Singapore

Out-of-distribution (OOD) detection is committed to delineating the classification boundaries between in-distribution (ID) and OOD images. Recent advances in vision-language models (VLMs) have demonstrated remarkable OOD detection performance by integrating both visual and textual modalities. In this context, negative prompts are introduced to emphasize the dissimilarity between image features and prompt content. However, these prompts often include a broad range of non-ID features, which may result in suboptimal outcomes due to the capture of overlapping or misleading information. To address this issue, we propose Positive and Negative Prompt Supervision, which encourages negative prompts to capture inter-class features and transfers this semantic knowledge to the visual modality to enhance OOD detection performance. Our method begins with class-specific positive and negative prompts initialized by large language models (LLMs). These prompts are subsequently optimized, with positive prompts focusing on features within each class, while negative prompts highlight features around category boundaries. Additionally, a graph-based architecture is employed to aggregate semantic-aware supervision from the optimized prompt representations and propagate it to the visual branch, thereby enhancing the performance of the energy-based OOD detector. Extensive experiments on two benchmarks, CIFAR-100 and ImageNet-1K, across eight OOD datasets and five different LLMs, demonstrate that our method outperforms state-of-the-art baselines.

AAAI 2026

Out-of-Distribution Detection with Positive and Negative Prompt Supervision Using Large Language Models

graph-based machine learning

multimodal learning

representation learning

poster

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.

Bayesian networks play a crucial role in various domains for unsupervised feature extraction and data interpretation. The Poisson gamma belief networks (PGBNs), as a type of Bayesian networks, have shown promise in analyzing high-dimensional count data. However, PGBNs encounter significant challenges when applied to sparse data, particularly in achieving accurate feature extraction and avoiding overfitting during missing value prediction. In this paper, we propose the sparse Poisson gamma belief networks (SPGBNs), a Bayesian network model designed to address these limitations. By incorporating sparse graph-structured priors over the weight matrices between adjacent layers, the proposed SPGBNs effectively capture the inherent sparsity and graph structures of latent features. Meanwhile, SPGBNs demonstrate superior generalization on missing data prediction and enable more stable extraction of meaningful latent features compared to existing approaches. Additionally, we develop an efficient Gibbs sampling algorithm that significantly improves the training stability and computational efficiency of SPGBNs. Extensive experiments on real-world datasets are conducted to validate the effectiveness of our approach.

Sparse Poisson Gamma Belief Networks for High-Dimensional Sparse Count Data

Advanced Persistent Threats (APTs) are difficult to detect due to their complexity and stealthiness. To mitigate such attacks, many approaches model entities and their relationship using provenance graphs to detect the stealthy and persistent characteristics of APTs. However, existing detection methods suffer from the flaws of missing indirect dependencies, noisy complex scenarios, and missing behavioral logical associations, which make it difficult to detect complex scenarios and effectively identify stealthy threats.
In this paper, we propose Sentient, an APT detection method that combines pre-training and intent analysis. It employs a graph transformer to learn structural and semantic information from provenance graphs to avoid missing indirect dependencies. We mitigate scenario noise by combining global and local information. Additionally, we design an Intent Analysis Module (IAM) to associate logical relationships between behaviors. Sentient is trained solely on easily obtainable benign data to detect malicious behaviors that deviate from benign behavioral patterns.
We evaluated Sentient on three widely-used datasets covering real-world attacks and simulated attacks. Notably, compared to six state-of-the-art methods, Sentient achieved an average reduction of 44\% in false positive rate(FPR) for detection.

Sentient: Detecting APTs via Capturing Indirect Dependencies and Behavioral Logic

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 a 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.

Learning to LEAP: Efficient Dense Point Tracking by Focusing Where It Matters

Reference Audio-Visual Segmentation (Ref-AVS) tasks challenge models to precisely locate sounding objects by integrating visual, auditory, and textual cues. Existing methods often lack genuine semantic understanding, tending to memorize fixed reasoning patterns. Furthermore, jointly training for reasoning and segmentation can compromise pixel-level precision.
To address these issues, we introduce AURORA, a novel framework designed to enhance genuine reasoning and language comprehension in reference audio-visual segmentation. We employ a structured Chain-of-Thought (CoT) prompting mechanism to guide the model through a step-by-step reasoning process and introduce a novel segmentation feature distillation loss to effectively integrate these reasoning abilities without sacrificing segmentation performance. To further cultivate the model's genuine reasoning capabilities, we devise a further two-stage training strategy: first, a ``corrective reflective-style training" stage utilizes self-correction to enhance the quality of reasoning paths, followed by reinforcement learning via Group Reward Policy Optimization (GRPO) to bolster robustness in challenging scenarios. Experiments demonstrate that AURORA achieves state-of-the-art performance on Ref-AVS benchmarks and generalizes effectively to unreferenced segmentation.

AURORA: Augmented Understanding via Structured Reasoning and Reinforcement Learning for Reference Audio-Visual Segmentation

Recent vision-language model (VLM)-based methods have achieved promising results in zero-shot out-of-distribution (OOD) detection by effectively leveraging the local patch features. 
However, the zero-shot nature inherently comes with two limitations: 1) imperfect local feature prototypes; 2) lack of OOD prototypes.
In this paper, we propose Intra-Image Mining (IIM), a lightweight framework designed to overcome these limitations in a few-shot manner.
IIM is motivated by the fact that local patches within an image often exhibit diverse semantics, with some patches deviating from the main class concept. Therefore, for each image, we first select the top-$k$ class prototype-related patches as positive samples and leverage them to refine and optimize the local feature prototype. Then, the next top-$k$ among the remaining patches are selected as negatives—serving as OOD signals to construct OOD prototypes. This process yields coherent local positives and challenging negatives, effectively enhancing the model’s local feature discrimination. 
Besides, we propose a novel OOD evaluation method named Symmetric Maximum Concept Matching (S-MCM). 
While existing approaches typically adopt an image-to-text scheme—comparing the image features to textual class prototypes—S-MCM further incorporate a text-to-image perspective, leading to more reliable OOD detection. We also propose two benchmarks to analyze
the impact of semantic diversity within ID dataset.
Built on a frozen VLM, IIM, in conjunction with S-MCM, achieves consistent gains in OOD detection on ImageNet-1k and other benchmarks, outperforming prior methods in FPR95 and AUROC across various few-shot settings.

Intra-Image Mining and Symmetric Maximum Concept Matching for Few Shot Out-of-Distribution Detection

Watermarking diffusion-generated images is crucial for copyright protection and user tracking.
However, current diffusion watermarking methods face significant limitations: zero-bit watermarking systems lack the capacity for large-scale user tracking, while multi-bit methods are highly sensitive to certain image transformations or generative attacks, resulting in a lack of comprehensive robustness.
In this paper, we propose **OptMark**, an optimization-based approach that embeds a robust multi-bit watermark into the intermediate latents of the diffusion denoising process. OptMark strategically inserts a structural watermark early to resist generative attacks and a detail watermark late to withstand image transformations, with tailored regularization terms to preserve image quality and ensure imperceptibility.
To address the challenge of memory consumption growing linearly with the number of denoising steps during optimization, OptMark incorporates adjoint gradient methods, reducing memory usage from $O(N)$ to $O(1)$. Experimental results demonstrate that OptMark achieves invisible multi-bit watermarking while ensuring robust resilience against valuemetric transformations, geometric transformations, editing, and regeneration attacks.

OptMark: Robust Multi-bit Diffusion Watermarking via Inference Time Optimization

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.

Beyond Simple Edits: X-Planner for Complex Instruction-Based Image Editing

Given the inherently costly and time-intensive nature of pixel-level annotation, the generation of synthetic datasets comprising sufficiently diverse synthetic images paired with ground-truth pixel-level annotations has garnered increasing attention recently for training high-performance semantic segmentation models. However, existing methods necessitate to either predict pseudo annotations after image generation or generate images conditioned on manual annotation masks, which incurs image-annotation semantic inconsistency or scalability problem. To migrate both problems with one stone, we present a novel dataset generative diffusion framework for semantic segmentation, termed JoDiffusion. Firstly, given a standard latent diffusion model, JoDiffusion incorporates an independent annotation variational auto-encoder (VAE) network to map annotation masks into the latent space shared by images. Then, the diffusion model is tailored to capture the joint distribution of each image and its annotation mask conditioned on a text prompt. By doing these, JoDiffusion enables simultaneously generating paired images and semantically consistent annotation masks solely conditioned on text prompts, thereby demonstrating superior scalability. Additionally, a mask optimization strategy is developed to mitigate the annotation noise produced during generation. Experiments on Pascal VOC, COCO, and ADE20K datasets show that the annotated dataset generated by JoDiffusion yields substantial performance improvements in semantic segmentation compared to existing methods.

JoDiffusion: Jointly Diffusing Image with Pixel-Level Annotations for Semantic Segmentation Promotion

In robot manipulation, robot learning has become a prevailing approach. However, generative models within this field face a fundamental trade-off between the slow, iterative sampling of diffusion models and the architectural constraints of faster Flow-based methods, which often rely on explicit consistency losses. To address these limitations, we introduce MP1, which pairs 3D point-cloud inputs with the MeanFlow paradigm to generate action trajectories in one network function evaluation (1-NFE). By directly learning the interval-averaged velocity via the "MeanFlow Identity", our policy avoids any additional consistency constraints. This formulation eliminates numerical ODE-solver errors during inference, yielding more precise trajectories. MP1 further incorporates CFG for improved trajectory controllability while retaining 1-NFE inference without reintroducing structural constraints. Because subtle scene-context variations are critical for robot learning, especially in few-shot learning, we introduce a lightweight Dispersive Loss that repels state embeddings during training, boosting generalization without slowing inference. We validate our method on the Adroit and Meta-World benchmarks, as well as in real-world scenarios. Experimental results show MP1 achieves superior average task success rates, outperforming DP3 by 10.2% and FlowPolicy by 7.3%. Its average inference time is only 6.8 ms—19 times faster than DP3 and nearly 2 times faster than FlowPolicy. Our code can be accessed at https://github.com/LogSSim/MP1.

MP1: MeanFlow Tames Policy Learning in 1-step for Robotic Manipulation

Natural Language-based Egocentric Task Verification (NLETV) aims to verify the alignment between action sequences in egocentric videos and their corresponding textual descriptions. 
However, existing NLETV approaches are still facing two critical challenges: 
(1) These methods are designed for simulating environments, ignoring the domain gap between synthetic and realistic data. 
(2) The matching processes are regarded as a simple binary classification problem, which undermines model reliability due to evaluation bias and uncalibrated decision settings. 
To address these challenges, we propose a novel method termed Prototypical Evidential Learning (PEL), which can be adapted to existing NLETV approaches and boost the model generalization and mitigate prediction bias. 
Our method leverages prototypes to guide cross-domain alignment and evidence collection. 
Specifically, PEL consists of two key components: 
(1) Prototypical Domain Adaptation module enabling cross-domain feature alignment and intra-domain prototype preservation between synthetic and realistic domains; 
(2) Matching Evidence Collector module, which quantifies prediction uncertainty on the prototypical representations through evidential deep learning. 
It enforces the model to collect the vision-text consistency and discrepancy evidence, thus addressing the issues of biased decisions in binary classification. 
Extensive experiments on two public datasets demonstrate that our PEL method outperforms existing state-of-the-art NLETV methods and shows remarkable generalizability.

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Sparse Poisson Gamma Belief Networks for High-Dimensional Sparse Count Data

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