Singapore

Aerial multi-modal visual streams registration and fusion can generate more comprehensive scene information representations for UAVs&#39; cross-modal perception. However, current challenges lie primarily in the essential difficulty of joint spatiotemporal representation learning from dynamic background and moving targets, and a critical shortage exists in large-scale, well-annotated multi-modal visual streams benchmark for UAV platforms. In this paper, we propose AerialFusion, a co-motion-driven unified UAVs visual streams registration and fusion that fully mines modality-invariant common features based on motion-aware, enabling spatiotemporally coherent registration and fusion. Specifically, 1) a Skewed Motion Distribution Field Co-Motion-Driven Image Registration, 2) a Co-Motion Generative Fusion, 3) a Streams-based Unified Learning. Furthermore, we introduce EUM3D, a registration and fusion benchmark for UAVs cross-modal perception. This benchmark contains 60 synchronized visible-infrared visual streams, or 122k spatially and temporally aligned pairs, most of which were taken at low-light scenes. And EUM3D provides pixel-level alignment guarantees via perspective-transform ground-truth. Extensive experiments reveal that AerialFusion surpasses current focus on image and static background fusion methods in aerial sequence scenarios, addressing spatiotemporal mismatches while suppressing cross-modal interference.

AAAI 2026

AerialFusion: Co-Motion-Driven Unified Registration and Fusion on Multi-modal Data Streams from Aerial View

rob: embodied ai

rob: multimodal perception & sensor fusion

cv: multi-modal vision

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

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. 

Aerial multi-modal visual streams registration and fusion can generate more comprehensive scene information representations for UAVs' cross-modal perception. However, current challenges lie primarily in the essential difficulty of joint spatiotemporal representation learning from dynamic background and moving targets, and a critical shortage exists in large-scale, well-annotated multi-modal visual streams benchmark for UAV platforms. In this paper, we propose AerialFusion, a co-motion-driven unified UAVs visual streams registration and fusion that fully mines modality-invariant common features based on motion-aware, enabling spatiotemporally coherent registration and fusion. Specifically, 1) a Skewed Motion Distribution Field Co-Motion-Driven Image Registration, 2) a Co-Motion Generative Fusion, 3) a Streams-based Unified Learning. Furthermore, we introduce EUM3D, a registration and fusion benchmark for UAVs cross-modal perception. This benchmark contains 60 synchronized visible-infrared visual streams, or 122k spatially and temporally aligned pairs, most of which were taken at low-light scenes. And EUM3D provides pixel-level alignment guarantees via perspective-transform ground-truth. Extensive experiments reveal that AerialFusion surpasses current focus on image and static background fusion methods in aerial sequence scenarios, addressing spatiotemporal mismatches while suppressing cross-modal interference.

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.

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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.

Large Language Models (LLMs) hold significant potential for enhancing healthcare applications, yet their deployment is hindered by high computational and memory demands. Model compression techniques, such as quantization and sparsification, offer solutions to reduce these demands, but their impact on medical LLMs remains underexplored. We introduce CMedBench, the first comprehensive benchmark for evaluating compressed LLMs in medical contexts. LLMCMedBench assesses five core dimensions: Medical Knowledge Ability, Medical Application Ability, Trustworthiness Maintenance, Compression Cross Combination, and Computational Efficiency. Through extensive empirical studies, we analyze the trade-offs between model efficiency and clinical performance across diverse models, datasets, and compression strategies. Our findings highlight critical limitations in current evaluation practices and provide a robust framework for aligning compression strategies with medical requirements. CMedBench serves as a vital resource for researchers and practitioners, guiding the development of efficient, trustworthy, and clinically effective LLMs for healthcare applications.

CMedBench: A Comprehensive Benchmark for Efficient Medical Large Language Models

Large Language Models (LLMs) have demonstrated strong performance in various NLP tasks but remain limited in emotional intelligence (EI). Benchmarks such as EmoBench attribute this gap to deficiencies in cognitively demanding tasks that require inferring others’ latent mental states, intentions, and emotions in nuanced social contexts. To address this, we propose MACRo, a Multi-Agent Cognitive Reasoning framework that generates a structured Cognitive Chain of Thought comprising Situation, Clue, Thought, Action, and Emotion. Each component is generated by a specialized agent, enabling modular, interpretable multi-step reasoning. To ensure coherence and mitigate hallucinations, a coordinator agent verifies outputs, and a consensus game mechanism enforces alignment across reasoning steps. Extensive Experiments on EmoBench show that MACRo significantly enhances both emotional understanding and application across LLMs. Further evaluations confirm its generalizability to real-world social applications such as emotional support conversations.

Consensus-Driven Multi-Agent Cognitive Reasoning for Enhancing the Emotional Intelligence of Large Language Models

Counterfactual decision-making in the face of uncertainty involves selecting the optimal action from several alternatives using causal reasoning. Decision-makers often rank expected potential outcomes (or their corresponding utility and desirability) to compare the preferences of candidate actions. In this paper, we study new counterfactual decision-making rules by introducing two new metrics: the probabilities of potential outcome ranking (PoR) and the probability of achieving the best potential outcome (PoB). PoR reveals the most probable ranking of potential outcomes for an individual, and PoB indicates the action most likely to yield the top-ranked outcome for an individual. We then establish identification theorems and derive bounds for these metrics, and present estimation methods. Finally, we perform numerical experiments to illustrate the finite-sample properties of the estimators and demonstrate their application to a real-world dataset.

Potential Outcome Rankings for Counterfactual Decision Making

Post-Training Quantization enables efficient Vision Transformer (ViTs) deployment with a small calibration data, and its prevalent use of uniform quantization harnesses AI accelerator matrix cores for high-speed inference. However, the application of uniform quantization is fundamentally challenged by the extreme non-uniformity of activation distributions.Specifically, the power-law nature of post-Softmax attention scores and the significant inter-channel variance in post-GELU activations create a dilemma for conventional quantization, as it struggles to preserve critical high-magnitude values without sacrificing overall precision. To resolve this core conflict, we introduce UQ-ViT (Uniform Quantization for Vision Transformers), a novel uniform quantization framework designed to reconcile high precision with hardware efficiency. Central to UQ-ViT are two operators: Dynamic Elimination of Maximum (DeMax) and Normalization Quantization (NormQuant). DeMax is a quantization operator for post-Softmax attention scores that utilizes uniform quantization. It dynamically eliminates and preserves dominant values, effectively mitigating quantization loss from the extreme values in the power-law distribution. NormQuant utilizes a per-channel quantization strategy during quantization and reverts to a per-tensor format for dequantization, achieving both high accuracy and computational efficiency. Crucially, it is applicable to any linear layer, enabling effective quantization of post-GELU activations in ViTs. Through extensive experiments on various ViTs and vision tasks, including image classification, object detection, and instance segmentation, we demonstrate that our proposed approach outperforms existing methods, achieving superior accuracy while ensuring hardware friendliness.

UQ-ViT: Harmonizing Extreme Activations with Hardware-Friendly Uniform Quantization in Vision Transformers

The proliferation of the tampered images on social media can pose serious societal risks, influencing public opinion and causing panic. Image Manipulation Localization technique has advanced to address this, but some methods focus on microscopic traces, overlooking macroscopic semantics that deceive viewers. To address this problem, we propose a novel Image Manipulation Localization framework called Collaborative Transformers (Co-Transformers), designed to fully explore and utilize the collaborative information between macroscopic semantics and microscopic traces. This framework is based on two Vision Transformer variants. The first variant captures the semantic logic of the image. The second variant delves into microscopic tampering traces. By dynamically fusing these two complementary features, the framework enables interaction between macroscopic semantic inconsistencies and microscopic abnormal traces, effectively coordinating their relationship in the latent space. Furthermore, we introduce a new Multi-Level Forensic Attention (MLF-Attention) mechanism to enhance the model's ability to extract various tampered traces, this mechanism can be integrated into our framework. Compared with existing methods, our proposed framework achieves state-of-the-art results in localization accuracy and shows good robustness against various attacks.

Collaborative Transformers with Multi-Level Forensic Attention for Image Manipulation Localization

Current low-light image restoration methods suffer from severe efficiency bottlenecks, primarily stemming from: (1) computational burden and error correction costs associated with reliance on external priors (manual or cross-modal); (2) redundant operations in complex multi-stage enhancement pipelines; and (3) indiscriminate processing across frequency components in frequency-domain methods, leading to excessive global computational demands. To address these challenges, we propose an Efficient Self-Mining Prior-Guided Joint Frequency Enhancement Network (SPJFNet). Specifically, we first introduce a Self-Mining Guidance Module (SMGM) that generates lightweight endogenous guidance directly from the network, eliminating dependence on external priors and thereby bypassing error correction overhead while improving inference speed. Second, through meticulous analysis of different frequency domain characteristics, we reconstruct and compress multi-level operation chains into a single efficient operation via lossless wavelet decomposition and joint Fourier-based advantageous frequency enhancement, significantly reducing parameters. Building upon this foundation, we propose a Dual-Frequency Guidance Framework (DFGF) that strategically deploys specialized high/low frequency branches (wavelet-domain high-frequency enhancement and Fourier-domain low-frequency restoration), decoupling frequency processing to substantially reduce computational complexity. Rigorous evaluation across multiple benchmarks demonstrates that SPJFNet not only surpasses state-of-the-art performance but also achieves significant efficiency improvements, substantially reducing model complexity and computational overhead.

SPJFNet: Self-Mining Prior-Guided Joint Frequency Enhancement for Ultra-Efficient Dark Image Restoration

Recent advancements in Large Language Models (LLMs) have increasingly demonstrated their potential for event reasoning. However, current LLMs struggle with this task due to their limited capacity to explicitly model the structured semantics of events, resulting in insufficient schema knowledge and low reasoning performance. To address these challenges, we propose SGER, a Schema-Guided Event Reasoning framework. It constructs a systematic solution by decomposing complex event reasoning tasks into three interrelated subtasks: schema extraction, schema prediction, and event reasoning. In the schema extraction stage, the model maps event descriptions with diverse surface forms to potential semantic structure representations, achieving an abstract transformation from instances to schemas. The schema prediction stage captures the potential associations between historical event schemas to make forward-looking inferences about possible future event schemas. In the event reasoning stage, we integrate historical events and predicted schemas into prompts to guide LLMs in generating specific, contextually consistent predicted events. Experimental evaluations demonstrate that our framework significantly improves event reasoning performance of LLMs.

Schema-Guided Event Reasoning: A Plug-and-Play Event Reasoning Framework Based on Large Language Models

Multi-sensor fusion using LiDAR and RGB cameras significantly enhances 3D object detection task. However, conventional LiDAR sensors perform dense, stateless scans, ignoring the strong temporal continuity in real-world scenes. This leads to substantial sensing redundancy and excessive power consumption, limiting their practicality on resource-constrained platforms. To address this inefficiency, we propose a predictive, history-aware adaptive scanning framework that anticipates informative regions of interest (ROI) based on past observations. Our approach introduces a lightweight predictor network that distills historical spatial and temporal contexts into refined query embeddings. These embeddings guide a differentiable mask generator network, which leverages Gumbel-Softmax sampling to produce binary masks identifying critical ROIs for the upcoming frame. Our method significantly reduces unnecessary data acquisition by concentrating dense LiDAR scanning only within these ROIs and sparsely sampling elsewhere. 
Experiments on nuScenes and Lyft benchmarks demonstrate that our adaptive scanning strategy reduces LiDAR energy consumption by over 65% while maintaining competitive or even superior 3D object detection performance compared to traditional LiDAR-camera fusion methods with dense LiDAR scanning.

Adaptive LiDAR Scanning: Harnessing Temporal Cues for Efficient 3D Object Detection via Multi-Modal Fusion

Given the high cost of large language model (LLM) training from scratch, safeguarding LLM intellectual property (IP) becomes increasingly crucial. As the standard paradigm for IP ownership verification, LLM fingerprinting thus plays a vital role in addressing this challenge. Existing LLM fingerprinting methods verify ownership by extracting or injecting model-specific features. However, they overlook potential attacks during the verification process, leaving them ineffective when the model thief fully controls the LLM's inference process. In such settings, attackers may share prompt-response pairs to enable fingerprint unlearning, or manipulate outputs to evade exact-match verification. We propose iSeal, the first fingerprinting method designed for reliable verification when the model thief controls the suspected LLM in an end-to-end manner. It injects unique features into both the model and an external module, reinforced by an error-correction mechanism and a similarity-based verification strategy. These components are resistant to verification-time attacks, including collusion-based fingerprint unlearning and response manipulation, backed by both theoretical analysis and empirical results. iSeal achieves 100% Fingerprint Success Rate (FSR) on 12 LLMs against more than 10 attacks, while baselines fail under unlearning and response manipulations.

iSeal: Encrypted Fingerprinting for Reliable LLM Ownership Verification

Recommender systems have been widely deployed across various domains such as e-commerce and social media, and intelligently suggest items like products and potential friends to users based on their preferences and interaction history, which are often privacy-sensitive. Recent studies have revealed that recommender systems are prone to membership inference attacks (MIAs), where an attacker aims to infer whether or not a user's data has been used for training a target recommender system. However, existing MIAs fail to exploit the unique characteristic of recommender systems, and therefore are only applicable to mixed recommender systems consisting of two recommendation algorithms. This leaves a gap in investigating MIAs against hybrid-based recommender systems where the same algorithm utilising user-item historical interactions and attributes of users and items serves and produces personalised recommendations. To investigate how the personalisation in hybrid-based recommender systems influences MIA, we propose a novel metric-based MIA. Specifically, we leverage the characteristic of personalisation to obtain reference recommendation for any target users. Then, a relative membership metric is proposed to exploit a target user's historical interactions, target recommendation, and reference recommendation to infer the membership of the target user's data. Finally, we theoretically and empirically demonstrate the efficacy of the proposed metric-based MIA on hybrid-based recommender systems.

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