Enhancing Sound Source Localization via False Negative Elimination

Sound source localization aims to localize objects emitting the sound in visual scenes. Recent works obtaining impressive results typically rely on contrastive learning. However, the common practice of randomly sampling negatives in prior arts can lead to the false negative issue, where the sounds semantically similar to visual instance are sampled as negatives and incorrectly pushed away from the visual anchor/query. As a result, this misalignment of audio and visual features could yield inferior performance. To address this issue, we propose a novel audio-visual learning framework which is instantiated with two individual learning schemes: self-supervised predictive learning (SSPL) and semantic-aware contrastive learning (SACL). SSPL explores image-audio positive pairs alone to discover semantically coherent similarities between audio and visual features, while a predictive coding module for feature alignment is introduced to facilitate the positive-only learning. In this regard SSPL acts as a negative-free method to eliminate false negatives. By contrast, SACL is designed to compact visual features and remove false negatives, providing reliable visual anchor and audio negatives for contrast. Different from SSPL, SACL releases the potential of audio-visual contrastive learning, offering an effective alternative to achieve the same goal. Comprehensive experiments demonstrate the superiority of our approach over the state-of-the-arts. Furthermore, we highlight the versatility of the learned representation by extending the approach to audio-visual event classification and object detection tasks. Code and models are available at: https://github.com/zjsong/SACL.

Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning

Contemporary automatic first break (FB) picking methods typically analyze 1D signals, 2D source gathers, or 3D source-receiver gathers. Utilizing higher-dimensional data, such as 2D or 3D, incorporates global features, improving the stability of local picking. Despite the benefits, high-dimensional data requires structured input and increases computational demands. Addressing this, we propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information. In this graph, each seismic trace is represented as a node, connected by edges that reflect similarities. To manage the size of the graph, we develop a subgraph sampling technique to streamline model training and inference. Our proposed framework, DGL-FB, leverages deep graph learning for FB picking. It encodes subgraphs into global features using a deep graph encoder. Subsequently, the encoded global features are combined with local node signals and fed into a ResUNet-based 1D segmentation network for FB detection. Field survey evaluations of DGL-FB show superior accuracy and stability compared to a 2D U-Net-based benchmark method.

Image Fusion via Vision-Language Model

Image fusion integrates essential information from multiple source images into a single composite, emphasizing the highlighting structure and textures, and refining imperfect areas. Existing methods predominantly focus on pixel-level and semantic visual features for recognition. However, they insufficiently explore the deeper semantic information at a text-level beyond vision. Therefore, we introduce a novel fusion paradigm named image Fusion via vIsion-Language Model (FILM), for the first time, utilizing explicit textual information in different source images to guide image fusion. In FILM, input images are firstly processed to generate semantic prompts, which are then fed into ChatGPT to obtain rich textual descriptions. These descriptions are fused in the textual domain and guide the extraction of crucial visual features from the source images through cross-attention, resulting in a deeper level of contextual understanding directed by textual semantic information. The final fused image is created by vision feature decoder. This paradigm achieves satisfactory results in four image fusion tasks: infrared-visible, medical, multi-exposure, and multi-focus image fusion. We also propose a vision-language dataset containing ChatGPT-based paragraph descriptions for the ten image fusion datasets in four fusion tasks, facilitating future research in vision-language model-based image fusion. Code and dataset will be released.

Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy

Recently, sharpness-aware minimization (SAM) has attracted a lot of attention because of its surprising effectiveness in improving generalization performance.However, training neural networks with SAM can be highly unstable since the loss does not decrease along the direction of the exact gradient at the current point, but instead follows the direction of a surrogate gradient evaluated at another point nearby. To address this issue, we propose a simple renormalization strategy, dubbed StableSAM, so that the norm of the surrogate gradient maintains the same as that of the exact gradient. Our strategy is easy to implement and flexible enough to integrate with SAM and its variants, almost at no computational cost. With elementary tools from convex optimization and learning theory, we also conduct a theoretical analysis of sharpness-aware training, revealing that compared to stochastic gradient descent (SGD), the effectiveness of SAM is only assured in a limited regime of learning rate. In contrast, we show how StableSAM extends this regime of learning rate and when it can consistently perform better than SAM with minor modification. Finally, we demonstrate the improved performance of StableSAM on several representative data sets and tasks.

ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning

Image fusion aims to combine information from multiple source images into a single and more informative image. A major challenge for deep learning-based image fusion algorithms is the absence of a definitive ground truth and distance measurement. Thus, the manually specified loss functions aiming to steer the model learning, include hyperparameters that need to be manually thereby limiting the model's flexibility and generalizability to unseen tasks. To overcome the limitations of designing loss functions for specific fusion tasks, we propose a unified meta-learning based fusion framework named ReFusion, which learns optimal fusion loss from reconstructing source images. ReFusion consists of a fusion module, a loss proposal module, and a reconstruction module. Compared with the conventional methods with fixed loss functions, ReFusion employs a parameterized loss function, which is dynamically adapted by the loss proposal module based on the specific fusion scene and task. To ensure that the fusion network preserves maximal information from the source images, makes it possible to reconstruct the original images from the fusion image, a meta-learning strategy is used to make the reconstruction loss continually refine the parameters of the loss proposal module. Adaptive updating is achieved by alternating between inter update, outer update, and fusion update, where the training of the three components facilitates each other. Extensive experiments affirm that our method can successfully adapt to diverse fusion tasks, including infrared-visible, multi-focus, multi-exposure, and medical image fusion problems. The code will be released.

Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling

The incompleteness of the seismic data caused by missing traces along the spatial extension is a common issue in seismic acquisition due to the existence of obstacles and economic constraints, which severely impairs the imaging quality of subsurface geological structures. Recently, deep learningbased seismic interpolation methods have attained promising progress, while achieving stable training of generative adversarial networks is not easy, and performance degradation is usually notable if the missing patterns in the testing and training do not match. In this paper, we propose a novel seismic denoising diffusion implicit model with resampling. The model training is established on the denoising diffusion probabilistic model, where U-Net is equipped with the multi-head self-attention to match the noise in each step. The cosine noise schedule, serving as the global noise configuration, promotes the high utilization of known trace information by accelerating the passage of the excessive noise stages. The model inference utilizes the denoising diffusion implicit model, conditioning on the known traces, to enable high-quality interpolation with fewer diffusion steps. To enhance the coherency between the known traces and the missing traces within each reverse step, the inference process integrates a resampling strategy to achieve an information recap on the former interpolated traces. Extensive experiments conducted on synthetic and field seismic data validate the superiority of our model and its robustness to various missing patterns. In addition, uncertainty quantification and ablation studies are also investigated.

Leveraging Uncertainty Quantification for Picking Robust First Break Times

In seismic exploration, the selection of first break times is a crucial aspect in the determination of subsurface velocity models, which in turn significantly influences the placement of wells. Many deep neural network (DNN)-based automatic first break picking methods have been proposed to speed up this picking processing. However, there has been no work on the uncertainty of the first picking results of the output of DNN. In this paper, we propose a new framework for first break picking based on a Bayesian neural network to further explain the uncertainty of the output. In a large number of experiments, we evaluate that the proposed method has better accuracy and robustness than the deterministic DNN-based model. In addition, we also verify that the uncertainty of measurement is meaningful, which can provide a reference for human decision-making.

Equivariant Multi-Modality Image Fusion

Multi-modality image fusion is a technique used to combine information from different sensors or modalities, allowing the fused image to retain complementary features from each modality, such as functional highlights and texture details. However, effectively training such fusion models is difficult due to the lack of ground truth fusion data. To address this issue, we propose the Equivariant Multi-Modality imAge fusion (EMMA) paradigm for end-to-end self-supervised learning. Our approach is based on the prior knowledge that natural images are equivariant to specific transformations. Thus, we introduce a novel training framework that includes a fusion module and a learnable pseudo-sensing module, which allow the network training to follow the principles of physical sensing and imaging process, and meanwhile satisfy the equivariant prior for natural images. Our extensive experiments demonstrate that our method produces high-quality fusion results for both infrared-visible and medical images, while facilitating downstream multi-modal segmentation and detection tasks. The code will be released.

Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution

Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in high-resolution (HR) RGB images from the same scene. The critical step of this task is to effectively extract domain-shared and domain-private RGB/depth features. In addition, three detailed issues, namely blurry edges, noisy surfaces, and over-transferred RGB texture, need to be addressed. In this paper, we propose the Spherical Space feature Decomposition Network (SSDNet) to solve the above issues. To better model cross-modality features, Restormer block-based RGB/depth encoders are employed for extracting local-global features. Then, the extracted features are mapped to the spherical space to complete the separation of private features and the alignment of shared features. Shared features of RGB are fused with the depth features to complete the GDSR task. Subsequently, a spherical contrast refinement (SCR) module is proposed to further address the detail issues. Patches that are classified according to imperfect categories are input to the SCR module, where the patch features are pulled closer to the ground truth and pushed away from the corresponding imperfect samples in the spherical feature space via contrastive learning. Extensive experiments demonstrate that our method can achieve state-of-the-art results on four test datasets and can successfully generalize to real-world scenes. Code will be released.

DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion

Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and texture details. To leverage strong generative priors and address challenges such as unstable training and lack of interpretability for GAN-based generative methods, we propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM). The fusion task is formulated as a conditional generation problem under the DDPM sampling framework, which is further divided into an unconditional generation subproblem and a maximum likelihood subproblem. The latter is modeled in a hierarchical Bayesian manner with latent variables and inferred by the expectation-maximization algorithm. By integrating the inference solution into the diffusion sampling iteration, our method can generate high-quality fused images with natural image generative priors and cross-modality information from source images. Note that all we required is an unconditional pre-trained generative model, and no fine-tuning is needed. Our extensive experiments indicate that our approach yields promising fusion results in infrared-visible image fusion and medical image fusion. The code will be released.