GenAI4ELab/papercli-papers huggingface dataset:
FGNet: Leveraging Feature-Guided Attention to Refine SAM2 for 3D EM Neuron Segmentation
Authors: Zhenghua Li; Hang Chen; Zihao Sun; Kai Li; Xiaolin Hu
Venue: AAAI 2026 (main)

Abstract: Accurate segmentation of neural structures in Electron Microscopy (EM) images is paramount for neuroscience. However, this task is challenged by intricate morphologies, low signal-to-noise ratios, and scarce annotations, limiting the accuracy and generalization of existing methods. To address these challenges, we seek to leverage the priors learned by visual foundation models on a vast amount of natural images to better tackle this task. Specifically, we propose a novel framework that can effectively transfer knowledge from Segment Anything 2 (SAM2), which is pre-trained on natural images, to the EM domain. We first use SAM2 to extract powerful, general-purpose features. To bridge the domain gap, we introduce a Feature-Guided Attention module that leverages semantic cues from SAM2 to guide a lightweight encoder, the Fine-Grained Encoder (FGE), in focusing on these challenging regions. Finally, a dual-affinity decoder generates both coarse and refined affinity maps. Experimental results demonstrate that our method achieves performance comparable to state-of-the-art (SOTA) approaches with the SAM2 weights frozen. Upon further fine-tuning on EM data, our method significantly outperforms existing SOTA methods. This study validates that transferring representations pre-trained on natural images, when combined with targeted domain-adaptive guidance, can effectively address the specific challenges in neuron segmentation.
DTTNet: Improving Video Shadow Detection via Dark-Aware Guidance and Tokenized Temporal Modeling
Authors: Zhicheng Li; Kunyang Sun; Rui Yao; Hancheng Zhu; Fuyuan Hu; Jiaqi Zhao; Zhiwen Shao; Yong Zhou
Venue: AAAI 2026 (main)

Abstract: Video shadow detection confronts two entwined difficulties: distinguishing shadows from complex backgrounds and modeling dynamic shadow deformations under varying illumination. To address shadow-background ambiguity, we leverage linguistic priors through the proposed Vision-language Match Module (VMM) and a Dark-aware Semantic Block (DSB), extracting text-guided features to explicitly differentiate shadows from dark objects. Furthermore, we introduce adaptive mask reweighting to downweight penumbra regions during training and apply edge masks at the final decoder stage for better supervision. For temporal modeling of variable shadow shapes, we propose a Tokenized Temporal Block (TTB) that decouples spatiotemporal learning. TTB summarizes cross-frame shadow semantics into learnable temporal tokens, enabling efficient sequence encoding with minimal computation overhead. Comprehensive Experiments on multiple benchmark datasets demonstrate state-of-the-art accuracy and real-time inference efficiency.
HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval
Authors: Zixu Li; Yupeng Hu; Zhiwei Chen; Shiqi Zhang; Qinlei Huang; Zhiheng Fu; Yinwei Wei
Venue: AAAI 2026 (main)

Abstract: Composed Image Retrieval (CIR) is a flexible image retrieval paradigm that enables users to accurately locate the target image through a multimodal query composed of a reference image and modification text. Although this task has demonstrated promising applications in personalized search and recommendation systems, it encounters a severe challenge in practical scenarios known as the Noise Triplet Correspondence (NTC) problem. This issue primarily arises from the high cost and subjectivity involved in annotating triplet data. To address this problem, we identify two central challenges: the precise estimation of composed semantic discrepancy and the insufficient progressive adaptation to modification discrepancy. To tackle these challenges, we propose a cHrono-synergiA roBust progressIve learning framework for composed image reTrieval (HABIT), which consists of two core modules. First, the Mutual Knowledge Estimation Module quantifies sample cleanliness by calculating the Transition Rate of mutual information between the composed feature and the target image, thereby effectively identifying clean samples that align with the intended modification semantics. Second, the Dual-consistency Progressive Learning Module introduces a collaborative mechanism between the historical and current models, simulating human habit formation to retain good habits and calibrate bad habits, ultimately enabling robust learning under the presence of NTC. Extensive experiments conducted on two standard CIR datasets demonstrate that HABIT significantly outperforms most methods under various noise ratios, exhibiting superior robustness and retrieval performance.
Object-Centric Framework for Video Moment Retrieval
Authors: Zongyao Li; Yongkang Wong; Satoshi Yamazaki; Jianquan Liu; Mohan Kankanhalli
Venue: AAAI 2026 (main)

Abstract: Most existing video moment retrieval methods rely on temporal sequences of frame- or clip-level features that primarily encode global visual and semantic information. However, such representations often fail to capture fine-grained object semantics and appearance, which are crucial for localizing moments described by object-oriented queries involving specific entities and their interactions. In particular, temporal dynamics at the object level have been largely overlooked, limiting the effectiveness of existing approaches in scenarios requiring detailed object-level reasoning. To address this limitation, we propose a novel object-centric framework for moment retrieval. Our method first extracts query-relevant objects using a scene graph parser and then generates scene graphs from video frames to represent these objects and their relationships. Based on the scene graphs, we construct object-level feature sequences that encode rich visual and semantic information. These sequences are processed by a relational tracklet transformer, which models spatio-temporal correlations among objects over time. By explicitly capturing object-level state changes, our framework enables more accurate localization of moments aligned with object-oriented queries. We evaluated our method on three benchmarks: Charades-STA, QVHighlights, and TACoS. Experimental results demonstrate that our method outperforms existing state-of-the-art methods across all benchmarks.
Diffusion-Based Contextual Reconstruction for Point Cloud Segmentation with Limited Annotations
Authors: Jiawei Lian; Zhengxue Wang; Wentao Qu; Haobo Jiang; Le Hui; Jian Yang
Venue: AAAI 2026 (main)

Abstract: Point cloud semantic segmentation is fundamental to 3D scene understanding, but dense annotation requirements limit scalability. 
Although recent label propagation and contrastive learning methods enhance local consistency, the incomplete object coverage caused by sparse annotations hinders global context modeling, ultimately limiting overall performance.
To this end, we propose a diffusion-based contextual reconstruction framework for point cloud semantic segmentation with limited annotations. 
At its core, our framework guides denoising with semantic predictions, using better context reconstruction to enhance the conditional model for better segmentation.
Specifically, our contributions include:
(1) Diffusion-based segmentation framework: reconstructs contextual semantics from noise under conditional guidance, sharing the decoder with the segmentation module for robust contextual semantic learning.
(2) Dynamically aggregates local context from segmentation features and guides denoising with global spatial structure, significantly enhancing denoising quality and contextual awareness. 
Notably, we pioneer diffusion models for 3D semantic segmentation with limited annotations, enabling efficient single-step inference.
Experiments show robustness across varying annotation ratios and state-of-the-art performance on benchmarks.
MoSs: Mixture of Scales for Efficient High-Resolution Autoregressive Image Generation
Authors: Yaoxiu Lian; Hao Liang; Zhihong Gou; Yijia Zhang; Jiaming Xu; Guohao Dai; Ningyi Xu
Venue: AAAI 2026 (main)

Abstract: Since next-scale prediction was introduced as a new paradigm for autoregressive image generation, it has attracted extensive research interest. By progressively increasing resolution in a draft-to-refinement process, next-scale prediction demonstrates great potential in both generation quality and efficiency. However, at high resolutions, this paradigm faces a fundamental challenge: token sequences grow quadratically and accumulate across multiple scales, resulting in a key performance bottleneck. Our systematic study uncovers two critical observations: (1) most image regions have stabilized during early drafting stages, making later refinement across the full-scale image token-inefficient; (2) different scales inherently trade off efficiency and fidelity, suggesting that adaptive token dispatch on different scales can focus resources where they yield the greatest quality gains. Motivated by these insights, we propose a training-free Mixture of Scales (MoSs) method for efficient high-resolution autoregressive image generation. MoSs breaks the strict causal dependency across scales in the final refinement steps by parallelizing scales of different resolutions, each responsible for a subset of spatial regions. A lightweight frequency-based token dispatcher analyzes the drafted image and assigns regions to the appropriate scale. The outputs are then composited over the draft to produce the final high-resolution image. The scale-mixture method exhibits remarkable efficiency with little impact on generation quality on various models.  For instance, our implementation achieves 2.05-4.96x speedup on the transformer backbone, up to 85.62% KV cache reduction, incurring only 0.1-2.4% loss on GenEval quality, based on the state-of-the-art Infinity model.
DW-DGAT: Dynamically Weighted Dual Graph Attention Network for Neurodegenerative Disease Diagnosis
Authors: Chengjia Liang; Zhenjiong Wang; Chao Chen; Ruizhi Zhang; Songxi Liang; Hai Xie; Haijun Lei; Zhongwei Huang
Venue: AAAI 2026 (main)

Abstract: Parkinson's disease (PD) and Alzheimer's disease (AD) are the two most prevalent and incurable neurodegenerative diseases (NDs) worldwide, for which early diagnosis is critical to delay their progression. However, the high dimensionality of multi-metric data with diverse structural forms, the heterogeneity of neuroimaging and phenotypic data, and class imbalance collectively pose significant challenges to early ND diagnosis. To address these challenges, we propose a dynamically weighted dual graph attention network (DW-DGAT) that integrates: (1) a general-purpose data fusion strategy to merge three structural forms of multi-metric data; (2) a dual graph attention architecture based on brain regions and inter-sample relationships to extract both micro- and macro-level features; and (3) a class weight generation mechanism combined with two stable and effective loss functions to mitigate class imbalance. Rigorous experiments, based on the Parkinson Progression Marker Initiative (PPMI) and Alzhermer's Disease Neuroimaging Initiative (ADNI) studies, demonstrate the state-of-the-art performance of our approach.
Multi-Agent Undercover Gaming: Hallucination Removal Through Counterfactual Test for Multimodal Reasoning
Authors: Dayong Liang; Xiao-Yong Wei; Changmeng Zheng
Venue: AAAI 2026 (main)

Abstract: Hallucination continues to pose a major obstacle in the reasoning capabilities of large language models (LLMs). Although the Multi-Agent Debate (MAD) paradigm offers a promising solution by promoting consensus among multiple agents to enhance reliability, it relies on the unrealistic assumption that all debaters are rational and reflective, which is a condition that may not hold when agents themselves are prone to hallucinations. To address this gap, we introduce the Multi-agent Undercover Gaming (MUG) protocol, inspired by social deduction games like ''Who is Undercover?''. MUG reframes MAD as a process of detecting ''undercover'' agents (those suffering from hallucinations) by employing multimodal counterfactual tests.  Specifically, we modify reference images to introduce counterfactual evidence and observe whether agents can accurately identify these changes, providing ground-truth for identifying hallucinating agents and enabling robust, crowd-powered multimodal reasoning. MUG advances MAD protocols along three key dimensions: (1) enabling factual verification beyond statistical consensus through counterfactual testing; (2) introducing cross-evidence reasoning via dynamically modified evidence sources instead of relying on static inputs; and (3) fostering active reasoning, where agents engage in probing discussions rather than passively answering questions. Collectively, these innovations offer a more reliable and effective framework for multimodal reasoning in LLMs.
Improved Masked Image Generation with Knowledge-Augmented Token Representations
Authors: Guotao Liang; Baoquan Zhang; Zhiyuan Wen; Zihao Han; Yunming Ye
Venue: AAAI 2026 (main)

Abstract: Masked image generation (MIG) has demonstrated remarkable efficiency and high-fidelity images by enabling parallel token prediction. Existing methods typically rely solely on the model itself to learn semantic dependencies among visual token sequences. However, directly learning such semantic dependencies from data is challenging because the individual tokens lack clear semantic meanings, and these sequences are usually long. To address this limitation, we propose a novel Knowledge-Augmented Masked Image Generation framework, named KA-MIG, which introduces explicit knowledge of token-level semantic dependencies (i.e., extracted from the training data) as priors to learn richer representations for improving performance. In particular, we explore and identify three types of advantageous token knowledge graphs, including two positive and one negative graphs (i.e., the co-occurrence graph, the semantic similarity graph, and the position-token incompatibility graph). Based on three prior knowledge graphs, we design a graph-aware encoder to learn token and position-aware representations. After that, a lightweight fusion mechanism is introduced to integrate these enriched representations into the existing MIG methods. Resorting to such prior knowledge, our method effectively enhances the model's ability to capture semantic dependencies, leading to improved generation quality. Experimental results demonstrate that our method improves upon existing MIG for class-conditional image generation on ImageNet.
OTI: A Model-free and Visually Interpretable Measure of Image Attackability
Authors: Jiaming Liang; Haowei Liu; Chi-Man Pun
Venue: AAAI 2026 (main)

Abstract: Despite the tremendous success of neural networks, benign images can be corrupted by adversarial perturbations to deceive these models. Intriguingly, images differ in their attackability. Specifically, given an attack configuration, some images are easily corrupted, whereas others are more resistant. Evaluating image attackability has important applications in active learning, adversarial training, and attack enhancement. This prompts a growing interest in developing attackability measures. However, existing methods are scarce and suffer from two major limitations: (1) They rely on a model proxy to provide prior knowledge (e.g., gradients or minimal perturbation) to extract model-dependent image features. Unfortunately, in practice, many task-specific models are not readily accessible. (2) Extracted features characterizing image attackability lack visual interpretability, obscuring their direct relationship with the images. To address these, we propose a novel Object Texture Intensity (OTI), a model-free and visually interpretable measure of image attackability, which measures image attackability as the texture intensity of the image's semantic object. Theoretically, we describe the principles of OTI from the perspectives of decision boundaries as well as the mid- and high-frequency characteristics of adversarial perturbations. Comprehensive experiments demonstrate that OTI is effective and computationally efficient. In addition, our OTI provides the adversarial machine learning community with a visual understanding of attackability.
MM-R1: Unleashing the Power of Unified Multimodal Large Language Models for Personalized Image Generation
Authors: Qian Liang; Yujia Wu; Kuncheng Li; Jiwei Wei; Shiyuan He; Jinyu Guo; Ning Xie
Venue: AAAI 2026 (main)

Abstract: Multimodal Large Language Models (MLLMs) with unified architectures excel across a wide range of vision-language tasks, yet aligning them with personalized image generation remains a significant challenge. Existing methods for MLLMs are frequently subject-specific, demanding a data-intensive fine-tuning process for every new subject, which  limits their scalability. In this paper, we introduce MM-R1, a framework that integrates a cross-modal Chain-of-Thought (X-CoT) reasoning strategy to unlock the inherent potential of unified MLLMs for personalized image generation. Specifically, we structure personalization as an integrated visual reasoning and generation process: (1) grounding subject concepts by interpreting and understanding user-provided images and contextual cues, and (2) generating personalized images conditioned on both the extracted subject representations and user prompts. To further enhance the reasoning capability, we adopt Grouped Reward Proximal Policy Optimization(GRPO) to explicitly align the generation. Experiments demonstrate that MM-R1 unleashes the personalization capability of unified MLLMs to generate images with high subject fidelity and strong text alignment in a zero-shot manner.
Rethinking Surgical Smoke: A Smoke-Type-Aware Laparoscopic Video Desmoking Method and Dataset
Authors: Qifan Liang; Junlin Li; Zhen Han; Xihao Wang; Zhongyuan Wang; Bin Mei
Venue: AAAI 2026 (main)

Abstract: Electrocautery or lasers will inevitably generate surgical smoke, which hinders the visual guidance of laparoscopic videos for surgical procedures. The surgical smoke can be classified into different types based on its motion patterns, leading to distinctive spatio-temporal characteristics across smoky laparoscopic videos. However, existing desmoking methods fail to account for such smoke-type-specific distinctions. Therefore, we propose the first Smoke-Type-Aware Laparoscopic Video Desmoking Network (STANet) by introducing two smoke types: Diffusion Smoke and Ambient Smoke. Specifically, a smoke mask segmentation sub-network is designed to jointly conduct smoke mask and smoke type predictions based on the attention-weighted mask aggregation, while a smokeless video reconstruction sub-network is proposed to perform specially desmoking on smoky features guided by two types of smoke mask. To address the entanglement challenges of two smoke types, we further embed a coarse-to-fine disentanglement module into the mask segmentation sub-network, which yields more accurate disentangled masks through the smoke-type-aware cross attention between non-entangled and entangled regions. In addition, we also construct the first large-scale synthetic video desmoking dataset with smoke type annotations. Extensive experiments demonstrate that our method not only outperforms state-of-the-art approaches in quality evaluations, but also exhibits superior generalization across multiple downstream surgical tasks.
Tensor Decomposition and Language Description for Open-Vocabulary Object Detection
Authors: Qiuyu Liang; Yongqiang Zhang
Venue: AAAI 2026 (main)

Abstract: Open-vocabulary object detection (OVOD) aims at detecting and recognizing objects beyond a fixed set of classes. Although region-word alignment and knowledge distillation have been explored for training a strong open-vocabulary detector, our analysis reveals three main issues (inaccurate alignment, redundant distillation, and low-quality class embedding) that limit OVOD's performance. In this paper, we explore the well-designed Tensor decomposition and Language descriptions for open-vocabulary object Detection (called TLDet).
Proposals with the highest similarity score often correspond to discriminative but incomplete regions (e.g., object heads), resulting in inaccurate region-word alignment. To mitigate this issue, we propose a low-rank proposal filtering module that quantitatively assesses the completeness of each proposal by performing singular value decomposition and computing the sum of its singular values. This allows the model to reduce discriminative proposals and enhance the precision of alignment between visual regions and textual concepts. Furthermore, to mitigate redundant knowledge transfer, we introduce a core tensor distillation approach that decomposes teacher and student features into core tensors via Tucker decomposition and performs distillation through optimized tensor alignment. This ensures that the student acquires the most essential knowledge from the teacher. Finally, to improve the quality of class embedding, a language description enhancement method is proposed by exploring the knowledge of LLM to enrich the representations of categories during inference. Extensive experiments on popular datasets demonstrate the superior performance of our TLDet, achieving 36.1% mAP on COCO and 30.1% mask mAP on LVIS, and outperforming existing methods on novel categories.
Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving
Authors: Shiyi Liang; Xinyuan Chang; Changjie Wu; Huiyuan Yan; Yifan Bai; Xinran Liu; Hang Zhang; Yujian Yuan; Shuang Zeng; Mu Xu; Xing Wei
Venue: AAAI 2026 (main)

Abstract: Safe autonomous driving requires both accurate HD map construction and persistent awareness of traffic rules, even when their associated signs are no longer visible. However, existing methods either focus solely on geometric elements or treat rules as temporary classifications, failing to capture their persistent effectiveness across extended driving sequences. In this paper, we present PAMR (Persistent Autoregressive Mapping with Traffic Rules), a novel framework that performs autoregressive co-construction of lane vectors and traffic rules from visual observations. Our approach introduces two key mechanisms: Map-Rule Co-Construction for processing driving scenes in temporal segments, and Map-Rule Cache for maintaining rule consistency across these segments. To properly evaluate continuous and consistent map generation, we develop MapDRv2, featuring improved lane geometry annotations. Extensive experiments demonstrate that PAMR achieves superior performance in joint vector-rule mapping tasks, while maintaining persistent rule effectiveness throughout extended driving sequences.