GenAI4ELab/papercli-papers huggingface dataset:
Conformable Convolution for Topologically Constrained Learning of Complex Anatomical Structures
Authors: Yousef Yeganeh; Goktug Guvercin; Nassir Navab; Azade Farshad
Venue: AAAI 2026 (main)

Abstract: While conventional computer vision emphasizes pixel-level and feature-based objectives, medical image analysis of intricate biological structures necessitates explicit representation of their complex topological properties. Despite their successes, deep learning models often struggle to accurately capture the connectivity and continuity of fine, sometimes pixel-thin, yet critical structures due to their reliance on implicit learning from data. To address this challenge, we introduce Conformable Convolution, a novel convolutional layer designed to explicitly impose topological consistency. Conformable Convolution learns adaptive kernel offsets that focus on regions of high topological significance within an image. This prioritization is guided by our proposed Topological Posterior Generator (TPG) module, which leverages persistent homology. The TPG module identifies key topological features and guides the convolutional layers by applying persistent homology to feature maps transformed into cubical complexes. Unlike existing approaches that are merely aware of topology, our method explicitly constrains the learning process to ensure topological correctness. The proposed modules are architecture-agnostic, enabling them to be integrated seamlessly into various architectures. We showcase the effectiveness of our framework in the segmentation task, where preserving the interconnectedness of structures is critical. The results on three diverse datasets demonstrate that our framework effectively preserves the topology both quantitatively and qualitatively.
Beyond Simple Edits: X-Planner for Complex Instruction-Based Image Editing
Authors: Chun-Hsiao Yeh; Yilin Wang; Nanxuan Zhao; Richard Zhang; Yuheng Li; Yi Ma; Krishna Kumar Singh
Venue: AAAI 2026 (main)

Abstract: Recent diffusion-based image editing methods have made great strides in text-guided tasks but often struggle with complex, indirect instructions. Additionally, current models frequently exhibit poor identity preservation, unintended edits, or rely on manual masks. To overcome these limitations, we introduce X-Planner, a Multimodal Large Language Model (MLLM)-based planning system that bridges user intent with editing model capabilities. X-Planner uses chain-of-thought reasoning to systematically break down complex instructions into simpler sub-instructions. For each one, X-Planner automatically generates precise edit types and segmentation masks, enabling localized, identity-preserving edits without applying external tools or models during inference. To enable the training of such a planner, we also introduce a fully automated, reproducible pipeline to generate large-scale, high-quality training data. Our complete system achieves state-of-the-art results on both existing and newly proposed complex instruction-based editing benchmarks.
Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs
Authors: Chun-Hsiao Yeh; Chenyu Wang; Shengbang Tong; Ta-Ying Cheng; Ruoyu Wang; Tianzhe Chu; Yuexiang Zhai; Yubei Chen; Shenghua Gao; Yi Ma
Venue: AAAI 2026 (main)

Abstract: Multi-view understanding, the ability to  reconcile visual information across diverse viewpoints for effective navigation, manipulation, and 3D scene comprehension, is a fundamental challenge in Multi-Modal Large Language Models (MLLMs) to be used as embodied agents. While recent MLLMs have shown impressive advances in high-level reasoning and planning, they frequently fall short when confronted with multi-view geometric consistency and cross-view correspondence. To comprehensively evaluate the challenges of MLLMs in multi-view scene reasoning, we introduce All-Angles Bench, a human carefully benchmark with over 2,100 question-answer pairs from 90 diverse, real-world scenes. Our broad evaluation across 38 general-purpose and 3D spatial reasoning MLLMs reveals a substantial performance gap compared to humans. More critically, our analysis identifies two root failure modes: (1) cross-view object mismatch—the inability to establish consistent object correspondence across views; and (2) cross-view spatial misalignment—the failure to infer accurate camera poses and spatial layouts. These findings underscore a lack of multi-view awareness in current MLLMs, calling for architectural innovations beyond prompt tuning alone. We believe that our benchmark offers valuable insights toward building spatially-intelligent MLLMs.
DiffRefiner: Coarse to Fine Trajectory Planning via Diffusion Refinement with Semantic Interaction for End to End Autonomous Driving
Authors: Liuhan Yin; Runkun Ju; Guodong Guo; Erkang Cheng
Venue: AAAI 2026 (main)

Abstract: Unlike discriminative approaches in autonomous driving that predict a fixed set of candidate trajectories of the ego vehicle, generative methods, such as diffusion models, learn the underlying distribution of future motion, enabling more flexible trajectory prediction. However, since these methods typically rely on denoising human-craft trajectory anchors or random noise, there remains significant room for improvement. In this paper, we propose DiffRefiner, a novel two-stage trajectory prediction framework. The first stage employs a transformer-based Proposal Decoder to generate coarse trajectory predictions by regressing from sensor inputs using predefined trajectory anchors. The second stage applies a Diffusion Refiner that iteratively denoises and refines these initial predictions. In this way, we enhance the performance of diffusion-based planning by incorporating a discriminative trajectory proposal module, which provides strong guidance for the generative refinement process. Furthermore, we design a fine-grained denoising decoder to enhance scene compliance, enabling more accurate trajectory prediction through enhanced alignment with the surrounding environment. Experimental results demonstrate that DiffRefiner achieves state-of-the-art performance, attaining 87.4 EPDMS on NAVSIM v2, and 87.1 DS along with 71.4 SR on Bench2Drive, thereby setting new records on both public benchmarks. The effectiveness of each component is validated via ablation studies as well.
Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Function
Authors: Shuo Yin; Zhiyuan Yin; Yuqing Hou; Rui Liu; Yong Chen; Dell Zhang
Venue: AAAI 2026 (main)

Abstract: Hash center-based deep hashing methods improve upon pairwise or triplet-based approaches by assigning fixed hash centers to each class as learning targets, thereby avoiding the inefficiency of local similarity optimization. However, random center initialization often disregards inter-class semantic relationships. While existing two-stage methods mitigate this by first refining hash centers with semantics and then training the hash function, they introduce additional complexity, computational overhead, and suboptimal performance due to stage-wise discrepancies.
To address these limitations, we propose Center-Reassigned Hashing (CRH), an end-to-end framework that dynamically reassigns hash centers from a preset codebook while jointly optimizing the hash function. Unlike previous methods, CRH adapts hash centers to the data distribution without explicit center optimization phases, enabling seamless integration of semantic relationships into the learning process. Furthermore, a multi-head mechanism enhances the representational capacity of hash centers, capturing richer semantic structures. Extensive experiments on three benchmarks demonstrate that CRH learns semantically meaningful hash centers and outperforms state-of-the-art deep hashing methods in retrieval tasks.
Learning to Tell Apart: Weakly Supervised Video Anomaly Detection via Disentangled Semantic Alignment
Authors: Wenti Yin; Huaxin Zhang; Xiang Wang; Yuqing Lu; Yicheng Zhang; Bingquan Gong; Jialong Zuo; Li Yu; Changxin Gao; Nong Sang
Venue: AAAI 2026 (main)

Abstract: Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal foundation models such as CLIP to highlight anomalous instances and classify categories.
However, their objectives may tend to detect the most salient response segments, while neglecting to mine diverse normal patterns separated from anomalies, and are prone to category confusion due to similar appearance, leading to unsatisfactory fine-grained classification results.
Therefore, we propose a novel Disentangled Semantic Alignment Network (DSANet) to explicitly separate abnormal and normal features from coarse-grained and fine-grained aspects, enhancing the distinguishability.
Specifically, at the coarse-grained level, we introduce a self-guided normality modeling branch that reconstructs input video features under the guidance of learned normal prototypes, encouraging the model to exploit normality cues inherent in the video, thereby improving the temporal separation of normal patterns and anomalous events.
At the fine-grained level, we present a decoupled contrastive semantic alignment mechanism, which first temporally decomposes each video into event-centric and background-centric components using frame-level anomaly scores and then applies visual-language contrastive learning to enhance class-discriminative representations.
Comprehensive experiments on two standard benchmarks, namely XD-Violence and UCF-Crime, demonstrate that DSANet outperforms existing state-of-the-art methods.
KPLM-STA: Physically-Accurate Shadow Synthesis for Human Relighting via Keypoint-Based Light Modeling
Authors: Xinhui Yin; Qifei Li; Yilin Guo; Hongxia Xie; Xiaoli Zhang
Venue: AAAI 2026 (main)

Abstract: Image composition aims to seamlessly integrate a foreground object into a background, where generating realistic and geometrically accurate shadows remains a persistent challenge. While recent diffusion-based methods have outperformed GAN-based approaches, existing techniques, such as the diffusion-based relighting framework IC-Light, still fall short in producing shadows with both high appearance realism and geometric precision, especially in composite images. To address these limitations, we propose a novel shadow generation framework based on a Keypoints Linear Model (KPLM) and a Shadow Triangle Algorithm (STA). KPLM models articulated human bodies using nine keypoints and one bounding block, enabling physically plausible shadow projection and dynamic shading across joints, thereby enhancing visual realism. STA further improves geometric accuracy by computing shadow angles, lengths, and spatial positions through explicit geometric formulations. 
Extensive experiments demonstrate that our method achieves state-of-the-art performance on shadow realism benchmarks, particularly under complex human poses, and generalizes effectively to multi-directional relighting scenarios such as those supported by IC-Light.
Spatiotemporal-Untrammelled Mixture of Experts for Multi-Person Motion Prediction
Authors: Zheng Yin; Chengjian Li; Xiangbo Shu; Meiqi Cao; Rui Yan; Jinhui Tang
Venue: AAAI 2026 (main)

Abstract: Comprehensively and flexibly capturing the complex spatio-temporal dependencies of human motion is critical for multi-person motion prediction. Existing methods grapple with two primary limitations: i) Inflexible spatiotemporal representation due to reliance on positional encodings for capturing spatiotemporal information. ii) High computational costs stemming from the quadratic time complexity of conventional attention mechanisms. To overcome these limitations, we propose the Spatiotemporal-Untrammelled Mixture of Experts (ST-MoE), which flexibly explores complex spatio-temporal dependencies in human motion and significantly reduces computational cost. To adaptively mine complex spatio-temporal patterns from human motion, our model incorporates four distinct types of spatiotemporal experts, each specializing in capturing different spatial or temporal dependencies. To reduce the potential computational overhead while integrating multiple experts, we introduce bidirectional spatiotemporal Mamba as experts, each sharing bidirectional temporal and spatial Mamba in distinct combinations to achieve model efficiency and parameter economy. Extensive experiments on four multi-person benchmark datasets demonstrate that our approach not only outperforms state-of-art in accuracy but also reduces model parameter by 41.38% and achieves a 3.6× speedup in training.
RefSTAR: Blind Face Image Restoration with Reference Selection, Transfer, and Reconstruction
Authors: Zhicun Yin; Junjie Chen; Ming Liu; Zhixin Wang; Fan Li; Renjing Pei; Xiaoming Li; Rynson W. H. Lau; Wangmeng Zuo
Venue: AAAI 2026 (main)

Abstract: Introducing high-quality references can largely alleviate the uncertainty in blind face image restoration tasks, yet the equivocal utilization of reference priors makes it still a struggle to well preserve the human identity. We attribute the identity inconsistency to two deficiencies of existing reference-based face restoration methods, namely the inability to effectively determine which features need to be transferred, and the failure to preserve the structure and details of the selected features. This work mainly focuses on these two issues, and we present a novel blind face image restoration method that considers reference selection, transfer, and reconstruction (RefSTAR) to introduce proper features from reference images. Specifically, we construct a reference selection (RefSel) module, which can generate accurate masks to select reference features. For training the RefSel module, we construct a RefSel-HQ dataset through a mask generation pipeline, which contains annotated masks for 10,000 ground truth-reference pairs. To guarantee the exact introduction of selected reference features, a feature fusion paradigm is designed for reference feature transferring, and a Mask-Compatible Cycle-Consistency Loss is redesigned based on reference reconstruction to further ensure the presence of selected reference image features in the output image. Experiments on various backbone models demonstrate superior performance, showing better identity preservation ability and reference feature transfer quality.
Exploiting All Mamba Fusion for Efficient RGB-D Tracking
Authors: Ge Ying; Dawei Zhang; Chengzhuan Yang; Wei Liu; Sang-Woon Jeon; Hua Wang; Changqin Huang; Zhonglong Zheng
Venue: AAAI 2026 (main)

Abstract: Despite the progress made through deep learning, existing Visual Object Tracking (VOT) frameworks struggle with real-world challenges. Recent approaches incorporate additional modalities like Depth, Thermal Infrared, and Language to enhance the robustness of VOT, particularly with the improvement of the depth sensor precision, facilitating RGB-D tracking. However, current RGB-D trackers often copy RGB tracking paradigms, leading to inefficiency due to two-stream architectures that fail to exploit heterogeneous features, and reliance on simplistic or large-parameter fusion methods. To address these challenges, we propose AMTrack, a one-stream RGB-D tracker leveraging Mamba's linear complexity for simultaneous feature extraction and two-stage cross-modal feature fusion. Our innovation also includes a low-parameter Multimodal Mix Mamba (3M) module, which optimizes deep feature fusion and reduces computational overhead. The advantage of the 3M module stems from our Multimodal State Space Model (MSSM), a multimodal feature interaction component reconstructed based on SSM. Experiments across multiple RGB-D tracking datasets indicate that AMTrack achieves superior performance with lower parameters and memory demands compared to state-of-the-arts.
PeriUn: Enhancing Unlearning by Selectively Forgetting Peripheral Samples
Authors: Hee Bin Yoo; Dong-Sig Han; Jaein Kim; Byoung-Tak Zhang
Venue: AAAI 2026 (main)

Abstract: Once trained, neural networks memorize information in diffusely encoded parameters, making it difficult to forget in support of the right to be forgotten. Unlearning aims to remove the influence of data, with performance measured against a retrained model that excludes the data. However, understanding the behavior of gold-standard retraining remains underexplored. We compare original and retrained models and observe that most prediction changes occur in peripheral samples near decision boundaries. Consequently, we propose PeriUn, a selective strategy that unlearns only peripheral samples to mimic retrained model behavior with minimal disruption, unlike prior works that remove the entire request. Combined with the Random Label based method, PeriUn significantly improves both generalization and privacy metrics. Specifically, on TinyImageNet with VGG16, PeriUn increases the Tug-of-War score by 22 points compared to the strongest. Besides, the MIA gap score surpasses the state-of-the-art method, improving by 8.7 points after applying PeriUn. Further analyses confirm that PeriUn better preserves the feature space and aligns closely with the retrained model.
Through the Water: Refractive Gaussian Splatting for Water Surface Scenes
Authors: Yeonghun Yoon; Hojoon Jung; Jaeyoon Lee; Taegwan Kim; Gyuhyun Kim; Jongwon Choi
Venue: AAAI 2026 (main)

Abstract: Scenes with water surfaces present a significant challenge for Gaussian Splatting due to the simultaneous presence of refraction and reflection, as well as the difficulty of accurately estimating the geometry of transparent water surfaces. To address this, we propose a novel framework for reconstructing scenes involving both reflection and refraction caused by water surfaces. The water surface is modeled as a trainable plane, and 2D Gaussian ray tracing is applied to account for refraction through the water. We extend 2D Gaussian Splatting by introducing a soft mask parameter and a dual set of Gaussian primitives, which handle both reflected and refracted effects. Our method achieves state-of-the-art performance on newly constructed water surface datasets, including both synthetic and real scenes, and significantly outperforms prior approaches in water-interacting regions. Furthermore, we demonstrate the editability of our model by manipulating the index of refraction to suppress or modify refractive effects, enabling scene transformations into different liquids.
Dense Cross-Scale Image Alignment with Fully Spatial Correlation and Just Noticeable Difference Guidance
Authors: Jinkun You; Jiaxue Li; Jie Zhang; Yicong Zhou
Venue: AAAI 2026 (main)

Abstract: Existing unsupervised image alignment methods exhibit limited accuracy and high computational complexity. To address these challenges, we propose a dense cross-scale image alignment model. It takes into account the correlations between cross-scale features to decrease the alignment difficulty. Our model supports flexible trade-offs between accuracy and efficiency by adjusting the number of scales utilized. Additionally, we introduce a fully spatial correlation module to further improve accuracy while maintaining low computational costs. We incorporate the just noticeable difference to encourage our model to focus on image regions more sensitive to distortions, eliminating noticeable alignment errors. Extensive quantitative and qualitative experiments demonstrate that our method surpasses state-of-the-art approaches.
KPDM: Key Phrase Dynamic Masking for Robust Text-to-Image Person Retrieval
Authors: Shaofeng You; Tianle Miao; Qihang Chen; Xin Li; Zhuo Cheng; Dapeng Luo
Venue: AAAI 2026 (main)

Abstract: Text-to-image person re-identification (TIReID) aims to retrieve the most relevant pedestrian images from an image gallery based on natural language descriptions. Recent studies have achieved significant performance improvements by leveraging Masked Language Modeling (MLM) to align fine-grained information through local matching. However, in the text feature extraction, randomly masking text tokens may disrupt the semantic relationships between these local tokens, leading to feature misalignment; on the other hand, from an image feature perspective, redundant patches in pedestrian images hinder the information interaction across modalities. Moreover, the presence of noisy image-text pairs further complicates the learning process, as the model may be misled into recognizing incorrect patterns. To address these issues, we propose a robust fine-grained local alignment framework based on Key Phrase Dynamic Mask (KPDM). First, we strengthen the semantic relationships between text tokens by implementing a "adjective + noun'' phrase-level masking strategy, and design a frequency-based masked language loss (FMLM) to supervise fine-grained semantic-level local alignment. Second, we integrate cross-layer importance estimation to highlight key pedestrian image representations while removing redundant image features. Third, we propose a trusted consensus partitioning mechanism, utilizing intra-identity image-text similarity distributions to identify noisy pairs, enhancing the model robustness. Extensive experiments show that our method achieves 67.95% Rank-1 and 51.88% mAP on the RSTPReid dataset, exceeding the previous state-of-the-art by 2.6% and 1%. Furthermore, KPDM achieves Rank-1 accuracies of 75.97% on the CUHK-PEDES dataset and 67.78% on the ICFG-PEDES dataset, outperforming earlier methods.
Knowledge Completes the Vision: A Multimodal Entity-aware Retrieval-Augmented Generation Framework for News Image Captioning
Authors: Xiaoxing You; Qiang Huang; Lingyu Li; Chi Zhang; Xiaopeng Liu; Min Zhang; Jun Yu
Venue: AAAI 2026 (main)

Abstract: News image captioning aims to produce journalistically informative descriptions by combining visual content with contextual cues from associated articles. Despite recent advances, existing methods struggle with three key challenges: (1) incomplete information coverage, (2) weak cross-modal alignment, and (3) suboptimal visual-entity grounding. To address these issues, we introduce MERGE, the first Multimodal Entity-aware Retrieval-augmented GEneration framework for news image captioning. MERGE constructs an entity-centric multimodal knowledge base (EMKB) that integrates textual, visual, and structured knowledge, enabling enriched background retrieval. It improves cross-modal alignment through a multistage hypothesis-caption strategy and enhances visual-entity matching via dynamic retrieval guided by image content. Extensive experiments on GoodNews and NYTimes800k show that MERGE significantly outperforms state-of-the-art baselines, with CIDEr gains of +6.84 and +1.16 in caption quality, and F1-score improvements of +4.14 and +2.64 in named entity recognition. Notably, MERGE also generalizes well to the unseen Visual News dataset, achieving +20.17 in CIDEr and +6.22 in F1-score, demonstrating strong robustness and domain adaptability.