GenAI4ELab/papercli-papers huggingface dataset:
Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring
Authors: Sizhuo Li; Dimitri Gominski; Martin Brandt; Xiaoye Tong; Philippe Ciais
Venue: ECCV 2024 (main)

Abstract: "Image-level regression is an important task in Earth observation, where visual domain and label shifts are a core challenge hampering generalization. However, cross-domain regression within remote sensing data remains understudied due to the absence of suited datasets. We introduce a new dataset with aerial and satellite imagery in five countries with three forest-related regression tasks2 . To match real-world applicative interests, we compare methods through a restrictive setup where no prior on the target domain is available during training, and models are adapted with limited information during testing. Building on the assumption that ordered relationships generalize better, we propose manifold diffusion for regression as a strong baseline for transduction in low-data regimes. Our comparison highlights the comparative advantages of inductive and transductive methods in cross-domain regression. 2 Dataset and code available here: dgominski.github.io/drift/"
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion
Authors: Daniel Winter; Matan Cohen; Shlomi Fruchter; Yael Pritch; Alex Rav-Acha; Yedid Hoshen
Venue: ECCV 2024 (main)

Abstract: "[width=0.7]figures/frontp age.pdf Figure 1: Object removal and insertion. Our method models the effects of an object on the scene including occlusions, reflections, and shadows, enabling photorealistic object removal and insertion. It significantly outperforms state-of-the-art baselines. Diffusion models have revolutionized image editing but often generate images that violate physical laws, particularly the effects of objects on the scene, e.g., occlusions, shadows, and reflections. By analyzing the limitations of self-supervised approaches, we propose a practical solution centered on a counterfactual dataset. Our method involves capturing a scene before and after removing a single object, while minimizing other changes. By fine-tuning a diffusion model on this dataset, we are able to not only remove objects but also their effects on the scene. However, we find that applying this approach for photorealistic object insertion requires an impractically large dataset. To tackle this challenge, we propose bootstrap supervision; leveraging our object removal model trained on a small counterfactual dataset, we synthetically expand this dataset considerably. Our approach significantly outperforms prior methods in photorealistic object removal and insertion, particularly in modeling the effects of objects on the scene."
CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning
Authors: ZiYang Gong; FuHao Li; Yupeng Deng; Deblina Bhattacharjee; Xianzheng Ma; Xiangwei Zhu; Zhenming Ji
Venue: ECCV 2024 (main)

Abstract: "Unsupervised Domain Adaptation (UDA) aims to adapt models from labeled source domains to unlabeled target domains. When adapting to adverse scenes, existing UDA methods fail to perform well due to the lack of instructions, leading their models to overlook discrepancies within all adverse scenes. To tackle this, we propose CoDA which instructs models to distinguish, focus, and learn from these discrepancies at scene and image levels. Specifically, CoDA consists of a Chain-of-Domain (CoD) strategy and a Severity-Aware Visual Prompt Tuning (SAVPT) mechanism. CoD focuses on scene-level instructions to divide all adverse scenes into easy and hard scenes, guiding models to adapt from source to easy domains with easy scene images, and then to hard domains with hard scene images, thereby laying a solid foundation for whole adaptations. Building upon this foundation, we employ SAVPT to dive into more detailed image-level instructions to boost performance. SAVPT features a novel metric Severity that divides all adverse scene images into low-severity and high-severity images. Then Severity directs visual prompts and adapters, instructing models to concentrate on unified severity features instead of scene-specific features, without adding complexity to the model architecture. CoDA achieves SOTA performances on widely-used semantic segmentation benchmarks under all adverse scenes. Notably, CoDA outperforms the existing ones by 4.6%, and 10.3% mIoU on the Foggy Driving, and Foggy Zurich benchmarks, respectively. Our code is available at https://github.com/Cuzyoung/CoDA."
Curved Diffusion: A Generative Model With Optical Geometry Control
Authors: Andrey Voynov; Amir Hertz; Moab Arar; Shlomi Fruchter; Daniel Cohen-Or
Venue: ECCV 2024 (main)

Abstract: "State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlooked is the specific camera geometry used during image capture. The influence of different optical systems on the final scene appearance is frequently overlooked. This study introduces a framework that intimately integrates a text-to-image diffusion model with the particular lens geometry used in image rendering. Our method is based on a per-pixel coordinate conditioning method, enabling the control over the rendering geometry. Notably, we demonstrate the manipulation of curvature properties, achieving diverse visual effects, such as fish-eye, panoramic views, and spherical texturing using a single diffusion model."
Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians
Authors: Guangchi Fang; Bing Wang
Venue: ECCV 2024 (main)

Abstract: "In this study, we explore the challenge of efficiently representing scenes with a constrained number of Gaussians. Our analysis shifts from traditional graphics and 2D computer vision to the perspective of point clouds, highlighting the inefficient spatial distribution of Gaussian representation as a key limitation in model performance. To address this, we introduce strategies for densification including blur split and depth reinitialization, and simplification through intersection preserving and sampling. These techniques reorganize the spatial positions of the Gaussians, resulting in significant improvements across various datasets and benchmarks in terms of rendering quality, resource consumption, and storage compression. Our Mini-Splatting integrates seamlessly with the original rasterization pipeline, providing a strong baseline for future research in Gaussian-Splatting-based works. Code is available."
MeshSegmenter: Zero-Shot Mesh Segmentation via Texture Synthesis
Authors: Ziming Zhong; Yanyu Xu; Jing Li; Jiale Xu; Zhengxin Li; Chaohui Yu; Shenghua Gao
Venue: ECCV 2024 (main)

Abstract: "We present MeshSegmenter, a simple yet effective framework designed for zero-shot 3D semantic segmentation. This model successfully extends the powerful capabilities of 2D segmentation models to 3D meshes, delivering accurate 3D segmentation across diverse meshes and segment descriptions. Specifically, our model leverages the Segment Anything Model (SAM) model to segment the target regions from images rendered from the 3D shape. In light of the importance of the texture for segmentation, we also leverage the pretrained stable diffusion model to generate images with textures from 3D shape, and leverage SAM to segment the target regions from images with textures. Textures supplement the shape for segmentation and facilitate accurate 3D segmentation even in geometrically non-prominent areas, such as segmenting a car door within a car mesh. To achieve the 3D segments, we render 2D images from different views and conduct segmentation for both textured and untextured images. Lastly, we develop a multi-view revoting scheme that integrates 2D segmentation results and confidence scores from various views onto the 3D mesh, ensuring the 3D consistency of segmentation results and eliminating inaccuracies from specific perspectives. Through these innovations, MeshSegmenter offers stable and reliable 3D segmentation results both quantitatively and qualitatively, highlighting its potential as a transformative tool in the field of 3D zero-shot segmentation. The code is available at https://github.com/zimingzhong/MeshSegmenter. Texture Synthesis"
OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
Authors: Kwanyoung Kim; Yujin Oh; Jong Chul Ye
Venue: ECCV 2024 (main)

Abstract: "The recent success of CLIP has demonstrated promising results in zero-shot semantic segmentation by transferring muiltimodal knowledge to pixel-level classification. However, leveraging pre-trained CLIP knowledge to closely align text embeddings with pixel embeddings still has limitations in existing approaches. To address this issue, we propose OTSeg, a novel multimodal attention mechanism aimed at enhancing the potential of multiple text prompts for matching associated pixel embeddings. We first propose Multi-Prompts Sinkhorn (MPS) based on the Optimal Transport (OT) algorithm, which leads multiple text prompts to selectively focus on various semantic features within image pixels. Moreover, inspired by the success of Sinkformers in unimodal settings, we introduce the extension of MPS, called Multi-Prompts Sinkhorn Attention (MPSA), which effectively replaces cross-attention mechanisms within Transformer framework in multimodal settings. Through extensive experiments, we demonstrate that OTSeg achieves state-of-the-art (SOTA) performance with significant gains on Zero-Shot Semantic Segmentation (ZS3) tasks across three benchmark datasets. We release our source code at https://github.com/cubeyoung/OTSeg."
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
Authors: Yannick Kirchhoff; Maximilian R Rokuss; Saikat Roy; Balint Kovacs; Constantin Ulrich; Tassilo Wald; Maximilian Zenk; Philipp Vollmuth; Jens Kleesiek; Fabian Isensee; Klaus H. Maier-Hein
Venue: ECCV 2024 (main)

Abstract: "Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation loss functions, such as Dice or Cross-Entropy, focus on volumetric overlap, often at the expense of preserving structural connectivity or topology. This can lead to segmentation errors that adversely affect downstream tasks, including flow calculation, navigation, and structural inspection. Although current topology-focused losses mark an improvement, they introduce significant computational and memory overheads. This is particularly relevant for 3D data, rendering these losses infeasible for larger volumes as well as increasingly important multi-class segmentation problems. To mitigate this, we propose a novel Skeleton Recall Loss, which effectively addresses these challenges by circumventing intensive GPU-based calculations with inexpensive CPU operations. It demonstrates overall superior performance to current state-of-the-art approaches on five public datasets for topology-preserving segmentation, while substantially reducing computational overheads by more than 90%. In doing so, we introduce the first multi-class capable loss function for thin structure segmentation, excelling in both efficiency and efficacy for topology-preservation. Our code is available to the community, providing a foundation for further advancements, at: https: //github.com/MIC-DKFZ/Skeleton-Recall."
Conceptual Codebook Learning for Vision-Language Models
Authors: Yi Zhang; Ke Yu; Siqi Wu; Zhihai He
Venue: ECCV 2024 (main)

Abstract: "In this paper, we propose Conceptual Codebook Learning (CoCoLe), a novel fine-tuning method for vision-language models (VLMs). CoCoLe aims to address the challenge of enhancing the generalization capability of VLMs while adapting them to downstream tasks in a few-shot setting. We recognize that visual concepts like shapes, colors, and textures are inherently transferable across different domains and are essential for generalization tasks. Motivated by this critical finding, we learn a conceptual codebook consisting of visual concepts as keys and conceptual prompts as values, which serves as a link between the image encoder’s outputs and the text encoder’s inputs. Specifically, for a given image, we leverage the codebook to identify the most relevant conceptual prompts associated with the class embeddings to perform the classification. Additionally, we incorporate a handcrafted concept cache as a regularization to alleviate the overfitting issues in low-shot scenarios. This conceptual codebook learning method has been shown to improve the alignment between visual and linguistic modalities. Extensive experimental results demonstrate that our CoCoLe method remarkably outperforms the existing state-of-the-art methods across various evaluation settings, including base-to-new generalization, cross-dataset evaluation, and domain generalization tasks. Detailed ablation studies further confirm the efficacy of each component in CoCoLe."
LingoQA: Video Question Answering for Autonomous Driving
Authors: Ana-Maria Marcu; Long Chen; Jan Hünermann; Alice Karnsund; Benoit Hanotte; Prajwal Chidananda; Saurabh Nair; Vijay Badrinarayanan; Alex Kendall; Jamie Shotton; Elahe Arani; Oleg Sinavski
Venue: ECCV 2024 (main)

Abstract: "We introduce LingoQA, a novel dataset and benchmark for visual question answering in autonomous driving. The dataset contains 28K unique short video scenarios, and 419K annotations. Evaluating state-of-the-art vision-language models on our benchmark shows that their performance is below human capabilities, with GPT-4V responding truthfully to 59.6% of the questions compared to 96.6% for humans. For evaluation, we propose a truthfulness classifier, called Lingo-Judge, that achieves a 0.95 Spearman correlation coefficient to human evaluations, surpassing existing techniques like METEOR, BLEU, CIDEr, and GPT-4. We establish a baseline vision-language model and run extensive ablation studies to understand its performance. We release our dataset and benchmark1 as an evaluation platform for vision-language models in autonomous driving. 1 https://github.com/wayveai/LingoQA"
AnimateMe: 4D Facial Expressions via Diffusion Models
Authors: Dimitrios Gerogiannis; Foivos Paraperas Papantoniou; Rolandos Alexandros Potamias; Alexandros Lattas; Stylianos Moschoglou; Stylianos Ploumpis; Stefanos Zafeiriou
Venue: ECCV 2024 (main)

Abstract: "The field of photorealistic 3D avatar reconstruction and generation has garnered significant attention in recent years; however, animating such avatars remains challenging. Recent advances in diffusion models have notably enhanced the capabilities of generative models in 2D animation. In this work, we directly utilize these models within the 3D domain to achieve controllable and high-fidelity 4D facial animation. By integrating the strengths of diffusion processes and geometric deep learning, we employ Graph Neural Networks (GNNs) as denoising diffusion models in a novel approach, formulating the diffusion process directly on the mesh space and enabling the generation of 3D facial expressions. This facilitates the generation of facial deformations through a mesh-diffusion-based model. Additionally, to ensure temporal coherence in our animations, we propose a consistent noise sampling method. Under a series of both quantitative and qualitative experiments, we showcase that the proposed method outperforms prior work in 4D facial expression synthesis by generating high-fidelity extreme expressions. Furthermore, we applied our method to textured 4D facial expression generation, implementing a straightforward extension that involves training on a large-scale textured 4D facial expression database."
HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning
Authors: Zhecan Wang; Garrett Bingham; Adams Wei Yu; Quoc V. Le; Thang Luong; Golnaz Ghiasi
Venue: ECCV 2024 (main)

Abstract: "Hallucination has been a major problem for large language models and remains a critical challenge when it comes to multimodality in which vision-language models (VLMs) have to deal with not just textual but also visual inputs. Despite rapid progress in VLMs, resources for evaluating and addressing multimodal hallucination are limited and mostly focused on evaluation. This work introduces HaloQuest, a novel visual question answering dataset that captures various aspects of multimodal hallucination such as false premises, insufficient contexts, and visual challenges. A novel idea from HaloQuest is to leverage synthetic images, apart from real ones, to enable dataset creation at scale. With over 7.7K examples spanning across a wide variety of categories, HaloQuest was designed to be both a challenging benchmark for VLMs and a fine-tuning dataset for advancing multimodal reasoning. Our experiments reveal that current models struggle with HaloQuest, with all open-source VLMs achieving below 36% accuracy. On the other hand, fine-tuning on HaloQuest significantly reduces hallucination rates while preserving performance on standard reasoning tasks. Our results discover that benchmarking with generated images is highly correlated (r = 0.97) with real images. Last but not least, we propose a novel Auto-Eval mechanism that is highly correlated with human raters (r = 0.99) for evaluating VLMs. In sum, this work makes concrete strides towards understanding, evaluating, and mitigating hallucination in VLMs, serving as an important step towards more reliable multimodal AI systems in the future."
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
Authors: Kevin Xie; Tianshi Cao; Jonathan P Lorraine; Jun Gao; James R Lucas; Antonio Torralba; Sanja Fidler; Xiaohui Zeng
Venue: ECCV 2024 (main)

Abstract: "Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt. Amortized methods like ATT3D optimize multiple prompts simultaneously to improve efficiency, enabling fast text-to-3D synthesis. However, they cannot capture high-frequency geometry and texture details and struggle to scale to large prompt sets, so they generalize poorly. We introduce , addressing these limitations to achieve fast, high-quality generation on a significantly larger prompt set. Key to our method is 1) building a scalable architecture and 2) leveraging 3D data during optimization through 3D-aware diffusion priors, shape regularization, and model initialization to achieve robustness to diverse and complex training prompts. amortizes both neural field and textured surface generation to produce highly detailed textured meshes in a single forward pass. generates 3D objects in , and can be further enhanced with fast test-time optimization."
PreSight: Enhancing Autonomous Vehicle Perception with City-Scale NeRF Priors
Authors: Tianyuan Yuan; Yucheng Mao; Jiawei Yang; Yicheng LIU; Yue Wang; Hang Zhao
Venue: ECCV 2024 (main)

Abstract: "Autonomous vehicles rely extensively on perception systems to navigate and interpret their surroundings. Despite significant advancements in these systems recently, challenges persist under conditions like occlusion, extreme lighting, or in unfamiliar urban areas. Unlike these systems, humans do not solely depend on immediate observations to perceive the environment. In navigating new cities, humans gradually develop a preliminary mental map to supplement real-time perception during subsequent visits. Inspired by this human approach, we introduce a novel framework, PreSight, that leverages past traversals to construct static prior memories, enhancing online perception in later navigations. Our method involves optimizing a city-scale neural radiance field with data from previous journeys to generate neural priors. These priors, rich in semantic and geometric details, are derived without manual annotations and can seamlessly augment various state-of-the-art perception models, improving their efficacy with minimal additional computational cost. Experimental results on the nuScenes dataset demonstrate the framework’s high compatibility with diverse online perception models. Specifically, it shows remarkable improvements in HD-map construction and occupancy prediction tasks, highlighting its potential as a new perception framework for autonomous driving systems. Our code will be released at https://github.com/yuantianyuan01/PreSight."
Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention
Authors: Jie Ren; Yaxin Li; Shenglai Zeng; Han Xu; Lingjuan Lyu; Yue Xing; Jiliang Tang
Venue: ECCV 2024 (main)

Abstract: "Recent advancements in text-to-image (T2I) diffusion models have demonstrated their remarkable capability to generate high-quality images from textual prompts. However, increasing research indicates that these models memorize and replicate images from their training data, raising concerns about potential copyright infringement and privacy risks. In our study, we provide a novel perspective to understand this memorization phenomenon by examining its relationship with cross-attention mechanisms. We reveal that during memorization, the cross-attention tends to focus disproportionately on the embeddings of specific tokens. The diffusion model is overfitted to these token embeddings, memorizing corresponding training images. To elucidate this phenomenon, we further identify and discuss various intrinsic findings of cross-attention that contribute to memorization. Building on these insights, we introduce an innovative approach to detect and mitigate memorization in diffusion models. The advantage of our proposed method is that it will not compromise the speed of either the training or the inference processes in these models while preserving the quality of generation. Our code is available at github.com/renjie3/MemAttn."
iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning
Authors: Tom Fischer; Yaoyao Liu; Artur Jesslen; Noor Ahmed; Prakhar Kaushik; Angtian Wang; Alan Yuille; Adam Kortylewski; Eddy Ilg
Venue: ECCV 2024 (main)

Abstract: "Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have recently addressed handling continual data streams. However, extending these methods to manage out-of-distribution (OOD) scenarios has not effectively been investigated. On the other hand, it has recently been shown that non-continual neural mesh models exhibit strong performance in generalizing to such OOD scenarios. To leverage this decisive property in a continual learning setting, we propose incremental neural mesh models that can be extended with new meshes over time. In addition, we present a latent space initialization strategy that enables us to allocate feature space for future unseen classes in advance and a positional regularization term that forces the features of the different classes to consistently stay in respective latent space regions. We demonstrate the effectiveness of our method through extensive experiments on the Pascal3D and ObjectNet3D datasets and show that our approach outperforms the baselines for classification by 2 − 6% in the in-domain and by 6−50% in the OOD setting. Our work also presents the first incremental learning approach for pose estimation. Our code and model can be found at github.com/Fischer-Tom/iNeMo."