearance editing according to user needs is a pivotal task in video editing. Existing text-guided methods often lead to ambiguities regarding user intentions and restrict fine-grained control over editing specific aspects of objects. To overcome these limitations, this paper introduces a novel approach named Zero-to-Hero, which focuses on reference-based video editing by disentangling the editing process into two distinct problems. It achieves this by first editing an anchor frame to satisfy user requirements as a reference image and then consistently propagating its appearance across the other frames in the video. To achieve accurate appearance propagation, in the first stage of Zero-to-Hero, we leverage correspondences within the original frames to guide the attention mechanism, which is more robust than previously proposed optical flow or temporal modules in memory-friendly video generative models, especially when dealing with objects exhibiting large motions. This offers a solid zero-shot initialization that ensures both accuracy and temporal consistency. However, intervention in the attention mechanism results in compounded imaging degradation with unknown blurring and color-missing issues. Following the Zero-Stage, our Hero-Stage holistically learns a conditional generative model for video restoration. To accurately evaluate appearance consistency, we construct a set of videos with multiple appearances using Blender, enabling a fine-grained and deterministic evaluation. Our method outperforms the best-performing baseline with a PSNR improvement of 2.6 dB.
Weather-Robust LiDAR Perception: Point Cloud Restoration from Adverse Weather
Authors: Chenghao Sun; Pengpeng Sun; Xiangmo Zhao
Venue: AAAI 2026 (main)

Abstract: Adverse weather conditions—such as rain, fog, and snow—significantly degrade LiDAR point cloud quality, causing substantial performance deterioration in detection models trained on clean data. To address this, we propose LTDNet, a novel point cloud quality improvement net-work that restores degraded LiDAR scans by learning an end-to-end mapping from corrupted to clean geometry. LTDNet leverages position encoding, spatial–frequency joint feature extraction, weather-aware refinement, and probabilistic pruning to effectively recover structural in-tegrity while suppressing weather-induced noise. To fa-cilitate standardized evaluation, we introduce IQA3D, a new benchmark comprising both synthetic and real-world sequences under adverse weather. This dual-design benchmark serves two complementary purposes: synthet-ic sequences provide pixel-wise correspondences between degraded and clean point clouds for quantitatively as-sessing restoration fidelity, while real-world sequences enable evaluation of the practical impact of improvement methods on downstream 3D object detection under au-thentic weather conditions. This makes IQA3D particular-ly suitable for jointly measuring both perceptual quality and task-level robustness of point cloud improvement models. Extensive experiments on IQA3D demonstrate that LTDNet significantly improves detection perfor-mance across various state-of-the-art 3D detectors and three tested weather conditions, making it a practical and effective solution for robust LiDAR-based detection.
AlignTrack: Top-Down Spatiotemporal Resolution Alignment for RGB-Event Visual Tracking
Authors: Chuanyu Sun; Jiqing Zhang; Yang Wang; Yuanchen Wang; Yutong Jiang; Baocai Yin; Xin Yang
Venue: AAAI 2026 (main)

Abstract: Most existing RGB-Event trackers rely on strictly aligned datasets, overlooking the asynchronous spatio-temporal resolutions common in real-world scenarios. 
This methodological limitation impedes effective RGB-Event feature alignment and ultimately degrades tracking performance.
To overcome this limitation, we propose AlignTrack, a novel tracking framework built upon a Top-Down Alignment (TDA) strategy inspired by the human visual system. 
Our TDA framework follows an encode-decode-align paradigm: it first encodes multimodal features to generate target-related priors, which are then progressively decoded to guide a subsequent feature alignment pass. 
Within this framework, we introduce two key innovations: (1) a Cross-Prior Attention (CPA) module that effectively generates and integrates cross-modal priors, and (2) a Cross-Modal Semantic Alignment (CSA) loss that maximizes mutual information to enforce semantic consistency between modalities. 
Extensive experiments show that AlignTrack achieves state-of-the-art performance on four challenging RGB-Event tracking benchmarks, demonstrating its robustness in both aligned and unaligned scenarios. 
Ablation studies further validate the significant contribution of each proposed component.
HumanPro: Single-view 3D Clothed Human Reconstruction with Progressive Normal Guidance
Authors: Jianchi Sun; Fei Luo; Wenzhuo Fan; Yu Jiang; Chunxia Xiao
Venue: AAAI 2026 (main)

Abstract: Reconstructing fine-grained geometry of clothed human from single-view image is a challenging task, particularly in accurately recovering complex shapes and generating clothes details. To address these limitations, we propose a novel approach named HumanPro, which estimates high-quality human normals via a generative model, and progressively deforms a parametric body into the final clothed human mesh guided by normals. First, we propose a geometry-aware latent diffusion model with a normal enhancer to estimate high-quality human normals from four views. Then, we propose a progressive mesh optimization consisting of shape-aware deformation alignment and global-to-patch detail refinement for human mesh reconstruction. The shape-aware deformation alignment applies image morphing to learn the shape-level gap of normals, addressing large-scale deformation of complex clothes. It can recover the overall silhouette of a clothed human, and serves as an initialization for the global-to-patch detail refinement. Our detail refinement combines global and patch-wise optimization strategies to iteratively produce the clothed human mesh by minimizing the pixel-level difference of normals. This way effectively recovers fine-grained details while avoiding local minima. Extensive experiments demonstrate that HumanPro can deal with various challenging scenarios and outperforms state-of-the-art methods.
VaccineRAG: Boosting Multimodal Large Language Models’ Immunity to Harmful RAG Samples
Authors: Qixin Sun; Ziqin Wang; Hengyuan Zhao; Yilin Li; Kaiyou Song; Si Liu; Xiaolin Hu; Qingpei Guo; Linjiang Huang
Venue: AAAI 2026 (main)

Abstract: Retrieval Augmented Generation enhances the response accuracy of Large Language Models (LLMs) by integrating retrieval and generation modules with external knowledge, demonstrating particular strength in real-time queries and Visual Question Answering tasks. 
However, the effectiveness of RAG is frequently hindered by the precision of the retriever: many retrieved samples fed into the generation phase are irrelevant or misleading, posing a critical bottleneck to LLMs’ performance.
To address this challenge, we introduce \textbf{VaccineRAG}, a novel Chain-of-Thought-based retrieval-augmented generation dataset. 
On one hand, VaccineRAG employs a benchmark to evaluate models using data with varying positive/negative sample ratios, systematically exposing inherent weaknesses in current LLMs. 
On the other hand, it enhances models’ sample-discrimination capabilities by prompting LLMs to generate explicit Chain-of-Thought (CoT) analysis for each sample before producing final answers.
Furthermore, to enhance the model’s ability to learn long-sequence complex CoT content, we propose \textbf{Partial-GRPO}. 
By modeling the outputs of LLMs as multiple components rather than a single whole, our model can make more informed preference selections for complex sequences, thereby enhancing its capacity to learn complex CoT.
Comprehensive evaluations and ablation studies on VaccineRAG validate the effectiveness of the proposed scheme.
SNN-Driven Event-Based Flow and Rotation Estimation with SO(3) Refinement
Authors: Ruimin Sun; Haoran Xu; De Ma
Venue: AAAI 2026 (main)

Abstract: Spiking Neural Networks (SNNs) offer a promising direction for energy-efficient event-based vision by leveraging sparse, temporally precise spikes. We propose a directly trained, fully spiking model for optical flow estimation, featuring a novel Spike GRU and membrane potential carryover for improved temporal modeling. On the DSEC-Flow benchmark, our model achieves competitive accuracy while reducing energy consumption by 42.88× over EV-FlowNet and 38× over TIDNet. Building on the predicted motion field, we infer camera rotation and, to the best of our knowledge, are the first to construct panoramic event images from SNN-based flow. We further introduce an optional unsupervised SO(3) refinement step that improves rotation accuracy by maximizing panorama consistency—without IMU or pose supervision. Our results achieve comparable visual quality to CMax-SLAM, showing that SNNs can enable fast and high-level spatial perception using only event-based input.
CoMA: Compositional Human Motion Generation with Multi-modal Agents
Authors: Shanlin Sun; Jiaqi Xu; Gabriel de Araujo; Shenghan Zhou; Hanwen Zhang; Ziheng Huang; Chenyu You; Xiaohui Xie
Venue: AAAI 2026 (main)

Abstract: 3D human motion generation has seen substantial advancement in recent years. While state-of-the-art approaches have improved performance significantly, they still struggle with complex and detailed motions unseen in training data, largely due to the scarcity of motion datasets and the prohibitive cost of generating new training examples. To address these challenges, we introduce CoMA, an agent-based solution for complex human motion generation, editing, and comprehension. CoMA leverages multiple collaborative agents powered by large language and vision models, alongside a mask transformer-based motion generator featuring body part-specific encoders and codebooks for fine-grained control. Our framework enables generation of both short and long motion sequences with detailed instructions, text-guided motion editing, and self-correction for improved quality. Evaluations on the HumanML3D dataset demonstrate competitive performance against state-of-the-art methods. Additionally, we create a set of context-rich, compositional, and long text prompts, where user studies show our method significantly outperforms existing approaches.
Bridging Granularity Gaps: Hierarchical Semantic Learning for Cross-domain Few-shot Segmentation
Authors: Sujun Sun; Haowen Gu; Cheng Xie; Yanxu Ren; Mingwu Ren; Haofeng Zhang
Venue: AAAI 2026 (main)

Abstract: Cross-domain Few-shot Segmentation (CD-FSS) aims to segment novel classes from target domains that are not involved in training and have significantly different data distributions from the source domain, using only a few annotated samples, and recent years have witnessed significant progress on this task. However, existing CD-FSS methods primarily focus on style gaps between source and target domains while ignoring segmentation granularity gaps, resulting in insufficient semantic discriminability for novel classes in target domains. Therefore, we propose a Hierarchical Semantic Learning (HSL) framework to tackle this problem. Specifically, we introduce a Dual Style Randomization (DSR) module and a Hierarchical Semantic Mining (HSM) module to learn hierarchical semantic features, thereby enhancing the model's ability to recognize semantics at varying granularities. DSR simulates target domain data with diverse foreground-background style differences and overall style variations through foreground and global style randomization respectively, while HSM leverages multi-scale superpixels to guide the model to mine intra-class consistency and inter-class distinction at different granularities. Additionally, we also propose a Prototype Confidence-modulated Thresholding (PCMT) module to mitigate segmentation ambiguity when foreground and background are excessively similar. Extensive experiments are conducted on four popular target domain datasets, and the results demonstrate that our method achieves state-of-the-art performance.