Singapore

Recent progress in robot learning has produced impressive
results, yet many systems still require learning from large
datasets of demonstrations and are less effective in clutter
or with highly deformable objects. This talk presents work
on data-efficient manipulation using (i) diffusion-based
augmentation that synthesizes geometrically consistent
images
and action labels to reduce demonstration requirements and
(ii) Vision-Language Models (VLMs) that inject high-level
semantics for contact-rich motion planning in clutter. We
will
also introduce ManipBench, which evaluates VLMs’ abilities
for low-level manipulation. Together, we show how to move
the community towards achieving robot manipulators that can
learn and operate with reduced demonstration requirements
across cluttered and real-world environments.

AAAI 2026

Data-Efficient and Contact-Rich Manipulation Through Diffusion Augmentation and Vision-Language Models

bimanual robot manipulation

diffusion models

data augmentation

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

technical paper

Egocentric visual query localization is vital for embodied AI and VR/AR, yet remains challenging due to camera motion, viewpoint changes, and appearance variations. We present $\textbf{EAGLE}$, a novel framework that leverages $\textbf{e}$pisodic $\textbf{a}$ppearance- and $\textbf{g}$eometry-aware memory to achieve unified 2D-3D visual query $\textbf{l}$ocalization in $\textbf{e}$gocentric vision. Inspired by avian memory consolidation, EAGLE synergistically integrates segmentation guided by an appearance-aware meta-learning memory (AMM), with tracking driven by a geometry-aware localization memory (GLM). This memory consolidation mechanism, through structured appearance and geometry memory banks, stores high-confidence retrieval samples, effectively supporting both long- and short-term modeling of target appearance variations. This enables precise contour delineation with robust spatial discrimination, leading to significantly improved retrieval accuracy. Furthermore, by integrating the VQL-2D output with a visual geometry grounded Transformer (VGGT), we achieve a efficient unification of 2D and 3D tasks, enabling rapid and accurate back-projection into 3D space. Our method achieves state-of-the-art performance on the Ego4D-VQ benchmark. Code will be released soon.

EAGLE: Episodic Appearance- and Geometry-aware Memory for Unified 2D-3D Visual Query Localization in Egocentric Vision

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 \textit{FourierPET}, a Fourier-based unrolled reconstruction framework grounded in the Alternating Direction Method of Multipliers. It consists of three tailored modules: a \textit{spectral consistency module} that enforces global frequency alignment to maintain data fidelity, an \textit{amplitude–phase correction module} that decouples and compensates for high-frequency phase distortions and low-frequency amplitude suppression, and a \textit{dual adjustment module} that accelerates convergence during iterative reconstruction. Extensive experiments demonstrate that \textit{FourierPET} achieves state-of-the-art performance with significantly fewer parameters, while offering enhanced interpretability through frequency-aware correction.

FourierPET: Deep Fourier-based Unrolled Network for Low-count PET Reconstruction

Multivariate time series anomaly detection is a crucial factor in real-world applications but a challenging task due to the complex temporal dependencies and system dynamics. Reconstruction-based methods have made great improvements in recent years. However, we observe an issue these methods are suffering, that they primarily measure deviations in the time points themselves when performing anomaly detection but ignore changes in the dynamic properties of the system. In these cases, they are unable to produce sufficient reconstruction errors to detect anomalies, so some potential abnormal time points caused by the dynamic evolution of the system are missing. To address this problem, we propose a novel method, SDA2D, which models system dynamics by the derivative of the NCDE-derived state vector with respect to time, enabling the learning of reconstruction deviation and system evolution jointly. Our experimental results show that SDA2D achieves noticeable improvements in four benchmark datasets, and the visualization also provides further instructions for anomaly diagnosis, which helps locate the sources of these anomalies.

State-Derivative-Aware Neural Controlled Differential Equations for Multivariate Time Series Anomaly Detection and Diagnosis

Infrared imaging plays a critical role in low-light and adverse weather conditions. However, due to the distinct characteristics of infrared images, existing foundation models such as Masked Autoencoder (MAE) trained on visible data perform suboptimal in infrared image interpretation tasks. To bridge this gap, an infrared foundation model known as InfMAE was developed and pre-trained on large-scale infrared datasets. Despite its effectiveness, InfMAE still faces several limitations, including the omission of informative tokens, insufficient modeling of global associations, and neglect of non-uniform noise. In this paper, we propose a Dual-domain Guided Infrared foundation model based on MAE (DuGI-MAE). First, we design a deterministic masking strategy based on token entropy, preserving only high-entropy tokens for reconstruction to enhance informativeness. Next, we introduce a Dual-Domain Guidance (DDG) module, which simultaneously captures global token relationships and adaptively filters non-uniform background noise commonly present in infrared imagery. To facilitate large-scale pretraining, we construct Inf-590K, a comprehensive infrared image dataset encompassing diverse scenes, various target types, and multiple spatial resolutions. Pretrained on Inf-590K, DuGI-MAE demonstrates strong generalization capabilities across various downstream tasks, including infrared object detection, semantic segmentation, and small target detection. Experimental results validate the superiority of the proposed method over both supervised and self-supervised comparison methods. Code: https://github.com/Xtingsu/DuGI-MAE.

DuGI-MAE: Improving Infrared Mask Autoencoders via Dual-Domain Guidance

The perception of moving objects is crucial for autonomous
robots performing collision avoidance in dynamic
environments. LiDARs and cameras tremendously enhance scene
interpretation but do not provide direct motion information
and face limitations under adverse weather. Radar sensors
overcome these limitations and provide Doppler velocities,
delivering direct information on dynamic objects. In this
paper, we address the problem of moving instance
segmentation in radar point clouds to enhance scene
interpretation for safety-critical tasks. Our Radar
Instance Transformer enriches the current radar scan with
temporal information without passing aggregated scans
through a neural network. We propose a full-resolution
backbone to prevent information loss in sparse point cloud
processing. Our instance transformer head incorporates
essential information to enhance segmentation but also
enables reliable, class-agnostic instance assignments. In
sum, our approach shows superior performance on the new
moving instance segmentation benchmarks, including diverse
environments, and provides model-agnostic modules to
enhance scene interpretation. The benchmark is based on the
RadarScenes dataset and is available at
https://doi.org/10.5281/zenodo.10203864.

Radar Instance Transformer: Reliable Moving Instance
Segmentation in Sparse Radar Point Clouds

Multi-Instance Generation has advanced significantly in spatial placement and attribute binding. However, existing approaches still face challenges in fine-grained semantic understanding, particularly when dealing with complex textual descriptions.To overcome these limitations, we propose $\textbf{DEIG}$, a novel framework for fine-grained and controllable multi-instance generation. DEIG integrates an $\textit{Instance Detail Extractor}$ (IDE) that transforms text encoder embeddings into compact, instance-aware representations, and a $\textit{Detail Fusion Module}$ (DFM) that applies instance-based masked attention to prevent attribute leakage across instances. These components enable DEIG to generate visually coherent multi-instance scenes that precisely match rich, localized textual descriptions.
To support fine-grained supervision, we construct a high-quality dataset with detailed, compositional instance captions generated by VLMs. We also introduce $\textbf{DEIG-Bench}$, a new benchmark with region-level annotations and multi-attribute prompts for both humans and objects.Experiments demonstrate that DEIG consistently outperforms existing approaches across multiple benchmarks in spatial consistency, semantic accuracy, and compositional generalization. Moreover, DEIG functions as a plug-and-play module, making it easily integrable into standard diffusion-based pipelines.

DEIG: Detail-Enhanced Instance Generation with Fine-Grained Semantic Control

Deep neural networks (DNNs) have significantly advanced diabetic retinopathy (DR) diagnosis, yet their black-box nature limits clinical acceptance due to a lack of interpretability. Concept bottleneck model (CBM) offers a promising solution by enabling concept-level reasoning and test-time intervention, with recent DR studies modeling lesions as concepts and grades as outcomes. However, current methods often ignore relationships between lesion concepts across different DR grades and struggle when fine-grained lesion concepts are unavailable, limiting their interpretability and real-world applicability. To bridge these gaps, we propose VLM-GCR, a vision-language model guided graph concept reasoning framework for interpretable DR diagnosis. VLM-GCR emulates the diagnostic process of ophthalmologists by constructing a grading-aware lesion concept graph that explicitly models the interactions among lesions and their relationships to disease grades. In concept-free clinical scenarios, our method introduces a vision-language guided dynamic concept pseudo-labeling mechanism to mitigate the challenges of existing concept-based models in fine-grained lesion recognition. Additionally, we introduce a multi-level intervention method that supports error correction, enabling transparent and robust human-AI collaboration. Experiments on two public DR benchmarks show that VLM-GCR achieves strong performance in both lesion and grading tasks, while delivering clear and clinically meaningful reasoning steps.

Vision-Language Models Guided Graph Concept Reasoning for Interpretable Diabetic Retinopathy Diagnosis

Detecting mirror regions in RGB videos is essential for scene understanding in applications such as scene reconstruction and robotic navigation. Existing video mirror detectors typically rely on cues like inside-outside mirror correspondences and 2D motion inconsistencies. However, these methods often yield noisy or incomplete predictions when confronted with complex real-world video scenes, especially in areas with occlusion or limited visual features and motions. We observe that human perceive and navigate 3D occluded environments with remarkable ease, owing to Motion-in-Depth (MiD) perception. MiD integrates information from visual appearance (image colors and textures), the way objects move around us in 3D space (3D motions), and their relative distance from us (depth) to determine whether something is approaching or receding and to support navigation. Motivated by this neuroscience mechanism, we introduce MiD-VMD, the first approach to explicitly model MiD for video mirror detection. MiD-VMD jointly utilizes contrastive 3D motion, depth, and image features through two novel modules based on a combinational QKV transformer architecture. The Motion-in-Depth Attention Learning (MiD-AL) module captures complementary relationships across these modalities with combinatorial attention and enforces a compact encoding to represent global 3D transformations, resulting in more accurate mirror detection and reduced motion artifacts. The Motion-in-Depth Boundary Detection (MiD-BD) module further sharpens mirror boundaries by leveraging cross-modal attention on 3D motion and depth features. Extensive experiments show that MiD-VMD outperforms current SOTAs. We will release our code.

Video Mirror Detection with the Motion-in-Depth Cue

Irregular time series (IRTS) are prevalent in real-world applications, where uneven sampling and missing data pose fundamental challenges to deep learning-based feature modeling. Although existing methods attempt to retain timestamp information, they often overlook the structured patterns embedded within the missingness itself, and tend to perform poorly when confronted with class imbalance exacerbated by data incompleteness. Specifically, temporal irregularity hinders the modeling of long-range dependencies
and local patterns, while sparse observations limit representational capacity, disproportionately impairing minority classes and leading to severe classification bias. To address these deeply coupled challenges, we propose SPECTRA (Structured Pattern and Enriched Context-aware Temporal Representation Architecture), a unified framework for robust IRTS classification. SPECTRA introduces a frequency-guided observation encoder that reconstructs temporal dependencies in a stable manner, mitigating spectral distortion and information corruption. Complementarily, a missingness pattern encoder explicitly captures the dynamic evolution of missing data and leverages it as a discriminative signal. In addition, a prototype-constrained classification paradigm directly optimizes the geometric structure of the feature space, enhancing intra-class compactness and alleviating generalization bottlenecks caused by class imbalance. Extensive experiments on three public IRTS datasets—P12, P19, and PAM—demonstrate the superior performance of SPECTRA under both missing and imbalanced conditions.

Beyond Missing Data Imputation: Information-Theoretic Coupling of Missingness and Class Imbalance for Optimal Irregular Time Series Classification

Sequential Recommenders, which exploit dynamic user intents through interaction sequences, are vulnerable to adversarial attacks. While existing attacks primarily rely on data poisoning, they require large-scale user access or fake profiles thus lacking practicality. In this paper, we focus on the Profile Pollution Attack (PPA) that subtly contaminates partial user interactions to induce targeted mispredictions. Previous PPA methods suffer from two limitations, i.e., i) over-reliance on sequence horizon impact restricts fine-grained perturbations on item transitions, and ii) holistic modifications cause detectable distribution shifts. To address these challenges, we propose a constrained reinforcement driven attack CREAT that synergizes a bi-level optimization framework with multi-reward reinforcement learning to balance adversarial efficacy and stealthiness. We first develop a Pattern Balanced Rewarding Policy, which integrates pattern inversion rewards to invert critical patterns and distribution consistency rewards to minimize detectable shifts via unbalanced co-optimal transport. Then we employ a Constrained Group Relative Reinforcement Learning paradigm, enabling step-wise perturbations through dynamic barrier constraints and group-shared experience replay, achieving targeted pollution with minimal detectability. Extensive experiments demonstrate the effectiveness of CREAT. Our codes are available at https://anonymous.4open.science/r/CREAT-5B36.

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