GenAI4ELab/papercli-papers huggingface dataset:
GenPTW: Latent Image Watermarking for Provenance Tracing and Tamper Localization
Authors: Zhenliang Gan; Chunya Liu; Yichao Tang; Binghao Wang; Shiwen Cui; Weiqiang Wang; Xinpeng Zhang
Venue: AAAI 2026 (main)

Abstract: The proliferation of generative image models has revolutionized AIGC creation while amplifying concerns over content provenance and manipulation forensics.
Existing methods are typically either unable to localize tampering or restricted to specific generative settings, limiting their practical utility.
We propose GenPTW, a General watermarking framework that unifies Provenance tracing and Tamper localization in latent space.
It supports both in-generation and post-generation embedding without altering the generative process, and is plug-and-play compatible with latent diffusion models (LDMs) and visual autoregressive (VAR) models.
To achieve precise provenance tracing and tamper localization, we embed the watermark using two complementary mechanisms: cross-attention fusion aligned with latent semantics and spatial fusion providing explicit spatial guidance for edit sensitivity.
A tamper-aware extractor jointly conducts provenance tracing and tamper localization by leveraging watermark features together with high-frequency features.
Experiments show that GenPTW maintains high visual fidelity and strong robustness against diverse AIGC-editing.
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
Authors: Bin-Bin Gao; Yue Zhou; Jiangtao Yan; Yuezhi Cai; Weixi Zhang; Meng Wang; Jun Liu; Yong Liu; Lei Wang; Chengjie Wang
Venue: AAAI 2026 (main)

Abstract: Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle to design prompt templates, handle complex token interactions, or require fine-tuning on target domains, resulting in limited flexibility. In this work, we present a simple yet effective AdaptCLIP based on two key insights. First, adaptive visual and textual representations should be learned alternately rather than jointly. Second, comparative learning between query and normal image prompt should incorporate both contextual and aligned residual features, rather than relying solely on residual features. AdaptCLIP treats CLIP models as a foundational service, adding only three simple adapters, visual adapter, textual adapter, and prompt-query adapter, at its input or output ends. AdaptCLIP supports zero-/few-shot generalization across domains and provides a training-free approach on target domains once trained on a base dataset. AdaptCLIP achieves state-of-the-art performance on 12 anomaly detection benchmarks from industrial and medical domains, significantly outperforming existing competitive methods.
OrgaCast: A Trustworthy Spatiotemporal Diffusion Model for Fluorescence Organoid Forecasting
Authors: Dawei Gao; Angello Huerta Gomez; Mingchen Li; Marcel El-Mokahal; Huaxiao Yang; Yunhe Feng
Venue: AAAI 2026 (main)

Abstract: Accurately forecasting the spatiotemporal dynamics of biological systems, such as human pluripotent stem cell (hPSC)-derived cardiac organoids, from microscopy time-series is a critical challenge in biomedicine with profound implications for drug discovery. Existing generative models often fail to capture the intricate dynamics of organoid development, struggling with their irregular morphology, indistinct boundaries, and complex spatiotemporal patterns. To overcome these limitations, we introduce OrgaCast, a novel multimodal conditional diffusion model for high-fidelity organoid forecasting. OrgaCast uniquely conditions the generative process on three synergistic modalities: (i) historical image sequences, captured by a dedicated spatiotemporal control module; (ii) structured numerical metadata defining experimental conditions; and (iii) descriptive text captions summarizing the biological context. This comprehensive conditioning enables the generation of forecasts with high visual accuracy and biological plausibility. Furthermore, to enhance the model's utility in critical research settings, we introduce a post-hoc uncertainty quantification method that produces intuitive confidence maps, bolstering the interpretability and trustworthiness of predictions. Extensive experiments on a challenging cardiac organoid dataset demonstrate that OrgaCast outperforms baselines in metrics such as SSIM, PSNR, and LPIPS. Our framework presents a robust solution for biological forecasting, promising to accelerate research discovery while minimizing experimental costs and manual effort.
APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information Retrieval
Authors: Hong Gao; Yiming Bao; Xuezhen Tu; Bin Zhong; Linan Yue; Min-Ling Zhang
Venue: AAAI 2026 (main)

Abstract: Current multimodal large language models (MLLMs) struggle with hour-level video understanding, facing significant challenges not only in modeling the substantial information volume of long videos but also in overcoming the memory wall and resource constraints during both training and inference. Although recent training-free approaches have alleviated resource demands by compressing visual features, their reliance on incomplete visual information limits the performance potential. To address these limitations, we propose Adaptive Pivot Visual information Retrieval (APVR), a training-free framework that hierarchically retrieves and retains sufficient and important visual information. It breakthroughs the memory wall limitation via two complementary components: Pivot Frame Retrieval employs query expansion and iterative spatio-semantic confidence scoring to identify relevant video frames, and Pivot Token Retrieval performs query-aware attention-driven token selection within up to 1024 pivot frames. This dual granularity approach enables the processing of hour-long videos while maintaining semantic fidelity. Experimental validations on three different baseline MLLMs demonstrate significant performance improvements up to 9.5%, 4.6% and 9.7% on LongVideoBench, VideoMME and MLVU, respectively. APVR achieves state-of-the-art results for both training-free and training-based approaches.
The Structure-Equivalent Prior: Unifying Temporal Dynamics and 3D Evolution in 4D Latent Space
Authors: Jingyuan Gao; Tianyu Shen; Ruosen Hao; Te Guo; Zhiwei Li; Kunfeng Wang
Venue: AAAI 2026 (main)

Abstract: Recent advances in deep learning-based 3D representation have achieved remarkable success, particularly in modeling static high-fidelity geometries. However, the extension of these techniques to dynamic 3D scenes introduces a critical challenge of effectively representing spatio-temporal dependencies, i.e., jointly modeling detailed spatial structures within frames and temporal dynamics across frames. To address this challenge, this paper proposes that the temporal evolution observed in dynamic 3D scenes is fundamentally attributable to the deformation of underlying spatial structures. To capture this relationship, we introduce a unified continuous 4D latent space representation incorporating a structure-equivalence prior, named SEP-4D. The core of SEP-4D is an efficient 4D tensor decomposition-fusion approach. This method fuses decomposed learnable 2D feature planes via a plane-wise spatio-temporal fusion mechanism of planar distributions, explicitly enforcing the principle that temporal evolution originates from geometric deformations of the 3D structure. To mitigate the associated computational demands, we sample the 3D probability volumes generated by VAE-based fusion into a spatio-temporally consistent 4D latent representation. The efficacy of our approach is validated through experiments on the fundamental task of 4D occupancy reconstruction. Extensive results demonstrate that, by leveraging the inherent equivalence of temporal dynamics and structural deformation, our method achieves high-quality reconstruction across various sequence lengths. Notably, for 4-frame scenes, we attain an impressive 91.68% mIoU, significantly outperforming state-of-the-art baselines on standard benchmarks.
ViType: High-Fidelity Visual Text Rendering via Glyph-Aware Multimodal Diffusion
Authors: Lishuai Gao; Jun-Yan He; Yingsen Zeng; Yujie Zhong; Xiaopeng Sun; Jie Hu; Zan Gao; Xiaoming Wei
Venue: AAAI 2026 (main)

Abstract: Current text-to-image models face challenges in visual text rendering: text encoders like CLIP and T5 lack glyph-level understanding and often struggle to distinguish between the specific words to be rendered and their intended semantic meaning within prompts. In addition, inconsistencies between the base model and its plugins further compromise the quality of synthesized images. In this paper, we enhance the existing text-to-image method by addressing the following aspects: (1) Text-Glyph Alignmentin a Visual Question Answering (VQA) manner to enable glyph understanding for the text encoder. This involves establishing an explicit alignment between the representations of the glyphs and their detailed attribute descriptions, which boosts the model's ability to capture fine-grained visual features of the text.
(2) Accurate and harmony visual text rendering:  integrating  pre-aligned glyph-visual embeddings with semantic text tokens through the Multimodal Diffusion Transformer(MMDiT) synchronously, ensuring coherent feature alignment and enhancing both the robustness and fidelity of visual text rendering. (3) Image Aesthetic Refinement: leveraging a multisource data training strategy that incorporates diverse, high-quality image-text pairs from various domains, exposing the model to extensive linguistic and visual diversity while maintaining superior aesthetic quality throughout training. Our experiments demonstrate that the proposed approach significantly outperforms the existing state-of-the-art method.
Remember Me: Bridging the Long-Range Gap in LVLMs with Three-Step Inference-Only Decay Resilience Strategies
Authors: Peng Gao; Yujian Lee; Xiaofeng Zhang; Zailong Chen; Hui Zhang
Venue: AAAI 2026 (main)

Abstract: Large Vision-Language Models (LVLMs) have achieved impressive performance across a wide range of multimodal tasks. However, they still face critical challenges in modeling long-range dependencies under the usage of Rotary Positional Encoding (ROPE). Although it can facilitate precise modeling of token positions, it induces progressive attention decay as token distance increases, especially with progressive attention decay over distant token pairs, which severely impairs the model's ability to remember global context. To alleviate this issue, we propose inference-only Three-step Decay Resilience Strategies (T-DRS), comprising (1) Semantic-Driven DRS (SD-DRS), amplifying semantically meaningful but distant signals via content-aware residuals, (2) Distance-aware Control DRS (DC-DRS), which can purify attention by smoothly modulating weights based on positional distances, suppressing noise while preserving locality, and (3) re-Reinforce Distant DRS (reRD-DRS), consolidating the remaining informative remote dependencies to maintain global coherence. Together, the T-DRS recover suppressed long-range token pairs without harming local inductive biases. Extensive experiments on Vision Question Answering (VQA) benchmarks demonstrate that T-DRS can consistently improve performance in an inference-only manner.
VisAssist: A Visually Impaired-Captured Video Question Answering Benchmark for Assistive Systems
Authors: Qi Gao; Heng Li; Yixin Zhou; Meixuan Zhou; Jieqiong Chen; Xinyu Chai
Venue: AAAI 2026 (main)

Abstract: We present VisAssist, the first large-scale video question-answering dataset with 13,413 real-world videos captured by visually impaired users, addressing a critical gap in assistive vision research. Unlike existing benchmarks relying on third-person footage, VisAssist provides authentic first-person perspectives that uniquely capture challenges in blind photography—including unconventional framing, motion artifacts, and frequent information omission. Benchmark evaluations of SOTA multimodal models reveal systematic limitations: severe deficiencies in spatial reasoning when processing dynamic first-person viewpoints, an inability to distinguish missing information from poor capture quality leading to hazardous hallucinations, and fragile text understanding especially for non-Latin scripts under suboptimal conditions. This work establishes a vital real-world benchmark and underscores the need for specialized architectures in visual assistance systems.
T-APT: Text-Guided Modality-Aware Prompt Tuning for Arbitrary Multimodal Remote Sensing Data Joint Classification
Authors: Qinghao Gao; Jiahui Qu; Wenqian Dong
Venue: AAAI 2026 (main)

Abstract: Multimodal remote sensing image joint classification has achieved significant progress. However, existing methods primarily focus on designing modality-specific networks, lacking adaptive generalization capabilities in diverse and dynamic modality combinations encountered in real-world scenarios. Inspired by the generalization capabilities of visual foundation model in downstream tasks, we propose a unified Text-guided Arbitrary Modalitiy Prompting (T-APT) framework, which leverages complementary fused features to drive the foundation model and employs text-guided modality-specific prior knowledge as cross-modal prompts to fine-tune a pretrained Vision Transformer (ViT) model. Specifically, a Mamba-Based Arbitrary Modal-Focused Feature Capture (MAMF-FC) module is designed to extract complementary joint features and modality-specific prior knowledge from arbitrary modalities through a shared-specific scanning encoder-decoder architecture. Subsequently, a Text-Guided Modality-Aware Prompt Tuning (TMPT) module is proposed to support the adaptation of fused features to the foundation model, enabling our arbitrary remote sensing image classification task. Extensive experiments on public datasets spanning multispectral (MS), hyperspectral (HS), light detection and ranging (LiDAR), and synthetic aperture radar (SAR) modalities demonstrate that our T-APT achieves classification performance comparable to specialized networks across arbitrary modal combinations.
VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding
Authors: Shibo Gao; Peipei Yang; Yangyang Liu; Yi Chen; Han Zhu; Xu-Yao Zhang; Linlin Huang
Venue: AAAI 2026 (main)

Abstract: For video anomaly detection, it's both important to detect when the event happens and what the event is. The tasks of temporal grounding and semantic understanding can benefit from joint learning, but no existing work support it. To address this problem, we introduce VAGU (Video Anomaly Grounding and Understanding), the first benchmark designed to jointly evaluate semantic understanding and precise temporal grounding of anomalies, with comprehensive annotations and objective multiple-choice Video QA. Besides, we propose Glance then Scrutinize (GtS), the first training-free framework that achieves the best balance performance in both accuracy and efficiency. GtS uniquely balances high temporal precision and semantic interpretability while meeting practical speed requirements, outperforming previous methods in real-world scenarios. Furthermore, we introduce the JeAUG metric for holistic evaluation of both speed and accuracy. Extensive experiments demonstrate the superior effectiveness and practicality of our benchmark, framework, and metric.
BrainLMM: A Label-Free Framework for Mapping Multi-Semantic Representation in the Human Visual Cortex
Authors: Tan Gao; Mufan Xue; Haofang Zheng; Shuo Lv; Jia Xu; Dabin Sheng; Ziming Mao; Xinyu Wu; Andrew Luo; Guoyuan Yang
Venue: AAAI 2026 (main)

Abstract: Previous studies leveraging artificial neural networks have been used to investigate the semantic coding within human visual cortex. However, building an interpretable label-free framework that can effectively map brain responses to multiple coexisting semantic concepts remains largely unexplored. Here, we propose BrainLMM, a label-free framework for multi-semantic mapping of voxel responses by combining diverse vision encoders with the Describe-and-Dissect strategy, enabling a hypothesis-free analysis of the human high-level visual cortex. First, we construct voxel-wise encoding models leveraging diverse vision encoders to predict visual cortical responses to natural scene images. Then, we use BrainLMM to map individual brain voxels to multiple semantics without requiring any predefined labels. To evaluate the effectiveness of our method, we compute Pearson correlation coefficients to compare the multi-semantic mappings produced by BrainLMM and CLIP-MSM with ground-truth voxel responses within selective cortical areas. Our findings indicate that BrainLMM achieves more accurate predictions of visual responses compared to CLIP-MSM. Finally, to demonstrate the multi-semantic mapping capability of our method, we project multiple representative semantic concepts onto the cortical surface for visualization. Our method enables the discovery of voxels that exhibit strong activation in response to previously undefined semantic concepts across two independent datasets: the Natural Scenes Dataset (NSD) and the Natural Object Dataset (NOD).
AdaDepth: Exploiting Inherent Scene Information for Self-Supervised Depth Estimation in Dynamic Scenes
Authors: Xuanang Gao; Xiongbin Wu; Zhiwei Ning; Runze Yang; Zhonglong Zheng; Jie Yang; Wei Liu
Venue: AAAI 2026 (main)

Abstract: Self-supervised monocular depth estimation methods severely compromise accuracy in dynamic objects due to their static scene assumption. 
Existing approaches for dynamic scenes suffer from two critical shortcomings: 1) reliance on supervised segmentation models (requiring costly annotations) or computationally intensive multi-branch models to isolate moving objects, and 2) simple integration of 2D/3D motion flow without reliable supervision for dynamic objects. 
We propose AdaDepth, a two‑stage framework that jointly performs unsupervised scene decomposition and dynamic-aware depth learning. In the initial structural stage, our geometry-motion joint scene decomposition (GMoDecomp) module ensures the robust generation of a depth prior and simultaneously partitions the scene into multiple regions through the fusion of geometric and motion cues. 
In the region-adaptive refinement stage, we exploit the depth prior and decomposed regions to introduce motion-aware and geometry-consistent constraints, effectively improving depth estimation in dynamic scenes. 
AdaDepth achieves accurate depth prediction in highly dynamic scenes without relying on external labels or specialized segmentation models. Extensive experiments on KITTI, Cityscapes, and Waymo Open demonstrate its superiority over state-of-the-art approaches.
IQGS: Instance Query-based Gaussian Segmentation
Authors: Yichao Gao; Xinyuan Liu; Yike Ma; Yucheng Zhang; Feng Dai
Venue: AAAI 2026 (main)

Abstract: In recent years, Gaussian scene representations have achieved a series of promising results in 3D reconstruction. Compared to the previous 3DGS paradigm, the latest reconstruction approach 2DGS can achieve more accurate geometric representation using fewer Gaussian points. Accordingly, developing a panoramic segmentation algorithm suitable for 2DGS-reconstructed scenes is of significant importance. However, existing segmentation methods are primarily designed for 3DGS. They either fail to account for all objects in complex segmentation scenes or suffer from significant performance degradation when applied to 2D Gaussian scenes. Moreover, these methods consistently exhibit poor cross-dataset generalization. To address these issues, we propose IQGS, a segmentation framework applicable to 2DGS representations. Specifically, IQGS employs per-instance query and relaxed object-level supervision instead of strict pixel-level ID supervision, effectively mitigating the segmentation performance degradation that occurs when applied to 2DGS. At the same time, by learning features independent of specific object ID assignments, IQGS enhances its ability to generalize across diverse datasets. Our method achieves impressive panoramic segmentation results across multiple datasets, with an average mIoU of 66.6%, surpassing the state-of-the-art method Gaussian Grouping, which achieves 57.17%.
HKAFER: Achieve Visual Parameter-Efficient Fine-Tuning via Heterogeneous Kronecker Adaptation for Facial Expression Recognition
Authors: Yu Gao; Haoyu Ji; Zhiyong Wang; Wenze Huang; Qian Dong; Zhihao Yang; Xueting Liu; Weihong Ren; Honghai Liu
Venue: AAAI 2026 (main)

Abstract: Facial Expression Recognition (FER) seeks to classify affective states from facial images, which remains a challenging problem due to variations in real-world conditions. FER task becomes particularly complex when handling unconstrained environments characterized by partial occlusions, different head poses, and so on. To address the above problems, current approaches rely on extensive learnable parameters and complex model architectures, which inevitably lead to overfitting and cause the FER model to focus on non-discriminative facial regions. In this work, we propose an HKAFER model that can adaptively enhance visual expression representations through efficiently fine-tuning the image encoder in large Visual Foundation Models (VFMs) and Vision-Language Models (VLMs). Specifically, we establish Heterogeneous Kronecker Adaptation (HeKA), which consists of multi-scale adapters based on Kronecker product in a parallel manner, offering significantly diverse subspaces to learn the incremental matrices. Besides, we also propose Dual-Branch Interactive Router (DBIR) to dynamically assign the weights of adapters, which promotes collaboration and information flow among them. In this way, our HKAFER can effectively capture robust spatial features and the regional associations. Experimental results demonstrate that our proposed model not only outperforms state-of-the-art methods on several FER benchmarks but also uses significantly fewer trainable parameters.
High-Quality Full-Head 3D Avatar Generation from Any Single Portrait Image
Authors: Yujie Gao; ChenCheng Wang; Xianbing Sun; Jiahui Zhan; Wentao Wang; Yiyi Zhang; Haohua Zhao; Liqing Zhang; Jianfu Zhang
Venue: AAAI 2026 (main)

Abstract: In this work, we introduce a novel high-fidelity full-head 3D avatar generation method from a single image, regardless of perspective, style, expression, or accessories. Prior works often fail to preserve consistent head geometry and facial details, primarily due to their limited capacity in modeling fine-grained facial textures and maintaining identity information. To address these challenges, we construct a new high-quality dataset containing 227 sequences of digital human portraits captured from 96 different perspectives, totalling 21,792 frames, featuring high-quality facial texture details. To further improve performance, we propose a novel multi-view diffusion named ID-TS diffusion model, which integrate identity and expression information into the two-stage multi-view diffusion process. The low-resolution stage ensures structural consistency of heads across multiple views, while the high-resolution stage preserves facial detail fidelity and coherence. Finally, we propose an enhanced feed-forward Gaussian avatar reconstruction method that optimizes the network on multi-view images of each single subject, significantly improving 3D facial texture details. Extensive experiments show that our method demonstrates robust performance across challenging scenarios, while showcasing broad applicability across numerous downstream tasks.