Singapore

Single-channel audio separation, which separates individual audio sources from monophonic mixtures, remains challenging in audio processing. Supervised approaches, typically trained on synthetically generated mixtures, are the prevailing solution. However, these methods depend on high-quality paired training data, which is often scarce or difficult to acquire in real-world scenarios. This data scarcity can hinder model performance on unseen mixing conditions, compromising generalization capabilities. To this end, in this work, we approach the problem from an unsupervised perspective, framing it as a probabilistic inverse problem. Our method requires only training diffusion priors on individual sources. Separation is then achieved by iteratively guiding an initial state towards the solution through reconstruction guidance. Crucially, we introduce an advanced inverse solver designed for separation, which mitigates gradient conflicts arising from the interference between the diffusion prior and the reconstruction guidance during the denoising process. This effectively ensures high-quality and balanced separation performance across individual sources. In addition, we found that using an augmented mixture instead of pure Gaussian noise to initialize the denoising process is effective, and this informative prior significantly improves the final performance. Furthermore, to enhance audio prior modeling, we designed a novel Time-Frequency (TF) attention-based network architecture that demonstrates powerful audio modeling capabilities. These collective improvements significantly enhance separation performance, as demonstrated by our experimental results across speech-sound event, sound event, and speech separation tasks. Audio demonstrations are available in the Supplementary Material.

AAAI 2026

Unsupervised Single-Channel Audio Separation with Diffusion Source Priors

diffusion model. inverse problem

audio separation

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.

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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.

Deep learning have achieved remarkable success in image recognition, yet their inherent opacity poses challenges for deployment in critical domains. Concept-based interpretations aim to address this by explaining model reasoning through human-understandable concepts. However, existing post-hoc methods and ante-hoc concept bottleneck models (CBMs), suffer from limitations such as unreliable concept relevance, non-visual or labor-intensive concept definitions, and model or data-agnostic assumptions. This paper introduces 
$\textbf{P}$ost-hoc $\textbf{C}$oncept $\textbf{B}$ottleneck $\textbf{M}$odel via $\textbf{Re}$presentation $\textbf{D}$ecomposition ($\textbf{PCBM-ReD}$), a novel pipeline that synergizes the strengths of both paradigms. PCBM-ReD automatically extracts visual concepts from a pre-trained encoder, employs multimodal large language models (MLLMs) to label and filter concepts based on visual identifiability and task relevance, and selects an independent subset via reconstruction-guided optimization. Leveraging CLIP’s visual-text alignment, it decomposes image representations into linear combination of concept text embeddings to fit into the CBMs abstraction. Our extensive experiments across 11 image classification tasks demonstrate that PCBM-ReD achieves state-of-the-art accuracy, narrows the performance gap with end-to-end models, and exhibits better interpretability. The code will be released.

Concepts from Representations: Post-hoc Concept Bottleneck Models via Sparse Decomposition of Visual Representations

The Contrastive Language-Image Pre-Training (CLIP) model excels in few-shot learning by aligning visual and textual representations. Our study shows that template-sample similarity (TSS), defined as the resemblance between a text template and an image sample, introduces bias. This bias leads the model to rely on template proximity rather than true sample-to-category alignment, reducing both accuracy and robustness in classification.
We present a framework that uses empty prompts, textual inputs that convey the idea of “emptiness” without category information. These prompts capture unbiased template features and offset TSS bias. The framework employs two stages. During pre-training, empty prompts reveal and reduce template-induced bias within the CLIP encoder. During few-shot fine-tuning, a bias calibration loss enforces correct alignment between images and their categories, ensuring the model focuses on relevant visual cues.
Experiments across multiple benchmarks demonstrate that our template correction method significantly reduces performance fluctuations caused by TSS, yielding higher classification accuracy and stronger robustness.

Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learning

Recently, rapid advancements have been made in multimodal large language models (MLLMs), especially in video understanding tasks. However, current research focuses on simple video scenarios, failing to reflect the complex and diverse nature of real-world audio-visual events in videos. To bridge this gap, we firstly introduce R-AVST, a dataset for audio-visual reasoning featuring fine-grained spatio-temporal annotations. In constructing this, we design a pipeline consisting of LLM-based key object extraction, automatic spatial annotation and manual quality inspection, resulting in over 5K untrimmed videos with 27K objects across 100 types of audio-visual events. Building on this dataset, we define three core tasks for spatio-temporal reasoning in audio-visual scenes and generate more than 8K high-quality, evenly distributed question-answer pairs to effectively benchmark model performance. To further enhance reasoning, we propose AVST-Zero, a reinforcement learning-based model that avoids intermediate supervision, directly optimizing behavior via carefully designed multi-dimensional rewards. Extensive experiments validate the effectiveness of our R-AVST in advancing audio-visual spatio-temporal reasoning, upon which AVST-Zero demonstrates competitive performance compared to existing models. To the best of our knowledge, R-AVST is the first dataset designed for real-world audio-visual spatio-temporal reasoning, and AVST-Zero offers a novel perspective for tackling future challenges in this domain.

R-AVST: Empowering Video-LLMs with Fine-Grained Spatio-Temporal Reasoning in Complex Audio-Visual Scenarios

Active Label Correction (ALC) has emerged as a promising solution to the high cost and error-prone nature of manual pixel-wise annotation in semantic segmentation, by selectively identifying and correcting mislabeled data. Although recent work has improved correction efficiency by generating pseudo-labels using foundation models, substantial inefficiencies still remain. In this paper, we propose Active and Automated Label Correction for semantic segmentation (A$^2$LC), a novel and efficient ALC framework that integrates an automated correction stage into the conventional pipeline. Specifically, the automated correction stage leverages annotator feedback to perform label correction beyond the queried samples, thereby maximizing cost efficiency. In addition, we further introduce an adaptively balanced acquisition function that emphasizes underrepresented tail classes and complements the automated correction mechanism. Extensive experiments on Cityscapes and PASCAL VOC 2012 demonstrate that A$^2$LC significantly outperforms previous state-of-the-art methods. Notably, A$^2$LC achieves high efficiency by outperforming previous methods using only 20\% of their budget, and demonstrates strong effectiveness by yielding a 27.23\% performance improvement under an equivalent budget constraint on the Cityscapes dataset.

A²LC: Active and Automated Label Correction for Semantic Segmentation

Improving the quality of hyperspectral images (HSIs), such as through super-resolution, is a crucial research area. However, generative modeling for HSIs presents several challenges. Due to their high spectral dimensionality, HSIs are too memory-intensive for direct input into conventional diffusion models. Furthermore, general generative models lack an understanding of the topological and geometric structures of ground objects in remote sensing imagery. In addition, most diffusion models optimize loss functions at the noise level, leading to a non-intuitive convergence behavior and suboptimal generation quality for complex data. To address these challenges, we propose a Geometric Enhanced Wavelet-based Diffusion Model (GEWDiff), a novel framework for reconstructing hyperspectral images at 4-times super-resolution. A wavelet-based encoder-decoder is introduced that efficiently compresses HSIs into a latent space while preserving spectral-spatial information. To avoid distortion during generation, we incorporate a geometry-enhanced diffusion process that preserves the geometric features. Furthermore, a multi-level loss function was designed to guide the diffusion process, promoting stable convergence and improved reconstruction fidelity. Our model demonstrated state-of-the-art results across multiple dimensions, including fidelity, spectral accuracy, visual realism, and clarity.

GEWDiff: Geometric Enhanced Wavelet-based Diffusion Model for Hyperspectral Image Super-resolution

While existing underwater image compression (UIC) methods optimize for human perception or basic redundancies, they neglect inter-image correlations and fail to prioritize machine-friendly features essential for automated analysis. This paper introduces a novel -quantized (VQ) codebook-driven framework for machine-centric UIC. We leverage VQ codebooks -- pre-trained as external priors on diverse underwater data -- to unify three critical stages: (1) Machine-friendly feature extraction via contrastive learning with high/low-quality codebooks, enhancing degradation robustness; (2) Compact compression using variable-size codebooks to map discriminative features to entropy-coded indices, enabling ultra-low bitrates ($<$0.04bpp); and (3) Feature refinement at the decoder, restoring semantic fidelity for downstream tasks. In addition, we contribute the first Underwater Visual Question Answering (UVQA) benchmark to holistically evaluate machine perception across object presence, counting, and localization. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art codecs in machine vision task performance at ultra-low bitrates. The VQ-codebook effectively harnesses inter-image redundancy, combats joint degradation, and delivers compact, analysis-friendly representations, establishing a new paradigm for machine-centric UIC.

Codebook-Empowered Analysis-Friendly Extreme Underwater Image Compression

Design generation, in its essence, is a step-by-step process where designers progressively refine and enhance their work through careful modifications. Despite this fundamental characteristic, existing approaches mainly treat design synthesis as a single-step generation problem, significantly underestimating the inherent complexity of the creative process. To bridge this gap, we propose a novel problem setting called Step-by-step Layered Design Generation, that tasks a machine learning model to generate a design, adhering to a sequence of instructions from a designer. Leveraging the recent advancements in Multi-modal LLMs, we propose SLEDGE: Step-by-step LayEred Design GEnerator to model each update to a design as an atomic layered change over its previous state, while being grounded on the instruction.To complement our new problem setting, we introduce a new evaluation suite, including a dataset and a benchmark. Our exhaustive experimental analysis and comparison with state-of-the-art approaches adapted to our new setup bring out the efficacy of our approach. We hope our work will attract attention to this pragmatic and under-explored research area.

Step-by-step Layered Design Generation

Machine learning and data processing techniques relying on covariance information are widespread as they identify meaningful patterns in unsupervised and unlabeled settings. As a prominent example, Principal Component Analysis (PCA) projects data points onto the eigenvectors of their covariance matrix, capturing the directions of maximum variance. This mapping, however, falls short in two directions: it fails to capture information in low-variance directions, relevant when, e.g., the data contains high-variance noise; and it provides unstable results in low-sample regimes, especially when covariance eigenvalues are close. CoVariance Neural Networks (VNNs), i.e., graph neural networks using the covariance matrix as a graph, show improved stability to estimation errors and learn more expressive functions in the covariance spectrum than PCA, but require training and operate in a labeled setup. To get the benefits of both worlds, we propose Covariance Scattering Transforms (CSTs), deep untrained networks that sequentially apply filters localized in the covariance spectrum to the input data and produce expressive hierarchical representations via nonlinearities. We define the filters as covariance wavelets that capture specific and detailed covariance spectral patterns. We improve CSTs' computational and memory efficiency via a pruning mechanism, and we prove that their error due to finite-sample covariance estimations is less sensitive to close covariance eigenvalues compared to PCA, improving their stability. Our experiments on age prediction from cortical thickness measurements on 4 datasets collecting patients with neurodegenerative diseases show that CSTs produce stable representations in low-data settings, as VNNs but without any training, and lead to comparable or better predictions w.r.t. more complex learning models.

Covariance Scattering Transforms

CLIP (Contrastive Language-Image Pre-training) has attracted widespread attention for its multimodal generalizable knowledge, which is significant for downstream tasks. However, the computational overhead of a large number of parameters and large-scale pre-training poses challenges of pre-training a different scale of CLIP. Learngene extracts the generalizable components termed as learngene from an ancestry model and initializes diverse descendant models with it. Previous Learngene paradigms fail to handle the generalizable knowledge in multimodal scenarios. In this paper, we put forward the idea of utilizing a multimodal block to extract the multimodal generalizable knowledge, which inspires us to propose MM-LG (Multimodal Learngene), a novel framework designed to extract and leverage generalizable components from CLIP. Specifically, we first establish multimodal and unimodal blocks to extract the multimodal and unimodal generalizable knowledge in a weighted-sum manner. Subsequently, we employ these components to numerically initialize descendant models of varying scales and modalities. Extensive experiments demonstrate MM-LG's effectiveness, which achieves performance gains over existing learngene approaches (e.g.,+3.1% on Oxford-IIIT PET and +4.13% on Flickr30k) and comparable or superior results to the pre-training and fine-tuning paradigm (e.g.,+1.9% on Oxford-IIIT PET and +3.65% on Flickr30k). Notably, MM-LG requires only around 25% of the parameter storage while reducing around 2.8× pre-training costs for diverse model scales compared to the pre-training and fine-tuning paradigm, making it particularly suitable for efficient deployment across diverse downstream tasks.

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge

While Transformers have revolutionized time series forecasting, they remain trapped by manual architecture design—every model uses the same attention mechanism, normalization, and activation choices. What if we could automatically discover the perfect architectural recipe for each dataset? This work introduces STrans (Spontaneous Transformer), a comprehensive neural architecture search framework for time series Transformers that simultaneously explores attention variants, normalization techniques, activation functions, and encoding operations. Using differentiable architecture search, STrans automatically discovers architectures that outperform manually designed baselines. However, the experiments reveal a surprising and counterintuitive finding: complex searched architectures often fail catastrophically, while simpler configurations generalize better. This "search overfitting" phenomenon challenges fundamental assumptions about automated architecture design in time series domains. The work not only advances automated model design but uncovers critical insights that will reshape how we think about neural architecture search for temporal data.

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