GenAI4ELab/papercli-papers huggingface dataset:
FourierPET: Deep Fourier-based Unrolled Network for Low-count PET Reconstruction
Authors: Zheng Zhang; Hao Tang; Yingying Hu; Zhanli Hu; Jing Qin
Venue: AAAI 2026 (main)

Abstract: Low-count positron emission tomography (PET) reconstruction is a challenging inverse problem due to severe degradations arising from Poisson noise, photon scarcity, and attenuation correction errors. Existing deep learning methods
typically address these in the spatial domain with an undifferentiated optimization objective, making it difficult to disentangle overlapping artifacts and limiting correction effectiveness. In this work, we perform a Fourier-domain analysis and reveal that these degradations are spectrally separable: Poisson noise and photon scarcity cause high-frequency
phase perturbations, while attenuation errors suppress low-frequency amplitude components. Leveraging this insight, we
propose FourierPET, a Fourier-based unrolled reconstruction
framework grounded in the Alternating Direction Method of
Multipliers. It consists of three tailored modules: a spectral
consistency module that enforces global frequency alignment
to maintain data fidelity, an amplitude–phase correction module that decouples and compensates for high-frequency phase
distortions and low-frequency amplitude suppression, and a
dual adjustment module that accelerates convergence during
iterative reconstruction. Extensive experiments demonstrate
that FourierPET achieves state-of-the-art performance with
significantly fewer parameters, while offering enhanced interpretability through frequency-aware correction.
Evolving Generalist Virtual Agents with Generative and Associative Memory
Authors: Zhenkui Zhang; Wendong Bu; Kaihang Pan; Bingchen Miao; Wenqiao Zhang; Guoming Wang; Wei Ji; Rui Tang; Juncheng Li; Siliang Tang
Venue: AAAI 2026 (main)

Abstract: Generalist Virtual Agents (GVAs) powered by Multimodal Large Language Models (MLLMs) exhibit impressive capabilities. However, their long-term learning is hampered by a core limitation: a failure to evolve beyond existing trajectories. This stems from memory systems that treat experiences as isolated fragments and rely on brittle semantic retrieval, preventing the synthesis of novel solutions from disparate knowledge. To address this, we introduce CA3Mem, a framework inspired by the human hippocampus that organizes experiences into a structured memory graph. Leveraging this graph, CA3Mem features two key innovations: 1) a generative memory recombination mechanism that synthesizes novel solutions to drive agent evolution, and 2) an associative retrieval algorithm that employs spreading activation to recall a comprehensive and contextually-aware set of experiences. Experiments on OSWorld and WebArena demonstrate that CA3Mem significantly enhances agent capabilities, leading to marked improvements in long-horizon planning, compositional generalization for novel tasks, and continuous adaptation from experience.
Adaptive Morph-Patch Transformer for Aortic Vessel Segmentation
Authors: Zhenxi Zhang; Fuchen Zheng; Adnan Iltaf; Yifei Han; Zhenyu Cheng; Yue Du; Bin Li; Tianyong Liu; Shoujun Zhou
Venue: AAAI 2026 (main)

Abstract: Accurate segmentation of aortic vascular structures is critical for diagnosing and treating cardiovascular diseases. Traditional Transformer-based models have shown promise in this domain by capturing long-range dependencies between vascular features. 
However, their reliance on fixed-size rectangular patches often influences the integrity of complex vascular structures, leading to suboptimal segmentation accuracy. 
To address this challenge, we propose the adaptive Morph-Patch Transformer (MPT), a novel architecture specifically designed for aortic vascular segmentation. 
Specifically, MPT introduces an adaptive patch partitioning strategy that dynamically generates morphology-aware patches aligned with complex vascular structures.
This strategy can preserve semantic integrity of complex vascular structures within individual patches.
Moreover, a Semantic Clustering Attention (SCA) method is proposed to dynamically aggregate features from various patches with similar semantic characteristics.
This method enhances the model's capability to segment vessels of varying sizes, preserving the integrity of vascular structures.
Extensive experiments on three open-source datasets (AVT, AortaSeg24 and TBAD) demonstrate that MPT achieves state-of-the-art performance, with improvements in segmenting intricate vascular structures.
GEMA-Score: Granular Explainable Multi-Agent Scoring Framework for Radiology Report Evaluation
Authors: Zhenxuan Zhang; KinHei Lee; Peiyuan Jing; Weihang Deng; Huichi Zhou; Zihao Jin; Jiahao Huang; Zhifan Gao; Dominic C. Marshall; Yingying Fang; Guang Yang
Venue: AAAI 2026 (main)

Abstract: Automatic medical report generation has the potential to support clinical diagnosis, reduce the workload of radiologists, and demonstrate potential for enhancing diagnostic consistency. However, current evaluation metrics often fail to reflect the clinical reliability of generated reports. Overlap-based methods overlook fine-grained details (e.g., location, severity), diagnostic metrics are constrained by fixed vocabularies. Some diagnostic metrics are limited by fixed vocabularies or templates, reducing their ability to capture diverse clinical expressions. LLM-based metrics lack interpretable reasoning, limiting trust in clinical settings. Therefore, we propose a Granular Explainable Multi-Agent Score (GEMA-Score) in this paper,  which conducts both objective quantification and subjective evaluation through a large language model-based multi-agent workflow. Our GEMA-Score parses structured reports and employs stable calculations through interactive exchanges of information among agents to assess disease diagnosis, location, severity, and uncertainty. Additionally, an LLM-based scoring agent evaluates completeness, readability, and clinical terminology while providing explanatory feedback. Extensive experiments show that GEMA-Score achieves the highest correlation with human experts on public datasets (Kendall = 0.69 on ReXVal; 0.45 on RadEvalX), demonstrating improved clinical scoring reliability.
D²-VPR: A Parameter-efficient Visual-foundation-model-based Visual Place Recognition Method via Knowledge Distillation and Deformable Aggregation
Authors: Zheyuan Zhang; Jiwei Zhang; Boyu Zhou; Linzhimeng Duan; Hong Chen
Venue: AAAI 2026 (main)

Abstract: Visual Place Recognition (VPR) aims to determine the geographic location of a query image by retrieving its most visually similar counterpart from a geo-tagged reference database. Recently, the emergence of the powerful visual foundation model, DINOv2, trained in a self-supervised manner on massive datasets, has significantly improved VPR performance. This improvement stems from DINOv2’s exceptional feature generalization capabilities but is often accompanied by increased model complexity and computational overhead that impede deployment on resource-constrained devices. To address this challenge, we propose D2-VPR, a Distillation- and Deformable-based framework that retains the strong feature extraction capabilities of visual foundation models while significantly reducing model parameters and achieving a more favorable performance-efficiency trade-off. Specifically, first, we employ a two-stage training strategy that integrates knowledge distillation and fine-tuning. Additionally, we introduce a Distillation Recovery Module (DRM) to better align the feature spaces between the teacher and student models, thereby minimizing knowledge transfer losses to the greatest extent possible. Second, we design a Top-Down-attention-based Deformable Aggregator (TDDA) that leverages global semantic features to dynamically and adaptively adjust the Regions of Interest (ROI) used for aggregation, thereby improving adaptability to irregular structures. Extensive experiments demonstrate that our method achieves competitive performance compared to state-of-the-art approaches. Meanwhile, it reduces the parameter count by approximately 64.2% (compared to CricaVPR).
RPE-PAD: Relative Pose Estimation for Pose-agnostic Anomaly Detection
Authors: Zhipeng Zhang; Mengzan Qi; Rongkang Ma; Yingying Fang; Guixu Zhang; Tieyong Zeng; Zhi Li
Venue: AAAI 2026 (main)

Abstract: Pose-agnostic Anomaly Detection (PAD) aims to detect anomalies when the poses of query images are unknown and differ from those in the training set. Therefore, accurately estimating the camera poses for the query images in the test set is critical for this task. Existing query-specific framework methods require re-optimizing a new set of parameters for each query image, limiting their generalization and increasing computational burden. To overcome these limitations, we propose a novel method, Relative Pose Estimation for Pose-agnostic Anomaly Detection (RPE-PAD), which enhances both generalization and efficiency with a query-independent framework. Specifically, we propose a Random View Synthesis Scheme (RVSS) that generates new poses by adding Gaussian perturbations to the original poses, then renders the corresponding views to augment the dataset. To estimate the relative camera pose between two input images, we introduce an Iterative Relative Pose Refinement Network (IRPRN), which incorporates a hierarchical coarse-to-fine refinement strategy. Furthermore, we employ a Multi-Pair Training Strategy (MPTS) to train the proposed IRPRN, leveraging multiple image pairs to expand the relative pose transformation space during training. Extensive experiments demonstrate that our method achieves robust anomaly detection performance while significantly improving inference efficiency.
Disentangling for Transfer: Boosting Limited Modalities via Information-Theoretic Regularization and Cross-Modal Reconstruction
Authors: Zhiyun Zhang; Yan-Jie Zhou; Yujian Hu; Xiyao Ma; Zhouhang Yuan; Zirui Wang; Hongkun Zhang; Minfeng Xu
Venue: AAAI 2026 (main)

Abstract: Missing critical modalities in medical imaging poses significant challenges for AI-driven diagnostic systems, particularly in scenarios where limited modalities must suffice for downstream tasks. Existing approaches often fail to fully leverage privileged features available only at training or address the information gap between privileged and limited modalities, resulting in suboptimal performance. To address this, we propose a unified, dual-stage Disentanglement-AligNmenT framEwork (DANTE), which uses InformationTheoretic Regularization and Cross-Modal Reconstruction to decompose full-modality information into alignable and privileged-exclusive components. In the first stage, a self-supervised pre-training strategy based on cross-modal reconstruction acts as a proxy task to implicitly incentivize disentangled representations. In the second stage, we present an information-theoretic regularization to explicitly maximize the transfer of privileged knowledge through two novel modules: (1) a Mutual Alignment Module that employs multilevel bidirectional alignment between limited-modality features and alignable features, enhancing cross-modal representation consistency; (2) a Privileged Compaction Module that restricts the privileged-exclusive information flow, promoting the integration of task-relevant content into alignable representations. Experimental results on three challenging medical datasets demonstrate that DANTE achieves state-of-the-art performance, demonstrating its effectiveness in leveraging privileged guidance under modality scarcity, and exhibits broad applicability across diverse medical imaging scenarios.
Simulating Distribution Dynamics: Liquid Temporal Feature Evolution for Single-Domain Generalized Object Detection
Authors: Zihao Zhang; Yang Li; Aming Wu; Yahong Han
Venue: AAAI 2026 (main)

Abstract: In this paper, we focus on Single-Domain Generalized Object Detection (Single-DGOD), aiming to transfer a detector trained on one source domain to multiple unknown domains.
Existing methods for Single-DGOD typically rely on discrete data augmentation or static perturbation methods to expand data diversity, thereby mitigating the lack of access to target domain data. However, in real-world scenarios such as changes in weather or lighting conditions, domain shifts often occur continuously and gradually. 
Discrete augmentations and static perturbations fail to effectively capture the dynamic variation of feature distributions, thereby limiting the model's ability to perceive fine-grained cross-domain differences.
To this end, we propose a new method, i.e., Liquid Temporal Feature Evolution, which simulates the progressive evolution of features from the source domain to simulated latent distributions by incorporating temporal modeling and liquid neural network–driven parameter adjustment. Specifically, we introduce controllable Gaussian noise injection and multi-scale Gaussian blurring to simulate initial feature perturbations, followed by temporal modeling and a liquid parameter adjustment mechanism to generate adaptive modulation parameters, enabling a smooth and continuous adaptation across domains.
By capturing progressive cross-domain feature evolution and dynamically regulating adaptation paths, our method bridges the source-unknown domain distribution gap, significantly boosting generalization and robustness to unseen shifts.
Significant performance improvements on the Diverse Weather dataset and Real-to-Art benchmark demonstrate the superiority of our method.
Diffusion Distillation with Direct Preference Optimization for Efficient 3D LiDAR Scene Completion
Authors: An Zhao; Shengyuan Zhang; Zejian Li; Ling Yang; Pei Chen; Jiale Wu; Haoran Xu; AnYang Wei; Perry Pengyun Gu; Lingyun Sun
Venue: AAAI 2026 (main)

Abstract: The slow sampling speed of diffusion models hinders their application in 3D LiDAR scene completion.  To address this, we propose Distillation-DPO, a novel framework that accelerates sampling through score distillation while simultaneously enhancing generation quality via preference alignment.  Distillation-DPO follows a three-step procedure. First, the student model generates paired completion scenes with different initial noises. Second, using LiDAR scene evaluation metrics as preference, we construct winning and losing sample pairs.  Third, as our core innovation, Distillation-DPO optimizes the student model by exploiting the difference in score functions between the teacher and student models on the paired completion scenes. This operation performs variational score distillation of the student model but simultaneously encourages the distilled student to prefer the winning samples over the losing ones. Extensive experiments demonstrate that Distillation-DPO achieves higher-quality scene completion than state-of-the-art diffusion models, while accelerating sampling by over 5-fold.  To our knowledge, our work is the first to integrate the preference learning principle of DPO into the distillation of diffusion models, offering a new framework of preference-aligned distillation.
CondDiff-AMO: Integrating Conditional Diffusion Mechanism for Unified Amodal Mask Generation
Authors: CaiJie Zhao; Bob Zhang
Venue: AAAI 2026 (main)

Abstract: Aiming to estimate the full extent of partially occluded objects, amodal segmentation is a critical capability for visual intelligence. Existing methods suffer from limitations in efficiency and precision, due to their reliance on auxiliary information or two-stage architectures. Furthermore, they lack generalizability, failing to meet practical requirements. To overcome these challenges, we proposed a new paradigm, CondDiff-AMO, that interprets amodal segmentation as a denoising problem by leveraging diffusion models. Methodologically, the designed novel framework consists of three key innovations to adapt the task characteristics and unlocks the diffusion models’ potential in amodal segmentation, including a masking strategy in the forward process, an adaptive transformer for conditional feature extraction, and visual-guided sampling. In the forward process, progressive masking strategy converts ground-truth masks to visible masks, simulating amodal segmentation process to enhance reasoning regarding occluded areas. For architectural design, a pyramid network with feature refinement extracts adaptive and representative conditional priors, improving the guidance in the denoising process of diffusion models. As for the sampling stage, a visible mask is incorporated with an ensemble strategy, restricting the prediction on occluded part. Experiments were conducted on five well-known datasets under supervised and zero-shot learning, with the results confirming that CondDiff-AMO outperforms state-of-the-art methods.
MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss
Authors: Can Zhao; Pengfei Guo; Dong Yang; Yufan He; Yucheng Tang; Benjamin Simon; Mason Belue; Stephanie Harmon; Baris Turkbey; Daguang Xu
Venue: AAAI 2026 (main)

Abstract: Medical image synthesis is an important topic for both clinical and research applications. Recently, diffusion models have become a leading approach in this area. Despite their strengths, many existing methods struggle with (1) limited generalizability, only working for specific body regions or voxel spacings, (2) slow inference, which is a common issue for diffusion models, and (3) weak alignment with input conditions, which is a critical issue for medical imaging. MAISI, a previously proposed framework, addresses generalizability issues but still suffers from slow inference and limited condition consistency. In this work, we present MAISI-v2, the first accelerated 3D medical image synthesis framework that integrates rectified flow to enable fast and high-quality generation. To further enhance condition fidelity, we introduce a novel region-specific contrastive loss to improve sensitivity to the region of interest. Our experiments show that MAISI-v2 can achieve state-of-the-art image quality with 33× acceleration for latent diffusion models. We also conducted a downstream segmentation experiment to show that the synthetic images can be used for data augmentation. We release our code, training details, model weights, and a GUI demo to facilitate reproducibility and promote further development within the community.
Real-time 3D Object Detection with Inference-Aligned Learning
Authors: Chenyu Zhao; Xianwei Zheng; Zimin Xia; Linwei Yue; Nan Xue
Venue: AAAI 2026 (main)

Abstract: Real-time 3D object detection from point clouds is essential for dynamic scene understanding in applications such as augmented reality, robotics, and navigation. We introduce a novel Spatial-prioritized and Rank-aware 3D object detection (SR3D) framework for indoor point clouds, to bridge the gap between how detectors are trained and how they are evaluated. This gap stems from the lack of spatial reliability and ranking awareness during training, which conflicts with the ranking-based prediction selection used at inference. Such a training-inference gap hampers the model’s ability to learn representations aligned with inference-time behavior. To address the limitation, SR3D consists of two components tailored to the spatial nature of point clouds during training: a novel spatial-prioritized optimal transport assignment that dynamically emphasizes well-located and spatially reliable samples, and a rank-aware adaptive self-distillation scheme that adaptively injects ranking perception via a self-distillation paradigm. Extensive experiments on ScanNet V2 and SUN RGB-D show that SR3D effectively bridges the training-inference gap and significantly outperforms prior methods in accuracy while maintaining real-time speed.
Learning to LEAP: Efficient Dense Point Tracking by Focusing Where It Matters
Authors: Chenzhi Zhao; Wufan Wang; Bo Zhang; Wendong Wang
Venue: AAAI 2026 (main)

Abstract: Tracking Any Point (TAP) is a foundational task in computer vision with broad applicability. The state-of-the-art self-supervised TAP method leverages a global matching transformer and contrastive random walks to learn point correspondences. However, its dense all-pairs attention and correlation volume computation tend to introduce irrelevant features and produce less informative training signals, degrading both learning efficiency and tracking accuracy. To address these limitations, we introduce LEAP-Track, a self-supervised TAP approach that computes the attention matrices and correlation volume over adaptively selected sparse pairs. It consists of two core designs: (1) Curriculum-based Sparse Attention (CSA), which dynamically focuses on the most relevant keys, promoting the learning of discriminative features; and (2) Progressive k-NN Transition (PkT), which reformulates the contrastive random walk to operate on an increasingly sparse k-NN affinity graph to reinforce the learning of the most informative correspondences. By integrating the above two designs into a two-stage training paradigm, LEAP-Track is shown both theoretically and empirically to effectively boost learning efficiency,  achieving superior tracking accuracy over existing self-supervised TAP methods.
Partially Shared Concept Bottleneck Models
Authors: Delong Zhao; Qiang Huang; Di Yan; Yiqun Sun; Jun Yu
Venue: AAAI 2026 (main)

Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by introducing a layer of human-understandable concepts between inputs and predictions. While recent methods automate concept generation using Large Language Models (LLMs) and Vision-Language Models (VLMs), they still face three fundamental challenges: poor visual grounding, concept redundancy, and the absence of principled metrics to balance predictive accuracy and concept compactness. We introduce PS-CBM, a Partially Shared CBM framework that addresses these limitations through three core components: (1) a multimodal concept generator that integrates LLM-derived semantics with exemplar-based visual cues; (2) a Partially Shared Concept Strategy that merges concepts based on activation patterns to balance specificity and compactness; and (3) Concept-Efficient Accuracy (CEA), a post-hoc metric that jointly captures both predictive accuracy and concept compactness. Extensive experiments on eleven diverse datasets show that PS-CBM consistently outperforms state-of-the-art CBMs, improving classification accuracy by 1.0%–7.4% and CEA by 2.0%–9.5%, while requiring significantly fewer concepts. These results underscore PS-CBM’s effectiveness in achieving both high accuracy and strong interpretability.
Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
Authors: Haoyu Zhao; Linghao Zhuang; Xingyue Zhao; Cheng Zeng; Haoran Xu; Yuming Jiang; Jun Cen; Kexiang Wang; Jiayan Guo; Siteng Huang; Xin Li; Deli Zhao; Hua Zou
Venue: AAAI 2026 (main)

Abstract: A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream manipulation.  To address these limitations, we propose AffordDex, a novel framework with two-stage training that learns a universal grasping policy with an inherent understanding of both motion priors and object affordances.  In the first stage, a trajectory imitator is pre-trained on a large corpus of human hand motions to instill a strong prior for natural movement. In the second stage, a residual module is trained to adapt these general human-like motions to specific object instances. This refinement is critically guided by two components: our Negative Affordance-aware Segmentation (NAA) module, which identifies functionally inappropriate contact regions, and a privileged teacher-student distillation process that ensures the final vision-based policy is highly successful. Extensive experiments demonstrate that AffordDex not only achieves universal dexterous grasping but also remains remarkably human-like in posture and functionally appropriate in contact location. As a result, AffordDex significantly outperforms state-of-the-art baselines across seen objects, unseen instances, and even entirely novel categories.