GenAI4ELab/papercli-papers huggingface dataset:
RouterNet: Hierarchical Point Routing Network for Robust Vertebral Landmark Localization on AP X-ray Images
Authors: Yingjie Guo; Jinxin Lv; Wei Fang; Qiang Li; Zhiwei Wang
Venue: AAAI 2026 (main)

Abstract: Locating vertebral landmarks on anteroposterior (AP) X-ray images is challenging due to the tissue overlap. Despite the great progress of heatmap-based methods, they often predict missing/false points, which are intolerable in the downstream applications like scoliosis assessment. In this paper, we instead modernize the classic point-regression scheme, and propose a novel model termed RouterNet to locate the 68 vertebral landmarks completely and accurately. RouterNet starts from an initial root point, and then gradually routes it onto more and more points with finer and finer semantics. RouterNet naturally couples such point routing process with its hierarchical and multi-scale feature learning. That is, lower-scale feature maps are utilized to regress points with coarser semantics, and the regressed points pilot a more focused local feature extraction on the next higher-scale map to route onto their subsequent positions with finer semantics. With this divide-and-conquer, RouterNet alleviates the task difficulty, and can robustly localize by routing from the whole spinal center to 17 vertebral centers, and further to their 68 corner points. Extensive and comprehensive experiments on both public and private datasets demonstrate our superior performance over other state-of-the-arts, by decreasing NMSE by 73.8% for landmark localization, and SMAPE by 14.8% for the downstream scoliosis assessment.
SymGS: Leveraging Reflective Symmetries for 3DGS Compression
Authors: Keshav Gupta; Akshat Sanghvi; Shreyas Reddy Palley; Astitva Srivastava; Charu Sharma; Avinash Sharma
Venue: AAAI 2026 (main)

Abstract: 3D Gaussian Splatting has emerged as a transformative technique in novel view synthesis, primarily due to its high rendering speed and photorealistic fidelity. However, its memory footprint scales rapidly with scene complexity, often reaching several gigabytes. Existing methods address this issue by introducing compression strategies that exploit primitive level redundancy through similarity detection and quantization. We aim to surpass the compression limits of such methods by incorporating symmetry aware techniques, specifically targeting mirror symmetries to eliminate redundant primitives. We propose a novel compression framework, SymGS, introducing learnable mirrors into the scene, thereby eliminating local and global reflective redundancies for compression. Our framework functions as a plug and play enhancement to state of the art compression methods, (e.g. HAC) to achieve further compression. Compared to HAC, we achieve 1.66x compression across benchmark datasets (upto 3x on large scale scenes). On an average, SymGS enables 108x compression over 3DGS, while preserving rendering quality.
O3SLM: Open Weight, Open Data, and Open Vocabulary Sketch-Language Model
Authors: Rishi Gupta; Mukilan Karuppasamy; Shyam Marjit; Aditay Tripathi; Anirban Chakraborty
Venue: AAAI 2026 (main)

Abstract: While Large Vision Language Models (LVLMs) are increasingly deployed in real-world applications, their ability to interpret abstract visual inputs remains limited. Specifically, they struggle to comprehend hand-drawn sketches, a modality that offers an intuitive means of expressing concepts that are difficult to describe textually. We identify the primary bottleneck as the absence of a large-scale dataset that jointly models sketches, photorealistic images, and corresponding natural language instructions. To address this, we present two key contributions: (1) a new, large-scale dataset of image-sketch-instruction triplets designed to facilitate both pretraining and instruction tuning, and (2) O3SLM, an LVLM trained on this dataset. Comprehensive evaluations on multiple sketch-based tasks: (a) object localization, (b) counting, (c) image retrieval i.e., (SBIR and fine-grained SBIR), and (d) visual question answering (VQA); while incorporating the three existing sketch datasets, namely QuickDraw!, Sketchy, and Tu-Berlin, along with our generated SketchVCL dataset, show that O3SLM achieves state-of-the-art performance, substantially outperforming existing LVLMs in sketch comprehension and reasoning.
I-INR: Iterative Implicit Neural Representations
Authors: Ali Haider; Muhammad Salman Ali; Maryam Qamar; Tahir Khalil; Soo Ye Kim; Jihyong Oh; Enzo Tartaglione; Sung-Ho Bae
Venue: AAAI 2026 (main)

Abstract: Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable functions parameterized by neural networks. However, INRs are prone to the spectral bias problem, limiting their ability to retain high-frequency information, and often struggle with noise robustness. Motivated by recent trends in iterative refinement processes, we propose Iterative Implicit Neural Representations (I-INRs). This novel plug-and-play framework iteratively refines signal reconstructions to restore high-frequency details, improve noise robustness, and enhance generalization, ultimately delivering superior reconstruction quality. I-INRs integrate seamlessly into existing INR architectures with only a 0.5–2% increase in parameters. During reconstruction, the iterative refinement adds just 0.8–1.6% additional FLOPs over the baseline while delivering a substantial performance boost of up to +2.0 PSNR. Extensive experiments demonstrate that I-INRs consistently outperform WIRE, SIREN, and Gauss across various computer vision tasks, including image fitting, image denoising, and object occupancy prediction.
Universal Compressed Image Restoration via Codec-Aware Conditioning with Reinforcement Learning
Authors: Changwoo Han; Hongil Kim; Donghyun Kim; Sung-Chang Lim; Seung-Won Jung
Venue: AAAI 2026 (main)

Abstract: We address the task of universal compressed image restoration, which involves recovering high-quality images degraded by a wide range of codecs and compression levels. While prior methods have made significant progress, they typically target specific degradation types and struggle to generalize across both traditional and learning-based codecs. To overcome this limitation, we propose a unified framework that leverages codec-aware conditioning and reinforcement learning-based fine-tuning. Specifically, we introduce a conditioning module that encodes both codec type and compression level, enabling the restoration network to adapt its behavior to diverse degradation settings. To further improve generalization, we incorporate reward-based objectives during fine-tuning, providing complementary signals that enhance training across both conventional and learned compression schemes. Experimental results demonstrate the effectiveness of our method in restoring images across a wide range of compression artifacts and scenarios.
Realistic Face Reconstruction from Facial Embeddings via Diffusion Models
Authors: Dong Han; Yong Li; Joachim Denzler
Venue: AAAI 2026 (main)

Abstract: With the advancement of face recognition (FR) systems, privacy-preserving face recognition (PPFR) systems have gained popularity for their accurate recognition, enhanced facial privacy protection, and robustness to various attacks. However, there are limited studies to further verify privacy risks by reconstructing realistic high-resolution face images from embeddings of these systems, especially for PPFR. In this work, we propose the face embedding mapping (FEM), a general framework that explores Kolmogorov-Arnold Network (KAN) for conducting the embedding-to-face attack by leveraging pre-trained Identity-Preserving diffusion model against state-of-the-art (SOTA) FR and PPFR systems. Based on extensive experiments, we verify that reconstructed faces can be used for accessing other real-word FR systems. Besides, the proposed method shows the robustness in reconstructing faces from the partial and protected face embeddings. Moreover, FEM can be utilized as a tool for evaluating safety of FR and PPFR systems in terms of privacy leakage. All images used in this work are from public datasets.
E-MaT:Event-oriented Mamba for Egocentric Point Tracking
Authors: Han Han; Wei Zhai; Baocai Yin; Yang Cao; Bin Li; Zheng-jun Zha
Venue: AAAI 2026 (main)

Abstract: Egocentric point tracking aims to localize points on object surfaces from a first-person perspective and serves as a critical step toward embodied intelligence. Recent methods rely on video input, tracking query points through feature matching across consecutive frames. 
However, these methods struggle in highly dynamic settings—a common challenge in first-person perspectives, where the head-mounted camera undergoes frequent and abrupt rotations, resulting in high angular velocities, motion blur, and large inter-frame displacements.
In contrast, event cameras capture motion at microsecond temporal resolution, naturally avoiding blur and delivering low-latency, high-fidelity cues crucial for egocentric point tracking.
Moreover, rapid egocentric motion disrupts local smoothness, breaking the assumption that spatially adjacent regions share similar motion. Event dynamics expose global motion trends, guiding coherent modeling and consistent feature flow.
Therefore, this paper proposes a mamba-based tracking framework that constructs feature modeling paths aligned with the dominant motion trend extracted from events, and modulates feature propagation along these paths based on local motion intensity, enhancing stability by suppressing unreliable signals and emphasizing consistent cues.
Additionally, a motion-adaptive suppression module enhances temporal robustness by adaptively suppressing correlation features based on motion intensity variations, mitigating the effects of intensity fluctuations and partial observability.
To facilitate research in this domain, a multimodal dataset named DVS-EgoPoints with both events and videos for egocentric point tracking is collected. Experiments on the DVS-EgoPoints dataset and a simulation benchmark demonstrate superior performance over state-of-the-art methods, especially under challenging motion and occlusion conditions.
GS-Checker: Tampering Localization for 3D Gaussian Splatting
Authors: Haoliang Han; Ziyuan Luo; Jun Qi; Anderson Rocha; Renjie Wan
Venue: AAAI 2026 (main)

Abstract: Recent advances in editing technologies for 3D Gaussian Splatting (3DGS) have made it simple to manipulate 3D scenes. However, these technologies raise concerns about potential malicious manipulation of 3D content. To avoid such malicious applications, localizing tampered regions becomes crucial. In this paper, we propose GS-Checker, a novel method for locating tampered areas in 3DGS models. Our approach integrates a 3D tampering attribute into the 3D Gaussian parameters to indicate whether the Gaussian has been tampered. Additionally, we design a 3D contrastive mechanism by comparing the similarity of key attributes between 3D Gaussians to seek tampering cues at 3D level. Furthermore, we introduce a cyclic optimization strategy to refine the 3D tampering attribute, enabling more accurate tampering localization. Notably, our approach does not require expensive 3D labels for supervision. Extensive experimental results demonstrate the effectiveness of our proposed method to locate the tampered 3DGS area.
GOAL: Geometrically Optimal Alignment for Continual Generalized Category Discovery
Authors: Jizhou Han; Chenhao Ding; SongLin Dong; Yuhang He; Shaokun Wang; Qiang Wang; Yihong Gong
Venue: AAAI 2026 (main)

Abstract: Continual Generalized Category Discovery (C-GCD) requires identifying novel classes from unlabeled data while retaining knowledge of known classes over time. Existing methods typically update classifier weights dynamically, resulting in forgetting and inconsistent feature alignment. We propose GOAL, a unified framework that introduces a fixed Equiangular Tight Frame (ETF) classifier to impose a consistent geometric structure throughout learning. GOAL conducts supervised alignment for labeled samples and confidence-guided alignment for novel samples, enabling stable integration of new classes without disrupting old ones. Experiments on four benchmarks show that GOAL outperforms prior methods, reducing forgetting by 16.1% and boosting novel class discovery by 3.2%, establishing a strong solution for long-horizon continual discovery.
MODA: The First Challenging Benchmark for Multispectral Object Detection in Aerial Images
Authors: Shuaihao Han; Tingfa Xu; Peifu Liu; Jianan Li
Venue: AAAI 2026 (main)

Abstract: Aerial object detection faces significant challenges in real-world scenarios, such as small objects and extensive background interference, which limit the performance of RGB-based detectors with insufficient discriminative information. Multispectral images (MSIs) capture additional spectral cues across multiple bands, offering a promising alternative. However, the lack of training data has been the primary bottleneck to exploiting the potential of MSIs. To address this gap, we introduce the first large-scale dataset for Multispectral Object Detection in Aerial images (MODA), which comprises 14,041 MSIs and 330,191 annotations across diverse, challenging scenarios, providing a comprehensive data foundation for this field. Furthermore, to overcome challenges inherent to aerial object detection using MSIs, we propose OSSDet, a framework that integrates spectral and spatial information with object-aware cues. OSSDet employs a cascaded spectral-spatial modulation structure to optimize target perception, aggregates spectrally related features by exploiting spectral similarities to reinforce intra-object correlations, and suppresses irrelevant background via object-aware masking. Moreover, cross-spectral attention further refines object-related representations under explicit object-aware guidance. Extensive experiments demonstrate that OSSDet outperforms existing methods with comparable parameters and efficiency.
EvalMuse-40K: A Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Alignment Evaluation
Authors: Shuhao Han; Haotian Fan; Jiachen Fu; Liang Li; Tao Li; Junhui Cui; Yunqiu Wang; Yang Tai; Jingwei Sun; Chun-Le Guo; Chongyi Li
Venue: AAAI 2026 (main)

Abstract: Text-to-Image (T2I) generation models have achieved significant advancements. Correspondingly, many automated methods emerge to evaluate the image-text alignment capabilities of generative models. However, the performance comparison among these automated methods is constrained by the limited scale of existing datasets. Additionally, existing datasets lack the capacity to assess the performance of automated methods at a fine-grained level. In this study, we contribute an EvalMuse-40K dataset, gathering 40K image-text pairs with fine-grained human annotations for image-text alignment-related tasks. In the construction process, we employ various strategies such as balanced prompt sampling and data re-annotation to ensure the diversity and reliability of our dataset. This allows us to comprehensively evaluate the performance of image-text alignment methods for T2I models. Based on this dataset, we introduce an efficient automated evaluation method termed FGA-BLIP2, which enables Fine-Grained Alignment evaluation solely by inputting images and text leveraging BLIP2, without visual question answering for each fine-grained element. Experimental results show the proposed FGA-BLIP2 efficiently achieves good performance on multiple image-text alignment datasets. Meanwhile, benefiting from the high efficiency and fine-grained evaluation capability of FGA-BLIP2, we apply it as a reward model to improve text-to-image models, which effectively enhances the image-text alignment ability of text-to-image models.
Polysemic Semantic Instance Network for Cross-Modal Hashing
Authors: Shuo Han; Qibing Qin; Kezhen Xie; Wenfeng Zhang; Lei Huang
Venue: AAAI 2026 (main)

Abstract: Hashing techniques are widely adopted in large-scale cross-modal retrieval due to their efficiency and low storage cost. However, semantic ambiguities, including polysemy, multi-object images, and missing semantic descriptions, significantly degrade the accuracy of alignment and retrieval performance. Most existing methods rely on one-to-one mappings that preserve only global average semantics, which fail to capture the intrinsic polysemous structures embedded within individual samples. To address this issue, we propose a novel Deep Polysemic Semantic Instance Hashing (DPSIH) method and design a Diverse Semantic Instance Embedding (DSIE) module. This module integrates local and global features through multi-head self-attention and residual learning, generating multiple diverse embeddings per sample to effectively capture fine-grained and polysemous semantic structures. Furthermore, we design a multi-embedding semantic correlation constraint that relaxes strict alignment restrictions to improve robustness under partial alignment, and introduce Maximum Mean Discrepancy (MMD) regularization to alleviate cross-modal distribution shifts. Additionally, an embedding diversity mechanism is proposed to prevent all embeddings from collapsing into a central or averaged representation, thereby enhancing semantic diversity. Extensive experiments on four benchmark datasets demonstrate that DPSIH significantly outperforms state-of-the-art methods and effectively improves the modeling of semantic ambiguity in cross-modal retrieval tasks.
Filter, Correlate, Compress: Training-Free Token Reduction for MLLM Acceleration
Authors: Yuhang Han; Xuyang Liu; Zihan Zhang; Pengxiang Ding; Junjie Chen; Honggang Chen; Donglin Wang; Qingsen Yan; Siteng Huang
Venue: AAAI 2026 (main)

Abstract: The quadratic complexity of Multimodal Large Language Models (MLLMs) with respect to context length poses significant computational and memory challenges, hindering their real-world deployment. In the paper, we devise a ''filter-correlate-compress'' framework to accelerate the MLLM by systematically optimizing multimodal context length during prefilling. The framework first implements FiCoCo-V, a training-free method operating within the vision encoder. It employs a redundancy-based token discard mechanism that uses a novel integrated metric to accurately filter out redundant visual tokens. To mitigate information loss, the framework introduces a correlation-based information recycling mechanism that allows preserved tokens to selectively recycle information from correlated discarded tokens with a self-preserving compression, thereby preventing the dilution of their own core content. The framework's FiCoCo-L variant further leverages task-aware textual priors to perform token reduction directly within the LLM decoder. Extensive experiments demonstrate that the FiCoCo series effectively accelerates a range of MLLMs, achieves up to 14.7× FLOPs reduction with 93.6% performance retention. Our methods consistently outperform state-of-the-art training-free approaches, showcasing effectiveness and generalizability across model architectures, sizes, and tasks without requiring retraining.
Dual-Geometry Graph Network: Unifying Local and Global Priors for Few-Shot Learning
Authors: Zheng Han; Xiaobin Zhu; Chun Yang; Jingyan Qin; Xu-Cheng Yin
Venue: AAAI 2026 (main)

Abstract: In few-shot learning, utilizing local and global geometric priors to capture both subtle local class metrics and coarse global structures within the meta-task are important to obtain discriminative embeddings. However, existing graph-based and curvature-based few-shot approaches only focus on either one kind of geometric prior but neglect the other. To effectively utilize the pros of these two paradigms, we propose a novel Dual-Geometry Graph Network (DGGN) to adaptively integrate the local and global geometric priors via two key pathways. Specifically, the local-wise metric modeling pathway utilizes Ollivier-Ricci curvature to capture task-specific local class metrics among the instances, and the global-wise connectivity modeling pathway utilizes resistive embedding to capture global instance distributions and connectivity patterns of the entire meta-task. In addition, we introduce two new regularization loss functions to explicitly enhance the geometric representation ability of the local and global pathways respectively. We validate that DGGN's superior performance stems from its adaptively topological refinements by measuring the graph edit distance, demonstrating its ability to match the underlying data distribution. Extensive experiments show that DGGN sets a new state-of-the-art on standard, cross-domain, and semi-supervised few-shot benchmarks.
A Geometric Perspective on Optimizing Vector Quantized Latent Diffusion Model for Image Restoration
Authors: Chen Hang; Haoming Chen; Xuwei Fang; Weisheng Xie; Xiangxiang Gao; Faming Fang; Guixu Zhang; Haichuan Song
Venue: AAAI 2026 (main)

Abstract: In this paper, we investigate the limitations of the Vector Quantized Latent Diffusion Model (VQ-LDM) in restoration tasks. We identify a performance gap between the Vector Quantization (VQ) and Diffusion Model components, manifested as a significant discrepancy between the reconstruction quality of ground truth images processed via VQ autoregression and degraded images restored by VQ-LDM. Through experiments, we attribute this gap primarily to the lack of robustness in the mapped points of VQ within the original VQ-LDM framework. To address this issue, we propose a geometric based optimization approach. First, we introduce a simple yet effective method, termed interpolation-based latent initial state optimization, which mitigates the performance gap by replacing the original mapped points with interpolated values, supported by theoretical analysis. Here, the latent initial state refers specifically to the input of the diffusion model. Building upon this, we further propose a Chebyshev center-based latent initial state optimization, an elegant theoretical solution from a geometric perspective, that further enhances restoration performance. Our improvements consistently achieve superior results across nine benchmark datasets.
StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving
Authors: Ruiyang Hao; Bowen Jing; Haibao Yu; Zaiqing Nie
Venue: AAAI 2026 (main)

Abstract: Personalization, while extensively studied in conventional autonomous driving pipelines, has been largely overlooked in the context of end-to-end autonomous driving (E2EAD), despite its critical role in fostering user trust, safety perception, and real-world adoption. A primary bottleneck is the absence of large-scale real-world datasets that systematically capture driving preferences, severely limiting the development and evaluation of personalized E2EAD models. In this work, we introduce the first large-scale real-world dataset explicitly curated for personalized E2EAD, integrating comprehensive scene topology with rich dynamic context derived from agent dynamics and semantics inferred via a fine-tuned vision-language model (VLM). We propose a hybrid annotation pipeline that combines behavioral analysis, rule-and-distribution-based heuristics, and subjective semantic modeling guided by VLM reasoning, with final refinement through human-in-the-loop verification. Building upon this dataset, we introduce the first standardized benchmark for systematically evaluating personalized E2EAD models. Empirical evaluations on state-of-the-art architectures demonstrate that incorporating personalized driving preferences significantly improves behavioral alignment with human demonstrations.