GenAI4ELab/papercli-papers huggingface dataset:
Dynamic-Static Collaboration for Unsupervised Domain Adaptive Video-Based Visible-Infrared Person Re-Identification
Authors: Jiaxu Leng; Zhengjie Wang; Shuang Li; Xinbo Gao
Venue: AAAI 2026 (main)

Abstract: Video-based visible-infrared person re-identification (VVI-ReID) aims to match pedestrian sequences across modalities for all-day surveillance. While supervised methods have shown progress, their dependence on large-scale cross-modal annotations limits scalability.
We investigate the task of unsupervised domain adaptation for VVI-ReID (UDA-VVI-ReID), where a model trained on a labeled source domain is adapted to an unlabeled target domain. Directly extending existing image-based unsupervised VI-ReID methods to video scenarios by simply averaging frame-level features is suboptimal, as this naive strategy neglects the rich temporal dynamics in video data and leads to unreliable pseudo-labels due to occlusion-induced noise.
To overcome these limitations, we propose a Dynamic-Static Collaboration (DSC) framework that explicitly leverages the complementary strengths of motion and appearance cues. The Dynamic-Static Label Unification (DSLU) module refines pseudo-labels by validating the consistency between static and dynamic predictions. Based on these labels, the Dynamic-Static Joint Learning (DSJL) module performs neighbor-aware contrastive learning in both feature spaces, promoting robust representation learning under cross-modal and temporal variations.
Experiments on HITSZ-VCM and BUPTCampus show that DSC sets a strong baseline for this new task, enabling robust cross-modal video ReID without target labels.
PIF-Net: Ill-Posed Prior Guided Multispectral and Hyperspectral Image Fusion via Invertible Mamba and Fusion-Aware LoRA
Authors: Baisong Li; Xingwang Wang; Haixiao Xu
Venue: AAAI 2026 (main)

Abstract: The goal of multispectral and hyperspectral image fusion (MHIF) is to generate high-quality images that simultaneously possess rich spectral information and fine spatial details. However, due to the inherent trade-off between spectral and spatial information and the limited availability of observations, this task is fundamentally ill-posed. Previous studies have not effectively addressed the ill-posed nature caused by data misalignment. To tackle this challenge, we propose a fusion framework named PIF-Net, which explicitly incorporates ill-posed priors to effectively fuse multispectral images and hyperspectral images. To balance global spectral modeling with computational efficiency, we design a method based on an invertible Mamba architecture that maintains information consistency during feature transformation and reconstruction, ensuring stable gradient flow and process reversibility.
Furthermore, we introduce a novel fusion module called the Fusion-Aware Low-Rank Adaptation module, which dynamically calibrates spectral and spatial features while keeping the model lightweight. Extensive experiments on multiple benchmark datasets demonstrate that PIF-Net achieves significantly better image restoration performance than current state-of-the-art methods while maintaining model efficiency.
Test-Time Preference Optimization for Image Restoration
Authors: Bingchen Li; Xin Li; Jiaqi Xu; Jiaming Guo; Wenbo Li; Renjing Pei; Zhibo Chen
Venue: AAAI 2026 (main)

Abstract: Image restoration (IR) models are typically trained to recover high-quality images using L1 or LPIPS loss. To handle diverse unknown degradations, zero-shot IR methods have also been introduced. However, existing pre-trained and zero-shot IR approaches often fail to align with human preferences, resulting in restored images that may not be favored. This highlights the critical need to enhance restoration quality and adapt flexibly to various image restoration tasks or backbones without requiring model retraining and ideally without labor-intensive preference data collection. In this paper, we propose the first Test-Time Preference Optimization (TTPO) paradigm for image restoration, which enhances perceptual quality, generates preference data on-the-fly, and is compatible with any IR model backbone. Specifically, we design a training-free, three-stage pipeline: (i) generate candidate preference images online using diffusion inversion and denoising based on the initially restored image; (ii) select preferred and dispreferred images using automated preference-aligned metrics or human feedback; and (iii) use the selected preference images as reward signals to guide the diffusion denoising process, optimizing the restored image to better align with human preferences. Extensive experiments across various image restoration tasks and models demonstrate the effectiveness and flexibility of the proposed pipeline.
Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing
Authors: Bingyu Li; Haocheng Dong; Da Zhang; Zhiyuan Zhao; Hao Sun; Junyu Gao
Venue: AAAI 2026 (main)

Abstract: Open-Vocabulary Remote Sensing Image Segmentation (OVRSIS), an emerging task that adapts Open-Vocabulary Segmentation (OVS) to the remote sensing (RS) domain, remains underexplored due to the absence of a unified evaluation benchmark and the domain gap between natural and RS images.
To bridge these gaps, we first establish a standardized OVRSIS benchmark (OVRSISBench) based on widely-used RS segmentation datasets, enabling consistent evaluation across methods. Using this benchmark, we comprehensively evaluate several representative OVS/OVRSIS models and reveal their limitations when directly applied to remote sensing scenarios.
Building on these insights, we propose RSKT-Seg, a novel open-vocabulary segmentation framework tailored for remote sensing. RSKT-Seg integrates three key components: (1) a Multi-Directional Cost Map Aggregation (RS-CMA) module that captures rotation-invariant visual cues by computing vision-language cosine similarities across multiple directions; (2) an Efficient Cost Map Fusion (RS-Fusion) transformer, which jointly models spatial and semantic dependencies with a lightweight dimensionality reduction strategy; and (3) a Remote Sensing Knowledge Transfer (RS-Transfer) module that injects pre-trained knowledge and facilitates domain adaptation via enhanced upsampling.
Extensive experiments on the benchmark show that RSKT-Seg consistently outperforms strong OVS baselines by +3.8 mIoU and +5.9 mACC, while achieving 2× faster inference through efficient aggregation.
FreLay: Frequency-aware Energy Function for Training-free Layout-to-Image Generation
Authors: Bonan Li; Yinhan Hu; Songhua Liu; Zeyu Xiao; Xinchao Wang
Venue: AAAI 2026 (main)

Abstract: Layout-to-Image generation has significantly advanced content creation by enabling the rendering of visual text under predefined spatial layouts. Current approaches achieve training-free layout guidance by constructing attention-based energy functions to derive correction gradients. In this paper, we demonstrate that vanilla energy functions suffer from two limitations, resulting in imprecise layout control and visually unrealistic artifacts. First, the normalizing factor of the Boltzmann distribution defined by the energy functions is non-negligible when calculating correction gradients, yet current energy functions cannot compute this factor exactly. Furthermore, while attention varies over time during the denoising process, existing approaches employ a fixed formulation. To address these challenges, we introduce FreLay, a novel training-free approach equipped with a frequency-aware energy function. Our method first reformulates the energy function to handle the normalization factor, enabling accurate computation of correction gradients. Simultaneously, leveraging the prior knowledge that low-frequency information deteriorates slower during noise addition, we design a time-specific energy function for each timestep from a frequency-domain perspective. Experimental results demonstrate that FreLay consistently outperforms existing state-of-the-art training-free methods by a large margin both qualitatively and quantitatively across multiple datasets.
DigimonGPT: An Evolvable Agent with Hierarchical Human-like Memory for Video Question Answering
Authors: Borui Li; Xingcai Zhang; Tianen Liu; Shuai Wang; Yun Cheng; Shuai Wang
Venue: AAAI 2026 (main)

Abstract: Video question answering (VideoQA), whose goal is to produce answers through the integration of linguistic and visual understanding, has emerged as a significant research focus. Although Large Multimodal Models (LMMs) and autonomous agent methods have achieved notable advances in VideoQA, excessive computational overhead and restricted multimodal interaction capabilities limit their ability to facilitate the continuous evolution of the VideoQA system. To address the challenge, we introduce DigimonGPT, an evolvable VideoQA agent inspired by cognitive psychology. Specifically, DigimonGPT integrates a multimodal memory mechanism to achieve the continuous evolution of VideoQA systems.  An intra-video declarative memory contains fundamental features of the video and semantic contexts extracted from historical QA pairs. Another inter-task procedural memory encodes task-solving experience for further question answering. Additionally, we introduce a hierarchical memory replay mechanism for VideoQA that selects appropriate memories by their relevance and question complexity. Extensive experiments demonstrate that DigimonGPT's accuracy averagely outperforms 13.71% on NExT-QA datasets and 9.89% on Intent-QA datasets over LMM and autonomous agents.
MR-COSMO: Visual-Text Memory Recall and Direct CrOSs-MOdal Alignment Method for Query-Driven 3D Segmentation
Authors: Chade Li; Pengju Zhang; Yihong Wu
Venue: AAAI 2026 (main)

Abstract: The rapid advancement of vision-language models (VLMs) in 3D domains has accelerated research in text-query-guided point cloud processing, though existing methods underperform in point-level segmentation due to inadequate 3D-text alignment that limits local feature-text context linking. To address this limitation, we propose MR-COSMO, a Visual-Text Memory Recall and Direct CrOSs-MOdal Alignment Method for Query-Driven 3D Segmentation, establishing explicit alignment between 3D point clouds and text/2D image data through a dedicated direct cross-modal alignment module while implementing a visual-text memory module with specialized feature banks. This direct alignment mechanism enables precise fusion of geometric and semantic features, while the memory module employs specialized banks storing text features, visual features, and their correspondence mappings to dynamically enhance scene-specific representations via attention-based knowledge recall. Comprehensive experiments across 3D instruction, reference, and semantic segmentation benchmarks confirm state-of-the-art performance.
Exploring Surround-View Fisheye Camera 3D Object Detection
Authors: Changcai Li; Wenwei Lin; Zuoxun Hou; Gang Chen; Wei Zhang; Huihui Zhou; Weishi Zheng
Venue: AAAI 2026 (main)

Abstract: In this work, we explore the technical feasibility of implementing end-to-end 3D object detection (3DOD) with surround-view fisheye camera system. Specifically, we first investigate the performance drop incurred when transferring classic pinhole-based 3D object detectors to fisheye imagery. To mitigate this, we then develop two methods that incorporate the unique geometry of fisheye images into mainstream detection frameworks: one based on the bird's-eye-view (BEV) paradigm, named FisheyeBEVDet, and the other on the query-based paradigm, named FisheyePETR. Both methods adopt spherical spatial representations to effectively capture fisheye geometry. In light of the lack of dedicated evaluation benchmarks, we release Fisheye3DOD, a new open dataset synthesized using CARLA and featuring both standard pinhole and fisheye camera arrays. Experiments on Fisheye3DOD demonstrate that our fisheye-compatible modeling improves accuracy by up to 6.2% compared to baseline methods.
Polarization Uncertainty-Guided Diffusion Model for Color Polarization Image Demosaicking
Authors: Chenggong Li; Yidong Luo; Junchao Zhang; Degui Yang
Venue: AAAI 2026 (main)

Abstract: Color polarization demosaicking (CPDM) aims to reconstruct full-resolution polarization images of four directions from the color-polarization filter array (CPFA) raw image. Due to the challenge of predicting numerous missing pixels and the scarcity of high-quality training data, existing network-based methods, despite effectively recovering scene intensity information, still exhibit significant errors in reconstructing polarization characteristics (degree of polarization, DOP, and angle of polarization, AOP). To address this problem, we introduce the image diffusion prior from text-to-image (T2I) models to overcome the performance bottleneck of network-based methods, with the additional diffusion prior compensating for limited representational capacity caused by restricted data distribution. To effectively leverage the diffusion prior, we explicitly model the polarization uncertainty during reconstruction and use uncertainty to guide the diffusion model in recovering high error regions. Extensive experiments demonstrate that the proposed method accurately recovers scene polarization characteristics with both high fidelity and strong visual perception.
Dual-Teacher Interactive Knowledge Distillation Network for Text-to-Visible & Infrared Person Retrieval
Authors: Chenglong Li; Zhengyu Chen; Yifei Deng; Aihua Zheng
Venue: AAAI 2026 (main)

Abstract: Text-to-visible & infrared person retrieval aims to retrieve the corresponding visible (RGB) and thermal infrared (TIR) images given the text descriptions. Existing methods perform semantic decoupling by aligning RGB and TIR features separately to different attributes, thereby facilitating the alignment between the fused multimodal representation and the text. However, insufficient TIR representation ability and cross-view representation capabilities of RGB and TIR modalities limit the retrieval accuracy and robustness. To address these issues, we propose a novel Dual-teacher Interactive Knowledge Distillation Network called DIKDNet, which performs the interactive knowledge distillation between two modality-specific teachers with rich cross-view representation capabilities to enhance TIR representations and the collaborative knowledge distillation from both teachers to the corresponding students to enhance the cross-modal cross-view representations, for robust text-to-visible & infrared person retrieval. Specifically, to enhance the representation ability of the TIR backbone network while preserving modality-specific characteristics, we design an Interactive Knowledge Distillation Module (IKDM), which introduces a boundary-constrained distillation strategy between RGB and TIR backbones, to transfer the semantic features of RGB backbone to TIR one. To enhance the cross-modal cross-view representation capability, we design a Collaborative Knowledge Distillation Module (CKDM) to transfer the cross-modal similarity relations and the cross-view multimodal representations from teacher networks to student ones. Experimental results demonstrate that our method consistently achieves significant performance gains on both the RGBT-PEDES and RGBNT201-PEDES datasets. The code will be released upon the acceptance.
MotivDance: Fine-Grained Text-Guided Motivation Choreography with Music Synchronization
Authors: Chenguang Li; Yu-Hui Wen; Liping Jing
Venue: AAAI 2026 (main)

Abstract: Realistic choreography demands simultaneous attention to rhythm and motivation. Prevailing automated dance generation methods mainly depend on musical input, overlooking the motivations that drive meaningful dance creation. Inspired by the motivation choreography, we aim to articulate dance motivations through textual guidance. However, the absence of high-quality datasets concurrently containing music, textual descriptions, and motion data presents a challenge in achieving accurate fine-grained textual control. To address this limitation, we present MotivDance, a novel framework integrating fine-grained textual guidance with music to synthesize semantically coherent dance sequences. Our approach first synthesizes text-guided key poses as motivations. We then introduce an Adaptive Keyframe Locator that dynamically positions these motivations within the musical context through beat-aware synchronization and cross-modal latent space alignment. Finally, a Transformer-based U-Net diffusion model performs the motion in-betweening while preserving motivational integrity. Extensive qualitative and quantitative experiments demonstrate that MotivDance effectively integrates music with fine-grained text control to generate high-fidelity dance motions.
RSOD: Reliability-Guided Sonar Image Object Detection with Extremely Limited Labels
Authors: Chengzhou Li; Ping Guo; Guanchen Meng; Qi Jia; Jinyuan Liu; Zhu Liu; Xiaokang Liu; Yu Liu; Zhongxuan Luo; Xin Fan
Venue: AAAI 2026 (main)

Abstract: Object detection in sonar images is a key technology in underwater detection systems. Compared to natural images, sonar images contain fewer texture details and are more susceptible to noise, making it difficult for non-experts to distinguish subtle differences between classes. This leads to their inability to provide precise annotation data for sonar images. Therefore, designing effective object detection methods for sonar images with extremely limited labels is particularly important. To address this, we propose a teacher-student framework called RSOD, which aims to fully learn the characteristics of sonar images and develop a pseudo-label strategy suitable for these images to mitigate the impact of limited labels. First, RSOD calculates a reliability score by assessing the consistency of the teacher's predictions across different views. To leverage this score, we introduce an object mixed pseudo-label method to tackle the shortage of labeled data in sonar images. Finally, we optimize the performance of the student by implementing a reliability-guided adaptive constraint. By taking full advantage of unlabeled data, the student can perform well even in situations with extremely limited labels. Notably, on the UATD dataset, our method, using only 5% of labeled data, achieves results that can compete against those of our baseline algorithm trained on 100% labeled data. We also collected a new dataset to provide more valuable data for research in the field of sonar.
Learning a Fix and Explore Framework for Continuous Generalized Category Discovery
Authors: Chunming Li; Shidong Wang; Haofeng Zhang
Venue: AAAI 2026 (main)

Abstract: To address the limitations of transductive learning in evolving real-world scenarios where unknown categories may continuously emerge, Continual Generalized Category Discovery (C-GCD) presents a novel paradigm that extends conventional category discovery frameworks. Unlike traditional static learning environments, C-GCD requires models to incrementally discover novel categories across multiple operational phases while maintaining discrimination capabilities for previously learned classes, posing significant challenges in balancing stability and plasticity. Prior approaches typically employ parameter-level knowledge distillation from historical models to alleviate catastrophic forgetting, which effectively preserves prior knowledge and optimizes computational efficiency. However, our analysis reveals that the persistent availability of samples from previous stages enables more sophisticated knowledge preservation strategies. Specifically, we present a Fix and Explore strategy that employs distinct learning methodologies for different types of potential data, aiming to preserve the features of old categories as much as possible and gradually exploring the potential distribution of new class latent spaces, we can enhance the model's ability to discover novel categories. This paper investigates this effect and introduces a novel heuristic paradigm to solve the C-GCD problem, called Fix and Explore (FaE), which aims to provide sufficient imaginative space for new classes while preserving the classification ability for old tasks. We conducted experiments across multiple datasets and performed detailed comparisons. The results demonstrate that our method achieves state-of-the-art performance at each stage across all datasets.
Refine3D: Scene-Adaptive Reference Point Refinement for Sparse 3D Object Detection
Authors: Fan Li; Jing Lu; Yunlu Xu; Changhong Wu; Tao Xu; Zhaoyi Xiang; Yi Niu
Venue: AAAI 2026 (main)

Abstract: Sparse query-based detectors have emerged as the dominant paradigm in camera-only 3D object detection, owing to their exceptional performance and computational efficiency. 
A central component of these approaches is the use of reference points, which serve as learnable spatial anchors to guide queries in localizing target objects. 
However, existing methods typically employ a unified set of reference points across all scenes, a design we find suboptimal for handling complex scenarios with highly imbalanced object distributions, such as road intersections or occluded environments.
In this paper, we investigate the adaptability of reference points and propose Refine3D, an adaptive refinement mechanism that achieves scene-level alignment between the distribution of reference points and ground-truth objects. 
In particular, we introduce a novel Reference Point Distribution Loss (RPD-Loss) to ensure reference points converge globally toward object positions, and a Scene-Adaptive Refinement head (SAR-Head) that predicts dynamic offsets for each reference point. 
Both components can be seamlessly integrated into mainstream sparse detectors. 
Extensive experiments on two challenging autonomous driving datasets demonstrate that Refine3D outperforms the state-of-the-art with improved detection accuracy and robustness.
Ambiguity-aware Truncated Flow Matching for Ambiguous Medical Image Segmentation
Authors: Fanding Li; Xiangyu Li; Xianghe Su; Xingyu Qiu; Suyu Dong; Wei Wang; Kuanquan Wang; Gongning Luo; Shuo Li
Venue: AAAI 2026 (main)

Abstract: A simultaneous enhancement of accuracy and diversity of predictions remains a challenge in ambiguous medical image segmentation (AMIS) due to the inherent trade-offs. While truncated diffusion probabilistic models (TDPMs) hold strong potential with a paradigm optimization, existing TDPMs suffer from entangled accuracy and diversity of predictions with insufficient fidelity and plausibility. To address the aforementioned challenges, we propose Ambiguity-aware Truncated Flow Matching (ATFM), which introduces a novel inference paradigm and dedicated model components. Firstly, we propose Data-Hierarchical Inference, a redefinition of AMIS-specific inference paradigm, which enhances accuracy and diversity at data-distribution and data-sample level, respectively, for an effective disentanglement. Secondly, Gaussian Truncation Representation (GTR) is introduced to enhance both fidelity of predictions and reliability of truncation distribution, by explicitly modeling it as a Gaussian distribution at Ttrunc instead of using sampling-based approximations. Thirdly, Segmentation Flow Matching (SFM) is proposed to enhance the plausibility of diverse predictions by extending semantic-aware flow transformation in Flow Matching (FM). Comprehensive evaluations on LIDC and ISIC3 datasets demonstrate that ATFM outperforms SOTA methods and simultaneously achieves a more efficient inference. ATFM improves GED and HM-IoU by up to 12% and 7.3% compared to advanced methods.