mtybilly/Instruct2DS huggingface dataset:
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
Authors: Hang Yao, Ming Liu*, Zhicun Yin, Zifei Yan, Xiaopeng Hong, Wangmeng Zuo
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Diffusion models have shown superior performance on unsupervised anomaly detection tasks. Since trained with normal data only, diffusion models tend to reconstruct normal counterparts of test images with certain noises added. However, these methods treat all potential anomalies equally, which may cause two main problems. From the global perspective, the difficulty of reconstructing images with different anomalies is uneven. For example, adding back a missing element is harder than dealing with a scratch, thus requiring a larger number of denoising steps. Therefore, instead of utilizing the same setting for all samples, we propose to predict a particular denoising step for each sample by evaluating the difference between image contents and the priors extracted from diffusion models. From the local perspective, reconstructing abnormal regions differs from normal areas even in the same image. Theoretically, the diffusion model predicts a noise for each step, typically following a standard Gaussian distribution. However, due to the difference between the anomaly and its potential normal counterpart, the predicted noise in abnormal regions will inevitably deviate from the standard Gaussian distribution. To this end, we propose introducing synthetic abnormal samples in training to encourage the diffusion models to break through the limitation of standard Gaussian distribution, and a spatial-adaptive feature fusion scheme is utilized during inference. With the above modifications, we propose a global and local adaptive diffusion model (abbreviated to GLAD) for unsupervised anomaly detection, which introduces appealing flexibility and achieves anomaly-free reconstruction while retaining as much normal information as possible. Extensive experiments are conducted on three commonly used anomaly detection datasets (MVTec-AD, MPDD, and VisA) and a printed circuit board dataset (PCB-Bank) we integrated, showing the effectiveness of the proposed method. The source code and pre-trained models are publicly available at https://github.com/hyao1/GLAD."
MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks
Authors: Elad Hirsch*, Gefen Dawidowicz, Ayellet Tal
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Medical report generation from X-ray images is a challenging task, particularly in an unpaired setting where paired image-report data is unavailable for training. To address this challenge, we propose a novel model that leverages the available information in two distinct datasets, one comprising reports and the other consisting of images. The core idea of our model revolves around the notion that combining auto-encoding report generation with multi-modal (report-image) alignment can offer a solution. However, the challenge persists regarding how to achieve this alignment when pair correspondence is absent. Our proposed solution involves the use of auxiliary tasks, particularly contrastive learning and classification, to position related images and reports in close proximity to each other. This approach differs from previous methods that rely on pre-processing steps, such as using external information stored in a knowledge graph. Our model, named MedRAT, surpasses previous state-of-the-art methods, demonstrating the feasibility of generating comprehensive medical reports without the need for paired data or external tools. Our code is publicly available 1 . 1 https://github.com/eladhi/MedRAT"
Are Synthetic Data Useful for Egocentric Hand-Object Interaction Detection?
Authors: Rosario Leonardi*, Antonino Furnari, Francesco Ragusa, Giovanni Maria Farinella
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this study, we investigate the effectiveness of synthetic data in enhancing egocentric hand-object interaction detection. Via extensive experiments and comparative analyses on three egocentric datasets, VISOR, EgoHOS, and ENIGMA-51, our findings reveal how to exploit synthetic data for the HOI detection task when real labeled data are scarce or unavailable. Specifically, by leveraging only 10% of real labeled data, we achieve improvements in Overall AP compared to baselines trained exclusively on real data of: +5.67% on EPIC-KITCHENS VISOR, +8.24% on EgoHOS, and +11.69% on ENIGMA-51. Our analysis is supported by a novel data generation pipeline and the newly introduced HOI-Synth benchmark which augments existing datasets with synthetic images of hand-object interactions automatically labeled with hand-object contact states, bounding boxes, and pixel-wise segmentation masks. Data, code, and data generation tools to support future research are released at: https://fpv-iplab. github.io/HOI-Synth/."
"PoseEmbroider: Towards a 3D, Visual, Semantic-aware Human Pose Representation"
Authors: Ginger Delmas*, Philippe Weinzaepfel, Francesc Moreno-Noguer, Gregory Rogez
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Aligning multiple modalities in a latent space, such as images and texts, has shown to produce powerful semantic visual representations, fueling tasks like image captioning, text-to-image generation, or image grounding. In the context of human-centric vision, albeit CLIP-like representations encode most standard human poses relatively well (such as standing or sitting), they lack sufficient acuteness to discern detailed or uncommon ones. Actually, while 3D human poses have been often associated with images (to perform pose estimation or pose-conditioned image generation), or more recently with text (for text-to-pose generation), they have seldom been paired with both. In this work, we combine 3D poses, person’s pictures and textual pose descriptions to produce an enhanced 3D-, visual- and semantic-aware human pose representation. We introduce a new transformer-based model, trained in a retrieval fashion, which can take as input any combination of the aforementioned modalities. When composing modalities, it outperforms a standard multi-modal alignment retrieval model, making it possible to sort out partial information (image with the lower body occluded). We showcase the potential of such an embroidered pose representation for (1) SMPL regression from image with optional text cue; and (2) on the task of fine-grained instruction generation, which consists in generating a text that describes how to move from one 3D pose to another (as a fitness coach). Unlike prior works, our model can take any kind of input (image and/or pose) without retraining."
A Comparative Study of Image Restoration Networks for General Backbone Network Design
Authors: Xiangyu Chen*, Zheyuan Li, Yuandong Pu, Yihao Liu, Jiantao Zhou*, Yu Qiao, Chao Dong*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Despite the significant progress made by deep models in various image restoration tasks, existing image restoration networks still face challenges in terms of task generality. An intuitive manifestation is that networks which excel in certain tasks often fail to deliver satisfactory results in others. To illustrate this point, we select five representative networks and conduct a comparative study on five classic image restoration tasks. First, we provide a detailed explanation of the characteristics of different image restoration tasks and backbone networks. Following this, we present the benchmark results and analyze the reasons behind the performance disparity of different models across various tasks. Drawing from this comparative study, we propose that a general image restoration backbone network needs to meet the functional requirements of diverse tasks. Based on this principle, we design a new general image restoration backbone network, X-Restormer. Extensive experiments demonstrate that X-Restormer possesses good task generality and achieves state-of-the-art performance across a variety of tasks."
Learned Image Enhancement via Color Naming
Authors: David Serrano-Lozano*, Luis Herranz, Michael S Brown, Javier Vazquez-Corral
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "A popular method for enhancing images involves learning the style of a professional photo editor using pairs of training images comprised of the original input with the editor-enhanced version. When manipulating images, many editing tools offer a feature that allows the user to manipulate a limited selection of familiar colors. Editing by color name allows easy adjustment of elements like the ”blue” of the sky or the ”green” of trees. Inspired by this approach to color manipulation, we propose NamedCurves, a learning-based image enhancement technique that separates the image into a small set of named colors. Our method learns to globally adjust the image for each specific named color via tone curves and then combines the images using an attention-based fusion mechanism to mimic spatial editing. We demonstrate the effectiveness of our method against several competing methods on the well-known Adobe 5K dataset and the PPR10K dataset, showing notable improvements."
Synthesizing Time-varying BRDFs via Latent Space
Authors: Takuto Narumoto*, Hiroaki Santo, Fumio Okura
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper introduces a method for synthesizing time-varying bidirectional reflectance distribution functions (BRDFs) by applying learned temporal changes to static BRDFs. Achieving realistic and natural changes in material appearance over time is crucial in computer graphics and virtual reality. Existing methods employ a parametric BRDF model, and the temporal changes in BRDFs are modeled by polynomial functions that represent the transitions of the BRDF parameters. However, the limited representational capabilities of both the parametric BRDF model and the polynomial temporal model restrict the fidelity of the appearance reproduction. In this paper, to overcome this limitation, we introduce a neural embedding for BRDFs and propose a neural temporal model that represents the temporal changes of BRDFs in the latent space, which allows flexible representations of BRDFs and temporal changes. The experiments using synthetic and real-world datasets demonstrate that the flexibility of the proposed approach achieves a faithful synthesis of temporal changes in material appearance."
HoloADMM: High-Quality Holographic Complex Field Recovery
Authors: Mazen Mel*, Paul Springer, Pietro Zanuttigh, Haitao Zhou, Alexander Gatto
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Holography enables intriguing microscopic imaging modalities, particularly through Quantitative Phase Imaging (QPI), which utilizes the phase of coherent light as a way to reveal the contrast in transparent and thin microscopic specimens. Despite the limitation of image sensors, which detect only light intensity, phase information can still be recorded within a two-dimensional interference pattern between two distinct light waves. Numerical reconstruction is later needed to retrieve the amplitude and phase from such holographic measurements. To this end, we introduce HoloADMM, a novel interpretable, learning-based approach for in-line holographic image reconstruction. HoloADMM enhances imaging capability with spatial image super-resolution, offering a versatile framework that accommodates multiple illumination wavelengths and supports extensive refocusing ranges with up to 10 µm precision. Our results indicate a substantial improvement in reconstruction quality over existing methods and demonstrate HoloADMM’s effective adaptation to real holographic data captured by our Digital in-line Holographic Microscope (DIHM). This work not only advances holographic imaging techniques but also broadens the potential for non-invasive microscopic analysis applications."
Fundamental Matrix Estimation Using Relative Depths
Authors: Yaqing Ding*, Václav Vávra, Snehal Bhayani, Qianliang Wu, Jian Yang, Zuzana Kukelova
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We propose a novel approach to estimate the fundamental matrix from point correspondences and their relative depths. Relative depths can be approximated from the scales of local features, which are commonly available or can be obtained from non-metric monocular depth estimates provided by popular deep learning-based methods. This makes the considered problem very relevant. To derive efficient solutions, we explore new geometric constraints on the fundamental matrix with known relative depths and present new algebraic constraints between the fundamental matrix and the translation vector. Using point correspondences and their relative depths, we derive novel efficient minimal solvers for two fully uncalibrated cameras, two cameras with different unknown focal lengths, and two cameras with equal unknown focal lengths, respectively. We propose different variants of these solvers based on the source of the relative depth information. We present detailed analyses and comparisons with state-of-the-art solvers, including results with 86, 306 image pairs from three large-scale datasets."
Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion
Authors: Otto Seiskari*, Jerry Ylilammi, Valtteri Kaatrasalo, Pekka Rantalankila, Matias Turkulainen, Juho Kannala, Esa Rahtu, Arno Solin
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "High-quality scene reconstruction and novel view synthesis based on Gaussian Splatting (3DGS) typically require steady, high-quality photographs, often impractical to capture with handheld cameras. We present a method that adapts to camera motion and allows high-quality scene reconstruction with handheld video data suffering from motion blur and rolling shutter distortion. Our approach is based on detailed modelling of the physical image formation process and utilizes velocities estimated using visual-inertial odometry (VIO). Camera poses are considered non-static during the exposure time of a single image frame and camera poses are further optimized in the reconstruction process. We formulate a differentiable rendering pipeline that leverages screen space approximation to efficiently incorporate rolling-shutter and motion blur effects into the 3DGS framework. Our results with both synthetic and real data demonstrate superior performance in mitigating camera motion over existing methods, thereby advancing 3DGS in naturalistic settings."
MTaDCS: Moving Trace and Feature Density-based Confidence Sample Selection under Label Noise
Authors: Qingzheng Huang, Xilin He, Xiaole Xian, Qinliang Lin, Weicheng Xie*, Siyang Song, Linlin Shen, Zitong Yu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Learning from noisy labels is a challenging task, as noisy labels can compromise decision boundaries and result in suboptimal generalization performance. Most previous approaches for dealing noisy labels are based on sample selection, which utilized the small loss criterion to reduce the adverse effects of noisy labels. Nevertheless, they encounter a critical limitation in being unable to effectively separate challenging samples from those that were merely mislabeled. Meanwhile, there is a lack of researches on the trace changes of samples during training. To this end, we propose a novel moving trace and feature density-based confidence sample selection strategy (called MTaDCS). Different from existing small loss-based approaches, the local feature density of samples in the latent space is explored to construct a confidence set by selectively choosing confident samples in a progressive manner in terms of moving trace. Therefore, our MTaDCS can gradually isolate noisy labels through the setting of confidence set and achieve the goal of learning discriminative features from hard samples. Extensive experiments conducted on datasets with simulated and real-world noises validate that the proposed MTaDCS outperforms the state-of-the-art methods in terms of various metrics. The code is available at https://github.com/QZ-CODER/-ECCV-24-MTaD"
Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis
Authors: Brian Kostadinov Shalon Isaac-Medina*, Yona Falinie Abdul Gaus*, Neelanjan Bhowmik, Toby P Breckon
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Object detection is a pivotal task in computer vision that has received significant attention in previous years. Nonetheless, the capability of a detector to localise objects out of the training distribution remains unexplored. Whilst recent approaches in object-level out-of-distribution (OoD) detection heavily rely on class labels, such approaches contradict truly open-world scenarios where the class distribution is often unknown. In this context, anomaly detection focuses on detecting unseen instances rather than classifying detections as OoD. This work aims to bridge this gap by leveraging an open-world object detector and an OoD detector via virtual outlier synthesis. This is achieved by using the detector backbone features to first learn object pseudo-classes via self-supervision. These pseudo-classes serve as the basis for class-conditional virtual outlier sampling of anomalous features that are classified by an OoD head. Our approach empowers our overall object detector architecture to learn anomaly-aware feature representations without relying on class labels, hence enabling truly open-world object anomaly detection. Empirical validation of our approach demonstrates its effectiveness across diverse datasets encompassing various imaging modalities (visible, infrared, and X-ray). Moreover, our method establishes state-of-the-art performance on object-level anomaly detection, achieving an average recall score improvement of over 5.4% for natural images and 23.5% for a security X-ray dataset compared to the current approaches. In addition, our method detects anomalies in datasets where current approaches fail. Code available at https: //github.com/KostadinovShalon/oln-ssos."
GroundUp: Rapid Sketch-Based 3D City Massing
Authors: Gizem Esra Unlu*, Mohamed Sayed, Yulia Gryaditskaya, Gabriel Brostow
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We propose GroundUp, the first sketch-based ideation tool for 3D city massing of urban areas. We focus on early-stage urban design, where sketching is a common tool and the design starts from balancing planned building volumes (masses) and open spaces. With Human-Centered AI in mind, we aim to help architects quickly revise their ideas by easily switching between 2D sketches and 3D models, allowing for smoother iteration and sharing of ideas. Inspired by feedback from architects and existing workflows, our system takes as a first input a user sketch of multiple buildings in a top-down view. The user then draws a perspective sketch of the envisioned site. Our method is designed to exploit the complementarity of information in the two sketches and allows users to quickly preview and adjust the inferred 3D shapes. Our model, driving the proposed urban massing system, has two main components. First, we propose a novel sketch-to-depth prediction network for perspective sketches that exploits top-down sketch shapes. Second, we amalgamate the complimentary but sparse 2D signals to condition a customarily trained latent diffusion model. Second, we use depth cues derived from the perspective sketch as a condition to our diffusion model, which ultimately completes the geometry in a top-down view. The diffusion model works in the domain of a heightfield, Thus, our final 3D geometry is represented as a heightfield, allowing users to construct the city “from the ground up”. We will release the code, datasets, and interface. The code, datasets, and interface are available at visual.cs.ucl.ac.uk/pubs/groundup."
Guide-and-Rescale: Self-Guidance Mechanism for Effective Tuning-Free Real Image Editing
Authors: Vadim Titov*, Madina Khalmatova*, Alexandra Ivanova*, Dmitry P Vetrov, Aibek Alanov*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Despite recent advances in large-scale text-to-image generative models, manipulating real images with these models remains a challenging problem. The main limitations of existing editing methods are that they either fail to perform with consistent quality on a wide range of image edits or require time-consuming hyperparameter tuning or fine-tuning of the diffusion model to preserve the image-specific appearance of the input image. We propose a novel approach that is built upon a modified diffusion sampling process via the guidance mechanism. In this work, we explore the self-guidance technique to preserve the overall structure of the input image and its local regions appearance that should not be edited. In particular, we explicitly introduce layout-preserving energy functions that are aimed to save local and global structures of the source image. Additionally, we propose a noise rescaling mechanism that allows to preserve noise distribution by balancing the norms of classifier-free guidance and our proposed guiders during generation. Such a guiding approach does not require fine-tuning the diffusion model and exact inversion process. As a result, the proposed method provides a fast and high-quality editing mechanism. In our experiments, we show through human evaluation and quantitative analysis that the proposed method allows to produce desired editing which is more preferable by humans and also achieves a better trade-off between editing quality and preservation of the original image. Our code is available at https://github.com/AIRI-Institute/Guide-and-Rescale."
DataDream: Few-shot Guided Dataset Generation
Authors: Jae Myung Kim*, Jessica Bader, Stephan Alaniz, Cordelia Schmid, Zeynep Akata
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "While text-to-image diffusion models have been shown to achieve state-of-the-art results in image synthesis, they have yet to prove their effectiveness in downstream applications. Previous work has proposed to generate data for image classifier training given limited real data access. However, these methods struggle to generate in-distribution images or depict fine-grained features, thereby hindering the generalization of classification models trained on synthetic datasets. We propose DataDream, a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few-shot examples of the target classes. DataDream fine-tunes LoRA weights for the image generation model on the few real images before generating the training data using the adapted model. We then fine-tune LoRA weights for CLIP using the synthetic data to improve downstream image classification over previous approaches on a large variety of datasets. We demonstrate the efficacy of DataDream through extensive experiments, surpassing state-of-the-art classification accuracy with few-shot data across 7 out of 10 datasets, while being competitive on the other 3. Additionally, we provide insights into the impact of various factors, such as the number of real-shot and generated images as well as the fine-tuning compute on model performance. The code is available at https://github.com/ExplainableML/DataD"
LPViT: Low-Power Semi-structured Pruning for Vision Transformers
Authors: Kaixin Xu*, Zhe Wang*, Chunyun Chen, Xue Geng, Jie Lin, Xulei Yang, Min Wu*, Xiaoli Li, Weisi Lin*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Vision transformers (ViTs) have emerged as a promising alternative to convolutional neural networks (CNNs) for various image analysis tasks, offering comparable or superior performance. However, one significant drawback of ViTs is their resource-intensive nature, leading to increased memory footprint, computation complexity, and power consumption. To democratize this high-performance technology and make it more environmentally friendly, it is essential to compress ViT models, reducing their resource requirements while maintaining high performance. In this paper, we introduce a new block-structured pruning to address the resource-intensive issue for ViTs, offering a balanced trade-off between accuracy and hardware acceleration. Unlike unstructured pruning or channel-wise structured pruning, block pruning leverages the block-wise structure of linear layers, resulting in more efficient matrix multiplications. To optimize this pruning scheme, our paper proposes a novel hardware-aware learning objective that simultaneously maximizes speedup and minimizes power consumption during inference, tailored to the block sparsity structure. This objective eliminates the need for empirical look-up tables and focuses solely on reducing parametrized layer connections. Moreover, our paper provides a lightweight algorithm to achieve post-training pruning for ViTs, utilizing second-order Taylor approximation and empirical optimization to solve the proposed hardware-aware objective. Extensive experiments on ImageNet are conducted across various ViT architectures, including DeiT-B and DeiT-S, demonstrating competitive performance with other pruning methods and achieving a remarkable balance between accuracy preservation and power savings. Especially, we achieve up to 3.93× and 1.79× speedups on dedicated hardware and GPUs respectively for DeiT-B, and also observe an inference power reduction by 1.4× on real-world GPUs. Code will be released soon."
CipherDM: Secure Three-Party Inference for Diffusion Model Sampling
Authors: Xin Zhao, Xiaojun Chen*, Xudong Chen, He Li, Tingyu Fan, Zhendong Zhao
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Diffusion Models (DMs) achieve state-of-the-art synthesis results in image generation and have been applied to various fields. However, DMs sometimes seriously violate user privacy during usage, making the protection of privacy an urgent issue. Using traditional privacy computing schemes like Secure Multi-Party Computation (MPC) directly in DMs faces significant computation and communication challenges. To address these issues, we propose CipherDM, the first novel, versatile and universal framework applying MPC technology to DMs for secure sampling, which can be widely implemented on multiple DM based tasks. We thoroughly analyze sampling latency breakdown, find time-consuming parts and design corresponding secure MPC protocols for computing nonlinear activations including SoftMax, SiLU and Mish. CipherDM is evaluated on popular architectures (DDPM, DDIM) using MNIST dataset and on SD deployed by diffusers. Compared to direct implementation on SPU, our approach improves running time by approximately 1.084× ∼ 2.328×, and reduces communication costs by approximately 1.212× ∼ 1.791×. Code is available at: https://github.com/Zhaoxinxinzi/CipherDM."
Weighted Ensemble Models Are Strong Continual Learners
Authors: Imad Eddine MAROUF*, Subhankar Roy, Enzo Tartaglione, Stéphane Lathuilière
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this work, we study the problem of continual learning (CL) where the goal is to learn a model on a sequence of tasks, under the assumption that the data from the previous tasks becomes unavailable while learning on the current task data. CL is essentially a balancing act between learning on the new task (plasticity) and maintaining the performance on the previously learned concepts (stability). To address the stability-plasticity trade-off, we propose to perform weight-ensembling of the model parameters of the previous and current tasks. This weighted-ensembled model, which we call Continual Model Averaging (or CoMA), attains high accuracy on the current task by leveraging plasticity, while not deviating too far from the previous weight configuration, ensuring stability. We also propose an improved variant of CoMA, named Continual Fisher-weighted Model Averaging (or CoFiMA), that selectively weighs each parameter in the weights ensemble by leveraging the Fisher information of the weights of the model. Both variants are conceptually simple, easy to implement, and effective in attaining state-of-the-art performance on several standard CL benchmarks. Code is available at: https://github.com/IemProg/CoFiMA."
GGRt: Towards Generalizable 3D Gaussians without Pose Priors in Real-Time
Authors: Hao Li, Yuanyuan Gao, Dingwen Zhang*, Chenming Wu, YALUN DAI, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang, Junwei Han
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper presents GGRt, a novel approach to generalizable novel view synthesis that alleviates the need for real camera poses, complexity in processing high-resolution images, and lengthy optimization processes, thus facilitating stronger applicability of 3D Gaussian Splatting (3D-GS) in real-world scenarios. Specifically, we design a novel joint learning framework that consists of an Iterative Pose Optimization Network (IPO-Net) and a Generalizable 3D-Gaussians (G-3DG) model. With the joint learning mechanism, the proposed framework can inherently estimate robust relative pose information from the image observations and thus primarily alleviate the requirement of real camera poses. Moreover, we implement a deferred back-propagation mechanism that enables high-resolution training and inference, overcoming the resolution constraints of previous methods. To enhance the speed and efficiency, we further introduce a progressive Gaussian cache module that dynamically adjusts during training and inference. As the first pose-free generalizable 3D-GS framework, GGRt achieves inference at ≥ 5 FPS and real-time rendering at ≥ 100 FPS. Through extensive experimentation, we demonstrate that our method outperforms existing NeRF-based pose-free techniques in terms of inference speed and effectiveness. It can also approach the real pose-based 3D-GS methods. Our contributions provide a significant leap forward for the integration of computer vision and computer graphics into practical applications, offering state-of-the-art results on LLFF, KITTI, and Waymo Open datasets and enabling real-time rendering for immersive experiences. Project page: https://3d-aigc.github.io/GGRt."
A Unified Image Compression Method for Human Perception and Multiple Vision Tasks
Authors: Sha Guo, Lin Sui, Chen-Lin Zhang, Zhuo Chen, Wenhan Yang, Lingyu Duan*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent advancements in end-to-end image compression demonstrate the potential to surpass traditional codecs regarding rate-distortion performance. However, current methods either prioritize human perceptual quality or solely optimize for one or a few predetermined downstream tasks, neglecting a more common scenario that involves a variety of unforeseen machine vision tasks. In this paper, we propose a Diffusion-based Multiple-Task Unified Image Compression framework that aims to expand the boundary of traditional image compression by incorporating human perception and multiple vision tasks in open-set scenarios. Our proposed method comprises a Multi-Task Collaborative Embedding module and a Diffusion-based Invariant Knowledge Learning module. The former module facilitates collaborative embedding for multiple tasks, while the latter module boosts generalization toward unforeseen tasks by distilling the invariant knowledge from seen vision tasks. Experiments show that the proposed method extracts compact and versatile embeddings for human and machine vision collaborative compression, resulting in superior performance. Specifically, our method outperforms the state-of-the-art by 52.25%/51.68%/48.87%/48.07%/6.29% BD-rate reduction in terms of mAP/mAP/aAcc/PQ-all/accuracy on the MS-COCO for object detection/instance segmentation/semantic segmentation/panoptic segmentation and video question answering tasks, respectively."
UniVoxel: Fast Inverse Rendering by Unified Voxelization of Scene Representation
Authors: Shuang Wu, Songlin Tang, Guangming Lu, Jianzhuang Liu, Wenjie Pei*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Typical inverse rendering methods focus on learning implicit neural scene representations by modeling the geometry, materials and illumination separately, which entails significant computations for optimization. In this work we design a Unified Voxelization framework for explicit learning of scene representations, dubbed UniVoxel, which allows for efficient modeling of the geometry, materials and illumination jointly, thereby accelerating the inverse rendering significantly. To be specific, we propose to encode a scene into a latent volumetric representation, based on which the geometry, materials and illumination can be readily learned via lightweight neural networks in a unified manner. Particularly, an essential design of UniVoxel is that we leverage local Spherical Gaussians to represent the incident light radiance, which enables the seamless integration of modeling illumination into the unified voxelization framework. Such novel design enables our UniVoxel to model the joint effects of direct lighting, indirect lighting and light visibility efficiently without expensive multi-bounce ray tracing. Extensive experiments on multiple benchmarks covering diverse scenes demonstrate that UniVoxel boosts the optimization efficiency significantly compared to other methods, reducing the per-scene training time from hours to 18 minutes, while achieving favorable reconstruction quality. Code is available at https://github.com/freemantom/UniVoxel."
Audio-visual Generalized Zero-shot Learning the Easy Way
Authors: Shentong Mo*, Pedro Morgado
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Audio-visual generalized zero-shot learning is a rapidly advancing domain that seeks to understand the intricate relations between audio and visual cues within videos. The overarching goal is to leverage insights from seen classes to identify instances from previously unseen ones. Prior approaches primarily utilized synchronized auto-encoders to reconstruct audio-visual attributes, which were informed by cross-attention transformers and projected text embeddings. However, these methods fell short of effectively capturing the intricate relationship between cross-modal features and class-label embeddings inherent in pre-trained language-aligned embeddings. To circumvent these bottlenecks, we introduce a simple yet effective framework for , named , that aligns audio-visual embeddings with transformed text representations. It utilizes a single supervised text audio-visual contrastive loss to learn an alignment between audio-visual and textual modalities, moving away from the conventional approach of reconstructing cross-modal features and text embeddings. Our key insight is that while class name embeddings are well aligned with language-based audio-visual features, they don’t provide sufficient class separation to be useful for zero-shot learning. To address this, our method leverages differential optimization to transform class embeddings into a more discriminative space while preserving the semantic structure of language representations. We conduct extensive experiments on VGGSound-GZSL, UCF-GZSL, and ActivityNet-GZSL benchmarks. Our results demonstrate that our achieves state-of-the-art performance in audio-visual generalized zero-shot learning."
PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition
Authors: Xiao Li*, Yining Liu, Na Dong, Sitian Qin, Xiaolin Hu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Deep learning-based object recognition systems can be easily fooled by various adversarial perturbations. One reason for the weak robustness may be that they do not have part-based inductive bias like the human recognition process. Motivated by this, several part-based recognition models have been proposed to improve the adversarial robustness of recognition. However, due to the lack of part annotations, the effectiveness of these methods is only validated on small-scale nonstandard datasets. In this work, we propose PIN++, short for PartImageNet++, a dataset providing high-quality part segmentation annotations for all categories of ImageNet-1K (IN-1K). With these annotations, we build part-based methods directly on the standard IN-1K dataset for robust recognition. Different from previous two-stage part-based models, we propose a Multi-scale Part-supervised Model (MPM), to learn a robust representation with part annotations. Experiments show that MPM yielded better adversarial robustness on the large-scale IN-1K over strong baselines across various attack settings. Furthermore, MPM achieved improved robustness on common corruptions and several out-of-distribution datasets. The dataset, together with these results, enables and encourages researchers to explore the potential of part-based models in more real applications. The dataset and the code are available at https://github.com/LixiaoTHU/ PartImageNetPP."
Learning Equilibrium Transformation for Gamut Expansion and Color Restoration
Authors: Jun Xiao*, Changjian Shui, Zhi-Song Liu, Qian Ye, Kin-Man Lam
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Existing imaging systems support wide-gamut images like ProPhoto RGB, but most images are typically encoded in a narrower gamut space (e.g., sRGB). To this end, these images can be enhanced by learning to recover the original color values beyond the sRGB gamut, or out-of-gamut values. Current methods incorporate the metadata from the target wide-gamut images to expand the gamut, while preventing distortion of in-gamut values. However, this metadata is hard to obtain in real-world scenarios. In this paper, we propose a novel method that requires no metadata. We formulate gamut expansion as a “root-finding” problem and learn an equilibrium transformation via a neural network. Specifically, our method defines a dynamic system that keeps in-gamut values stable to prevent color distortion and updates out-of-gamut values recurrently. Therefore, we employ an implicit recurrent mechanism to iteratively extract features, which can effectively mitigate the vanishing gradient problem, and reduce the GPU memory consumption to O(1) complexity during training. Experiments demonstrate the effectiveness and efficiency of our model, in terms of gamut expansion and color restoration, outperforming state-of-the-art models by 0.40dB, in terms of PSNR, with a size of 40K parameters only. The codes are available at: https://github.com/junxiao01/LETNet."
Dyn-Adapter: Towards Disentangled Representation for Efficient Visual Recognition
Authors: Yurong Zhang*, Honghao Chen, Zhang Xinyu, Xiangxiang Chu, Li Song
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Parameter-efficient transfer learning (PETL) is a promising task, aiming to adapt the large-scale pre-trained model to downstream tasks with a relatively modest cost. However, current PETL methods struggle in compressing computational complexity and bear a heavy inference burden due to the complete forward process. This paper presents an efficient visual recognition paradigm, called Dynamic Adapter (Dyn-Adapter), that boosts PETL efficiency by subtly disentangling features in multiple levels. Our approach is simple: first, we devise a dynamic architecture with balanced early heads for multi-level feature extraction, along with adaptive training strategy. Second, we introduce a bidirectional sparsity strategy driven by the pursuit of powerful generalization ability. These qualities enable us to fine-tune efficiently and effectively: we reduce FLOPs during inference by 50%, while maintaining or even yielding higher recognition accuracy. Extensive experiments on diverse datasets and pretrained backbones demonstrate the potential of Dyn-Adapter serving as a general efficiency booster for PETL in vision recognition tasks."
Physics-informed Knowledge Transfer for Underwater Monocular Depth Estimation
Authors: Jinghe Yang*, Mingming Gong, Ye Pu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Compared to the in-air case, underwater depth estimation has its own challenges. For instance, acquiring high-quality training datasets with groundtruth poses difficulties due to sensor limitations in aquatic environments. Additionally, the physics characteristics of underwater imaging diverge significantly from the in-air case, the methods developed for in-air depth estimation underperform when applied underwater, due to the domain gap. To address these challenges, our paper introduces a novel transfer-learning-based method - Physics-informed Underwater Depth Estimation (PUDE). The key idea is to transfer the knowledge of a pre-trained in-air depth estimation model to underwater settings utilizing a small underwater image set without groundtruth measurement, guided by a physical underwater imaging formation model. We propose novel bound losses based on the physical model to rectify the depth estimations to align with actual underwater physical properties. Finally, in the evaluations across multiple datasets, we compare PUDE model with other existing in-air and underwater methods. The results reveal that the PUDE model excels in both quantitative and qualitative comparisons."
Robust Nearest Neighbors for Source-Free Domain Adaptation under Class Distribution Shift
Authors: Antonio Tejero-de-Pablos*, Riku Togashi, Mayu Otani, Shin'ichi Satoh
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The goal of source-free domain adaptation (SFDA) is retraining a model fit on data from a source domain (drawings) to classify data from a target domain (photos) employing only the target samples. In addition to the domain shift, in a realistic scenario, the number of samples per class on source and target would also differ (class distribution shift, or CDS). Dealing label-less with CDS via target data only is challenging, and thus previous methods assume no class imbalance in the source data. We study the SFDA pipeline and, for the first time, propose a SFDA method that can deal with class imbalance in both source and target data. While pseudolabeling is the core technique in SFDA to estimate the distribution of the target data, it relies on nearest neighbors, which makes it sensitive to class distribution shifts (CDS). We are able to calculate robust nearest neighbors by leveraging additional generic features free of the source model’s CDS bias. This provides a “second-opinion” regarding which nearest neighbors are more suitable for adaptation. We evaluate our method using various types of features, datasets and tasks, outperforming previous methods in SFDA under CDS. Our code is available at https://github.com/ CyberAgentAILab/Robust_Nearest_Neighbors_SFDA-CDS."
Chains of Diffusion Models
Authors: Yanheng Wei*, Lianghua Huang*, Zhi-Fan Wu, Wei Wang, Yu Liu, Mingda Jia, Shuailei Ma
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent generative models excel in creating high-quality single-human images but fail in complex multi-human scenarios, failing to capture accurate structural details like quantities, identity accuracy, layouts and postures. We introduce a novel approach, Chains, which enhances initial text prompts into detailed human conditions using a step-by-step process. Chains utilize a series of condition nodes—text, quantity, layout, skeleton, and 3D mesh—each undergoing an independent diffusion process. This enables high-quality human generation and advanced scene layout management in diffusion models. We evaluate Chains against a new benchmark for complex multi-human scene synthesis, showing superior performance in human quality and scene accuracy over existing methods. Remarkably, Chains achieves this with under 0.45 seconds for a 20-step inference, demonstrating both effectiveness and efficiency."
Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models
Authors: Phuong Hoang Dam*, Jihoon Jeong*, Anh T Tran*, Daeyoung Kim*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This study discusses the critical issues of Virtual Try-On in contemporary e-commerce and the prospective metaverse, emphasizing the challenges of preserving intricate texture details and distinctive features of the target person and the clothes in various scenarios, such as clothing texture and identity characteristics like tattoos or accessories. In addition to the fidelity of the synthesized images, the efficiency of the synthesis process presents a significant hurdle. Various existing approaches are explored, highlighting the limitations and unresolved aspects, e.g., identity information omission, uncontrollable artifacts, and low synthesis speed. It then proposes a novel diffusion-based solution that addresses garment texture preservation and user identity retention during virtual try-on. The proposed network comprises two primary modules - a warping module aligning clothing with individual features and a try-on module refining the attire and generating missing parts integrated with a mask-aware post-processing technique ensuring the integrity of the individual’s identity. It demonstrates impressive results, surpassing the state-of-the-art in speed by nearly 20 times during inference, with superior fidelity in qualitative assessments. Quantitative evaluations confirm comparable performance with the recent SOTA method on the VITON-HD and Dresscode datasets. We named our model Fast and Identity Preservation Virtual TryON (FIP-VITON)."
Feature Diversification and Adaptation for Federated Domain Generalization
Authors: Seunghan Yang*, Seokeon Choi, Hyunsin Park, Sungha Choi, Simyung Chang, Sungrack Yun
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their limited domains, leading to a ‘domain shift’ across clients. Privacy concerns limit each client’s learning to its own domain data, which increase the risk of overfitting. Moreover, the process of aggregating models trained on own limited domain can be potentially lead to a significant degradation in the global model performance. To deal with these challenges, we introduce the concept of federated feature diversification. Each client diversifies the own limited domain data by leveraging global feature statistics, i.e., the aggregated average statistics over all participating clients, shared through the global model’s parameters. This data diversification helps local models to learn client-invariant representations while preserving privacy. Our resultant global model shows robust performance on unseen test domain data. To enhance performance further, we develop an instance-adaptive inference approach tailored for test domain data. Our proposed instance feature adapter dynamically adjusts feature statistics to align with the test input, thereby reducing the domain gap between the test and training domains. We show that our method achieves state-of-the-art performance on several domain generalization benchmarks within a federated learning setting."