Singapore

Blind image quality assessment (BIQA) methods often incorporate auxiliary tasks to improve performance. However, existing approaches face limitations due to insufficient integration and a lack of flexible uncertainty estimation, leading to suboptimal performance. To address these challenges, we propose a multitasks-based Deep Evidential Fusion Network (DEFNet) for BIQA, which performs multitask optimization with the assistance of scene and distortion type classification tasks. The framework employs a trustworthy information fusion strategy at two levels: cross sub-region and local-global. The former combines diverse features and patterns across sub-regions, while the latter fuses information from fine- to coarse-grained scales and balances local and global perspectives. Moreover, DEFNet exploits advanced uncertainty estimation technique inspired by evidential learning with the help of normal-inverse gamma distribution mixture. Extensive experiments on both synthetic and authentic distortion datasets demonstrate the effectiveness and robustness of the proposed framework in comparison with state-of-the-art BIQA methods. Additional evaluation and analysis are carried out to highlight its strong generalization capability and adaptability to previously unseen scenarios.

AAAI 2026

Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment

evidential fusion

image quality assessment

computer vision

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.

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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.

Recommendation systems often rely on implicit feedback, where only positive user-item interactions can be observed.
Negative sampling is therefore crucial to provide proper negative training signals.
However, existing methods tend to mislabel potentially positive but unobserved items as negatives and lack precise control over negative sample selection. 
We aim to address these by generating controllable negative samples, rather than sampling from the existing item pool.
In this context, we propose Adaptive Diffusion-based Augmentation for Recommendation (ADAR), a novel and model-agnostic augmentation module that leverages diffusion to synthesize informative negatives.
Inspired by the progressive corruption process in diffusion, ADAR simulates a continuous transition from positive to negative, allowing for fine-grained control over sample hardness. 
To mine suitable negative samples, we theoretically identify the transition point at which a positive sample turns negative and derive a score-aware function to adaptively determine the optimal sampling timestep.
By identifying this transition point, ADAR generates challenging negatives that effectively refine the model's decision boundary.
Experiments confirm that ADAR is broadly compatible and boosts the performance of existing recommendation models substantially, including collaborative filtering and sequential recommendation, without architectural modifications.

Adaptive Diffusion-based Augmentation for Recommendation

Deep learning methods for pansharpening have advanced rapidly, yet models pretrained on data from a specific sensor often generalize poorly to data from other sensors.
Existing methods to tackle such cross-sensor degradation include retraining model or zero-shot methods, but they are highly time-consuming or even need extra training data. 
To address these challenges, our method first performs modular decomposition on deep learning-based pansharpening models, revealing a general yet critical interface where high-dimensional fused features begin mapping to the channel space of the final image. % may need revisement
A Feature Tailor is then integrated at this interface to address cross-sensor degradation at the feature level, and is trained efficiently with physics-aware unsupervised losses. Moreover, our method operates in a patch-wise manner, training on partial patches and performing parallel inference on all patches to boost efficiency.
Our method offer two key advantages: (1) $\textit{Improved Generalization Ability}$: it significantly enhance performance in cross-sensor cases. (2) $\textit{Low Generalization Cost}$: it achieves sub-second training and inference, requiring only partial test inputs and no external data, whereas prior methods often take minutes or even hours.
Experiments on the real-world data from multiple datasets demonstrate that our method achieves state-of-the-art quality and efficiency in tackling cross-sensor degradation. For example, training and inference of $512\times512\times8$ image within $\textit{0.2 seconds}$ and $4000\times4000\times8$ image within $\textit{3 seconds}$ at the fastest setting on a commonly used RTX 3090 GPU, which is over 100 times faster than zero-shot methods.

Training and Inference Within 1 Second – Tackle Cross-Sensor Degradation of Real-World Pansharpening with Efficient Residual Feature Tailoring

While Diffusion Transformers (DiTs) have achieved breakthroughs in video generation, this long sequence generation task remains constrained by the quadratic complexity of attention mechanisms, resulting in significant inference latency. Through detailed analysis of attention maps in Video Diffusion Transformer (vDiT), we identify three recurring sparsity patterns: diagonal, multi-diagonal, and vertical-stripe structures. And even 3-6\% attention heads can be skipped. Crucially, these patterns exhibit strong layer-depth and head-position correlations but show limited dependence on the input content. Leveraging these findings, we propose Sparse-vDiT, a sparsity acceleration framework for vDiT comprising: 1) Pattern-optimized sparse kernels that replace dense attention with computationally efficient implementations for each identified sparsity pattern. 2) An offline sparse diffusion search algorithm that selects the optimal sparse computation strategy per layer and head via hardware-aware cost modeling. After determining the optimal configuration, we fuse heads within the same layer that share the same attention strategy, enhancing inference efficiency. Integrated into state-of-the-art vDiT models (CogVideoX1.5, HunyuanVideo, and Wan2.1), Sparse-vDiT achieves 2.09$\times$, 2.38$\times$, and 1.67$\times$ theoretical FLOP reduction, and actual inference speedups of 1.76$\times$, 1.85$\times$, and 1.58$\times$, respectively, while maintaining high visual fidelity, with PSNR values reaching 24.13, 27.09, and 22.59. Our work demonstrates that latent structural sparsity in vDiTs can be systematically exploited for long video synthesis.

Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers

Particle Image Velocimetry (PIV) is a widely adopted non-invasive imaging technique that tracks the motion of tracer particles across image sequences to capture the velocity distribution of fluid flows. It is commonly employed to analyze complex flow structures and validate numerical simulations. This study explores the untapped potential of spike cameras—ultra-high-speed, high-dynamic-range vision sensors—in high-speed fluid velocimetry. We propose a deep learning framework, Spike Imaging Velocimetry (SIV), tailored for high-resolution fluid motion estimation. To enhance the network’s performance, we design three novel modules specifically adapted to the characteristics of fluid dynamics and spike streams: the Detail-Preserving Hierarchical Transform (DPHT), the Graph Encoder (GE), and the Multi-scale Velocity Refinement (MSVR). Furthermore, we introduce a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), comprising labeled samples from three representative scenarios in fluid dynamics. Our proposed method outperforms existing baselines on the PSSD dataset, demonstrating its effectiveness. The implementation details of our project, including code and configuration settings, are provided in the supplementary material.

Spike Imaging Velocimetry: Dense Motion Estimation of Fluids Using Spike Streams

We propose PUNO, a novel deep operator-based framework for point cloud upsampling, addressing the challenge of reconstructing high-resolution geometries from sparse point clouds. PUNO first uses a lightweight network to achieve vertex displacement and manifold parameterization, forming a coarse geometric representation. It then employs a lifting function to encode coordinates into high-dimensional embeddings, followed by iterative kernel integral approximations in function space and a dimensionality reduction to generate the target coordinates. Unlike prior work, PUNO transforms both in data space and function domain, achieving finer and more continuous results that mitigate the ill-posed nature of sparse data. It also introduces multilayer Galerkin-type attention for nonlocal kernel integrals, benefiting global continuity. Extensive experiments demonstrate superior accuracy, robustness, and generalization.

PUNO: A Neural Operator Framework for Point Cloud Upsampling

Spiking Neural Networks (SNNs) offer a promising direction for energy-efficient event-based vision by leveraging sparse, temporally precise spikes. We propose a directly trained, fully spiking model for optical flow estimation, featuring a novel Spike GRU and membrane potential carryover for improved temporal modeling. On the DSEC-Flow benchmark, our model achieves competitive accuracy while reducing energy consumption by 42.88× over EV-FlowNet and 38× over TIDNet. Building on the predicted motion field, we infer camera rotation and, to the best of our knowledge, are the first to construct panoramic event images from SNN-based flow. We further introduce an optional unsupervised $SO(3)$ refinement step that improves rotation accuracy by maximizing panorama consistency—without IMU or pose supervision. Our results achieve comparable visual quality to CMax-SLAM, showing that SNNs can enable fast and high-level spatial perception using only event-based input.

SNN-Driven Event-Based Flow and Rotation Estimation with SO(3) Refinement

Synthesizing amyloid PET scans from the more widely available and accessible structural MRI modality offers a promising, cost-effective approach for large-scale Alzheimer's Disease (AD) screening. This is motivated by evidence that, while MRI does not directly detect amyloid pathology, it may nonetheless encode information correlated with amyloid deposition that can be uncovered through advanced modeling. However, the high dimensionality and structural complexity of 3D neuroimaging data pose significant challenges for existing MRI-to-PET translation methods. Modeling the cross-modality relationship in a lower-dimensional latent space can simplify the learning task and enable more effective translation. As such, we present CoCoLIT (ControlNet-Conditioned Latent Image Translation), a diffusion-based latent generative framework that incorporates three main innovations: (1) a novel Weighted Image Space Loss (WISL) that improves latent representation learning and synthesis quality; (2) a theoretical and empirical analysis of Latent Average Stabilization (LAS), an existing technique used in similar generative models to enhance inference consistency; and (3) the introduction of ControlNet-based conditioning for MRI-to-PET translation. We evaluate CoCoLIT's performance on publicly available datasets and find that our model significantly outperforms state-of-the-art methods on both image-based and amyloid-related metrics. Notably, in amyloid-positivity classification, CoCoLIT outperforms the second-best method with improvements of +10.5% on the internal dataset and +23.7% on the external dataset. The code and models of our approach are available at https://github.com/anon4access/CoCoLIT.

CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis

Existing diffusion-based 3D shape completion methods typically use a conditional paradigm, injecting incomplete shape information into the denoising network via deep feature interactions (e.g., concatenation, cross-attention) to guide sampling toward complete shapes, often represented by voxel-based distance functions. However, these approaches fail to explicitly model the optimal global transport path, leading to suboptimal completions. Moreover, performing diffusion directly in voxel space imposes resolution constraints, limiting the generation of fine-grained geometric details.
To address these challenges, we propose BridgeShape, a novel framework for 3D shape completion via latent diffusion Schrödinger bridge. The key innovations lie in two aspects:
(i) BridgeShape formulates shape completion as an optimal transport problem, explicitly modeling the transition between incomplete and complete shapes to ensure a globally coherent transformation.
(ii) We introduce a Depth-Enhanced Vector Quantized Variational Autoencoder (VQ-VAE) to encode 3D shapes into a compact latent space, leveraging self-projected multi-view depth information enriched with strong DINOv2 features to enhance geometric structural perception.
By operating in a compact yet structurally informative latent space, BridgeShape effectively mitigates resolution constraints and enables more efficient and high-fidelity 3D shape completion.
BridgeShape achieves state-of-the-art performance on 3D shape completion benchmarks, demonstrating superior fidelity at higher resolutions and for unseen object classes.

BridgeShape: Latent Diffusion Schrödinger Bridge for 3D Shape Completion

Large Language Model (LLM) agents have demonstrated strong potential in complex, interactive decision-making tasks. However, when training LLM agents end-to-end with reinforcement learning (RL), efficiently optimizing agent policies in dynamic environments remains a significant challenge. Existing RL-based LLM agent paradigms commonly organize interactions in a cycle where reasoning is followed by action. In our work, we observe a phenomenon we call Exploration Contraction, where the explicit introduction of a reasoning stage reduces the diversity of actions—quantified by lower action entropy—which in turn limits exploration and leads to premature policy convergence. To address this limitation, we propose Act-before-Reasoning (ActRe), a two-stage RL training framework. In the first stage, we reverse the typical rollout order, prompting the agent to generate actions prior to reasoning, which encourages exploration driven by model intuition. In the second stage, we restore the standard reasoning-then-action order for training and evaluation, ensuring robust and interpretable decision-making. Experiments on the ALFWorld and WebShop benchmarks show that ActRe effectively mitigates exploration contraction, yielding consistently higher task success rates and improved training robustness compared to strong RL baselines. Our analysis underscores the importance of action entropy in the exploration-exploitation trade-off during LLM agent training and provides a practical approach to maintain the benefits of explicit reasoning while promoting sufficient exploration.

When Instinct Guides and Insight Grounds: Staged RL Training for LLM Agents

One-shot pruning efficiently compresses Large Language Models but produces coarse sparse weights, causing significant performance degradation. Traditional fine-tuning approaches to refine these weights are prohibitively expensive for large models. This highlights the need for a training-free weight refinement method that works seamlessly with one-shot pruning and can efficiently recover the lost performance.
To tackle this problem, we propose Efficient Iterative Weight Refinement (EIWR), a lightweight, plug-and-play, and training-free method that refines pruned weights through layer-wise iterative optimization. 
EIWR achieves efficient weight refinement via three key components: a Global Soft Constraint that eliminates costly row-wise Hessian inversions and expands the solution space; a Historical Momentum Strategy that leverages one-shot pruning priors to accelerate convergence and enhance final performance; and Neumann Series Extrapolation that significantly speeds up per-iteration computation.
As a result, EIWR enables effective weight refinement with minimal time and memory overhead. Extensive experiments on LLaMA2/3 and Qwen under different pruning strategies and sparsity levels demonstrate that our method can efficiently refine sparse weights and mitigate performance degradation. For example, on LLaMA2-7B under 70\% sparsity, EIWR reduces perplexity by 15\% compared with SparseGPT on the WikiText2 benchmark, with only 1.81 additional minutes of computation and 1GB of additional memory.

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