pwc-archive/papers-with-abstracts huggingface dataset:
Neural Experts: Mixture of Experts for Implicit Neural Representations
Authors: Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Sameera Ramasinghe, Stephen Gould
Date: 2024-10-29 00:00:00
Tasks: Image Reconstruction, Mixture-of-Experts, Surface Reconstruction, Video Reconstruction

Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction. These INRs typically learn the implicit field from sampled input points. This is often done using a single network for the entire domain, imposing many global constraints on a single function. In this paper, we propose a mixture of experts (MoE) implicit neural representation approach that enables learning local piece-wise continuous functions that simultaneously learns to subdivide the domain and fit locally. We show that incorporating a mixture of experts architecture into existing INR formulations provides a boost in speed, accuracy, and memory requirements. Additionally, we introduce novel conditioning and pretraining methods for the gating network that improves convergence to the desired solution. We evaluate the effectiveness of our approach on multiple reconstruction tasks, including surface reconstruction, image reconstruction, and audio signal reconstruction and show improved performance compared to non-MoE methods.
Investigation of moving objects through atmospheric turbulence from a non-stationary platform
Authors: Nicholas Ferrante, Jerome Gilles, Shibin Parameswaran
Date: 2024-10-29 00:00:00
Tasks: Optical Flow Estimation

In this work, we extract the optical flow field corresponding to moving objects from an image sequence of a scene impacted by atmospheric turbulence \emph{and} captured from a moving camera. Our procedure first computes the optical flow field and creates a motion model to compensate for the flow field induced by camera motion. After subtracting the motion model from the optical flow, we proceed with our previous work, Gilles et al~\cite{gilles2018detection}, where a spatial-temporal cartoon+texture inspired decomposition is performed on the motion-compensated flow field in order to separate flows corresponding to atmospheric turbulence and object motion. Finally, the geometric component is processed with the detection and tracking method and is compared against a ground truth. All of the sequences and code used in this work are open source and are available by contacting the authors.
Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis
Authors: Deepak Sridhar, Abhishek Peri, Rohith Rachala, Nuno Vasconcelos
Date: 2024-10-29 00:00:00
Tasks: Continual Learning, Image Generation

Recent advances in generative modeling with diffusion processes (DPs) enabled breakthroughs in image synthesis. Despite impressive image quality, these models have various prompt compliance problems, including low recall in generating multiple objects, difficulty in generating text in images, and meeting constraints like object locations and pose. For fine-grained editing and manipulation, they also require fine-grained semantic or instance maps that are tedious to produce manually. While prompt compliance can be enhanced by addition of loss functions at inference, this is time consuming and does not scale to complex scenes. To overcome these limitations, this work introduces a new family of \textit{Factor Graph Diffusion Models} (FG-DMs) that models the joint distribution of images and conditioning variables, such as semantic, sketch, depth or normal maps via a factor graph decomposition. This joint structure has several advantages, including support for efficient sampling based prompt compliance schemes, which produce images of high object recall, semi-automated fine-grained editing, text-based editing of conditions with noise inversion, explainability at intermediate levels, ability to produce labeled datasets for the training of downstream models such as segmentation or depth, training with missing data, and continual learning where new conditioning variables can be added with minimal or no modifications to the existing structure. We propose an implementation of FG-DMs by adapting a pre-trained Stable Diffusion (SD) model to implement all FG-DM factors, using only COCO dataset, and show that it is effective in generating images with 15\% higher recall than SD while retaining its generalization ability. We introduce an attention distillation loss that encourages consistency among the attention maps of all factors, improving the fidelity of the generated conditions and image.
OFER: Occluded Face Expression Reconstruction
Authors: Pratheba Selvaraju, Victoria Fernandez Abrevaya, Timo Bolkart, Rick Akkerman, Tianyu Ding, Faezeh Amjadi, Ilya Zharkov
Date: 2024-10-29 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Face Reconstruction, Face Reconstruction

Reconstructing 3D face models from a single image is an inherently ill-posed problem, which becomes even more challenging in the presence of occlusions. In addition to fewer available observations, occlusions introduce an extra source of ambiguity, where multiple reconstructions can be equally valid. Despite the ubiquity of the problem, very few methods address its multi-hypothesis nature. In this paper we introduce OFER, a novel approach for single image 3D face reconstruction that can generate plausible, diverse, and expressive 3D faces, even under strong occlusions. Specifically, we train two diffusion models to generate the shape and expression coefficients of a face parametric model, conditioned on the input image. This approach captures the multi-modal nature of the problem, generating a distribution of solutions as output. Although this addresses the ambiguity problem, the challenge remains to pick the best matching shape to ensure consistency across diverse expressions. To achieve this, we propose a novel ranking mechanism that sorts the outputs of the shape diffusion network based on the predicted shape accuracy scores to select the best match. We evaluate our method using standard benchmarks and introduce CO-545, a new protocol and dataset designed to assess the accuracy of expressive faces under occlusion. Our results show improved performance over occlusion-based methods, with added ability to generate multiple expressions for a given image.
NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments
Authors: Taiyi Pan, Junyang He, Chao Chen, Yiming Li, Chen Feng
Date: 2024-10-28 00:00:00
Tasks: Simultaneous Localization and Mapping, Visual Place Recognition

Visual place recognition (VPR) enables autonomous robots to identify previously visited locations, which contributes to tasks like simultaneous localization and mapping (SLAM). VPR faces challenges such as accurate image neighbor retrieval and appearance change in scenery. Event cameras, also known as dynamic vision sensors, are a new sensor modality for VPR and offer a promising solution to the challenges with their unique attributes: high temporal resolution (1MHz clock), ultra-low latency (in {\mu}s), and high dynamic range (>120dB). These attributes make event cameras less susceptible to motion blur and more robust in variable lighting conditions, making them suitable for addressing VPR challenges. However, the scarcity of event-based VPR datasets, partly due to the novelty and cost of event cameras, hampers their adoption. To fill this data gap, our paper introduces the NYC-Event-VPR dataset to the robotics and computer vision communities, featuring the Prophesee IMX636 HD event sensor (1280x720 resolution), combined with RGB camera and GPS module. It encompasses over 13 hours of geotagged event data, spanning 260 kilometers across New York City, covering diverse lighting and weather conditions, day/night scenarios, and multiple visits to various locations. Furthermore, our paper employs three frameworks to conduct generalization performance assessments, promoting innovation in event-based VPR and its integration into robotics applications.
Topological numbers and their use to characterize simple points for 2D binary images
Authors: Christophe Lohou
Date: 2024-10-28 00:00:00

In this paper, we adapt the two topological numbers, which have been proposed to efficiently characterize simple points in specific neighborhoods for 3D binary images, to the case of 2D binary images. Unlike the 3D case, we only use a single neighborhood to define these two topological numbers for the 2D case. Then, we characterize simple points either by using the two topological numbers or by a single topological number linked to another one condition. We compare the characterization of simple points by topological numbers with two other ones based on Hilditch crossing number and Yokoi number. We also highlight the number of possible configurations corresponding to a simple point, which also represents the maximum limit of local configurations that a thinning algorithm operating by parallel deletion of simple (individual) points may delete while preserving topology (limit usually not reachable, depending on the deletion strategy).
MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweeps
Authors: Yating Xu, Chen Li, Gim Hee Lee
Date: 2024-10-28 00:00:00
Tasks: 3D Object Detection, Depth Estimation, Depth Prediction, NeRF, object-detection, Object Detection

The key challenge of multi-view indoor 3D object detection is to infer accurate geometry information from images for precise 3D detection. Previous method relies on NeRF for geometry reasoning. However, the geometry extracted from NeRF is generally inaccurate, which leads to sub-optimal detection performance. In this paper, we propose MVSDet which utilizes plane sweep for geometry-aware 3D object detection. To circumvent the requirement for a large number of depth planes for accurate depth prediction, we design a probabilistic sampling and soft weighting mechanism to decide the placement of pixel features on the 3D volume. We select multiple locations that score top in the probability volume for each pixel and use their probability score to indicate the confidence. We further apply recent pixel-aligned Gaussian Splatting to regularize depth prediction and improve detection performance with little computation overhead. Extensive experiments on ScanNet and ARKitScenes datasets are conducted to show the superiority of our model. Our code is available at https://github.com/Pixie8888/MVSDet.
Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation
Authors: Yuan Huang, Fugen Zhou, Jerome Gilles
Date: 2024-10-28 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Texture Classification

In this paper, we propose a new approach to perform supervised texture classification/segmentation. The proposed idea is to feed a Fully Convolutional Network with specific texture descriptors. These texture features are extracted from images by using an empirical curvelet transform. We propose a method to build a unique empirical curvelet filter bank adapted to a given dictionary of textures. We then show that the output of these filters can be used to build efficient texture descriptors utilized to finally feed deep learning networks. Our approach is finally evaluated on several datasets and compare the results to various state-of-the-art algorithms and show that the proposed method dramatically outperform all existing ones.
Super-resolution in disordered media using neural networks
Authors: Alexander Christie, Matan Leibovich, Miguel Moscoso, Alexei Novikov, George Papanicolaou, Chrysoula Tsogka
Date: 2024-10-28 00:00:00
Tasks: Super-Resolution

We propose a methodology that exploits large and diverse data sets to accurately estimate the ambient medium's Green's functions in strongly scattering media. Given these estimates, obtained with and without the use of neural networks, excellent imaging results are achieved, with a resolution that is better than that of a homogeneous medium. This phenomenon, also known as super-resolution, occurs because the ambient scattering medium effectively enhances the physical imaging aperture. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Detection of moving objects through turbulent media. Decomposition of Oscillatory vs Non-Oscillatory spatio-temporal vector fields
Authors: Jerome Gilles, Francis Alvarez, Nicholas B. Ferrante, Margaret Fortman, Lena Tahir, Alex Tarter, Anneke von Seeger
Date: 2024-10-28 00:00:00

In this paper, we investigate how moving objects can be detected when images are impacted by atmospheric turbulence. We present a geometric spatio-temporal point of view to the problem and show that it is possible to distinguish movement due to the turbulence vs. moving objects. To perform this task, we propose an extension of 2D cartoon+texture decomposition algorithms to 3D vector fields. Our algorithm is based on curvelet spaces which permit to better characterize the movement flow geometry. We present experiments on real data which illustrate the efficiency of the proposed method.
ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction
Authors: Wei Dong, Han Zhou, Yulun Zhang, Xiaohong Liu, Jun Chen
Date: 2024-10-28 00:00:00
Tasks: Exposure Correction, Mamba

Exposure Correction (EC) aims to recover proper exposure conditions for images captured under over-exposure or under-exposure scenarios. While existing deep learning models have shown promising results, few have fully embedded Retinex theory into their architecture, highlighting a gap in current methodologies. Additionally, the balance between high performance and efficiency remains an under-explored problem for exposure correction task. Inspired by Mamba which demonstrates powerful and highly efficient sequence modeling, we introduce a novel framework based on Mamba for Exposure Correction (ECMamba) with dual pathways, each dedicated to the restoration of reflectance and illumination map, respectively. Specifically, we firstly derive the Retinex theory and we train a Retinex estimator capable of mapping inputs into two intermediary spaces, each approximating the target reflectance and illumination map, respectively. This setup facilitates the refined restoration process of the subsequent Exposure Correction Mamba Module (ECMM). Moreover, we develop a novel 2D Selective State-space layer guided by Retinex information (Retinex-SS2D) as the core operator of ECMM. This architecture incorporates an innovative 2D scanning strategy based on deformable feature aggregation, thereby enhancing both efficiency and effectiveness. Extensive experiment results and comprehensive ablation studies demonstrate the outstanding performance and the importance of each component of our proposed ECMamba. Code is available at https://github.com/LowlevelAI/ECMamba.
Constrained Transformer-Based Porous Media Generation to Spatial Distribution of Rock Properties
Authors: Zihan Ren, Sanjay Srinivasan, Dustin Crandall
Date: 2024-10-28 00:00:00

Pore-scale modeling of rock images based on information in 3D micro-computed tomography data is crucial for studying complex subsurface processes such as CO2 and brine multiphase flow during Geologic Carbon Storage (GCS). While deep learning models can generate 3D rock microstructures that match static rock properties, they have two key limitations: they don't account for the spatial distribution of rock properties that can have an important influence on the flow and transport characteristics (such as permeability and relative permeability) of the rock and they generate structures below the representative elementary volume (REV) scale for those transport properties. Addressing these issues is crucial for building a consistent workflow between pore-scale analysis and field-scale modeling. To address these challenges, we propose a two-stage modeling framework that combines a Vector Quantized Variational Autoencoder (VQVAE) and a transformer model for spatial upscaling and arbitrary-size 3D porous media reconstruction in an autoregressive manner. The VQVAE first compresses and quantizes sub-volume training images into low-dimensional tokens, while we train a transformer to spatially assemble these tokens into larger images following specific spatial order. By employing a multi-token generation strategy, our approach preserves both sub-volume integrity and spatial relationships among these sub-image patches. We demonstrate the effectiveness of our multi-token transformer generation approach and validate it using real data from a test well, showcasing its potential to generate models for the porous media at the well scale using only a spatial porosity model. The interpolated representative porous media that reflect field-scale geological properties accurately model transport properties, including permeability and multiphase flow relative permeability of CO2 and brine.
SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization
Authors: Wanhua Li, Zibin Meng, Jiawei Zhou, Donglai Wei, Chuang Gan, Hanspeter Pfister
Date: 2024-10-28 00:00:00
Tasks: Relation, Visual Social Relationship Recognition

Social relation reasoning aims to identify relation categories such as friends, spouses, and colleagues from images. While current methods adopt the paradigm of training a dedicated network end-to-end using labeled image data, they are limited in terms of generalizability and interpretability. To address these issues, we first present a simple yet well-crafted framework named {\name}, which combines the perception capability of Vision Foundation Models (VFMs) and the reasoning capability of Large Language Models (LLMs) within a modular framework, providing a strong baseline for social relation recognition. Specifically, we instruct VFMs to translate image content into a textual social story, and then utilize LLMs for text-based reasoning. {\name} introduces systematic design principles to adapt VFMs and LLMs separately and bridge their gaps. Without additional model training, it achieves competitive zero-shot results on two databases while offering interpretable answers, as LLMs can generate language-based explanations for the decisions. The manual prompt design process for LLMs at the reasoning phase is tedious and an automated prompt optimization method is desired. As we essentially convert a visual classification task into a generative task of LLMs, automatic prompt optimization encounters a unique long prompt optimization issue. To address this issue, we further propose the Greedy Segment Prompt Optimization (GSPO), which performs a greedy search by utilizing gradient information at the segment level. Experimental results show that GSPO significantly improves performance, and our method also generalizes to different image styles. The code is available at https://github.com/Mengzibin/SocialGPT.
Domain Adaptation with a Single Vision-Language Embedding
Authors: Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette
Date: 2024-10-28 00:00:00
Tasks: Domain Adaptation, One-shot Unsupervised Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation

Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in some uncommon conditions. In this paper, we present a new framework for domain adaptation relying on a single Vision-Language (VL) latent embedding instead of full target data. First, leveraging a contrastive language-image pre-training model (CLIP), we propose prompt/photo-driven instance normalization (PIN). PIN is a feature augmentation method that mines multiple visual styles using a single target VL latent embedding, by optimizing affine transformations of low-level source features. The VL embedding can come from a language prompt describing the target domain, a partially optimized language prompt, or a single unlabeled target image. Second, we show that these mined styles (i.e., augmentations) can be used for zero-shot (i.e., target-free) and one-shot unsupervised domain adaptation. Experiments on semantic segmentation demonstrate the effectiveness of the proposed method, which outperforms relevant baselines in the zero-shot and one-shot settings.
Multi-path Exploration and Feedback Adjustment for Text-to-Image Person Retrieval
Authors: Bin Kang, Bin Chen, Junjie Wang, Yong Xu
Date: 2024-10-26 00:00:00
Tasks: cross-modal alignment, Person Retrieval, Retrieval, Text based Person Retrieval, Text-based Person Retrieval

Text-based person retrieval aims to identify the specific persons using textual descriptions as queries. Existing ad vanced methods typically depend on vision-language pre trained (VLP) models to facilitate effective cross-modal alignment. However, the inherent constraints of VLP mod-els, which include the global alignment biases and insuffi-cient self-feedback regulation, impede optimal retrieval per formance. In this paper, we propose MeFa, a Multi-Pathway Exploration, Feedback, and Adjustment framework, which deeply explores intrinsic feedback of intra and inter-modal to make targeted adjustment, thereby achieving more precise person-text associations. Specifically, we first design an intra modal reasoning pathway that generates hard negative sam ples for cross-modal data, leveraging feedback from these samples to refine intra-modal reasoning, thereby enhancing sensitivity to subtle discrepancies. Subsequently, we intro duce a cross-modal refinement pathway that utilizes both global information and intermodal feedback to refine local in formation, thus enhancing its global semantic representation. Finally, the discriminative clue correction pathway incorpo rates fine-grained features of secondary similarity as discrim inative clues to further mitigate retrieval failures caused by disparities in these features. Experimental results on three public benchmarks demonstrate that MeFa achieves superior person retrieval performance without necessitating additional data or complex structures.
Decoding Diffusion: A Scalable Framework for Unsupervised Analysis of Latent Space Biases and Representations Using Natural Language Prompts
Authors: E. Zhixuan Zeng, Yuhao Chen, Alexander Wong
Date: 2024-10-25 00:00:00
Tasks: Denoising, Image Captioning, Image Generation

Recent advances in image generation have made diffusion models powerful tools for creating high-quality images. However, their iterative denoising process makes understanding and interpreting their semantic latent spaces more challenging than other generative models, such as GANs. Recent methods have attempted to address this issue by identifying semantically meaningful directions within the latent space. However, they often need manual interpretation or are limited in the number of vectors that can be trained, restricting their scope and utility. This paper proposes a novel framework for unsupervised exploration of diffusion latent spaces. We directly leverage natural language prompts and image captions to map latent directions. This method allows for the automatic understanding of hidden features and supports a broader range of analysis without the need to train specific vectors. Our method provides a more scalable and interpretable understanding of the semantic knowledge encoded within diffusion models, facilitating comprehensive analysis of latent biases and the nuanced representations these models learn. Experimental results show that our framework can uncover hidden patterns and associations in various domains, offering new insights into the interpretability of diffusion model latent spaces.