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This paper introduces Gaussian Spatial Transport (GST), a novel framework that leverages Gaussian splatting to facilitate transport from the probability measure in the image coordinate space to the annotation map. We propose a Gaussian splatting-based method to estimate pixel-annotation correspondence, which is then used to compute a transport plan derived from Bayesian probability. To integrate the resulting transport plan into standard network optimization in typical computer vision tasks, we derive a loss function that measures discrepancy after transport. Extensive experiments on representative computer vision tasks, including crowd counting and landmark detection, validate the effectiveness of our approach. Compared to conventional optimal transport schemes, GST eliminates iterative transport plan computation during training, significantly improving efficiency. The code will be released upon acceptance.

AAAI 2026

2D Gaussians Spatial Transport for Point-supervised Density Regression

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.

The rise of AI-generated image tools has made localized forgeries increasingly realistic, posing challenges for visual content integrity.
Although recent efforts have explored localized AIGC detection, existing datasets predominantly focus on object-level forgeries while overlooking broader scene edits in regions such as sky or ground.
To address these limitations, we introduce \textbf{BR-Gen}, a large-scale dataset of 150,000 locally forged images with diverse scene-aware annotations, which are based on semantic calibration to ensure high-quality samples. 
BR-Gen is constructed through a fully automated ``Perception-Creation-Evaluation'' pipeline to ensure semantic coherence and visual realism.
In addition, we further propose \textbf{NFA-ViT}, a Noise-guided Forgery Amplification Vision Transformer that enhances the detection of localized forgeries by amplifying subtle forgery-related features across the entire image. 
NFA-ViT mines heterogeneous regions in images, \emph{i.e.}, potential edited areas, by noise fingerprints. Subsequently, attention mechanism is introduced to compel the interaction between normal and abnormal features, thereby propagating the traces throughout the entire image, allowing subtle forgeries to influence a broader context and improving overall detection robustness.
Extensive experiments demonstrate that BR-Gen constructs entirely new scenarios that are not covered by existing methods.
Take a step further, NFA-ViT outperforms existing methods on BR-Gen and generalizes well across current benchmarks. Our code are available at \url{https://github.com/clpbc/BR-Gen}.

Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach

Visual neural decoding is an important research topic at the intersection of cognitive neuroscience and machine learning. While recent progress has been made in EEG-based neural decoding, reconstructing dynamic visual content remains challenging. In the field of EEG decoding, current models either utilize pre-trained encoders for feature extraction or employ graph neural networks to represent the spatio-temporal information embedding, resulting in poor model representation and high complexity. We propose EVOKE -- an innovative framework for zero-shot decoding of high-fidelity videos from EEG signals. EVOKE employs Implicit Neural Representations to perform complete spatial modeling of EEG and continuously decouples information in the EEG-INR perceptual space. Additionally, we construct a Hierarchical-aware Attention Module (HAM) to decode EEG from three feature anchors: visual, semantic, motion, and progressively control task inference. The Motion Attention Flow (MAF) we developed overcomes the limitations of capturing motion features in dynamic stimuli, creating a more robust representation that enhances reconstruction consistency. Comprehensive experiments prove that SOTA performance of EVOKE (0.353 SSIM, 0.715 CLIP-pcc). We provide an effective method for converting brain activity into rich visual experiences and set a new benchmark for brain multimodal generation.

EVOKE: Efficient and High-Fidelity EEG-to-Video Reconstruction via Decoupling Implicit Neural Representation

Multi-annotator learning (MAL) aims to model annotator-specific labeling patterns. However, existing methods face a critical challenge: they simply skip updating annotator-specific model parameters when encountering missing labels—a common scenario in real-world crowdsourced datasets where each annotator labels only small subsets of samples. This leads to inefficient data utilization and overfitting risks. To this end, we propose a novel similarity-weighted semi-supervised learning framework (SimLabel) that leverages inter-annotator similarities to generate weighted soft labels for missing annotations, enabling the utilization of unannotated samples rather than skipping them entirely. We further introduce a confidence-based iterative refinement mechanism that combines maximum probability with entropy-based uncertainty to prioritize predicted high-quality pseudo-labels to impute missing labels, jointly enhancing similarity estimation and model performance over time. For evaluation, we contribute a new multimodal multi-annotator dataset, AMER2, with high and more variable missing rates, reflecting real-world annotation sparsity and enabling evaluation across different sparsity levels. Extensive experiments validate the effectiveness of our method.

SimLabel: Similarity-Weighted Semi-supervision for Multi-annotator Learning with Missing Labels

Modern image super-resolution (SR) networks match photographic detail but remain impractical for memory- and latency-constrained devices. Post-training quantization (PTQ) compresses such models without full retraining; yet standard PTQ treats weight and activation quantizers independently and therefore ignores their coupled behaviour in the frequency domain. A Fourier analysis reveals a consistent asymmetry: quantizing weights blurs colour and shape at low frequencies, while quantizing activations erodes edges and textures at high frequencies. Because these distortions interact multiplicatively rather than add linearly, separate optimisation leaves considerable perceptual quality unrealised. We introduce HarmoQ, a harmonised PTQ framework that accounts for this coupling. HarmoQ first applies a closed-form spectral residual projection, editing weights once to cancel high-frequency artefacts caused by activation clipping. It then enforces an equal-error scaling law that analytically balances mean-squared error between weights and activations, followed by an adaptive boundary refinement routine that fine-tunes clipping thresholds while periodically restoring that balance.

HarmoQ: Harmonized Post-Training Quantization for High-Fidelity Image Super-Resolution

Market making (MM) through Reinforcement Learning (RL) has attracted significant attention in financial trading. With the development of Large Language Models (LLMs), more and more attempts are being made to apply LLMs to financial areas. A simple, direct application of LLM as an agent shows significant performance. Such methods are hindered by their slow inference speed, while most of the current research has not studied LLM distillation for this specific task. To address this, we first propose the normalized fluorescent probe to study the mechanism of the LLM’s feature. Based on the observation found by our investigation, we propose Cooperative Market Making(CMM), a novel framework that decouples LLM features across three orthogonal dimensions: layer, task, and data. Various student models collaboratively learn simple LLM features along with different dimensions, with each model responsible for a distinct feature to achieve knowledge distillation. Furthermore, CMM introduces an H´ajek-MoE to integrate the output of the student models by investigating the contribution of different models in a kernel function-generated common feature space. Extensive experimental results on four real-world market datasets demonstrate the superiority of CMM over the current distillation method and RL-based market-making strategies. The code is provided in the supplementary material.

Two Heads Are Better than One: Distilling Large Language Model Features into Small Models with Feature Decomposition and Mixture

Reflective imaging enables the mirror imagings and physical entities to possess identical attributes, e.g., color and shape. Current mirror detection (MD) methods primarily rely on designing functional components to establish the correlation and disparities between the imagings and entities, thereby identifying the mirror regions. However, the exploration of extended scenes with dynamic content changes is rarely investigated. Therefore, we propose the MirrorSAM designed for MD based on the Segment Anything Model (SAM). Specifically, due to the varying reflections produced by mirrors in different positions and the complex visual space that interferes with localization, we design the hierarchical mixture of direction experts (HMDE) in the low-rank space to reduce biases towards entities in SAM and dynamically adjust experts based on the input scene. We observe differences in depth between mirrors and adjacent areas, and propose the depth token calibration (DTC), which introduces a learnable depth token to generate the depth map and serve as an error correction factor. We further formulate the selective pixel-prototype contrastive (SPPC) loss, selecting partially confusable samples to promote the decoupling of mirror and non-mirror representations. Extensive experiments conducted on four mirror benchmarks and two settings demonstrate that our approach surpasses state-of-the-art methods with few trainable parameters and FLOPs. We further extend to four transparent surface benchmarks to validate generalization.

Seeing Beyond Illusion: Generalized and Efficient Mirror Detection

Currently, almost all traditional infrared small target detection methods work on the assumption that training and test sets always belong to a same domain, and training samples are sufficient. However, in real applications, a new detection task could often have no sufficient training samples from a special domain. In this situation, adopting the auxiliary data from big-sample domains is usually believed to be one of the most potential solutions. However, exceeding expectations, it is found that simply adding auxiliary samples cannot often be always effective, even causing performance decline, due to existing infrared domain shift. To overcome this unexpected problem, we propose the first infrared moving small target detection framework with domain-auxiliary supports by Learning to Overlook Domain Discrepancy (Loddis). This framework consists of three primary processing stages: correlation weakening, domain confusing, and target consistency contrastive learning. Breaking through traditional learning paradigm, through auxiliary data, it enables the model to focus more on targets themselves, and less on image backgrounds, minimizing the sensitivity to domain discrepancy. The extensive experiments on 6 different-domain datasets show the effectiveness and superiority of proposed Loddis framework. Codes will be open after acceptance.

Domain-Auxiliary Infrared Moving Small Target Detection by Learning to Overlook Domain Discrepancy

Aligning the decision-making process of deep learning models with that of experienced sonographers is essential for ultrasound-based reliable disease diagnosis. Although existing methods have made significant progress in this aspect, their alignments are primarily associational rather than causal, leading to pseudo-correlations between features and diagnostic results. Such a biased diagnosis blindly models the sonographer's diagnostic skills and attention to specific patterns, which we argue hardly produces an AI diagnoser that is comparable to human experts. To address this issue, we propose a causality-based diagnostic framework to align the model's diagnostic behaviors with those of experts. Specifically, by delving into both conspicuous and inconspicuous confounders within the ultrasound images, the back-door and front-door adjustment causal learning modules are proposed to promote unbiased learning by mitigating potential pseudo-correlations. In addition, we integrate causal inference into a well-designed dual-branch model with feature interaction bridges for compatibility with multimodal ultrasound inputs. To fully evaluate our method, we conduct comparative studies on different diseases and ultrasound modalities. In particular, we publish a carefully constructed multimodal ultrasound dataset for breast lesion diagnosis and segmentation. Sufficient comparative and ablation studies on this dataset emphasize that our method outperforms state-of-the-art methods.

Towards Ultrasound-based Reliable Disease Diagnosis Using Causal Inference

Entity hallucination poses a major challenge in radiology report generation (RRG), particularly for 3D CT scans where complex spatial contexts amplify factual errors. To address this, medical entity phrases serve as key carriers for multi-modal prompting, integrating expert knowledge into the vision-language model. Current methods use unified cross-attention for volume-phrase alignment, failing to account for anatomical specificity during the alignment process. In this work, we introduce the Dual-stream Entity Alignment Reporting network (DEAR) that separately models organ and lesion entities to resolve anatomical bias. Specifically, the dual-stream entity aligner is designed to partition medical entity phrases into organ and lesion streams, feeding them into separate cross-attention blocks in parallel to achieve fine-grained volume–phrase alignment. For structurally regular and spatially stable organ entities, an organ-guided cross-attention (OGCA) block is proposed to enforce structural consistency by retrieving the top-k voxel tokens via volume–phrase similarity and preserving spatial connectivity through morphological dilation. Meanwhile, a lesion-guided cross-attention (LGCA) block is introduced for structurally irregular and spatially variable lesion entities, enhancing anomaly sensitivity through phrase-weighted attention and refining discriminative boundaries via 3D residual Laplacian filtering. Experiments demonstrate that DEAR significantly reduces entity hallucinations and improves clinical factuality in 3D RRG benchmarks.

Mitigating Entity Hallucinations in 3D Radiology Report Generation via Dual-Stream Alignment

Machine unlearning, as a post-hoc processing technique, has gained widespread adoption in addressing challenges like bias mitigation and robustness enhancement. However, existing non-privacy unlearning-based solutions persist in using a binary data removal framework designed for privacy-driven motivation, even when repurposed for fairness or robustness improvements. This leads to significant utility loss, a phenomenon known as “over-unlearning”. While over-unlearning has been largely described in many studies as primarily causing utility degradation, we investigate deeper insights in this work through counterfactual leave-one-out analysis. 
Based on insights, we introduce a soft weighting strategy that assigns tailored weights to each sample by solving a convex quadratic programming problem analytically, which enables fine-grained model adjustments to address the over-unlearning. We demonstrate that the proposed soft-weighted scheme can be seamlessly integrated into most existing unlearning algorithms.
Extensive experiments show that in fairness- and robustness-driven tasks, the soft-weighted scheme significantly outperforms hard-weighted schemes in fairness/robustness metrics and alleviates the decline in utility metric, thereby enhancing unlearning algorithm as an effective correction solution.

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Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach

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