Singapore

Infrared small target detection often faces significant domain gaps across datasets due to varying sensors and scene distributions. Currently, most existing methods are typically based on single-domain learning ($i.e.$, training and test are on the same dataset), requiring training separate detectors when considering different datasets. However, they overlook the valuable public knowledge across domains and limit the applicability in multiple infrared scenarios. To break through single-domain learning, implementing only one universal detector simultaneously on multiple datasets, as the first exploration, we propose a cross-domain joint learning task framework with prototype-guided Mixture-of-Experts (CoMoE). Specifically, it designs a hyperspherical prototype learning to adaptively maintain both domain-specific prototypes and global prototypes, enhancing cross-domain feature representation. Meanwhile, a domain-aware Mixture-of-Experts with Top-K routing strategy is proposed to select the optimal domain experts. Moreover, to enhance cross-domain feature alignment, we design an adaptive cross-domain feature modulation with noise-guided contrastive learning. The extensive experiments on a newly constructed benchmark comprising three datasets verify the superiority of our CoMoE, even under limited data settings. It could often surpass general joint learning methods, and state-of-the-art (SOTA) single-domain ones. Codes will be open after acceptance.

AAAI 2026

Cross-domain Joint Learning with Prototype-guided Mixture-of-Experts for Infrared Moving Small Target Detection

domain aware mixture-of-experts

cross domain joint learning

infrared small target detection

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

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.

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.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

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.

Current methodologies for incremental object detection (IOD) primarily rely on Faster R-CNN or DETR series detectors; however, these approaches do not accommodate the real-time YOLO detection frameworks. In this paper, we first identify three primary types of knowledge conflicts that contribute to catastrophic forgetting in YOLO-based incremental detectors: foreground-background confusion, parameter interference, and misaligned knowledge distillation. Subsequently, we introduce YOLO-IOD, a real-time Incremental Object Detection (IOD) framework that is constructed upon the pretrained YOLO-World model, facilitating incremental learning via a stage-wise parameter-efficient finetuning process. Specifically, YOLO-IOD encompasses three principal components: 1) Conflict-Aware Pseudo-Label Refinement (CPR), which mitigates the foreground-background confusion by leveraging the confidence levels of pseudo labels and identifying potential objects relevant to future tasks. 2) Importance-based Kernel Selection (IKS), which identifies and updates the pivotal convolution kernels pertinent to the current task during the current learning stage. 3)Cross-Stage Asymmetric Knowledge Distillation (CAKD), which addresses the misaligned knowledge distillation conflict by transmitting the features of the student target detector through the detection heads of both the previous and current teacher detectors, thereby facilitating asymmetric distillation between existing and newly introduced categories. We further introduce LoCo COCO, a more realistic benchmark that eliminates data leakage across stages. Experiments on both conventional and LoCo COCO benchmarks show that YOLO-IOD achieves superior performance with minimal forgetting. Our code is available in the supplementary material.

YOLO-IOD: Towards Real Time Incremental Object Detection

Multimodal Emotion Recognition in Conversation (MERC) aims to predict speakers’ emotions by integrating textual, acoustic, and visual cues. Existing approaches either struggle to capture complex cross‑modal interactions or experience gradient conflicts and unstable training when using deeper architectures. To address these issues, we propose Cross-Space Synergy (CSS), which couples a representation component with an optimization component. Synergistic Polynomial Fusion (SPF) serves the representation role, leveraging low-rank tensor factorization to efficiently capture high-order cross-modal interactions. Pareto Gradient Modulator (PGM) serves the optimization role, steering updates along Pareto-optimal directions across competing objectives to alleviate gradient conflicts and improve stability. Experiments show that CSS outperforms existing representative methods on IEMOCAP and MELD in both accuracy and training stability, verifying its effectiveness in complex multimodal scenarios.

Cross-Space Synergy: A Unified Framework for Multimodal Emotion Recognition in Conversation

4D point cloud segmentation is crucial for autonomous driving with continuous LiDAR streams. While test-time adaptation (TTA) is the standard approach for handling dynamic environments, current methods suffer from catastrophic error accumulation due to over-reliance on pseudo-labels. Active learning could provide reliable annotations for critical samples, but combining it with TTA faces severe challenges: realtime processing requirements and expensive 3D labeling costs. In this paper, we propose ATTA-4DSeg, the first framework to achieve efficient active test-time adaptation for 4D point cloud segmentation under extreme budget constraints. Our key insight is a self-reinforcing loop: oracle annotations refine adaptation prototypes, which then guide the selection of subsequent high-value samples from regions with severe distribution shifts, maximizing each annotation’s impact. Specifically, we propose three key innovations: (1) dual-prototype comparison that precisely localizes distribution shift boundaries to narrow annotation scope, (2) Class-Inverse Budget Allocation (CIBA)
ensuring balanced adaptation across all categories, coupled with hybrid uncertainty scoring combining voxel-level geometry and point-wise variance for optimal sample selection, and (3) a refinement strategy leveraging sparse oracle annotations to improve predictions on unlabeled points, maximizing annotation utility. Extensive experiments show ATTA-4DSeg improves mIoU by 18.87%, 19.92%, and 3.6% on three domain adaptation benchmarks using only 1% annotation budget. Our method operates 2.28× faster than state-of-the-art methods. Remarkably, our approach reaches 90% of fully-supervised performance using only 5% annotation budget.

4D Point Cloud Segmentation via Active Test-Time Adaptation

Despite substantial progress in anomaly synthesis, existing diffusion-based and coarse inpainting pipelines commonly suffer from structural deficiencies such as micro-structural discontinuities, limited semantic controllability, and inefficient generation. To overcome these limitations, we introduce ARAS, a language-conditioned, auto-regressive anomaly synthesis approach that precisely injects local, text-specified defects into normal images via token-anchored latent editing. Leveraging a hard-gated auto-regressive operator and a training-free, context-preserving masked sampling kernel, ARAS significantly enhances defect realism, preserves fine-grained material textures, and provides continuous semantic control over synthesized anomalies. Integrated within our Quality-Aware Re-weighted Anomaly Detection (QARAD) framework, we propose a dynamic weighting strategy that emphasizes high-quality synthetic samples by computing an image-text similarity score with a dual-encoder model. Extensive experiments across three datasets, MVTec AD, VisA, and BTAD, demonstrate that our QARAD outperforms SOTA methods in both image- and pixel-level anomaly detection tasks, achieving improved accuracy, robustness, and a 5× synthesis speedup compared to diffusion-based alternatives.

Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection

The development of multimodal large language models (MLLMs) has advanced general video understanding. However, existing video evaluation benchmarks primarily focus on non-interactive videos, such as movies and recordings. To fill this gap, this paper proposes the first omnimodal benchmark for interactive livestream videos, LiViBench. It features a diverse set of 24 tasks, highlighting the perceptual, reasoning, and livestream-specific challenges. To efficiently construct the dataset, we design a standardized semi-automatic annotation workflow that incorporates the human-in-the-loop at multiple stages. The workflow leverages multiple MLLMs to form a multi-agent system for comprehensive video description and uses a seed-question-driven method to construct high-quality annotations. All interactive videos in the benchmark include audio, speech, and real-time comments modalities. To enhance models' understanding of interactive videos, we design tailored two-stage instruction-tuning and propose a Video-to-Comment Retrieval (VCR) module to improve the model's ability to utilize real-time comments. Based on these advancements, we develop LiVi-LLM-7B, an MLLM with enhanced knowledge of interactive livestreams. Experiments show that our model outperforms larger open-source models with up to 72B parameters, narrows the gap with leading proprietary models on LiViBench, and achieves enhanced performance on general video benchmarks, including VideoMME, LongVideoBench, MLVU, and VideoEval-Pro.

LiViBench: An Omnimodal Benchmark for Interactive Livestream Video Understanding

AI systems can perpetuate and amplify existing biases and discrimination, prompting academic efforts to develop mitigation techniques. Despite progress, real-world deployments often expose limitations in current methods and tools--- overlooking preprocessing, adopting poor evaluation protocols, and failing to integrate domain knowledge. These gaps hinder the effectiveness and reproducibility of fairness solutions.
AutoML has emerged as a promising approach to optimize AI pipelines and provide an evaluation framework.
However, challenges persist, especially around: intersectionality support, explainability, and stakeholder engagement, which are crucial for fairness and human-centric AI development.
We introduce HAMLET4Fairness, integrating AutoML with human-centered approaches grounded in logic and argumentation. This enhances interactivity and transparency in AI pipeline optimization while supporting intersectional fairness. HAMLET4Fairness leverages multi-objective optimization and bounds the search space by user-defined constraints, adapting the CRISP-DM methodology for co-design and collaborative problem-solving.
We validate HAMLET4Fairness through real-world case studies, showing improved fairness outcomes and scalability. The evaluation also offers insights into how preprocessing choices affect fairness performance.

HAMLET4Fairness: Enhancing Fairness in AI Pipelines Through Human-Centered AutoML and Argumentation

Online continual learning (OCL) aims at learning a non-stationary data stream in a way of reading each data sample only once, and hence suffers from the trade-off of catastrophic forgetting and insufficient learning. In this work, we firstly analytically establish relationship between loss functions and model parameters from the Bayesian perspective. Based on our analysis, we subsequently propose a parameter merging method with gradient-guided supermasks. Our method leverages 1-order and 2-order gradient information to construct supermasks that determine the merging weights between the old and new models. Our method performs direct arithmetic operations on parameters to update models, beyond traditional gradient descent. We further discover that a widely-used premise that 1-order gradients can be negligible is invalid in OCL, due to slow convergence incurred by insufficient learning. Additionally, we utilize a dual-model dual-view distillation strategy that can align output distributions of the new and merged models for each sample, further enhancing model performance. Extensive experiments are conducted on four benchmarks in OCL settings, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-100. Experimental results demonstrate that our method is effective, and achieves a substantial boost over previous methods.

Parameter Merging with Gradient-Guided Supermasks in Online Continual Learning

Human-AI cooperative classification (HAI-CC) aims to develop hybrid intelligent systems that enhance decision-making in various high-stakes real-world scenarios by leveraging both human expertise and AI capabilities. Current HAI-CC methods primarily focus on learning-to-defer (L2D), where decisions are deferred to human experts when AI is not confident, and learning-to-complement (L2C), where AI and human experts make predictions cooperatively. However, existing research in both L2D and L2C has not effectively been explored under diverse expert knowledge to improve decision-making, particularly when constrained by the operation cost of human involvement. In this paper, we address this research gap by proposing the Coverage-constrained Learning to Defer and Complement with Specific Experts (CL2DC) method. In particular, CL2DC assesses input data before making final decisions through either AI prediction alone or by deferring to or complementing a specific human expert. Furthermore, we propose a coverage-constrained optimisation to control the cooperation cost, ensuring it approximates a target probability for AI-only selection. This approach enables an effective assessment of system performance within a specified budget. Comprehensive evaluations on both synthetic and real-world datasets demonstrate that CL2DC achieves superior performance compared to state-of-the-art HAI-CC methods.

Coverage-Constrained Human-AI Cooperation with Multiple Experts

LLM-based multi-agent systems exhibit strong collaborative capabilities but often suffer from redundant communication and excessive token overhead. Existing methods typically enhance efficiency through pretrained GNNs or greedy algorithms, but often isolate pre- and post-task optimization, lacking a unified strategy. To this end, we present SafeSieve, a progressive and adaptive multi-agent pruning algorithm that dynamically refines the inter-agent communication through a novel dual-mechanism. SafeSieve integrates initial LLM-based semantic evaluation with accumulated performance feedback, enabling a smooth transition from heuristic initialization to experience-driven refinement. Unlike existing greedy Top-k pruning methods, SafeSieve employs 0-extension clustering to preserve structurally coherent agent groups while eliminating ineffective links. Experiments across benchmarks (SVAMP, HumanEval, etc.) showcase that SafeSieve achieves 94.01\% average accuracy while reducing token usage by 12.4\%-27.8\%. Results further demonstrate robustness under prompt injection attacks (1.23\% average accuracy drop). In heterogeneous settings, SafeSieve reduces deployment costs by 13.3\% while maintaining performance. These results establish SafeSieve as a robust, efficient, and scalable framework for practical multi-agent systems. Our code can be found in the Supplementary Material.

SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication

Conventional fairness in multi-tenant Large Language Model (LLM) inference services is typically defined by system-centric metrics, such as equitable resource allocation. This paper argues that this paradigm is fundamentally flawed, as it creates a gap between measured system performance and actual user-perceived quality. We challenge this notion by introducing and formalizing Experiential Fairness, a user-centric paradigm that shifts the objective from equality of opportunity (resource access) to equity of outcome (user experience). To operationalize this, we propose ExFairS, a lightweight scheduling framework that evaluates each user's state via a composite metric integrating SLO compliance with resource consumption, and then acts on this evaluation through a credit-based priority mechanism. Extensive experiments on an 8-GPU NVIDIA V100 node show that ExFairS reduces the SLO violation rate by up to 100% and improves system throughput by 14-21.9%, outperforming state-of-the-art schedulers and delivering a demonstrably higher degree of Experiential Fairness.

Downloads

Next from AAAI 2026

YOLO-IOD: Towards Real Time Incremental Object Detection

Similar lecture

Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAAI 2026

YOLO-IOD: Towards Real Time Incremental Object Detection

Similar lecture

Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention

Downloads