GenAI4ELab/papercli-papers huggingface dataset:
RAPTOR: Real-Time High-Resolution UAV Video Prediction with Efficient Video Attention
Authors: Zhan Chen; Zile Guo; Enze Zhu; Peirong Zhang; Xiaoxuan Liu; Lei Wang; Yidan Zhang
Venue: AAAI 2026 (main)

Abstract: Video prediction is plagued by a fundamental trilemma: achieving high-resolution and perceptual quality typically comes at the cost of real-time speed, hindering its use in latency-critical applications. This challenge is most acute for autonomous UAVs in dense urban environments, where foreseeing events from high-resolution imagery is non-negotiable for safety. Existing methods, reliant on iterative generation (diffusion, autoregressive models) or quadratic-complexity attention, fail to meet these stringent demands on edge hardware. To break this long-standing trade-off, we introduce RAPTOR, a video prediction architecture that achieves real-time, high-resolution performance. RAPTOR’s single-pass design avoids the error accumulation and latency of iterative approaches. Its core innovation is Efficient Video Attention (EVA), a novel translator module that factorizes spatiotemporal modeling. Instead of processing flattened spacetime tokens with O((ST)^2) or O(ST) complexity, EVA alternates operations along the spatial (S) and temporal (T) axes. This factorization reduces the time complexity to O(S + T) and memory complexity to O(max(S, T)), enabling global context modeling at 512^2 resolution and beyond, operating directly on dense feature maps with a patch-free design. Complementing this architecture is a 3-stage training curriculum that progressively refines predictions from coarse structure to sharp, temporally coherent details. Experiments show RAPTOR is the first predictor to exceed 30 FPS on a Jetson AGX Orin for 512^2 video, setting a new state-of-the-art on UAVid, KTH, and a custom high-resolution dataset in PSNR, SSIM, and LPIPS. Critically, RAPTOR boosts the mission success rate in a real-world UAV navigation task by 18%, paving the way for safer and more anticipatory embodied agents.
Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression
Authors: Zheng Chen; Mingde Zhou; Jinpei Guo; Jiale Yuan; Yifei Ji; Yulun Zhang
Venue: AAAI 2026 (main)

Abstract: Diffusion-based image compression has demonstrated impressive perceptual performance. However, it suffers from two critical drawbacks: (1) excessive decoding latency due to multi-step sampling, and (2) poor fidelity resulting from over-reliance on generative priors. To address these issues, we propose SODEC, a novel single-step diffusion image compression model. We argue that in image compression, a sufficiently informative latent renders multi-step refinement unnecessary. Based on this insight, we leverage a pre-trained VAE-based model to produce latents with rich information, and replace the iterative denoising process with a single-step decoding. Meanwhile, to improve fidelity, we introduce the fidelity guidance module, encouraging output that is faithful to the original image. Furthermore, we design the rate annealing training strategy to enable effective training under extremely low bitrates. Extensive experiments show that SODEC significantly outperforms existing methods, achieving superior rate-distortion-perception performance. Moreover, compared to previous diffusion-based compression models, SODEC improves decoding speed by more than 20×.
Empowering DINO Representations for Underwater Instance Segmentation via Aligner and Prompter
Authors: Zhiyang Chen; Chen Zhang; Hao Fang; Runmin Cong
Venue: AAAI 2026 (main)

Abstract: Underwater Instance Segmentation (UIS), integrating pixel-level understanding and instance-level discrimination, is a pivotal technology in marine resource exploration and ecological protection. In recent years, large-scale pretrained visual foundation models, exemplified by DINO, have advanced rapidly and demonstrated remarkable performance on complex downstream tasks. In this paper, we demonstrate that DINO can serve as an effective feature learner for UIS, and we introduce DiveSeg, a novel framework built upon two insightful components: (1) The AquaStyle Aligner, designed to embed underwater color style features into the DINO fine-tuning process, facilitating better adaptation to the underwater domain. (2) The ObjectPrior Prompter, which incorporates binary segmentation-based prompts to deliver object-level priors, provides essential guidance for instance segmentation task that requires both object- and instance-level reasoning. We conduct thorough experiments on the popular UIIS and USIS10K datasets, and the results show that DiveSeg achieves the state-of-the-art performance.
CaPro: Curvilinear-aware Prompt Learning with Single Unlabeled Image for Cost-effective Curvilinear Structure Segmentation
Authors: Zhuangzhuang Chen; Qiangyu Chen; Chubin Ou; Xiaomeng Li
Venue: AAAI 2026 (main)

Abstract: Curvilinear structure segmentation (CSS) plays a vital role in industrial applications, including medical imaging and structural health monitoring. Recently, the strong capacity of the Segment Anything Model (SAM) has inspired its downstream application in CSS tasks. To adapt SAM to CSS tasks, previous methods heavily rely on a certain number of samples and costly pixel-level annotation, which are hard to access for a new scenario. Considering this, the goal of our work is to adapt SAM in a very cost-effective setting where only a single unlabeled image is given. This is far more challenging than the typical supervised, unsupervised, or self-supervised learning manner that needs a large number of training samples. To tackle this problem, we propose a finetuning-free SAM for curvilinear structure segmentation, called curvilinear-aware prompt learning (CaPro), which aims to automatically learn visual prompts via a single unlabeled image. In the first stage, we generate extensive curvilinear structures and oriented sub-curvilinear box annotations. To increase the realism of generated curvilinear structures, we adapt these structures into real image domains via the Fourier Transform using a single real-world unlabeled image. Now, these adapted images can be used to train our oriented sub-curvilinear detector. In the second stage, we propose the curvilinear-aware discrete representation matching to filter those unreliable detection results. Afterward, these reliable detection results can be converted into informative prompts, contributing to the cost-effective SAM adaptation to CSS tasks. Experiments demonstrate the effectiveness of CaPro on medical image and crack segmentation tasks.
Force-Aware 3D Contact Modeling for Stable Grasp Generation
Authors: Zhuo Chen; Zhongqun Zhang; Yihua Cheng; Aleš Leonardis; Hyung Jin Chang
Venue: AAAI 2026 (main)

Abstract: Contact-based grasp generation plays a crucial role in various applications. Recent methods typically focus on the geometric structure of objects, producing grasps with diverse hand poses and plausible contact points. However, these approaches often overlook the physical attributes of the grasp, specifically the contact force, leading to reduced stability of the grasp. In this paper, we focus on stable grasp generation using explicit contact force predictions. First, we define a force-aware contact representation by transforming the normal force value into discrete levels and encoding it using a one-hot vector. Next, we introduce force-aware stability constraints. We define the stability problem as an acceleration minimization task and explicitly relate stability with contact geometry by formulating the underlying physical constraints. Finally, we present a pose optimizer that systematically integrates our contact representation and stability constraints to enable stable grasp generation. We show that these constraints can help identify key contact points for stability which provide effective initialization and guidance for optimization towards a stable grasp. Experiments are carried out on two public benchmarks, showing that our method brings about 20% improvement in stability metrics and adapts well to novel objects.
DCMM-Transformer: Degree-Corrected Mixed-Membership Attention for Medical Imaging
Authors: Huimin Cheng; Xiaowei Yu; Shushan Wu; Luyang Fang; Chao Cao; Jing Zhang; Tianming Liu; Dajiang Zhu; Wenxuan Zhong; Ping Ma
Venue: AAAI 2026 (main)

Abstract: Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit.
While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. 
We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. 
Comprehensive experiments across diverse medical imaging datasets, including brain, chest, breast, and ocular modalities, demonstrate the superior performance and generalizability of the proposed approach. Furthermore, the learned group structure and structured attention modulation substantially enhance interpretability by yielding attention maps that are anatomically meaningful and semantically coherent.
Phased One-Step Adversarial Equilibrium for Video Diffusion Models
Authors: Jiaxiang Cheng; Bing Ma; Xuhua Ren; Hongyi Henry Jin; Kai Yu; Peng Zhang; Wenyue Li; Yuan Zhou; Tianxiang Zheng; Qinglin Lu
Venue: AAAI 2026 (main)

Abstract: Video diffusion generation suffers from critical sampling efficiency bottlenecks, particularly for large-scale models and long contexts.
Existing video acceleration methods, adapted from image-based techniques, lack a single-step distillation ability for large-scale video models and task generalization for conditional downstream tasks.
To bridge this gap, we propose the Video Phased Adversarial Equilibrium (V-PAE), a distillation framework that enables high-quality, single-step video generation from large-scale video models. Our approach employs a two-phase process.
(i) Stability priming is a warm-up process to align the distributions of real and generated videos. It improves the stability of single-step adversarial distillation in the following process.
(ii) Unified adversarial equilibrium is a flexible self-adversarial process that reuses generator parameters for the discriminator backbone. It achieves a co-evolutionary adversarial equilibrium in the Gaussian noise space.
For the conditional tasks, we primarily preserve video-image subject consistency, which is caused by semantic degradation and conditional frame collapse during the distillation training in image-to-video (I2V) generation.
Comprehensive experiments on VBench-I2V demonstrate that V-PAE outperforms existing acceleration methods by an average of 5.8% in the overall quality score, including semantic alignment, temporal coherence, and frame quality.
In addition, our approach reduces the diffusion latency of the large-scale video model (e.g., Wan2.1-I2V-14B) by 100 times, while preserving competitive performance.
Thinking Aesthetics Assessment of Image Color Temperature: Models, Datasets and Benchmarks
Authors: Jinguang Cheng; Chunxiao Li; Shuai He; Taiyu Chen; Anlong Ming
Venue: AAAI 2026 (main)

Abstract: Color temperature, as a crucial attribute influencing image color, plays a critical role in Image Aesthetics Assessment (IAA). Yet, within the existing IAA field, little light has been shed on assessing the aesthetic quality of image color temperature. To bridge this gap, we introduce a new task: Image Color Temperature Aesthetics Assessment (ICTAA). However, this task poses the following challenges: 1) Perceptual Sensitivity: humans exhibit high sensitivity to subtle shifts in color temperature, necessitating a model to enable fine-grained discrimination; 2) Spectral Continuity: The theoretical modeling of color temperature aesthetics requires continuous labels; however, the just-noticeable-difference property of human perception makes continuous labeling infeasible, necessitating a well-designed labeling strategy. To address the aforementioned challenges, we make the following efforts. First, we propose a multi-modal contrastive learning framework, ICTA2Net, that models color temperature differences between image pairs while strictly controlling other visual attributes. Second, leveraging color temperature transitivity, we design a weakly supervised strategy that discretely samples images based on anchor images and human perception to build contrastive relations across color temperatures, enabling learning from discrete labels. Thirdly, we construct a color temperature aesthetics dataset, ICTAA240K, and a benchmark for validation. Additionally, we propose a new metric, Information Entropy-weighted Accuracy (IEA), which weights accuracy by the degree of annotation disagreement to reflect model performance across varying sample difficulties, complementing existing evaluation metrics. Experiments show our method outperforms existing state-of-the-art IAA methods on ICTAA240K, thereby setting an effective roadmap for ICTAA.
RFI: Rectified Flow Intervention for Mitigating Object Hallucination in Large Vision-Language Models
Authors: Junyu Cheng; Zhibiao Liang; Yidong Chen; Shuangyin Li
Venue: AAAI 2026 (main)

Abstract: Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation by integrating visual and textual data. However, these models frequently exhibit object hallucination problems: generating outputs that are inconsistent with the input image. Existing improved methods for mitigating hallucinations still suffer from two key limitations: dynamic approaches based on logits or attention mechanisms risk suppressing valuable linguistic priors, whereas static methods that employ fixed intervention vectors lack the flexibility to adapt to diverse images and questions. To address these issues, we propose RFI (Rectified Flow Intervention), a novel approach that harnesses the linear trajectory design of rectified flow for input-specific adaptation and employs gradient correction to ensure coherent generation, effectively combining the adaptability of dynamic methods with the stability of static ones. RFI dynamically predicts latent-space intervention vectors while requiring only a single forward pass in LVLMs per question, achieving computational efficiency (1.09x latency overhead for 100 new tokens). Extensive experiments show RFI significantly reduces hallucinations, achieving superior performance compared to existing advanced methods, highlighting its effectiveness as a lightweight plug-and-play method for reducing LVLM's hallucination in practical applications.
First Learn, Then Review: Human-Like Continual Learning for Cross-View Geo-Localization with Limited Field of View
Authors: Lei Cheng; Daikun Liu; Zhikun Chen; Teng Wang
Venue: AAAI 2026 (main)

Abstract: This paper addresses cross-view geo-localization in real-world scenarios, where the field-of-view (FoV) is restricted and the orientation is unknown for ground-view images. This task is extremely challenging due to the huge domain gap. Existing methods typically treat tasks with different FoVs as independent tasks. These approaches not only require separate retraining for each FoV, but also neglect the strong correlations between different FoVs, leading to poor performance under extremely limited FoV. To overcome these limitations, we propose HCL-Geo, a framework follows human-like continual learning paradigm of "first learn, then review" for geo-localization: in the first "learn" stage, tasks are presented to the model in an easy-to-hard sequence to enable gradual learning and knowledge retention, so that their natural correlations could be exploited to facilitate knowledge transfer. In the second "review" stage, expert modules are incorporated to efficiently handle tasks with varying FoVs. This approach eliminates the need for retraining separate models and demonstrates state-of-the-art performance across different FoVs with strong generalization capabilities. Remarkably, the recall rate@top-1 improves from 49.1% to 68.3% and from 24.6% to 34.3% respectively on CVUSA and CVACT benchmarks with 70° FoV.
Multi-Semantic Modeling for Glass Surface Detection in the Wild
Authors: Qianyu Cheng; Huankang Guan; Rynson W. H. Lau
Venue: AAAI 2026 (main)

Abstract: Glass surfaces challenge object detection models as they mix the transmitted background with the reflected surrounding, creating confusing visual patterns. Previous methods relying on low-level cues (e.g., reflections and boundaries) or surrounding semantics are often unreliable in complex real-world scenarios. A glass image inherently comprises three distinct semantic components: semantics of the transmitted content, semantics of the reflected content, and semantics of the surrounding content. In this work, we observe that there is a relationship among these three types of semantics, where reflection semantics closely resembles surrounding semantics, while these two types of semantics tend to be different from the transmission semantics. For example, when on a street, we may see into a cafeteria through a glass wall, intermixed with reflection of the street, while the glass is surrounded by other street contents like shops and pedestrians, thereby creating a unique multi-semantic signature. Based on this observation, we propose the Multi-Semantic Net, MSNet, which identifies transmission, reflection, and surrounding semantics from glass images and exploits their relationships for glass surface detection. MSNet consists of two novel modules: (1) A Semantic Decomposition Module (SDM) containing Dual-Semantics Extraction Block to extract original image and reflection semantics and Semantic Elimination Block to progressively derive transmission and surrounding semantics, and (2) An Adaptive Semantic Fusion Module (ASFM) to fuse these semantic components and adaptively learn their relationships to handle varying reflection conditions. Extensive experiments demonstrate that MSNet surpasses SOTA methods on public glass detection benchmarks.
RadarMP: Motion Perception for 4D mmWave Radar in Autonomous Driving
Authors: Ruiqi Cheng; Huijun Di; Jian Li; Feng Liu; Wei Liang
Venue: AAAI 2026 (main)

Abstract: Accurate 3D scene motion perception significantly enhances the safety and reliability of an autonomous driving system.
Benefiting from its all-weather operational capability and unique perceptual properties, 4D mmWave radar has emerged as an essential component in advanced autonomous driving.
However, sparse and noisy radar points often lead to imprecise motion perception, leaving autonomous vehicles with limited sensing capabilities when optical sensors degrade under adverse weather conditions.
In this paper, we propose RadarMP, a novel method for precise 3D scene motion perception using low-level radar echo signals from two consecutive frames. 
Unlike existing methods that separate radar target detection and motion estimation, RadarMP jointly models both tasks in a unified architecture, enabling consistent radar point cloud generation and pointwise 3D scene flow prediction.
Tailored to radar characteristics, we design specialized self-supervised loss functions guided by Doppler shifts and echo intensity, effectively supervising spatial and motion consistency without explicit annotations.
Extensive experiments on the public dataset demonstrate that RadarMP achieves reliable motion perception across diverse weather and illumination conditions, outperforming radar-based decoupled motion perception pipelines and enhancing perception capabilities for full-scenario autonomous driving systems.
HandMCM: Multi-modal Point Cloud-based Correspondence State Space Model for 3D Hand Pose Estimation
Authors: Wencan Cheng; Gim Hee Lee
Venue: AAAI 2026 (main)

Abstract: 3D hand pose estimation that involves accurate estimation of 3D human hand keypoint locations is crucial for many human-computer interaction applications such as augmented reality. However, this task poses significant challenges due to self-occlusion of the hands and occlusions caused by interactions with objects. In this paper, we propose HandMCM to address these challenges. Our HandMCM is a novel method based on the powerful state space model (Mamba). By incorporating modules for local information injection/filtering and correspondence modeling, the proposed correspondence Mamba effectively learns the highly dynamic kinematic topology of keypoints across various occlusion scenarios. Moreover, by integrating multi-modal image features, we enhance the robustness and representational capacity of the input, leading to more accurate hand pose estimation. Empirical evaluations on three benchmark datasets demonstrate that our model significantly outperforms current state-of-the-art methods, particularly in challenging scenarios involving severe occlusions. These results highlight the potential of our approach to advance the accuracy and reliability of 3D hand pose estimation in practical applications.
360Explorer: Exploring 4D Controllable World in Panoramic Videos
Authors: Xinhua Cheng; Haiyang Zhou; Wangbo Yu; Tanghui Jia; Bin Lin; Yunyang Ge; Weiqi Li; Li Yuan
Venue: AAAI 2026 (main)

Abstract: We present 360Explorer, a novel approach for generating 4D controllable panoramic videos conditioned on user-provided 3D instructions for exploring and manipulating dynamic worlds. Compared to existing perspective-based methods struggle to address spatial consistency during camera rotation in place, we introduce the panoramic view in controllable video generation models to inherently maintain the view recall consistency. By introducing dynamic point clouds as the 4D scene representations, 360Explorer unifies the modeling of camera transformations and object movements as incomplete renders to describe precise control instructions in 3D worlds. To tackle the data limitation in acquiring multi-viewpoint panoramic videos, we further propose a reverse warping strategy to construct the training dataset on easily accessible monocular panoramic videos.
Extensive experiments demonstrate that 360Explorer achieves superior performance in creating 4D controllable panoramic videos with camera transformation and object movements aligned with diverse provided instructions.