AAAI 2026 Papers

Tracking Any Point (TAP) is a foundational task in computer vision with broad applicability. The state-of-the-art self-supervised TAP method leverages a global matching transformer and contrastive random walks to learn point correspondences. However, its dense all-pairs attention and correlation volume computation tend to introduce irrelevant features and produce less informative training signals, degrading both learning efficiency and tracking accuracy. To address these limitations, we introduce LEAP-Track, a self-supervised TAP approach that computes the attention matrices and correlation volume over adaptively selected sparse pairs. It consists of two core designs: (1) Curriculum-based Sparse Attention (CSA), which dynamically focuses on the most relevant keys, promoting the learning of discriminative features; and (2) Progressive k-NN Transition (PkT), which reformulates the contrastive random walk to operate on an increasingly sparse k-NN affinity graph to reinforce the learning of the most informative correspondences. By integrating the above two designs into a two-stage training paradigm, LEAP-Track is shown both theoretically and empirically to effectively boost learning efficiency, achieving superior tracking accuracy over existing self-supervised TAP methods.

Real-time 3D object detection from point clouds is essential for dynamic scene understanding in applications such as augmented reality, robotics, and navigation. We introduce a novel Spatial-prioritized and Rank-aware 3D object detection (SR3D) framework for indoor point clouds, to bridge the gap between how detectors are trained and how they are evaluated. This gap stems from the lack of spatial reliability and ranking awareness during training, which conflicts with the ranking-based prediction selection used at inference. Such a training-inference gap hampers the model’s ability to learn representations aligned with inference-time behavior. To address the limitation, SR3D consists of two components tailored to the spatial nature of point clouds during training: a novel spatial-prioritized optimal transport assignment that dynamically emphasizes well-located and spatially reliable samples, and a rank-aware adaptive self-distillation scheme that adaptively injects ranking perception via a self-distillation paradigm. Extensive experiments on ScanNet V2 and SUN RGB-D show that SR3D effectively bridges the training-inference gap and significantly outperforms prior methods in accuracy while maintaining real-time speed.

Medical image synthesis is an important topic for both clinical and research applications. Recently, diffusion models have become a leading approach in this area. Despite their strengths, many existing methods struggle with (1) limited generalizability, only working for specific body regions or voxel spacings, (2) slow inference, which is a common issue for diffusion models, and (3) weak alignment with input conditions, which is a critical issue for medical imaging. MAISI, a previously proposed framework, addresses generalizability issues but still suffers from slow inference and limited condition consistency. In this work, we present MAISI-v2, the first accelerated 3D medical image synthesis framework that integrates rectified flow to enable fast and high-quality generation. To further enhance condition fidelity, we introduce a novel region-specific contrastive loss to improve sensitivity to the region of interest. Our experiments show that MAISI-v2 can achieve state-of-the-art image quality with 33× acceleration for latent diffusion models. We also conducted a downstream segmentation experiment to show that the synthetic images can be used for data augmentation. We release our code, training details, model weights, and a GUI demo to facilitate reproducibility and promote further development within the community.

Aiming to estimate the full extent of partially occluded objects, amodal segmentation is a critical capability for visual intelligence. Existing methods suffer from limitations in efficiency and precision, due to their reliance on auxiliary information or two-stage architectures. Furthermore, they lack generalizability, failing to meet practical requirements. To overcome these challenges, we proposed a new paradigm, CondDiff-AMO, that interprets amodal segmentation as a denoising problem by leveraging diffusion models. Methodologically, the designed novel framework consists of three key innovations to adapt the task characteristics and unlocks the diffusion models’ potential in amodal segmentation, including a masking strategy in the forward process, an adaptive transformer for conditional feature extraction, and visual-guided sampling. In the forward process, progressive masking strategy converts ground-truth masks to visible masks, simulating amodal segmentation process to enhance reasoning regarding occluded areas. For architectural design, a pyramid network with feature refinement extracts adaptive and representative conditional priors, improving the guidance in the denoising process of diffusion models. As for the sampling stage, a visible mask is incorporated with an ensemble strategy, restricting the prediction on occluded part. Experiments were conducted on five well-known datasets under supervised and zero-shot learning, with the results confirming that CondDiff-AMO outperforms state-of-the-art methods.

The slow sampling speed of diffusion models hinders their application in 3D LiDAR scene completion. To address this, we propose Distillation-DPO, a novel framework that accelerates sampling through score distillation while simultaneously enhancing generation quality via preference alignment. Distillation-DPO follows a three-step procedure. First, the student model generates paired completion scenes with different initial noises. Second, using LiDAR scene evaluation metrics as preference, we construct winning and losing sample pairs. Third, as our core innovation, Distillation-DPO optimizes the student model by exploiting the difference in score functions between the teacher and student models on the paired completion scenes. This operation performs variational score distillation of the student model but simultaneously encourages the distilled student to prefer the winning samples over the losing ones. Extensive experiments demonstrate that Distillation-DPO achieves higher-quality scene completion than state-of-the-art diffusion models, while accelerating sampling by over 5-fold. To our knowledge, our work is the first to integrate the preference learning principle of DPO into the distillation of diffusion models, offering a new framework of preference-aligned distillation.

In this paper, we focus on Single-Domain Generalized Object Detection (Single-DGOD), aiming to transfer a detector trained on one source domain to multiple unknown domains. Existing methods for Single-DGOD typically rely on discrete data augmentation or static perturbation methods to expand data diversity, thereby mitigating the lack of access to target domain data. However, in real-world scenarios such as changes in weather or lighting conditions, domain shifts often occur continuously and gradually. Discrete augmentations and static perturbations fail to effectively capture the dynamic variation of feature distributions, thereby limiting the model's ability to perceive fine-grained cross-domain differences. To this end, we propose a new method, i.e., Liquid Temporal Feature Evolution, which simulates the progressive evolution of features from the source domain to simulated latent distributions by incorporating temporal modeling and liquid neural network–driven parameter adjustment. Specifically, we introduce controllable Gaussian noise injection and multi-scale Gaussian blurring to simulate initial feature perturbations, followed by temporal modeling and a liquid parameter adjustment mechanism to generate adaptive modulation parameters, enabling a smooth and continuous adaptation across domains. By capturing progressive cross-domain feature evolution and dynamically regulating adaptation paths, our method bridges the source-unknown domain distribution gap, significantly boosting generalization and robustness to unseen shifts. Significant performance improvements on the Diverse Weather dataset and Real-to-Art benchmark demonstrate the superiority of our method.

Missing critical modalities in medical imaging poses significant challenges for AI-driven diagnostic systems, particularly in scenarios where limited modalities must suffice for downstream tasks. Existing approaches often fail to fully leverage privileged features available only at training or address the information gap between privileged and limited modalities, resulting in suboptimal performance. To address this, we propose a unified, dual-stage Disentanglement-AligNmenT framEwork (DANTE), which uses InformationTheoretic Regularization and Cross-Modal Reconstruction to decompose full-modality information into alignable and privileged-exclusive components. In the first stage, a self-supervised pre-training strategy based on cross-modal reconstruction acts as a proxy task to implicitly incentivize disentangled representations. In the second stage, we present an information-theoretic regularization to explicitly maximize the transfer of privileged knowledge through two novel modules: (1) a Mutual Alignment Module that employs multilevel bidirectional alignment between limited-modality features and alignable features, enhancing cross-modal representation consistency; (2) a Privileged Compaction Module that restricts the privileged-exclusive information flow, promoting the integration of task-relevant content into alignable representations. Experimental results on three challenging medical datasets demonstrate that DANTE achieves state-of-the-art performance, demonstrating its effectiveness in leveraging privileged guidance under modality scarcity, and exhibits broad applicability across diverse medical imaging scenarios.

Pose-agnostic Anomaly Detection (PAD) aims to detect anomalies when the poses of query images are unknown and differ from those in the training set. Therefore, accurately estimating the camera poses for the query images in the test set is critical for this task. Existing query-specific framework methods require re-optimizing a new set of parameters for each query image, limiting their generalization and increasing computational burden. To overcome these limitations, we propose a novel method, Relative Pose Estimation for Pose-agnostic Anomaly Detection (RPE-PAD), which enhances both generalization and efficiency with a query-independent framework. Specifically, we propose a Random View Synthesis Scheme (RVSS) that generates new poses by adding Gaussian perturbations to the original poses, then renders the corresponding views to augment the dataset. To estimate the relative camera pose between two input images, we introduce an Iterative Relative Pose Refinement Network (IRPRN), which incorporates a hierarchical coarse-to-fine refinement strategy. Furthermore, we employ a Multi-Pair Training Strategy (MPTS) to train the proposed IRPRN, leveraging multiple image pairs to expand the relative pose transformation space during training. Extensive experiments demonstrate that our method achieves robust anomaly detection performance while significantly improving inference efficiency.

Visual Place Recognition (VPR) aims to determine the geographic location of a query image by retrieving its most visually similar counterpart from a geo-tagged reference database. Recently, the emergence of the powerful visual foundation model, DINOv2, trained in a self-supervised manner on massive datasets, has significantly improved VPR performance. This improvement stems from DINOv2’s exceptional feature generalization capabilities but is often accompanied by increased model complexity and computational overhead that impede deployment on resource-constrained devices. To address this challenge, we propose D2-VPR, a Distillation- and Deformable-based framework that retains the strong feature extraction capabilities of visual foundation models while significantly reducing model parameters and achieving a more favorable performance-efficiency trade-off. Specifically, first, we employ a two-stage training strategy that integrates knowledge distillation and fine-tuning. Additionally, we introduce a Distillation Recovery Module (DRM) to better align the feature spaces between the teacher and student models, thereby minimizing knowledge transfer losses to the greatest extent possible. Second, we design a Top-Down-attention-based Deformable Aggregator (TDDA) that leverages global semantic features to dynamically and adaptively adjust the Regions of Interest (ROI) used for aggregation, thereby improving adaptability to irregular structures. Extensive experiments demonstrate that our method achieves competitive performance compared to state-of-the-art approaches. Meanwhile, it reduces the parameter count by approximately 64.2% (compared to CricaVPR).

Automatic medical report generation has the potential to support clinical diagnosis, reduce the workload of radiologists, and demonstrate potential for enhancing diagnostic consistency. However, current evaluation metrics often fail to reflect the clinical reliability of generated reports. Overlap-based methods overlook fine-grained details (e.g., location, severity), diagnostic metrics are constrained by fixed vocabularies. Some diagnostic metrics are limited by fixed vocabularies or templates, reducing their ability to capture diverse clinical expressions. LLM-based metrics lack interpretable reasoning, limiting trust in clinical settings. Therefore, we propose a Granular Explainable Multi-Agent Score (GEMA-Score) in this paper, which conducts both objective quantification and subjective evaluation through a large language model-based multi-agent workflow. Our GEMA-Score parses structured reports and employs stable calculations through interactive exchanges of information among agents to assess disease diagnosis, location, severity, and uncertainty. Additionally, an LLM-based scoring agent evaluates completeness, readability, and clinical terminology while providing explanatory feedback. Extensive experiments show that GEMA-Score achieves the highest correlation with human experts on public datasets (Kendall = 0.69 on ReXVal; 0.45 on RadEvalX), demonstrating improved clinical scoring reliability.

Accurate segmentation of aortic vascular structures is critical for diagnosing and treating cardiovascular diseases. Traditional Transformer-based models have shown promise in this domain by capturing long-range dependencies between vascular features. However, their reliance on fixed-size rectangular patches often influences the integrity of complex vascular structures, leading to suboptimal segmentation accuracy. To address this challenge, we propose the adaptive Morph-Patch Transformer (MPT), a novel architecture specifically designed for aortic vascular segmentation. Specifically, MPT introduces an adaptive patch partitioning strategy that dynamically generates morphology-aware patches aligned with complex vascular structures. This strategy can preserve semantic integrity of complex vascular structures within individual patches. Moreover, a Semantic Clustering Attention (SCA) method is proposed to dynamically aggregate features from various patches with similar semantic characteristics. This method enhances the model's capability to segment vessels of varying sizes, preserving the integrity of vascular structures. Extensive experiments on three open-source datasets (AVT, AortaSeg24 and TBAD) demonstrate that MPT achieves state-of-the-art performance, with improvements in segmenting intricate vascular structures.

Generalist Virtual Agents (GVAs) powered by Multimodal Large Language Models (MLLMs) exhibit impressive capabilities. However, their long-term learning is hampered by a core limitation: a failure to evolve beyond existing trajectories. This stems from memory systems that treat experiences as isolated fragments and rely on brittle semantic retrieval, preventing the synthesis of novel solutions from disparate knowledge. To address this, we introduce CA3Mem, a framework inspired by the human hippocampus that organizes experiences into a structured memory graph. Leveraging this graph, CA3Mem features two key innovations: 1) a generative memory recombination mechanism that synthesizes novel solutions to drive agent evolution, and 2) an associative retrieval algorithm that employs spreading activation to recall a comprehensive and contextually-aware set of experiences. Experiments on OSWorld and WebArena demonstrate that CA3Mem significantly enhances agent capabilities, leading to marked improvements in long-horizon planning, compositional generalization for novel tasks, and continuous adaptation from experience.

Low-count positron emission tomography (PET) reconstruction is a challenging inverse problem due to severe degradations arising from Poisson noise, photon scarcity, and attenuation correction errors. Existing deep learning methods typically address these in the spatial domain with an undifferentiated optimization objective, making it difficult to disentangle overlapping artifacts and limiting correction effectiveness. In this work, we perform a Fourier-domain analysis and reveal that these degradations are spectrally separable: Poisson noise and photon scarcity cause high-frequency phase perturbations, while attenuation errors suppress low-frequency amplitude components. Leveraging this insight, we propose FourierPET, a Fourier-based unrolled reconstruction framework grounded in the Alternating Direction Method of Multipliers. It consists of three tailored modules: a spectral consistency module that enforces global frequency alignment to maintain data fidelity, an amplitude–phase correction module that decouples and compensates for high-frequency phase distortions and low-frequency amplitude suppression, and a dual adjustment module that accelerates convergence during iterative reconstruction. Extensive experiments demonstrate that FourierPET achieves state-of-the-art performance with significantly fewer parameters, while offering enhanced interpretability through frequency-aware correction.

Movie dubbing seeks to synthesize speech from a given script using a specific voice, while ensuring accurate lip synchronization and emotion-prosody alignment with the character’s visual performance. However, existing alignment approaches based on visual features face two key limitations: (1) they rely on complex, handcrafted visual preprocessing pipelines, including facial landmark detection and feature extraction; and (2) they generalize poorly to unseen visual domains, often resulting in degraded alignment and dubbing quality. To address these issues, we propose InstructDubber, a novel instruction-based alignment dubbing method for both robust in-domain and zero-shot movie dubbing. Specifically, we first feed the video, script, and corresponding prompts into a multimodal large language model to generate natural language dubbing instructions regarding the speaking rate and emotion state depicted in the video, which is robust to visual domain variations. Second, we design an instructed duration distilling module to mine discriminative duration cues from speaking rate instructions to predict lip-aligned phoneme-level pronunciation duration. Third, for emotion-prosody alignment, we devise an instructed emotion calibrating module, which fine-tunes an LLM-based instruction analyzer using ground truth dubbing emotion as supervision and predicts prosody based on the calibrated emotion analysis. Finally, the predicted duration and prosody, together with the script, are fed into the audio decoder to generate video-aligned dubbing. Extensive experiments on three major benchmarks demonstrate that InstructDubber outperforms state‑of‑the‑art approaches across both in‑domain and zero‑shot scenarios.

The Mamba architecture has been widely applied to various low-level vision tasks due to its exceptional adaptability and strong performance. Although the Mamba architecture has been adopted for spectral reconstruction, it still faces the following two challenges: (1) Single spatial perception limits the ability to fully understand and analyze hyperspectral images; (2) Single-scale feature extraction struggles to capture the complex structures and fine details present in hyperspectral images. To address these issues, we propose a multi-scale, multi-perceptual Mamba architecture for the spectral reconstruction task, called M3SR. Specifically, we design a multi-perceptual fusion block to enhance the ability of the model to comprehensively understand and analyze the input features. By integrating the multi-perceptual fusion block into a U-Net structure, M3SR can effectively extract and fuse global, intermediate, and local features, thereby enabling accurate reconstruction of hyperspectral images at multiple scales. Extensive quantitative and qualitative experiments demonstrate that the proposed M3SR outperforms existing state-of-the-art methods while incurring a lower computational cost.

Pedestrian attribute recognition (PAR) has received increasing attention due to its wide application in video surveillance and pedestrian analysis. Some text-enhanced methods tackle this task by converting attributes into language descriptions to facilitate interactive learning between attributes and visual images. However, these generic languages fail to uniquely describe different pedestrian images, missing individual characteristics. In this paper, we propose a Joint Implicit and Explicit Language Guidance Enhancement Learning (JGEL) method, which converts each pedestrian image into a language description with dual language learning to effectively learn enhanced attribute information. Specifically, we first propose an Implicit Language Guidance Learning (ILGL) stream. It projects visual image features into the text embedding space to generate pseudo-word tokens, implicitly modeling image attributes and providing personalized descriptions. Moreover, we propose an Explicit Attribute Enhancement Learning (EAEL) stream to guide the generated pseudo-word tokens obtained by ILGL explicitly aligned with pedestrian attributes, which can effectively align the pseudo-word tokens with the attribute concepts in the text embedding space. Extensive experiments show that JGEL has significant advantages in improving the performance of PAR and the challenging zero-shot PAR task.

Cross-scene hyperspectral image (HSI) recognition aims to assign a unique label to each pixel in the target scene by transferring knowledge from the source scene. Existing methods primarily rely on fully labeled source data and either partially labeled or unlabeled target data. No prior work has addressed the more challenging scenario of cross-scene recognition without label guidance in both scenes. To bridge this gap, we present the first study on cross-scene HSI clustering, proposing an anchor-guided discriminative subspace alignment and clustering (ADSAC) framework that follows a well-structured three-step learning paradigm to effectively mitigate distribution shifts. Specifically, we first develop an anchor-promoted graph learning (APGL) model to efficiently derive accurate clustering labels for the source scene by leveraging anchor-based structural information. Next, we propose a discriminative cross-scene subspace alignment (DCSA) model to improve feature discriminability and reduce distribution discrepancies. Finally, labels of the target scene are inferred after source clustering and cross-scene alignment. To solve the formulated models, we design tailored optimization algorithms to ensure high-quality learning. Extensive experiments demonstrate the superiority of the proposed framework over state-of-the-art methods.

The task of image feature matching aims to establish correct correspondences between images from two different views. While approaches based on attention mechanisms have demonstrated remarkable advancements in image feature matching, they still encounter substantial limitations. Specifically, current graph attention network approaches face performance bottlenecks in complex scenarios, such as low-texture regions or occlusions. This limitation stems from the self-attention mechanism, which, when lacking effective guidance, can lead to divergent attention weights or incorrect focus on regions with low discriminability, resulting in matching failures in low-texture environments. Inspired by how humans focus on distinctive regions when performing cross-view matching, we enhance attention to singular points in images that are salient, unique and have high cross-view matching potential during information aggregation, thereby improving matching capability. To realize the aforementioned strategies, we develop a novel Singularity-enhanced Graph Attention Network (SGAT). SGAT leverages Co-potentiality and Multi-Scale Singularity as prior guidance, and designs a Singularity-aware Attention mechanism and a Co-potentiality Guided Attention mechanism , specifically enhancing the perception of singularity and matching potential during feature interaction. Experimental results on multiple datasets, including ScanNet1500, demonstrate that our method outperforms current state-of-the-art sparse matching methods. In particular, the improvement is most pronounced in complex scenarios such as low-texture environments, significantly enhancing the accuracy and robustness of image matching and its downstream tasks.

Label assignment is a critical component in object detectors, particularly within DETR-style frameworks where the one-to-one matching strategy, despite its end-to-end elegance, suffers from slow convergence due to sparse supervision. While recent works have explored one-to-many assignments to enrich supervisory signals, they often introduce complex, architecture-specific modifications and typically focus on a single auxiliary strategy, lacking a unified and scalable design. In this paper, we first systematically investigate the effects of ``one-to-many'' supervision and reveal a surprising insight that performance gains are driven not by the sheer quantity of supervision, but by the diversity of the assignment strategies employed. This finding suggests that a more elegant, parameter-efficient approach is attainable. Building on this insight, we propose LoRA-DETR, a flexible and lightweight framework that seamlessly integrates diverse assignment strategies into any DETR-style detector. Our method augments the primary network with multiple Low-Rank Adaptation (LoRA) branches during training, each instantiating a different one-to-many assignment rule. These branches act as auxiliary modules that inject rich, varied supervisory gradients into the main model and are discarded during inference, thus incurring no additional computational cost. This design promotes robust joint optimization while maintaining the architectural simplicity of the original detector. Extensive experiments on different baselines validate the effectiveness of our approach. Our work presents a new paradigm for enhancing detectors, demonstrating that diverse ``one-to-many'' supervision can be integrated to achieve state-of-the-art results without compromising model elegance.