GenAI4ELab/papercli-papers huggingface dataset:
Ultralight Polarity-Split Neuromorphic SNN for Event-Stream Super-Resolution
Authors: Chuanzhi Xu; Haoxian Zhou; Langyi Chen; Yuk Ying Chung; Qiang Qu
Venue: AAAI 2026 (main)

Abstract: Event cameras offer unparalleled advantages such as high temporal resolution, low latency, and high dynamic range. However, their limited spatial resolution poses challenges for fine-grained perception tasks. In this work, we propose an ultra-lightweight, stream-based event-to-event super-resolution method based on Spiking Neural Networks (SNNs), designed for real-time deployment on resource-constrained devices. To further reduce model size, we introduce a novel Dual-Forward Polarity-Split Event Encoding strategy that decouples positive and negative events into separate forward paths through a shared SNN. Furthermore, we propose a Learnable Spatio-temporal Polarity-aware Loss (LearnSTPLoss) that adaptively balances temporal, spatial, and polarity consistency using learnable uncertainty-based weights. Experimental results demonstrate that our method achieves competitive super-resolution performance on multiple datasets while significantly reducing model size and inference time. The lightweight design enables embedding the module into event cameras or using it as an efficient front-end preprocessing for downstream vision tasks.
BAT: Learning Event-based Optical Flow with Bidirectional Adaptive Temporal Correlation
Authors: Gangwei Xu; Haotong Lin; Zhaoxing Zhang; Hongcheng Luo; Haiyang Sun; Xin Yang
Venue: AAAI 2026 (main)

Abstract: Event cameras deliver visual information characterized by a high dynamic range and high temporal resolution, offering significant advantages in estimating optical flow for complex lighting conditions and fast-moving objects. Current advanced optical flow methods for event cameras largely adopt established image-based frameworks. However, the spatial sparsity of event data limits their performance. In this paper, we present BAT, an innovative framework that estimates event-based optical flow using bidirectional adaptive temporal correlation. BAT includes three novel designs: 1) a bidirectional temporal correlation that transforms bidirectional temporally dense motion cues into spatially dense ones, enabling accurate and spatially dense optical flow estimation; 2) an adaptive temporal sampling strategy for maintaining temporal consistency in correlation; 3) spatially adaptive temporal motion aggregation to efficiently and adaptively aggregate consistent target motion features into adjacent motion features while suppressing inconsistent ones. Our BAT achieves
state-of-the-art performance on the DSEC-Flow benchmark,
outperforming existing methods by a large margin while also
exhibiting sharp edges and high-quality details. Our BAT can
accurately predict future optical flow using only past events,
significantly outperforming E-RAFT’s warm-start approach.
Fine-Grained Representation for Lane Topology Reasoning
Authors: Guoqing Xu; Yiheng Li; Yang Yang
Venue: AAAI 2026 (main)

Abstract: Precise modeling of lane topology is essential for autonomous driving, as it directly impacts navigation and control decisions. Existing methods typically represent each lane with a single query and infer topological connectivity based on the similarity between lane queries.
However, this kind of design struggles to accurately model complex lane structures, leading to unreliable topology prediction. In this view, we propose a Fine-Grained lane topology reasoning framework (TopoFG). It divides the procedure from bird’s-eye-view (BEV) features to topology prediction via fine-grained queries into three phases, i.e., Hierarchical Prior Extractor (HPE), Region-Focused Decoder (RFD), and Robust Boundary-Point Topology Reasoning (RBTR). Specifically, HPE extracts global spatial priors from the BEV mask and local sequential priors from in-lane keypoint sequences to guide subsequent fine-grained query modeling. RFD constructs fine-grained queries by integrating the spatial and sequential priors. It then samples reference points in RoI regions of the mask and applies cross-attention with BEV features to refine the query representations of each lane. RBTR models lane connectivity based on boundary-point query features and further employs a topological denoising strategy to reduce matching ambiguity. By integrating spatial and sequential priors into fine-grained queries and applying a denoising strategy to boundary-point topology reasoning, our method precisely models complex lane structures and delivers trustworthy topology predictions. Extensive experiments on the OpenLane-V2 benchmark demonstrate that TopoFG achieves new state-of-the-art performance, with an OLS of 48.0% on subset_A and 45.4% on subset_B.
MoEG-HOI: Mixture of Expert Groups for One-Stage Hand-Object Interaction Motion Generation with Hand-Finger-Joint Semantic Guidance
Authors: Hang Xu; Yang Xiao; Changlong Jiang; Haohong Kuang; Kaidi Zhang; Min Du; Ran Wang
Venue: AAAI 2026 (main)

Abstract: In this paper, MoEG-HOI is proposed as a novel method for the challenging 3D hand-object interaction (HOI) motion generation task, by introducing Mixture-of-Experts (MoE) to this field for the first time. Almost all the mainstream approaches in HOI motion generation leverage diffusion model as its strong generative ability. Nevertheless, due to HOI’s fine-grained property, well training diffusion in one-stage way is actually not trivial. Existing state-of-the-art (SOTA) methods (e.g.,Text2HOI and MF-MDM) alleviate this mainly via a coarse-to-fine, multi-stage paradigm. Although effective and practical, this paradigm prevents end-to-end training for optimal performance. In contrast, MoEG-HOI applies MoE to address this in one-stage way, with end-to-end training ability. This allows each expert to specialize in certain distinct HOI patterns, which alleviates individual expert’s training difficulty. However, intuitively applying MoE is not optimal due to the issues of: (1) towards expert design, original MoE cannot well characterize hand’s articulated structure at the levels of hand, finger, and joint explicitly, and (2) for expert routing mechanism, the characteristics of variational HOI action classes and diffusion noise levels have not been concerned. Towards the first problem, MoE’s experts are designed into groups that correspond to motion generation for hand, finger, and joint respectively, under the semantic guidance from global to local. To facilitate this, HOI’s text description will be correspondingly refined at Hand-Finger-Joint levels using LLM. Secondly, during MoE routing, the information of HOI’s action label and diffusion noise level is concerned to select experts jointly, to better reveal actions’ inter-class variation and dynamics of diffusion generation. SOTA performance on ARCTIC, GRAB and H2O datasets demonstrates the effectiveness of our method.
TRT: Harnessing Tensor Ring Transformer for Hyperspectral Image Super-Resolution
Authors: Honghui Xu; Junwei Zhu; Yubin Gu; Yueqian Quan; Chuangjie Fang; Hong Qiu; Jianwei Zheng
Venue: AAAI 2026 (main)

Abstract: Deep unfolding networks (DUNs) have recently emerged as a promising approach for hyperspectral image super-resolution (HSISR) by combining the benefits of nonlinear deep learning architectures with interpretable optimization techniques. Despite their advantages, current DUNs face significant challenges, particularly in approximating degradation matrices across both spatial and spectral dimensions, which results in complex and cumbersome model construction. By analyzing the difference between the upsampled low-resolution hyperspectral images (LRHS) and the true target image, we observed that the residual image exhibits strong sparsity, akin to noise. Leveraging this insight, we reformulate the HSISR problem as a robust principal component analysis (RPCA)-based denoising task, effectively eliminating the need for the complex approximation of spatial degradation matrix and its transpose. In addition, we introduce a Tensor Ring Transformer based on multilinear products as the prior term, wherein tokens are mapped to a tensor ring factor domain and the traditional dot product is replaced with a multilinear tensor ring product. This significantly reduces the computational complexity of the Transformer model, from \( \mathcal{O}(N^2d) \) to \( \mathcal{O}(Nr^2) \), with \( r<
MCMoE: Completing Missing Modalities with Mixture of Experts for Incomplete Multimodal Action Quality Assessment
Authors: Huangbiao Xu; Huanqi Wu; Xiao Ke; Junyi Wu; Rui Xu; Jinglin Xu
Venue: AAAI 2026 (main)

Abstract: Multimodal Action Quality Assessment (AQA) has recently emerged as a promising paradigm. By leveraging complementary information across shared contextual cues, it enhances the discriminative evaluation of subtle intra-class variations in highly similar action sequences. However, partial modalities are frequently unavailable at the inference stage in reality. The absence of any modality often renders existing multimodal models inoperable. Furthermore, it triggers catastrophic performance degradation due to interruptions in cross-modal interactions. To address this issue, we propose a novel Missing Completion Framework with Mixture of Experts (MCMoE) that unifies unimodal and joint representation learning in single-stage training. Specifically, we propose an adaptive gated modality generator that dynamically fuses available information to reconstruct missing modalities. We then design modality experts to learn unimodal knowledge and dynamically mix the knowledge of all experts to extract cross-modal joint representations. With a mixture of experts, missing modalities are further refined and complemented. Finally, in the training phase, we mine the complete multimodal features and unimodal expert knowledge to guide modality generation and generation-based joint representation extraction. Extensive experiments demonstrate that our MCMoE achieves state-of-the-art results in both complete and incomplete multimodal learning on three public AQA benchmarks.
S2-UniSeg: Fast Universal Agglomerative Pooling for Scalable Segment Anything Without Supervision
Authors: Huihui Xu; Jin Ye; Hongqiu Wang; Changkai Ji; Jiashi Lin; Ming Hu; Ziyan Huang; Ying Chen; Chenglong Ma; Tianbin Li; Lihao Liu; Junjun He; Lei Zhu
Venue: AAAI 2026 (main)

Abstract: Recent self-supervised image segmentation models have achieved promising performance on semantic segmentation and class-agnostic instance segmentation.  
However, their pretraining schedule is multi-stage, requiring a time-consuming pseudo-masks generation process between each training epoch.  
This time-consuming offline process not only makes it difficult to scale with training dataset size, but also leads to sub-optimal solutions due to its discontinuous optimization routine.  
To solve these, we first present a novel pseudo-mask algorithm, Fast Universal Agglomerative Pooling (UniAP).  Each layer of UniAP can identify groups of similar nodes in parallel, allowing to generate both semantic-level and instance-level and multi-granular pseudo-masks within ens of milliseconds for one image.  
Based on the fast UniAP, we propose the Scalable Self-Supervised Universal Segmentation (S2-UniSeg), which employs a student and a momentum teacher for continuous pretraining.  
A novel segmentation-oriented pretext task, Query-wise Self-Distillation (QuerySD), is proposed to pretrain S2-UniSeg to learn the local-to-global correspondences.  
Under the same setting, S2-UniSeg outperforms the SOTA UnSAM model, achieving notable improvements of AP+6.9 on COCO, AR+11.1 on UVO, PixelAcc+4.5 on COCOStuff-27, RQ+8.0 on Cityscapes. After scaling up to a larger 2M-image subset of SA-1B, S2-UniSeg further achieves performance gains on all four benchmarks.
PulseMind: A Multi-Modal Medical Model for Real-World Clinical Diagnosis
Authors: Jiao Xu; Junwei Liu; Jiangwei Lao; Qi Zhu; Yunpeng Zhao; Congyun Jin; Shinan Liu; Zhihong Lu; Lihe Zhang; Xin Chen; Jian Wang; Ping Wang
Venue: AAAI 2026 (main)

Abstract: Recent advances in medical multi-modal models focus on specialized image analysis like dermatology, pathology, or radiology. However, they do not fully capture the complexity of real-world clinical diagnostics, which involve heterogeneous inputs and require ongoing contextual understanding during patient-physician interactions.
To bridge this gap, we introduce PulseMind, a new family of multi-modal diagnostic models that integrates a systematically curated dataset, a comprehensive evaluation benchmark, and a tailored training framework. Specifically, we first construct a diagnostic dataset, MediScope, which comprises 98,000 real-world multi-turn consultations and 601,500 medical images, spanning over 10 major clinical departments and more than 200 sub-specialties. Then, to better reflect the requirements of real-world clinical diagnosis, we develop the PulseMind Benchmark, a multi-turn diagnostic consultation benchmark with a four-dimensional evaluation protocol comprising proactiveness, accuracy, usefulness, and language quality. Finally, we design a training framework tailored for multi-modal clinical diagnostics, centered around a core component named Comparison-based Reinforcement Policy Optimization (CRPO). Compared to absolute score rewards, CRPO uses relative preference signals from multi-dimensional comparisons to provide stable and human-aligned training guidance. Extensive experiments demonstrate that PulseMind achieves competitive performance on both the diagnostic consultation benchmark and public medical benchmarks.
VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation
Authors: Jiazheng Xu; Yu Huang; Jiale Cheng; Yuanming Yang; Jiajun Xu; Yuan Wang; Wenbo Duan; Shen Yang; Qunlin Jin; Shurun Li; Jiayan Teng; Zhuoyi Yang; Wendi Zheng; Xiao Liu; Dan Zhang; Ming Ding; Xiaohan Zhang; Shiyu Huang; Xiaotao Gu; Minlie Huang; Jie Tang; Yuxiao Dong
Venue: AAAI 2026 (main)

Abstract: Visual generative models have achieved remarkable progress in synthesizing photorealistic images and videos, yet aligning their outputs with human preferences across critical dimensions remains a persistent challenge.
Though reinforcement learning from human feedback offers promise for preference alignment, existing reward models for visual generation face limitations, including black-box scoring without interpretability and potentially resultant unexpected biases.
We present VisionReward, a general framework for learning human visual preferences in both image and video generation.
Specifically, we employ a hierarchical visual assessment framework to capture fine-grained human preferences, and leverages linear weighting to enable interpretable preference learning.
Furthermore, we propose a multi-dimensional consistent strategy when using VisionReward as a reward model during preference optimization for visual generation.
Experiments show that VisionReward can significantly outperform existing image and video reward models on both machine metrics and human evaluation.
Notably, VisionReward surpasses VideoScore by 17.2% in preference prediction accuracy, and text-to-video models with VisionReward achieve a 31.6% higher pairwise win rate compared to the same models using VideoScore.
Identity-Aware Vision-Language Model for Explainable Face Forgery Detection
Authors: Junhao Xu; Jingjing Chen; Yang Jiao; Jiacheng Zhang; Zhiyu Tan; Hao Li; Yu-Gang Jiang
Venue: AAAI 2026 (main)

Abstract: Recent advances in generative artificial intelligence have enabled the creation of highly realistic image forgeries, raising significant concerns about digital media authenticity. While existing detection methods demonstrate promising results on benchmark datasets, they face critical limitations in real-world applications. First, existing detectors typically fail to detect semantic inconsistencies with the person’s identity, such as implausible behaviors or incompatible environmental contexts in given images. Second, these methods rely heavily on low-level visual cues, making them effective for known forgeries but less reliable against new or unseen manipulation techniques. To address these challenges, we present a novel personalized vision-language model (VLM) that integrates low-level visual artifact analysis and high-level semantic inconsistency detection. Unlike previous VLM-based methods, our approach avoids resource-intensive supervised fine-tuning that often struggles to preserve distinct identity characteristics. Instead, we employ a lightweight method that dynamically encodes identity-specific information into specialized identifier tokens. This design enables the model to learn distinct identity characteristics while maintaining robust generalization capabilities. We further enhance detection capabilities through a lightweight detection adapter that extracts fine-grained information from shallow features of the vision encoder, preserving critical low-level evidence. Comprehensive experiments demonstrate that our approach achieves 94.25% accuracy and 94.08% F1 score, outperforming both traditional forgery detectors and general VLMs while requiring only 10 extra tokens.
EccoMamba: Enhanced Cross-hierarchical Continuity Orthogonal Mamba for Medical Image Segmentation
Authors: Junlin Xu; Jincan Li; Feifei Cui; Zhuang Zhang; Jialiang Yang; Shuting Jin; Qiangguo Jin; Yajie Meng
Venue: AAAI 2026 (main)

Abstract: Medical image segmentation plays a crucial role in clinical diagnosis, lesion quantification, and preoperative planning. However, existing Mamba-based architectures, which rely on fixed-direction sequence modeling and flatten images into one-dimensional (1D) sequences, struggle to capture hierarchical anatomical features and spatial dependencies, thereby limiting their representational capacity for complex medical structures. To address these limitations, we propose EccoMamba (Enhanced Cross-hierarchical Continuity Orthogonal Mamba), a U-shaped encoder--decoder framework designed for medical image segmentation. In the encoder's downsampling path, we introduce a Hierarchical Aggregation Enhancement (HAE) module that integrates multi-scale convolutions with hierarchical attention mechanisms. The attention branch further incorporates cross-channel interactions, allowing the model to selectively enhance semantically relevant features while suppressing irrelevant background responses. For skip connections, we design a Structural Continuity Orthogonal (SCO) module to preserve spatial continuity by modeling cross-dimensional dependencies via orthogonal Axial Shifts (AS), thereby mitigating directional bias and improving anatomical consistency. Extensive experiments on four benchmark datasets---ISIC 2018, ISIC 2017, Synapse, and ACDC---show that EccoMamba consistently outperforms state-of-the-art methods in both segmentation accuracy and structural fidelity.
DeFT-LoRA: Decoupled and Fused Tuning with LoRA Experts for Universal Cross-Domain Retrieval
Authors: Ke Xu; Xiaozheng Shen; Shanshan Wang; Mengzhu Wang; Xun Yang
Venue: AAAI 2026 (main)

Abstract: Universal Cross-Domain Retrieval (UCDR) aims to retrieve images across unseen domains and categories, a critical capability for real-world applications. While large-scale Vision-Language Models (VLMs) like CLIP offer strong zero-shot category generalization, they struggle with domain shifts. Existing methods often improve domain robustness at the cost of high computational overhead or by compromising the VLM's inherent knowledge. To address this, we propose Decoupled and Fused Tuning with LoRA (DeFT-LoRA), a novel and parameter-efficient framework that integrates Low-Rank Adaptation (LoRA) with a Mixture-of-Experts (MoE) mechanism. This approach resolves the intrinsic conflict between domain-invariant and domain-specific knowledge in a single adapter, enabling our model to construct a domain adapters for each input image. We propose a three-stage training strategy, which first learns a shared Base LoRA for domain-invariant features, then derives Domain-Specific Experts to capture specific styles, and finally fuses them dynamically with a lightweight gating network. Extensive experiments on three UCDR benchmarks demonstrate that DeFT-LoRA achieves comparable or superior performance to state-of-the-art methods while requiring only 1.46 percent of CLIP's image-encoder parameters and reducing computational overhead, thereby establishing an exceptional balance between accuracy and efficiency.
RealRep: Generalized SDR-to-HDR Conversion via Attribute-Disentangled Representation Learning
Authors: Li Xu; Siqi Wang; Kepeng Xu; Lin Zhang; Gang He; Weiran Wang; Yu-Wing Tai
Venue: AAAI 2026 (main)

Abstract: High-Dynamic-Range Wide-Color-Gamut (HDR-WCG) technology is becoming increasingly widespread, driving a growing need for converting Standard Dynamic Range (SDR) content to HDR. Existing methods primarily rely on fixed tone mapping operators, which struggle to handle the diverse appearances and degradations commonly present in real-world SDR content. To address this limitation, we propose a generalized SDR-to-HDR framework that enhances robustness by learning attribute-disentangled representations. Central to our approach is Realistic Attribute-Disentangled Representation Learning (RealRep), which explicitly disentangles luminance and chrominance components to capture intrinsic content variations across different SDR distributions. Furthermore, we design a Luma-/Chroma-aware negative exemplar generation strategy that constructs degradation-sensitive contrastive pairs, effectively modeling tone discrepancies across SDR styles. Building on these attribute-level priors, we introduce the Degradation-Domain Aware Controlled Mapping Network (DDACMNet), a lightweight, two-stage framework that performs adaptive hierarchical mapping guided by a control-aware normalization mechanism. DDACMNet dynamically modulates the mapping process via degradation-conditioned features, enabling robust adaptation across diverse degradation domains. Extensive experiments demonstrate that RealRep consistently outperforms state-of-the-art methods in both generalization and perceptually faithful HDR color gamut reconstruction.
Heterogeneous Complementary Distillation
Authors: Liuchi Xu; Hao Zheng; Lu Wang; Lisheng Xu; Jun Cheng
Venue: AAAI 2026 (main)

Abstract: Knowledge distillation (KD) transfers the ``dark knowledge'' from a complex teacher model to a compact student model. However, heterogeneous architecture distillation, such as Vision Transformer (ViT) to ResNet18, faces challenges due to differences in spatial feature representations. Traditional KD methods are mostly designed for homogeneous architectures and hence struggle to effectively address the disparity. Although heterogeneous KD approaches have been developed recently to solve these issues, they often incur high computational costs and complex designs, or overly rely on logit alignment, which limits their ability to leverage the complementary features. To overcome these limitations, we propose Heterogeneous Complementary Distillation (HCD), a simple yet effective framework that integrates complementary teacher and student features to align representations in shared logits. These logits are decomposed and constrained to facilitate diverse knowledge transfer to the student. Specifically, HCD processes the student’s intermediate features through convolutional projector and adaptive pooling, concatenates them with teacher's feature from the penultimate layer and then maps them via the Complementary Feature Mapper (CFM) module, comprising fully connected layer, to produce shared logits.  We further introduce Sub-logit Decoupled Distillation (SDD) that partitions the shared logits into n sub-logits, which are fused with teacher's logits to rectify classification. To ensure sub-logit diversity and reduce redundant knowledge transfer, we propose an Orthogonality Loss (OL). By preserving student-specific strengths and leveraging teacher knowledge, HCD enhances robustness and generalization in students. Extensive experiments on the CIFAR-100, fine-grained (e.g., CUB200, Aircraft) and ImageNet-1K datasets demonstrate that HCD outperforms state-of-the-art KD methods, establishing it as an effective solution for heterogeneous KD.