GenAI4ELab/papercli-papers huggingface dataset:
Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities
Authors: Lorenzo Baraldi; Federico Cocchi; Marcella Cornia; Lorenzo Baraldi; Alessandro Nicolosi; Rita Cucchiara
Venue: ECCV 2024 (main)

Abstract: "Discerning between authentic content and that generated by advanced AI methods has become increasingly challenging. While previous research primarily addresses the detection of fake faces, the identification of generated natural images has only recently surfaced. This prompted the recent exploration of solutions that employ foundation vision-and-language models, like CLIP. However, the CLIP embedding space is optimized for global image-to-text alignment and is not inherently designed for deepfake detection, neglecting the potential benefits of tailored training and local image features. In this study, we propose CoDE (Contrastive Deepfake Embeddings), a novel embedding space specifically designed for deepfake detection. CoDE is trained via contrastive learning by additionally enforcing global-local similarities. To sustain the training of our model, we generate a comprehensive dataset that focuses on images generated by diffusion models and encompasses a collection of 9.2 million images produced by using four different generators. Experimental results demonstrate that CoDE achieves state-of-the-art accuracy on the newly collected dataset, while also showing excellent generalization capabilities to unseen image generators. Our source code, trained models, and collected dataset are publicly available at: https://github.com/aimagelab/CoDE. ∗ Equal contribution."
CriSp: Leveraging Tread Depth Maps for Enhanced Crime-Scene Shoeprint Matching
Authors: Samia Shafique; Shu Kong; Charless Fowlkes
Venue: ECCV 2024 (main)

Abstract: "Shoeprints are a common type of evidence found at crime scenes and are used regularly in forensic investigations. However, existing methods cannot effectively employ deep learning techniques to match noisy and occluded crime-scene shoeprints to a shoe database due to a lack of training data. Moreover, all existing methods match crime-scene shoeprints to clean reference prints, yet our analysis shows matching to more informative tread depth maps yields better retrieval results. The matching task is further complicated by the necessity to identify similarities only in corresponding regions (heels, toes, etc) of prints and shoe treads. To overcome these challenges, we leverage shoe tread images from online retailers and utilize an off-the-shelf predictor to estimate depth maps and clean prints. Our method, named CriSp, matches crime-scene shoeprints to tread depth maps by training on this data. CriSp incorporates data augmentation to simulate crime-scene shoeprints, an encoder to learn spatially-aware features, and a masking module to ensure only visible regions of crime-scene prints affect retrieval results. To validate our approach, we introduce two validation sets by reprocessing existing datasets of crime-scene shoeprints and establish a benchmarking protocol for comparison. On this benchmark, CriSp significantly outperforms state-of-the-art methods in both automated shoeprint matching and image retrieval tailored to this task."
Caltech Aerial RGB-Thermal Dataset in the Wild
Authors: Connor Lee; Matthew Anderson; Nikhil Ranganathan; Xingxing Zuo; Kevin T Do; Georgia Gkioxari; Soon-Jo Chung
Venue: ECCV 2024 (main)

Abstract: "We present the first publicly-available RGB-thermal dataset designed for aerial robotics operating in natural environments. Our dataset captures a variety of terrain across the United States, including rivers, lakes, coastlines, deserts, and forests, and consists of synchronized RGB, thermal, global positioning, and inertial data. We provide semantic segmentation annotations for 10 classes commonly encountered in natural settings in order to drive the development of perception algorithms robust to adverse weather and nighttime conditions. Using this dataset, we propose new and challenging benchmarks for thermal and RGB-thermal (RGB-T) semantic segmentation, RGB-T image translation, and motion tracking. We present extensive results using state-of-the-art methods and highlight the challenges posed by temporal and geographical domain shifts in our data. The dataset and accompanying code is available at https:// github.com/aerorobotics/caltech-aerial-rgbt-dataset."
Diffusion Soup: Model Merging for Text-to-Image Diffusion Models
Authors: Benjamin J Biggs; Arjun Seshadri; Yang Zou; Achin Jain; Aditya Golatkar; Yusheng Xie; Alessandro Achille; Ashwin Swaminathan; Stefano Soatto
Venue: ECCV 2024 (main)

Abstract: "We present Diffusion Soup, a compartmentalization method for Text-to-Image Generation that averages the weights of diffusion models trained on sharded data. By construction, our approach enables training-free continual learning and unlearning with no additional memory or inference costs, since models corresponding to data shards can be added or removed by re-averaging. We show that Diffusion Soup samples from a point in weight space that approximates the geometric mean of the distributions of constituent datasets, which offers anti-memorization guarantees and enables zero-shot style mixing. Empirically, Diffusion Soup outperforms a paragon model trained on the union of all data shards and achieves a 30% improvement in Image Reward (.34 → .44) on domain sharded data, and a 59% improvement in IR (.37 → .59) on aesthetic data. In both cases, souping also prevails in TIFA score (respectively, 85.5 → 86.5 and 85.6 → 86.8). We demonstrate robust unlearning—removing any individual domain shard only lowers performance by 1% in IR (.45 → .44)—and validate our theoretical insights on anti-memorization using real data. Finally, we showcase Diffusion Soup’s ability to blend the distinct styles of models finetuned on different shards, resulting in the zero-shot generation of hybrid styles."
Volumetric Rendering with Baked Quadrature Fields
Authors: Gopal Sharma; Daniel Rebain; Kwang Moo Yi; Andrea Tagliasacchi
Venue: ECCV 2024 (main)

Abstract: "We propose a novel Neural Radiance Field (NeRF) representation for non-opaque scenes that enables fast inference by utilizing textured polygons. Despite the high-quality novel view rendering that NeRF provides, a critical limitation is that it relies on volume rendering that can be computationally expensive and does not utilize the advancements in modern graphics hardware. Many existing methods fall short when it comes to modelling volumetric effects as they rely purely on surface rendering. We thus propose to model the scene with polygons, which can then be used to obtain the quadrature points required to model volumetric effects, and also their opacity and colour from the texture. To obtain such polygonal mesh, we train a specialized field whose zero-crossings would correspond to the quadrature points when volume rendering, and perform marching cubes on this field. We then perform ray-tracing and utilize the ray-tracing shader to obtain the final colour image. Our method allows an easy integration with existing graphics frameworks allowing rendering speed of over 100 frames-per-second for a 1920 × 1080 image, while still being able to represent non-opaque objects."
CityGuessr: City-Level Video Geo-Localization on a Global Scale
Authors: Parth Parag Kulkarni; Gaurav Kumar Nayak; Mubarak Shah
Venue: ECCV 2024 (main)

Abstract: "Video geolocalization is a crucial problem in current times. Given just a video, ascertaining where it was captured from can have a plethora of advantages. The problem of worldwide geolocalization has been tackled before, but only using the image modality. Its video counterpart remains relatively unexplored. Meanwhile, video geolocalization has also garnered some attention in the recent past, but the existing methods are all restricted to specific regions. This motivates us to explore the problem of video geolocalization at a global scale. Hence, we propose a novel problem of worldwide video geolocalization with the objective of hierarchically predicting the correct city, state/province, country, and continent, given a video. However, no large scale video datasets that have extensive worldwide coverage exist, to train models for solving this problem. To this end, we introduce a new dataset, “CityGuessr68k ” comprising of 68,269 videos from 166 cities all over the world. We also propose a novel baseline approach to this problem, by designing a transformer-based architecture comprising of an elegant “Self-Cross Attention” module for incorporating scenes as well as a “TextLabel Alignment” strategy for distilling knowledge from textlabels in feature space. To further enhance our location prediction, we also utilize soft-scene labels. Finally we demonstrate the performance of our method on our new dataset as well as Mapillary(MSLS)[?]. Our code and datasets are available here."
Pseudo-Labelling Should Be Aware of Disguising Channel Activations
Authors: Changrui Chen; Kurt Debattista; Jungong Han
Venue: ECCV 2024 (main)

Abstract: "The pseudo-labelling algorithm is highly effective across various tasks, particularly in semi-supervised learning, yet its vulnerabilities are not always apparent on benchmark datasets, leading to suboptimal real-world performance. In this paper, we identified some channel activations in pseudo-labelling methods, termed disguising channel activations (abbreviated as disguising activations in the following sections), which exacerbate the confirmation bias issue when the training data distribution is inconsistent. Even state-of-the-art semi-supervised learning models exhibit significantly different levels of activation on some channels for data in different distributions, impeding the full potential of pseudo labelling. We take a novel perspective to address this issue by analysing the components of each channel’s activation. Specifically, we model the activation of each channel as the mixture of two independent components. The mixture proportion enables us to identify the disguising activations, making it possible to employ our straightforward yet effective regularisation to attenuate the correlation between pseudo labels and disguising activations. This mitigation reduces the error risk of pseudo-label inference, leading to more robust optimization. The regularisation introduces no additional computing costs during the inference phase and can be seamlessly integrated as a plug-in into pseudo-labelling algorithms in various downstream tasks. Our experiments demonstrate that the proposed method achieves state-of-the-art results across 6 benchmark datasets in diverse vision tasks, including image classification, semantic segmentation, and object detection."
Bayesian Detector Combination for Object Detection with Crowdsourced Annotations
Authors: Zhi Qin Tan; Olga Isupova; Gustavo Carneiro; Xiatian Zhu; Yunpeng Li
Venue: ECCV 2024 (main)

Abstract: "Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise, especially in crowdsourcing scenarios. Most prior object detection methods assume accurate annotations; A few recent works have studied object detection with noisy crowdsourced annotations, with evaluation on distinct synthetic crowdsourced datasets of varying setups under artificial assumptions. To address these algorithmic limitations and evaluation inconsistency, we first propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations, with the unique ability of automatically inferring the annotators’ label qualities. Unlike previous approaches, BDC is model-agnostic, requires no prior knowledge of the annotators’ skill level, and seamlessly integrates with existing object detection models. Due to the scarcity of real-world crowdsourced datasets, we introduce large synthetic datasets by simulating varying crowdsourcing scenarios. This allows consistent evaluation of different models at scale. Extensive experiments on both real and synthetic crowdsourced datasets show that BDC outperforms existing state-of-the-art methods, demonstrating its superiority in leveraging crowdsourced data for object detection. Our code and data are available at: https://github.com/zhiqin1998/bdc."
Revising Densification in Gaussian Splatting
Authors: Samuel Rota Bulò; Lorenzo Porzi; Peter Kontschieder
Venue: ECCV 2024 (main)

Abstract: "In this paper, we address the limitations of Adaptive Density Control (ADC) in 3D Gaussian Splatting (3DGS), a scene representation method achieving high-quality, photorealistic results for novel view synthesis. ADC has been introduced for automatic 3D point primitive management, controlling densification and pruning, however, with certain limitations in the densification logic. Our main contribution is a more principled, pixel-error driven formulation for density control in 3DGS, leveraging an auxiliary, per-pixel error function as the criterion for densification. We further introduce a mechanism to control the total number of primitives generated per scene and correct a bias in the current opacity handling strategy of ADC during cloning operations. Our approach leads to consistent quality improvements across a variety of benchmark scenes, without sacrificing the method’s efficiency."
FlexiEdit: Frequency-Aware Latent Refinement for Enhanced Non-Rigid Editing
Authors: Gwanhyeong Koo; Sunjae Yoon; Ji Woo Hong; Chang D. Yoo
Venue: ECCV 2024 (main)

Abstract: "Current image editing methods primarily utilize DDIM Inversion, employing a two-branch diffusion approach to preserve the attributes and layout of the original image. However, these methods encounter challenges with non-rigid edits, which involve altering the image’s layout or structure. Our comprehensive analysis reveals that the high-frequency components of DDIM latent, crucial for retaining the original image’s key features and layout, significantly contribute to these limitations. Addressing this, we introduce FlexiEdit, which enhances fidelity to input text prompts by refining DDIM latent, by reducing high-frequency components in targeted editing areas. FlexiEdit comprises two key components: (1) Latent Refinement, which modifies DDIM latent to better accommodate layout adjustments, and (2) Edit Fidelity Enhancement via Re-inversion, aimed at ensuring the edits more accurately reflect the input text prompts. Our approach represents notable progress in image editing, particularly in performing complex non-rigid edits, showcasing its enhanced capability through comparative experiments."
"Smoothness, Synthesis, and Sampling: Re-thinking Unsupervised Multi-View Stereo with DIV Loss"
Authors: Alex Rich; Noah Stier; Pradeep Sen; Tobias Hollerer
Venue: ECCV 2024 (main)

Abstract: "Despite significant progress in unsupervised multi-view stereo (MVS), the core loss formulation has remained largely unchanged since its introduction. However, we identify fundamental limitations to this core loss and propose three major changes to improve the modeling of depth priors, occlusion, and view-dependent effects. First, we eliminate prominent stair-stepping and edge artifacts in predicted depth maps using a clamped depth-smoothness constraint. Second, we propose a learned view-synthesis approach to generate an image for photometric loss, avoiding the use of hand-coded heuristics for handling view-dependent effects. Finally, we sample additional views for supervision beyond those used as MVS input, challenging the network to predict depth that matches unseen views. Together, these contributions form an improved supervision strategy we call loss. The key advantage of our loss is that it can be easily dropped into existing unsupervised MVS training pipelines, resulting in significant improvements on competitive reconstruction benchmarks and drastically better qualitative performance around object boundaries for minimal training cost."
Text Motion Translator: A Bi-Directional Model for Enhanced 3D Human Motion Generation from Open-Vocabulary Descriptions
Authors: Yijun Qian; Jack Urbanek; Alexander Hauptmann; Jungdam Won
Venue: ECCV 2024 (main)

Abstract: "The field of 3D human motion generation from natural language descriptions, known as Text2Motion, has gained significant attention for its potential application in industries such as film, gaming, and AR/VR. To tackle a key challenge in Text2Motion, the deficiency of 3D human motions and their corresponding textual descriptions, we built a novel large-scale 3D human motion dataset, LaViMo, extracted from in-the-wild web videos and action recognition datasets. LaViMo is approximately 3.3 times larger and encompasses a much broader range of actions than the largest available 3D motion dataset. We then introduce a novel multi-task framework TMT (Text Motion Translator), aimed at generating faithful 3D human motions from natural language descriptions, especially focusing on complicated actions and those not existing in the training set. In contrast to prior works, TMT is uniquely regularized by multiple tasks, including Text2Motion, Motion2Text, Text2Text, and Motion2Motion. This multi-task regularization significantly bolsters the model’s robustness and enhances its ability of motion modeling and semantic understanding. Additionally, we devised an augmentation method for the textual descriptions using Large Language Models. This augmentation significantly enhances the model’s capability to interpret open-vocabulary descriptions while generating motions. The results demonstrate substantial improvements over existing state-of-the-art methods, particularly in handling diverse and novel motion descriptions, laying a strong foundation for future research in the field."
UL-VIO: Ultra-lightweight Visual-Inertial Odometry with Noise Robust Test-time Adaptation
Authors: Jinho Park; Se Young Chun; Mingoo Seok
Venue: ECCV 2024 (main)

Abstract: "Data-driven visual-inertial odometry (VIO) has received highlights for its performance since VIOs are a crucial compartment in autonomous robots. However, their deployment on resource-constrained devices is non-trivial since large network parameters should be accommodated in the device memory. Furthermore, these networks may risk failure post-deployment due to environmental distribution shifts at test time. In light of this, we propose UL-VIO – an ultra-lightweight (< 1M) VIO network capable of test-time adaptation (TTA) based on visual-inertial consistency. Specifically, we perform model compression to the network while preserving the low-level encoder part, including all BatchNorm parameters for resource-efficient test-time adaptation. It achieves 36× smaller network size than state-of-the-art with a minute increase in error – 1% on the KITTI dataset. For test-time adaptation, we propose to use the inertia-referred network outputs as pseudo labels and update the BatchNorm parameter for lightweight yet effective adaptation. To the best of our knowledge, this is the first work to perform noise-robust TTA on VIO. Experimental results on the KITTI, EuRoC, and Marulan datasets demonstrate the effectiveness of our resource-efficient adaptation method under diverse TTA scenarios with dynamic domain shifts."
PolyOculus: Simultaneous Multi-view Image-based Novel View Synthesis
Authors: Jason J. Yu; Tristan Aumentado-Armstrong; Fereshteh Forghani; Konstantinos G. Derpanis; Marcus A. Brubaker
Venue: ECCV 2024 (main)

Abstract: "This paper considers the problem of generative novel view synthesis (GNVS), generating novel, plausible views of a scene given a limited number of known views. Here, we propose a set-based generative model that can simultaneously generate multiple, self-consistent new views, conditioned on any number of views. Our approach is not limited to generating a single image at a time and can condition on a variable number of views. As a result, when generating a large number of views, our method is not restricted to a low-order autoregressive generation approach and is better able to maintain generated image quality over large sets of images. We evaluate our model on standard NVS datasets and show that it outperforms the state-of-the-art image-based GNVS baselines. Further, we show that the model is capable of generating sets of views that have no natural sequential ordering, like loops and binocular trajectories, and significantly outperforms other methods on such tasks. Our project page is available at: https://yorkucvil.github.io/PolyOculus-NVS/."
R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding
Authors: Qirui Wu; Sonia Raychaudhuri; Daniel Ritchie; Manolis Savva; Angel X Chang
Venue: ECCV 2024 (main)

Abstract: "We introduce the () dataset of synthetic 3D scenes mirroring the real-world scene arrangements from Matterport3D panoramas. Compared to prior work, has more complete and densely populated scenes with objects linked to real-world observations in panoramas. also provides an object support hierarchy, and matching object sets (e.g., same chairs around a dining table) for each scene. Overall, contains 19K objects represented by 3,784 distinct CAD models from over 100 object categories. We demonstrate the effectiveness of on the task. We find that: 1) training on enables better generalization; 2) support relation prediction trained with improves performance compared to heuristically calculated support; and 3) offers a challenging benchmark for future work on panoramic scene understanding."
A Graph-Based Approach for Category-Agnostic Pose Estimation
Authors: Or Hirschorn; Shai Avidan
Venue: ECCV 2024 (main)

Abstract: "Traditional 2D pose estimation models are limited by their category-specific design, making them suitable only for predefined object categories. This restriction becomes particularly challenging when dealing with novel objects due to the lack of relevant training data. To address this limitation, category-agnostic pose estimation (CAPE) was introduced. CAPE aims to enable keypoint localization for arbitrary object categories using a few-shot single model, requiring minimal support images with annotated keypoints. We present a significant departure from conventional CAPE techniques, which treat keypoints as isolated entities, by treating the input pose data as a graph. We leverage the inherent geometrical relations between keypoints through a graph-based network to break symmetry, preserve structure, and better handle occlusions. We validate our approach on the MP-100 benchmark, a comprehensive dataset comprising over 20,000 images spanning over 100 categories. Our solution boosts performance by 0.98% under a 1-shot setting, achieving a new state-of-the-art for CAPE. Additionally, we enhance the dataset with skeleton annotations. Our code and data are publicly available."
Depth-guided NeRF Training via Earth Mover’s Distance
Authors: Anita Rau; Josiah Aklilu; Floyd C Holsinger; Serena Yeung-Levy
Venue: ECCV 2024 (main)

Abstract: "Neural Radiance Fields (NeRFs) are trained to minimize the rendering loss of predicted viewpoints. However, the photometric loss often does not provide enough information to disambiguate between different possible geometries yielding the same image. Previous work has thus incorporated depth supervision during NeRF training, leveraging dense predictions from pre-trained depth networks as pseudo-ground truth. While these depth priors are assumed to be perfect once filtered for noise, in practice, their accuracy is more challenging to capture. This work proposes a novel approach to uncertainty in depth priors for NeRF supervision. Instead of using custom-trained depth or uncertainty priors, we use off-the-shelf pretrained diffusion models to predict depth and capture uncertainty during the denoising process. Because we know that depth priors are prone to errors, we propose to supervise the ray termination distance distribution with Earth Mover’s Distance instead of enforcing the rendered depth to replicate the depth prior exactly through L2 -loss. Our depth-guided NeRF outperforms all baselines on standard depth metrics by a large margin while maintaining performance on photometric measures."