Singapore

Condensed datasets offer a compact representation of larger datasets, but training models directly on them or using them to enhance model performance through knowledge distillation (KD) can result in suboptimal outcomes due to limited information. To address this, we propose a method that expands condensed datasets using model inversion, a technique for generating synthetic data based on the impressions of a pre-trained model on its training data. This approach is particularly well-suited for KD scenarios, as the teacher model is already pre-trained and retains knowledge of the original training data. By creating synthetic data that complements the condensed samples, we enrich the training set and better approximate the underlying data distribution, leading to improvements in student model accuracy during knowledge distillation. Our method demonstrates significant gains in KD accuracy compared to using condensed datasets alone and outperforms standard model inversion-based KD methods by up to 11.4% across various datasets and model architectures. Importantly, it remains effective even when using as few as one condensed sample per class, and can also enhance performance in few-shot scenarios where only limited real data samples are available.

AAAI 2026

Condensed Data Expansion Using Model Inversion for Knowledge Distillation

dataset condensation

model inversion

knowledge distillation

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

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.

Cross-city urban flow prediction is critical for democratizing smart application benefits in data-scarce developing cities. However, existing methods face an inherent performance ceiling, constrained by both the inevitably finite samples from the source city and the distributional gap between cities. In this paper, we present PLM-CUP, the first theoretically-grounded framework that breaks this bottleneck by leveraging a pre-trained language model (PLM) as an additional source domain. Through an information-theoretic analysis of the generalization error bound, we reveal that the key challenge lies in constructing a semantic bridge encoder and a task-specific adapter to enable cross-domain alignment when incorporating a PLM. Accordingly, PLM-CUP adopts a three-stage architecture, including a semantic bridge encoder that transforms spatiotemporal flow patterns into languagealigned representations via trend-periodicity decomposition, a PLM fine-tuned for knowledge transfer, and a task adapter with spatiotemporal self-attention to conduct multi-step prediction. We further introduce GDAConv, a graph convolution module with dual activation functions that enhances spatial modeling throughout the framework. Experiments on real-world datasets demonstrate that PLM-CUP significantly outperforms state-of-the-art baselines, validating the effectiveness of the proposed PLM enhanced cross-city transfer paradigm for urban flow prediction.

Exploiting Pre-trained Language Model for Cross-city Urban Flow Prediction Guided by Information-theoretic Analysis

Enabling robots to grasp disorganized cloth for efficient storage is valuable in robot-assisted room organization. Diverse deformations of cloth and the stacking of multiple items limit grasping-pose estimation that relies on annotations. This necessitates segmenting each cloth item in an unsupervised manner before estimating the grasping position. However, existing segmentation methods primarily focus on improving metrics such as Intersection-over-Union and Pixel Accuracy, which cannot effectively measure the segmentation errors of the cloth area and thus lead to failure grasping position estimation. To address this challenge, we use False Discovery Rate (FDR) as a novel measure of segmentation errors and analyze its impact on grasping success. Our preliminary study reveals a negative correlation between segmentation FDR and grasping success rate, highlighting the need for more reliable segmentation in cluttered cloth scenarios. Therefore, we propose an unsupervised cloth segmentation network based on feature distance-weighted constraints, designed to reduce the false discovery rate in cloth area perception without requiring expensive pixel-level manual annotations. Additionally, to estimate the grasping position on the perceived cloth area, we introduce a strategy based on cloth surface wrinkle analysis, which operates without the need for annotations or training. By integrating the proposed segmentation network and grasping strategy, we develop a robotic system capable of sequentially grasping cluttered cloth from a table. Extensive real-world robotic experiments demonstrate the effectiveness of our approach, outperforming multiple baseline methods in segmentation FDR and grasping success rate.

Effective Robotic Cloth Grasping Through Suppressing False Discoveries

Video snapshot compressive imaging (SCI) captures dynamic scene sequences through a two-dimensional (2D) snapshot, fundamentally relying on optical modulation for hardware compression and the corresponding software reconstruction. While mainstream video SCI using random binary modulation has demonstrated success, it inevitably results in temporal aliasing during compression. One-hot modulation, activating only one sub-frame per pixel, provides a promising solution for achieving perfect temporal decoupling, thereby alleviating issues associated with aliasing. However, no algorithms currently exist to fully exploit this potential. To bridge this gap, we propose an algorithm specifically designed for one-hot masks. First, leveraging the decoupling properties of one-hot modulation, we transform the reconstruction task into a generative video inpainting problem and introduce a stochastic differential equation (SDE) of the forward process that aligns with the hardware compression process. Next, we identify limitations of the pure diffusion method for video SCI and propose a novel framework that combines one-step regression initialization with one-step diffusion refinement. Furthermore, to mitigate the spatial degradation caused by one-hot modulation, we implement a dual optical path at the hardware level, utilizing complementary information from another path to enhance the inpainted video. To our knowledge, this is the first work integrating diffusion into video SCI reconstruction. Experiments conducted on synthetic datasets and real scenes demonstrate the effectiveness of our method.

3One2: One-Step Regression plus One-Step Diffusion for One-Hot Modulation in Dual-Path Video Snapshot Compressive Imaging

Diffusion models have recently shown promise in time series forecasting, particularly for probabilistic predictions. However, they often fail to achieve state-of-the-art point estimation performance compared to regression-based methods. This limitation stems from difficulties in providing sufficient contextual bias to track distribution shifts and in balancing output diversity with the stability and precision required for point forecasts. Existing diffusion-based approaches mainly focus on full-distribution modeling under probabilistic frameworks, often with likelihood maximization objectives, while paying little attention to dedicated strategies for high-accuracy point estimation. Moreover, other existing point prediction diffusion methods frequently rely on pre-trained or jointly trained mature models for contextual bias, sacrificing the generative flexibility of diffusion models.

To address these challenges, we propose SimDiff, a single-stage, end-to-end framework. SimDiff employs a single unified Transformer network carefully tailored to serve as both denoiser and predictor, eliminating the need for external pre-trained or jointly trained regressors. It achieves state-of-the-art point estimation performance by leveraging intrinsic output diversity and improving mean squared error accuracy through multiple inference ensembling. Key innovations, including normalization independence and the median-of-means estimator, further enhance adaptability and stability. Extensive experiments demonstrate that SimDiff significantly outperforms existing methods in time series point forecasting. The implementation is publicly available at https://anonymous.4open.science/r/SimDiff-A41D.

SimDiff: Simpler Yet Better Diffusion Model for Time Series Point Forecasting

3D object detection is critical for autonomous driving, yet it remains fundamentally challenging to simultaneously maximize computational
efficiency and capture long-range spatial dependencies.We observed that Mamba-based models, with their linear state-space design,
capture long-range dependencies at lower cost, offering a promising balance between efficiency and accuracy.However, existing methods
rely on axis-aligned scanning within a fixed window, inevitably discarding spatial information. To address this problem, we propose
WinMamba, a novel Mamba-based 3D feature-encoding backbone composed of stacked WinMamba blocks. To enhance the backbone with
robust multi-scale representation, the WinMamba block incorporates a window-scale-adaptive module that compensates voxel features
across varying resolutions during sampling. Meanwhile, to obtain rich contextual cues within the linear state space, we equip the WinMamba
layer with a learnable positional encoding and a window-shift strategy.Extensive experiments on the KITTI and Waymo datasets demonstrate
that WinMamba significantly outperforms the baseline. Ablation studies further validate the individual contributions of the WSF and AWF
modules in improving detection accuracy. The code will be made publicly available.

WinMamba: Multi-Scale Shifted Windows in State Space Model for 3D Object Detection

Watch time is widely used as a proxy for user satisfaction in video recommendation platforms. However, raw watch times are influenced by confounding factors such as video duration, popularity, and individual user behaviors, potentially distorting preference signals and resulting in biased recommendation models. We propose a novel relative advantage debiasing framework that corrects watch time by comparing it to empirically derived reference distributions conditioned on user and item groups. This approach yields a quantile-based preference signal and introduces a two-stage architecture that explicitly separates distribution estimation from preference learning. Additionally, we present distributional embeddings to efficiently parameterize watch-time quantiles without requiring online sampling or storage of historical data. Both offline and online experiments demonstrate significant improvements in recommendation accuracy and robustness compared to existing baseline methods.

Relative Advantage Debiasing for Watch-Time Prediction in Short-Video Recommendation

Despite advancements in Video Large Language Models (Vid-LLMs) improving multimodal understanding, challenges persist in streaming video reasoning due to its reliance on contextual information. Existing paradigms feed all available historical contextual information into Vid-LLMs, resulting in a significant computational burden for visual data processing. Furthermore, the inclusion of irrelevant context distracts models from key details. This paper introduces a challenging task called Context-guided Streaming Video Reasoning (CogStream), which simulates real-world streaming video scenarios, requiring models to identify the most relevant historical contextual information to deduce answers for questions about the current stream. To support CogStream, we present a densely annotated dataset featuring extensive and hierarchical question-answer pairs, generated by a semi-automatic pipeline. Additionally, we present CogReasoner as a baseline model. It effectively tackles this task by leveraging visual stream compression and historical dialogue retrieval. Extensive experiments prove the effectiveness of this method.

CogStream: Context-guided Streaming Video Question Answering

Despite the rapid progress of multimodal large language models (MLLMs), their capacity for low-level visual perception in underwater environments remains underexplored. To address this gap, we present UQ-Bench, the first systematically designed benchmark for evaluating the ability of MLLMs to perceive and assess underwater image quality at the low-level visual attribute level. UQ-Bench comprises three components: (1) UW-Perception, a dataset of 3,000 underwater images paired with targeted questions on key degradations such as color cast, blur, contrast, and exposure, covering both global and local perceptual dimensions; (2) UW-Describe, a dataset of 500 images with expert-annotated gold-standard descriptions for assessing the accuracy of model-generated text; and (3) UW-Eval, an evaluation protocol employing human mean opinion scores (MOS) for quantitative quality assessment. To ensure rigorous and reproducible benchmarking, we propose a GPT-assisted evaluation framework that aligns model outputs with expert references and enables fine-grained analysis of distortion perception. Experimental results demonstrate that while MLLMs exhibit preliminary competence in underwater low-level visual tasks, they still fall short in capturing subtle degradations and achieving human-level consistency, highlighting the need for further advances in foundation models for marine vision. Both the benchmark and code will be made publicly available.

UQ-Bench: A Benchmark for Evaluating Multimodal LLMs on Underwater Image Quality Assessment

Model robustness indicates a model's capability to generalize well on unforeseen distributional shifts, including data corruptions and adversarial attacks. Data augmentation is one of the most prevalent and effective ways to enhance the robustness. Despite the great success of the diverse augmentations in different fields, a unified theoretical understanding of their efficacy in improving model robustness is lacking. We theoretically reveal a general condition for label-preserving augmentations to bring robustness to diverse distribution shifts through the lens of flat minima and generalization bound, which de facto turns out to be strongly correlated with robustness against different distribution shifts in practice. Unlike most earlier works, our theoretical framework accommodates all the label-preserving augmentations and is not limited to particular distribution shifts. We substantiate our theories through different simulations on the existing common corruption and adversarial robustness benchmarks based on the CIFAR and ImageNet datasets.

A Flat Minima Perspective on Understanding Augmentations and Model Robustness

Point cloud quality assessment (PCQA) is essential for reliable 3D visual applications. While point-based methods face challenges in characterizing distortions due to point cloud disorder, projection-based approaches offer better efficiency but suffer from geometric distortion insensitivity and texture representation blind spots. This study proposes SAF-Net, a multi-view structure-aware feature fusion network for PCQA. We first identify two key limitations in projection-based methods: insufficient geometric distortion perception and representation blind spots (RBS) in texture images. To address these issues, SAF-Net innovatively integrates object mask maps and local binary pattern (LBP) maps. The mask maps enhance geometric distortion perception by extracting edge sharpness and curvature variations, while LBP maps capture essential structural information to overcome RBS and align with human visual system (HVS) sensitivity. SAF-Net employs a hybrid CNN-ViT architecture to balance local feature extraction and global context modeling, along with a progressive fusion strategy to optimize cross-modal feature interaction. Extensive experiments demonstrate the superior performance of SAF-Net on multiple benchmarks, establishing new state-of-the-art results in PCQA.

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Next from AAAI 2026

Exploiting Pre-trained Language Model for Cross-city Urban Flow Prediction Guided by Information-theoretic Analysis

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Knowledge Distillation Beyond Model Compression

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PRESENTATIONS

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