GenAI4ELab/papercli-papers huggingface dataset:
Decomposing Prompts, Composing Actions: A Multi-Granularity Prompting Approach for Incremental Action Learning
Authors: Xinyi Cheng; Chenghao Xu; Xi Wang; Jiexi Yan; Yanhua Yang
Venue: AAAI 2026 (main)

Abstract: Continual learning for action recognition is a critical capability for next-generation Extended Reality (XR) systems. Yet it faces a severe real-world challenge: strict user privacy that prohibits data rehearsal. While recent prompt-based continual learning methods show promise, we argue their core 'flat,' single-granularity design fundamentally misaligns with the complexity of human actions. This monolithic architecture fails to model the inherent hierarchical structure and overlooks standard action primitives shared across tasks, resulting in suboptimal performance and hindered knowledge transfer. To overcome this limitation, we propose DPCA, a novel spatio-temporal continual learning framework with multi-granularity adaptive prompting. DPCA learns three synergistic components to resolve this mismatch. First, the task-specific prompter employs a multi-granularity query system to capture the unique, compositional semantics of each action. Second, the task-agnostic prompter learns a globally shared vocabulary of ``action primitives," providing a stable and generalizable knowledge base to mitigate catastrophic forgetting. Finally, we introduce a Dissimilarity Attention Rectification at each granularity level, leveraging a reverse attention mechanism to model class-agnostic background information and effectively alleviating overfitting. The synergy between these components enables robust model adaptation without requiring access to past data. Rigorous experiments on multiple large-scale benchmarks (including NTU RGB+D), under a strict rehearsal-free, few-shot protocol, confirm that DPCA establishes a new state-of-the-art. This advance paves the way for the realization of truly adaptive and privacy-respecting XR systems.
RFNNS: Robust Fixed Neural Network Steganography with Universal Text-to-Image Models
Authors: Yu Cheng; Jiuan Zhou; Jiawei Chen; Zhaoxia Yin; Xinpeng Zhang
Venue: AAAI 2026 (main)

Abstract: With the rapid development of generative AI, image steganography has garnered widespread attention due to its unique concealment. Recent studies have demonstrated the practical advantages of Fixed Neural Network Steganography (FNNS), notably its ability to achieve stable information embedding and extraction without any additional network training. However, the stego images generated by FNNS still exhibit noticeable distortion and limited robustness. These drawbacks compromise the security of the embedded information and restrict the practical applicability of the method. To address these limitations, we propose Robust Fixed Neural Network Steganography (RFNNS). Specifically, a texture-aware localization technique selectively embeds perturbations carrying secret information into regions of complex textures, effectively preserving visual quality. Additionally, a robust steganographic perturbation generation (RSPG) strategy is designed to enhance the decoding accuracy, even under common and unknown attacks. These robust perturbations are combined with AI-generated cover images to produce stego images. Experimental results demonstrate that RFNNS significantly improves robustness compared to state-of-the-art FNNS methods, achieving an average increase in SSIM of 23% for recovered secret images under common attacks. Furthermore, the LPIPS value of recovered secrets images against previously unknown attacks achieved by RFNNS was reduced to 39% of the SOTA method, underscoring its practical value for covert communication.
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
Authors: Yuqi Cheng; Yihan Sun; Hui Zhang; Weiming Shen; Yunkang Cao
Venue: AAAI 2026 (main)

Abstract: In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs. To address this disparity, we propose a scalable pipeline for generating realistic and subtle 3D anomalies. Employing this pipeline, we developed MiniShift, the inaugural high-resolution 3D anomaly detection dataset, encompassing 2,577 point clouds, each with 500,000 points and anomalies occupying less than 1% of the total. We further introduce Simple3D, an efficient framework integrating Multi-scale Neighborhood Descriptors (MSND) and Local Feature Spatial Aggregation (LFSA) to capture intricate geometric details with minimal computational overhead, achieving real-time inference exceeding 20 fps. Extensive evaluations on MiniShift and established benchmarks demonstrate that Simple3D surpasses state-of-the-art methods in both accuracy and speed, highlighting the pivotal role of high-resolution data and effective feature aggregation in advancing practical 3D anomaly detection.
Adaptive Agent Selection and Interaction Network for Image-to-Point Cloud Registration
Authors: Zhixin Cheng; Xiaotian Yin; Jiacheng Deng; Bohao Liao; Yujia Chen; Xu Zhou; Baoqun Yin; Tianzhu Zhang
Venue: AAAI 2026 (main)

Abstract: Typical detection-free methods for image-to-point cloud registration leverage transformer-based architectures to aggregate cross-modal features and establish correspondences. However, they often struggle under challenging conditions, where noise disrupts similarity computation and leads to incorrect correspondences. Moreover, without dedicated designs, it remains difficult to effectively select informative and correlated representations across modalities, thereby limiting the robustness and accuracy of registration.
To address these challenges, we propose a novel cross-modal registration framework composed of two key modules: the Iterative Agents Selection (IAS) module and the Reliable Agents Interaction (RAI) module. IAS enhances structural feature awareness with phase maps and employs reinforcement learning principles to efficiently select reliable agents. RAI then leverages these selected agents to guide cross-modal interactions, effectively reducing mismatches and improving overall robustness.
Extensive experiments on the RGB-D Scenes v2 and 7-Scenes benchmarks demonstrate that our method consistently achieves state-of-the-art performance.
Learning 3D Texture-Aware Representations for Parsing Diverse Human Clothing and Body Parts
Authors: Kiran Chhatre; Christopher E. Peters; Srikrishna Karanam
Venue: AAAI 2026 (main)

Abstract: Existing methods for human parsing into body parts and clothing often use fixed mask categories with broad labels that obscure fine-grained clothing types. Recent open-vocabulary segmentation approaches leverage pretrained text-to-image (T2I) diffusion model features for strong zero-shot transfer, but typically group entire humans into a single person category, failing to distinguish diverse clothing or detailed body parts. To address this, we propose Spectrum, a unified network for part-level pixel parsing (body parts and clothing) and instance-level grouping. While diffusion-based open-vocabulary models generalize well across tasks, their internal representations are not specialized for detailed human parsing. We observe that, unlike diffusion models with broad representations, image-driven 3D texture generators maintain faithful correspondence to input images, enabling stronger representations for parsing diverse clothing and body parts. Spectrum introduces a novel repurposing of an Image-to-Texture (I2Tx) diffusion model—obtained by fine-tuning a T2I model on 3D human texture maps—for improved alignment with body parts and clothing. From an input image, we extract human-part internal features via the I2Tx diffusion model and generate semantically valid masks aligned to diverse clothing categories through prompt-guided grounding. Once trained, Spectrum produces semantic segmentation maps for every visible body part and clothing category, ignoring standalone garments or irrelevant objects, for any number of humans in the scene. We conduct extensive cross-dataset experiments—separately assessing body parts, clothing parts, unseen clothing categories, and full-body masks—and demonstrate that Spectrum consistently outperforms baseline methods in prompt-based segmentation.
VMChill: A Dataset for Fine-Grained Visual-Musical Synergy
Authors: Xiaowei Chi; Zeyue Tian; Jialiang Chen; Wei Xue
Venue: AAAI 2026 (main)

Abstract: Massive multi-modality datasets are fundamental to the success of large video-language models. However, existing datasets often focus on providing textual descriptions for visual content, treating audio, particularly music, as weakly related information. This overlooks the inherent semantic correlation between visual narratives and musical scores, limiting the development of models for fine-grained cross-modal understanding and generation. To address this gap, we introduce VMChill, a large-scale, fine-grained multimodal video dataset. We leverage trailers as our data source, as they are professionally edited to create a strong synergy between visual pacing, scene transitions, and background music for narrative and emotional impact. Our dataset comprises over 20 million video clips derived from more than 27.1k hours of high-resolution trailer videos.
To annotate this data, we propose a systematic multimodal captioning framework. This framework first employs specialized unimodal models to extract descriptive features from multiple perspectives, including visual content, motion dynamics, and musical attributes (e.g., genre, instruments, mood). Subsequently, a large language model (LLM) is utilized to adaptively fuse these diverse descriptions into a single, coherent, and rich multimodal caption. This process yields VMChill-2M, a high-quality subset of 2 million clips with detailed multimodal annotations, and VMChill-Test, a manually refined test set for evaluation. We conduct extensive experiments on downstream tasks, including video understanding and generation, to establish benchmarks and demonstrate the dataset's quality. The results validate that VMChill effectively enhances model performance, highlighting its potential to facilitate future research in fine-grained multimodal learning. We will release the dataset, annotation codebase, and processing pipelines to support community research.
4D Scaffold Gaussian Splatting with Dynamic-Aware Anchor Growing for Efficient and High-Fidelity Dynamic Scene Reconstruction
Authors: Woong Oh Cho; In Cho; Seoha Kim; Jeongmin Bae; Youngjung Uh; Seon Joo Kim
Venue: AAAI 2026 (main)

Abstract: Modeling dynamic scenes through 4D Gaussians offers high visual fidelity and fast rendering speeds, but comes with significant storage overhead.
Recent approaches mitigate this cost by aggressively reducing the number of Gaussians.
However, this inevitably removes Gaussians essential for high-quality rendering, leading to severe degradation in dynamic regions.
In this paper, we introduce a novel 4D anchor-based framework that tackles the storage cost in different perspective.
Rather than reducing the number of Gaussians, our method retains a sufficient quantity to accurately model dynamic contents, while compressing them into compact, grid-aligned 4D anchor features.
Each anchor is processed by an MLP to spawn a set of neural 4D Gaussians, which represent a local spatiotemporal region.
We design these neural 4D Gaussians to capture temporal changes with minimal parameters, making them well-suited for the MLP-based spawning.
Moreover, we introduce a dynamic-aware anchor growing strategy to effectively assign additional anchors to under-reconstructed dynamic regions.
Our method adjusts the accumulated gradients with Gaussians' temporal coverage, significantly improving reconstruction quality in dynamic regions.
Experimental results highlight that our method achieves state-of-the-art visual quality in dynamic regions, outperforming all baselines by a large margin with practical storage costs.
UAV4D: Dynamic Neural Rendering of Human-Centric UAV Imagery Using Gaussian Splatting
Authors: Jaehoon Choi; Dongki Jung; Chris Maxey; Sungmin Eum; Yonghan Lee; Dinesh Manocha; Heesung Kwon
Venue: AAAI 2026 (main)

Abstract: Despite significant advancements in dynamic neural rendering, existing methods fail to address the unique challenges posed by UAV-captured scenarios, particularly those involving monocular camera setups, top-down perspective, and multiple small, moving humans, which are not adequately represented in existing datasets. In this work, we introduce UAV4D, a framework for enabling photorealistic rendering for dynamic real-world scenes captured by UAVs. Specifically, we address the challenge of reconstructing dynamic scenes with multiple moving pedestrians from monocular video data without the need for additional sensors. We use a combination of a 3D foundation model and a human mesh reconstruction model to reconstruct both the scene background and humans. We propose a novel approach to resolve the scene scale ambiguity and place both humans and the scene in world coordinates by identifying human-scene contact points. Additionally, we exploit the SMPL model and background mesh to initialize Gaussian splats, enabling holistic scene rendering. We evaluated our method on three complex UAV-captured datasets: VisDrone, Manipal-UAV, and Okutama-Action, each with distinct characteristics and 10-50 humans. Our results demonstrate the benefits of our approach over existing methods in novel view synthesis, achieving a 1.5 dB PSNR improvement and superior visual sharpness.
Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation
Authors: Yuxuan Chou; Tao Yu; Wen Huang; ZhangYuHeng; Tao Dai; Shu-Tao Xia
Venue: AAAI 2026 (main)

Abstract: The generalization capability of deepfake detectors is crucial for real-world applications. Data augmentation to generate synthetic fake faces has served as an effective strategy to enhance generalization. Interestingly, current state-of-the-art (SoTA) methods rely on fixed augmentation strategies, raising a fundamental question: Can a single static augmentation approach suffice, or does the diversity of forgery features necessitate dynamic strategies? We argue that existing methods overlook the evolving complexity of real-world forgery patterns, such as facial warping, expression manipulation, and compression artifacts, which cannot be fully simulated by fixed policies.
To bridge this gap, we propose CRDA (Curriculum Reinforcement-Learning Data Augmentation), a novel framework that guides the detector to progressively master multi-domain forgery features from simple to complex. CRDA synthesizes augmented samples using a configurable pool of forgery operations and dynamically generates adversarial samples tailored to the detector’s current learning state. 
Key to our approach is the integration of reinforcement learning (RL) and causal inference. To efficiently explore the vast augmentation space, an RL agent dynamically selects augmentation actions based on the detector’s performance, ensuring continuous adaptation to increasingly challenging forgeries. Simultaneously, the agent’s output is designed to introduce variations in action spaces, generating heterogeneous forgery patterns. These variations are guided by causal inference theory, which mitigates spurious correlations by suppressing task-irrelevant biases and enforcing the model to focus on causally invariant features. This integration ensures robust generalization by decoupling synthetic augmentation patterns from the model’s learned representations.
Extensive experiments demonstrate that the proposed method significantly improves the generalizability of the detector, achieving superior performance compared to state-of-the-art methods on multiple cross-domain datasets.
TraveLLaMA: A Multimodal Travel Assistant with Large-Scale Dataset and Structured Reasoning
Authors: Meng Chu; Yukang Chen; Haokun Gui; Shaozuo Yu; Yi Wang; Jiaya Jia
Venue: AAAI 2026 (main)

Abstract: Tourism and travel planning increasingly rely on digital assistance, yet existing multimodal AI systems often lack specialized knowledge and contextual understanding of urban environments. We present TraveLLaMA, a specialized multimodal language model designed for comprehensive travel assistance. Our work addresses the fundamental challenge of developing practical AI travel assistants through three key contributions: (1) TravelQA, a novel dataset of 265k question-answer pairs combining 160k text QA from authentic travel sources, 100k vision-language QA featuring maps and location imagery, and 5k expert-annotated Chain-of-Thought reasoning examples; (2) Travel-CoT, a structured reasoning framework that decomposes travel queries into spatial, temporal, and practical dimensions, improving answer accuracy by 10.8% while providing interpretable decision paths; and (3) an interactive agent system validated through extensive user studies. Through fine-tuning experiments on state-of-the-art vision-language models (LLaVA, Qwen-VL, Shikra), we achieve 6.2-9.4% base improvements, further enhanced by Travel-CoT reasoning. Our model demonstrates superior capabilities in contextual travel recommendations, map interpretation, and scene understanding while providing practical information such as operating hours and cultural insights. User studies with 500 participants show TraveLLaMA achieves a System Usability Scale score of 82.5, significantly outperforming general-purpose models and establishing new standards for multimodal travel assistance systems.
uCLIP: Parameter-Efficient Multilingual Extension of Vision-Language Models with Unpaired Data
Authors: Dahyun Chung; Donghyun Shin; Yujin Sung; Seunggi Moon; Jinwoo Jeon; Byung-Jun Lee
Venue: AAAI 2026 (main)

Abstract: Contrastive Language–Image Pre-training (CLIP) has demonstrated strong generalization across a wide range of visual tasks by leveraging large-scale English–image pairs. However, its extension to low-resource languages remains limited due to the scarcity of high-quality multilingual image–text data. Existing multilingual vision–language models exhibit consistently low retrieval performance in underrepresented languages—including Czech, Finnish, Croatian, Hungarian, Romanian—on the Crossmodal-3600 (XM3600) benchmark. To address this, we propose a lightweight and data-efficient framework for multilingual vision–language alignment. Our approach requires no image–text pairs or text-text pairs and freezes both the pretrained image encoder and multilingual text encoder during training. Only a compact 1.7M-parameter projection module is trained, using a contrastive loss over English representations as semantic anchors. This minimal training setup enables robust multilingual alignment even for languages with limited supervision. Extensive evaluation across multiple multilingual retrieval benchmarks confirms the effectiveness of our method, showing significant gains in five underrepresented languages where existing models typically underperform. These findings highlight the effectiveness of our pivot-based, parameter-efficient alignment strategy for inclusive multimodal learning.
Rethinking Progression of Memory State in Robotic Manipulation: An Object-Centric Perspective
Authors: Nhat Chung; Taisei Hanyu; Toan Nguyen; Huy Le; Frederick Bumgarner; Duy Minh Ho Nguyen; Khoa Vo; Kashu Yamazaki; Chase Rainwater; Tung Kieu; Anh Nguyen; Ngan Le
Venue: AAAI 2026 (main)

Abstract: As embodied agents operate in increasingly complex environments, the ability to perceive, track, and reason about individual object instances over time becomes essential, especially in tasks requiring sequenced interactions with visually similar objects. In non-Markovian settings, critical decision cues lie in object histories rather than the current scene. Without persistent memory of prior interactions (what was used, where it was placed, or how it changed), visuomotor policies may fail, repeat past actions, or overlook completed ones. To surface this challenge, we introduce LIBERO-Mem, a non-Markovian task suite for stress-testing robotic manipulation under object-level partial observability. It combines short- and long-horizon object tracking with temporally sequenced subgoals, requiring reasoning beyond the current frame. However, vision-language-action (VLA) models often struggle in such settings, with token scaling quickly becoming intractable even for tasks spanning just a few hundred frames. We propose Embodied-SlotSSM, a slot-centric VLA framework built for temporal scalability. It maintains spatio-temporally consistent slot identities and leverages them through two mechanisms: (1) slot-state-space modeling for reconstructing short-term history, and (2) a relational encoder to align the input tokens with action decoding. Together, these components enable temporally grounded, context-aware action prediction. Experiments show Embodied-SlotSSM's baseline performance on LIBERO-Mem and general tasks, offering a scalable solution for non-Markovian reasoning in object-centric policies.
Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation
Authors: Runmin Cong; Anpeng Wang; Bin Wan; Cong Zhang; Xiaofei Zhou; Wei Zhang
Venue: AAAI 2026 (main)

Abstract: Cross-domain few-shot segmentation (CD-FSS) aims to tackle the dual challenge of recognizing novel classes and adapting to unseen domains with limited annotations. However, encoder features often entangle domain-relevant and category-relevant information, limiting both generalization and rapid adaptation to new domains. To address this issue, we propose a Divide-and-Conquer Decoupled Network (DCDNet). In the training stage, to tackle feature entanglement that impedes cross-domain generalization and rapid adaptation, we propose the Adversarial-Contrastive Feature Decomposition (ACFD) module. It decouples backbone features into category-relevant private and domain-relevant shared representations via contrastive learning and adversarial learning. Then, to mitigate the potential degradation caused by the disentanglement, the Matrix-Guided Dynamic Fusion (MGDF) module adaptively integrates base, shared, and private features under spatial guidance, maintaining structural coherence. In addition, in the fine-tuning stage, to enhanced model generalization, the Cross-Adaptive Modulation (CAM) module is placed before the MGDF, where shared features guide private features via modulation ensuring effective integration of domain-relevant information. Extensive experiments on four challenging datasets show that DCDNet outperforms existing CD-FSS methods, setting a new state-of-the-art for cross-domain generalization and few-shot adaptation.
Towards Efficient and Effective Interactive 3D Segmentation
Authors: Wei Cong; Yang Cong; Jiahua Dong; Gan Sun
Venue: AAAI 2026 (main)

Abstract: Interactive 3D segmentation embodies an advanced human-in-the-loop paradigm, where a model iteratively refines the segmentation of interested objects within a 3D point cloud through user feedback. Existing methods have achieved notable advancements at the expense of substantial resource consumption. To address this challenge, we introduce E2I3D, an efficient and effective model for interactive 3D segmentation. Specifically, we propose a two-stage efficiency-to-effectiveness framework to decouple efficiency and effectiveness, avoiding the high training cost of joint optimization. For efficiency in the first stage, we present heterogeneous pruning, which reliably compresses the model by ranking and pruning the constructed heterogeneous groups separately based on gradient compensation. For effectiveness in the second stage, we design hierarchical click-aware attention that integrates geometric details from high-resolution features with global context from low-resolution features to enhance click-guided interaction. Extensive experiments across public datasets demonstrate that E2I3D exceeds state-of-the-art methods in both efficiency and effectiveness. For instance, on the KITTI-360 dataset, E2I3D boosts the IoU for interactive single-object segmentation from 44.4% to 49.0% with 5 user clicks, while simultaneously reducing parameters from 39.3M to 5.7M.