pwc-archive/papers-with-abstracts huggingface dataset:
United Domain Cognition Network for Salient Object Detection in Optical Remote Sensing Images
Authors: Yanguang Sun, Jian Yang, Lei Luo
Date: 2024-11-11 00:00:00
Tasks: object-detection, Object Detection, Salient Object Detection

Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD methods consistently center around optimizing pixel features in the spatial domain, progressively distinguishing between backgrounds and objects. However, pixel information represents local attributes, which are often correlated with their surrounding context. Even with strategies expanding the local region, spatial features remain biased towards local characteristics, lacking the ability of global perception. To address this problem, we introduce the Fourier transform that generate global frequency features and achieve an image-size receptive field. To be specific, we propose a novel United Domain Cognition Network (UDCNet) to jointly explore the global-local information in the frequency and spatial domains. Technically, we first design a frequency-spatial domain transformer block that mutually amalgamates the complementary local spatial and global frequency features to strength the capability of initial input features. Furthermore, a dense semantic excavation module is constructed to capture higher-level semantic for guiding the positioning of remote sensing objects. Finally, we devise a dual-branch joint optimization decoder that applies the saliency and edge branches to generate high-quality representations for predicting salient objects. Experimental results demonstrate the superiority of the proposed UDCNet method over 24 state-of-the-art models, through extensive quantitative and qualitative comparisons in three widely-used ORSIs-SOD datasets. The source code is available at: \href{https://github.com/CSYSI/UDCNet}{\color{blue} https://github.com/CSYSI/UDCNet}.
Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs
Authors: Jia Syuen Lim, Yadan Luo, Zhi Chen, Tianqi Wei, Scott Chapman, Zi Huang
Date: 2024-11-11 00:00:00
Tasks: Multi-Object Tracking, Object, object-detection, Object Detection, Object Tracking

In the Detection and Multi-Object Tracking of Sweet Peppers Challenge, we present Track Any Peppers (TAP) - a weakly supervised ensemble technique for sweet peppers tracking. TAP leverages the zero-shot detection capabilities of vision-language foundation models like Grounding DINO to automatically generate pseudo-labels for sweet peppers in video sequences with minimal human intervention. These pseudo-labels, refined when necessary, are used to train a YOLOv8 segmentation network. To enhance detection accuracy under challenging conditions, we incorporate pre-processing techniques such as relighting adjustments and apply depth-based filtering during post-inference. For object tracking, we integrate the Matching by Segment Anything (MASA) adapter with the BoT-SORT algorithm. Our approach achieves a HOTA score of 80.4%, MOTA of 66.1%, Recall of 74.0%, and Precision of 90.7%, demonstrating effective tracking of sweet peppers without extensive manual effort. This work highlights the potential of foundation models for efficient and accurate object detection and tracking in agricultural settings.
HomoMatcher: Dense Feature Matching Results with Semi-Dense Efficiency by Homography Estimation
Authors: Xiaolong Wang, Lei Yu, Yingying Zhang, Jiangwei Lao, Lixiang Ru, Liheng Zhong, Jingdong Chen, Yu Zhang, Ming Yang
Date: 2024-11-11 00:00:00
Tasks: Homography Estimation, Patch Matching

Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved substantial performance enhancements and established a widely-accepted coarse-to-fine paradigm. However, the majority of existing methods focus on improving coarse feature representation rather than the fine-matching module. Prior fine-matching techniques, which rely on point-to-patch matching probability expectation or direct regression, often lack precision and do not guarantee the continuity of feature points across sequential images. To address this limitation, this paper concentrates on enhancing the fine-matching module in the semi-dense matching framework. We employ a lightweight and efficient homography estimation network to generate the perspective mapping between patches obtained from coarse matching. This patch-to-patch approach achieves the overall alignment of two patches, resulting in a higher sub-pixel accuracy by incorporating additional constraints. By leveraging the homography estimation between patches, we can achieve a dense matching result with low computational cost. Extensive experiments demonstrate that our method achieves higher accuracy compared to previous semi-dense matchers. Meanwhile, our dense matching results exhibit similar end-point-error accuracy compared to previous dense matchers while maintaining semi-dense efficiency.
Séparation en composantes structures, textures et bruit d'une image, apport de l'utilisation des contourlettes
Authors: Jerome Gilles
Date: 2024-11-11 00:00:00

In this paper, we propose to improve image decomposition algorithms in the case of noisy images. In \cite{gilles1,aujoluvw}, the authors propose to separate structures, textures and noise from an image. Unfortunately, the use of separable wavelets shows some artefacts. In this paper, we propose to replace the wavelet transform by the contourlet transform which better approximate geometry in images. For that, we define contourlet spaces and their associated norms. Then, we get an iterative algorithm which we test on two noisy textured images.
METRIC: a complete methodology for performances evaluation of automatic target Detection, Recognition and Tracking algorithms in infrared imagery
Authors: Jérôme Gilles, Stéphane Landeau, Tristan Dagobert, Philippe Chevalier, Eric Stiée, Damien Diaz, Jean-Luc Maillart
Date: 2024-11-11 00:00:00

In this communication, we deal with the question of automatic target detection, recognition and tracking (ATD/R/T) algorithms performance assessment. We propose a complete methodology of evaluation which approaches objective image datasets development and adapted metrics definition for the different tasks (detection, recognition and tracking). We present some performance results which are currently processed in a French-MoD program called 2ACI (``Acquisition Automatique de Cibles par Imagerie``).
Layout Control and Semantic Guidance with Attention Loss Backward for T2I Diffusion Model
Authors: Guandong Li
Date: 2024-11-11 00:00:00
Tasks: Attribute, Image Generation

Controllable image generation has always been one of the core demands in image generation, aiming to create images that are both creative and logical while satisfying additional specified conditions. In the post-AIGC era, controllable generation relies on diffusion models and is accomplished by maintaining certain components or introducing inference interferences. This paper addresses key challenges in controllable generation: 1. mismatched object attributes during generation and poor prompt-following effects; 2. inadequate completion of controllable layouts. We propose a train-free method based on attention loss backward, cleverly controlling the cross attention map. By utilizing external conditions such as prompts that can reasonably map onto the attention map, we can control image generation without any training or fine-tuning. This method addresses issues like attribute mismatch and poor prompt-following while introducing explicit layout constraints for controllable image generation. Our approach has achieved excellent practical applications in production, and we hope it can serve as an inspiring technical report in this field.
SeedEdit: Align Image Re-Generation to Image Editing
Authors: Yichun Shi, Peng Wang, Weilin Huang
Date: 2024-11-11 00:00:00
Tasks: Image Reconstruction

We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance between maintaining the original image, i.e. image reconstruction, and generating a new image, i.e. image re-generation. To this end, we start from a weak generator (text-to-image model) that creates diverse pairs between such two directions and gradually align it into a strong image editor that well balances between the two tasks. SeedEdit can achieve more diverse and stable editing capability over prior image editing methods, enabling sequential revision over images generated by diffusion models.
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation
Authors: Xinyang Huang, Chuang Zhu, BoWen Zhang, Shanghang Zhang
Date: 2024-11-11 00:00:00
Tasks: Contrastive Learning, Domain Adaptation, Semi-supervised Domain Adaptation

Semi-supervised domain adaptation (SSDA) has been widely studied due to its ability to utilize a few labeled target data to improve the generalization ability of the model. However, existing methods only consider designing certain strategies for target samples to adapt, ignoring the exploration of customized learning for different target samples. When the model encounters complex target distribution, existing methods will perform limited due to the inability to clearly and comprehensively learn the knowledge of multiple types of target samples. To fill this gap, this paper focuses on designing a framework to use different strategies for comprehensively mining different target samples. We propose a novel source-free framework (SOUF) to achieve semi-supervised fine-tuning of the source pre-trained model on the target domain. Different from existing SSDA methods, SOUF decouples SSDA from the perspectives of different target samples, specifically designing robust learning techniques for unlabeled, reliably labeled, and noisy pseudo-labeled target samples. For unlabeled target samples, probability-based weighted contrastive learning (PWC) helps the model learn more discriminative feature representations. To mine the latent knowledge of labeled target samples, reliability-based mixup contrastive learning (RMC) learns complex knowledge from the constructed reliable sample set. Finally, predictive regularization learning (PR) further mitigates the misleading effect of noisy pseudo-labeled samples on the model. Extensive experiments on benchmark datasets demonstrate the superiority of our framework over state-of-the-art methods.
LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection
Authors: Zhengyi Liu, Longzhen Wang, Xianyong Fang, Zhengzheng Tu, Linbo Wang
Date: 2024-11-11 00:00:00
Tasks: Mamba, object-detection, Object Detection, Relation, Salient Object Detection, Weakly-supervised Learning

A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information, enhancing applications in stereoscopic photography, virtual reality, and robotic vision. In this work, a state-of-the-art salient object detection model for multi-focus light field images, called LFSamba, is introduced to emphasize four main insights: (a) Efficient feature extraction, where SAM is used to extract modality-aware discriminative features; (b) Inter-slice relation modeling, leveraging Mamba to capture long-range dependencies across multiple focal slices, thus extracting implicit depth cues; (c) Inter-modal relation modeling, utilizing Mamba to integrate all-focus and multi-focus images, enabling mutual enhancement; (d) Weakly supervised learning capability, developing a scribble annotation dataset from an existing pixel-level mask dataset, establishing the first scribble-supervised baseline for light field salient object detection.https://github.com/liuzywen/LFScribble
Machine learning-enabled velocity model building with uncertainty quantification
Authors: Rafael Orozco, Huseyin Tuna Erdinc, Yunlin Zeng, Mathias Louboutin, Felix J. Herrmann
Date: 2024-11-11 00:00:00
Tasks: Uncertainty Quantification

Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional velocity model building methods such as Full-Waveform Inversion (FWI) are powerful but often struggle with the inherent complexities of the inverse problem, including noise, limited bandwidth, receiver aperture and computational constraints. To address these challenges, we propose a scalable methodology that integrates generative modeling, in the form of Diffusion networks, with physics-informed summary statistics, making it suitable for complicated imaging problems including field datasets. By defining these summary statistics in terms of subsurface-offset image volumes for poor initial velocity models, our approach allows for computationally efficient generation of Bayesian posterior samples for migration velocity models that offer a useful assessment of uncertainty. To validate our approach, we introduce a battery of tests that measure the quality of the inferred velocity models, as well as the quality of the inferred uncertainties. With modern synthetic datasets, we reconfirm gains from using subsurface-image gathers as the conditioning observable. For complex velocity model building involving salt, we propose a new iterative workflow that refines amortized posterior approximations with salt flooding and demonstrate how the uncertainty in the velocity model can be propagated to the final product reverse time migrated images. Finally, we present a proof of concept on field datasets to show that our method can scale to industry-sized problems.
Few-shot Semantic Learning for Robust Multi-Biome 3D Semantic Mapping in Off-Road Environments
Authors: Deegan Atha, Xianmei Lei, Shehryar Khattak, Anna Sabel, Elle Miller, Aurelio Noca, Grace Lim, Jeffrey Edlund, Curtis Padgett, Patrick Spieler
Date: 2024-11-10 00:00:00
Tasks: 2D Semantic Segmentation, Autonomous Navigation, Segmentation, Semantic Segmentation

Off-road environments pose significant perception challenges for high-speed autonomous navigation due to unstructured terrain, degraded sensing conditions, and domain-shifts among biomes. Learning semantic information across these conditions and biomes can be challenging when a large amount of ground truth data is required. In this work, we propose an approach that leverages a pre-trained Vision Transformer (ViT) with fine-tuning on a small (<500 images), sparse and coarsely labeled (<30% pixels) multi-biome dataset to predict 2D semantic segmentation classes. These classes are fused over time via a novel range-based metric and aggregated into a 3D semantic voxel map. We demonstrate zero-shot out-of-biome 2D semantic segmentation on the Yamaha (52.9 mIoU) and Rellis (55.5 mIoU) datasets along with few-shot coarse sparse labeling with existing data for improved segmentation performance on Yamaha (66.6 mIoU) and Rellis (67.2 mIoU). We further illustrate the feasibility of using a voxel map with a range-based semantic fusion approach to handle common off-road hazards like pop-up hazards, overhangs, and water features.
Adaptive and Temporally Consistent Gaussian Surfels for Multi-view Dynamic Reconstruction
Authors: Decai Chen, Brianne Oberson, Ingo Feldmann, Oliver Schreer, Anna Hilsmann, Peter Eisert
Date: 2024-11-10 00:00:00
Tasks: Dynamic Reconstruction, Novel View Synthesis, Surface Reconstruction

3D Gaussian Splatting has recently achieved notable success in novel view synthesis for dynamic scenes and geometry reconstruction in static scenes. Building on these advancements, early methods have been developed for dynamic surface reconstruction by globally optimizing entire sequences. However, reconstructing dynamic scenes with significant topology changes, emerging or disappearing objects, and rapid movements remains a substantial challenge, particularly for long sequences. To address these issues, we propose AT-GS, a novel method for reconstructing high-quality dynamic surfaces from multi-view videos through per-frame incremental optimization. To avoid local minima across frames, we introduce a unified and adaptive gradient-aware densification strategy that integrates the strengths of conventional cloning and splitting techniques. Additionally, we reduce temporal jittering in dynamic surfaces by ensuring consistency in curvature maps across consecutive frames. Our method achieves superior accuracy and temporal coherence in dynamic surface reconstruction, delivering high-fidelity space-time novel view synthesis, even in complex and challenging scenes. Extensive experiments on diverse multi-view video datasets demonstrate the effectiveness of our approach, showing clear advantages over baseline methods. Project page: \url{https://fraunhoferhhi.github.io/AT-GS}
Graph Neural Networks for modelling breast biomechanical compression
Authors: Hadeel Awwad, Eloy García, Robert Martí
Date: 2024-11-10 00:00:00
Tasks: Computational Efficiency, Image Registration, Inductive Learning

Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise alignment and improved analysis. Although Finite Element Analysis (FEA) is reliable for approximating soft tissue deformation, it struggles with balancing accuracy and computational efficiency. Recent studies have used data-driven models trained on FEA results to speed up tissue deformation predictions. We propose to explore Physics-based Graph Neural Networks (PhysGNN) for breast compression simulation. PhysGNN has been used for data-driven modelling in other domains, and this work presents the first investigation of their potential in predicting breast deformation during mammographic compression. Unlike conventional data-driven models, PhysGNN, which incorporates mesh structural information and enables inductive learning on unstructured grids, is well-suited for capturing complex breast tissue geometries. Trained on deformations from incremental FEA simulations, PhysGNN's performance is evaluated by comparing predicted nodal displacements with those from finite element (FE) simulations. This deep learning (DL) framework shows promise for accurate, rapid breast deformation approximations, offering enhanced computational efficiency for real-world scenarios.
Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement
Authors: Zhennan Chen, Yajie Li, Haofan Wang, Zhibo Chen, Zhengkai Jiang, Jun Li, Qian Wang, Jian Yang, Ying Tai
Date: 2024-11-10 00:00:00
Tasks: Attribute, Image Generation, RAG, Text to Image Generation, Text-to-Image Generation

Regional prompting, or compositional generation, which enables fine-grained spatial control, has gained increasing attention for its practicality in real-world applications. However, previous methods either introduce additional trainable modules, thus only applicable to specific models, or manipulate on score maps within cross-attention layers using attention masks, resulting in limited control strength when the number of regions increases. To handle these limitations, we present RAG, a Regional-Aware text-to-image Generation method conditioned on regional descriptions for precise layout composition. RAG decouple the multi-region generation into two sub-tasks, the construction of individual region (Regional Hard Binding) that ensures the regional prompt is properly executed, and the overall detail refinement (Regional Soft Refinement) over regions that dismiss the visual boundaries and enhance adjacent interactions. Furthermore, RAG novelly makes repainting feasible, where users can modify specific unsatisfied regions in the last generation while keeping all other regions unchanged, without relying on additional inpainting models. Our approach is tuning-free and applicable to other frameworks as an enhancement to the prompt following property. Quantitative and qualitative experiments demonstrate that RAG achieves superior performance over attribute binding and object relationship than previous tuning-free methods.
Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)
Authors: Faisal Mehmood, Xin Guo, Enqing Chen, Muhammad Azeem Akbar, Arif Ali Khan, Sami Ullah
Date: 2024-11-10 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data.
Image Segmentation from Shadow-Hints using Minimum Spanning Trees
Authors: Moritz Heep, Eduard Zell
Date: 2024-11-10 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation in RGB space is a notoriously difficult task where state-of-the-art methods are trained on thousands or even millions of annotated images. While the performance is impressive, it is still not perfect. We propose a novel image segmentation method, achieving similar segmentation quality but without training. Instead, we require an image sequence with a static camera and a single light source at varying positions, as used in for photometric stereo, for example.
PRISM: Privacy-preserving Inter-Site MRI Harmonization via Disentangled Representation Learning
Authors: Sarang Galada, Tanurima Halder, Kunal Deo, Ram P Krish, Kshitij Jadhav
Date: 2024-11-10 00:00:00
Tasks: Contrastive Learning, Privacy Preserving, Representation Learning, Variational Inference

Multi-site MRI studies often suffer from site-specific variations arising from differences in methodology, hardware, and acquisition protocols, thereby compromising accuracy and reliability in clinical AI/ML tasks. We present PRISM (Privacy-preserving Inter-Site MRI Harmonization), a novel Deep Learning framework for harmonizing structural brain MRI across multiple sites while preserving data privacy. PRISM employs a dual-branch autoencoder with contrastive learning and variational inference to disentangle anatomical features from style and site-specific variations, enabling unpaired image translation without traveling subjects or multiple MRI modalities. Our modular design allows harmonization to any target site and seamless integration of new sites without the need for retraining or fine-tuning. Using multi-site structural MRI data, we demonstrate PRISM's effectiveness in downstream tasks such as brain tissue segmentation and validate its harmonization performance through multiple experiments. Our framework addresses key challenges in medical AI/ML, including data privacy, distribution shifts, model generalizability and interpretability. Code is available at https://github.com/saranggalada/PRISM