GenAI4ELab/papercli-papers huggingface dataset:
Hierarchical Fine-Grained Image Forgery Detection and Localization
Authors: Xiao Guo; Xiaohong Liu; Zhiyuan Ren; Steven Grosz; Iacopo Masi; Xiaoming Liu
Venue: CVPR 2023 (main)

Abstract: Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine-grained formulation for IFDL representation learning. Specifically, we first represent forgery attributes of a manipulated image with multiple labels at different levels. Then we perform fine-grained classification at these levels using the hierarchical dependency between them. As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation. Our proposed IFDL framework contains three components: multi-branch feature extractor, localization and classification modules. Each branch of the feature extractor learns to classify forgery attributes at one level, while localization and classification modules segment the pixel-level forgery region and detect image-level forgery, respectively. Lastly, we construct a hierarchical fine-grained dataset to facilitate our study. We demonstrate the effectiveness of our method on 7 different benchmarks, for both tasks of IFDL and forgery attribute classification. Our source code and dataset can be found at https://github.com/CHELSEA234/HiFi_IFDL
CIMI4D: A Large Multimodal Climbing Motion Dataset Under Human-Scene Interactions
Authors: Ming Yan; Xin Wang; Yudi Dai; Siqi Shen; Chenglu Wen; Lan Xu; Yuexin Ma; Cheng Wang
Venue: CVPR 2023 (main)

Abstract: Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dancing, etc. Off-grounded actions such as climbing are largely overlooked. As an important type of action in sports and firefighting field, the climbing movements is challenging to capture because of its complex back poses, intricate human-scene interactions, and difficult global localization. The research community does not have an in-depth understanding of the climbing action due to the lack of specific datasets. To address this limitation, we collect CIMI4D, a large rock ClImbing MotIon on dataset from 12 persons climbing 13 different climbing walls. The dataset consists of around 180,000 frames of pose inertial measurements, LiDAR point clouds, RGB videos, high-precision static point cloud scenes, and reconstructed scene meshes. Moreover, we frame-wise annotate touch rock holds to facilitate a detailed exploration of human-scene interaction. The core of this dataset is a blending optimization process, which corrects for the pose as it drifts and is affected by the magnetic conditions. To evaluate the merit of CIMI4D, we perform four tasks which include human pose estimations (with/without scene constraints), pose prediction, and pose generation. The experimental results demonstrate that CIMI4D presents great challenges to existing methods and enables extensive research opportunities. We share the dataset with the research community in http://www.lidarhumanmotion.net/cimi4d/.
Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts
Authors: Nikolas Lamb; Cameron Palmer; Benjamin Molloy; Sean Banerjee; Natasha Kholgade Banerjee
Venue: CVPR 2023 (main)

Abstract: Automated shape repair approaches currently lack access to datasets that describe real-world damaged geometry. We present Fantastic Breaks (and Where to Find Them: https://terascale-all-sensing-research-studio.github.io/FantasticBreaks), a dataset containing scanned, waterproofed, and cleaned 3D meshes for 150 broken objects, paired and geometrically aligned with complete counterparts. Fantastic Breaks contains class and material labels, proxy repair parts that join to broken meshes to generate complete meshes, and manually annotated fracture boundaries. Through a detailed analysis of fracture geometry, we reveal differences between Fantastic Breaks and synthetic fracture datasets generated using geometric and physics-based methods. We show experimental shape repair evaluation with Fantastic Breaks using multiple learning-based approaches pre-trained with synthetic datasets and re-trained with subset of Fantastic Breaks.
Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer
Authors: Agus Gunawan; Soo Ye Kim; Hyeonjun Sim; Jae-Ho Lee; Munchurl Kim
Venue: CVPR 2023 (main)

Abstract: This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme. MROPM-Net stylizes old photos using multiple references via photorealistic style transfer (PST) and further enhances the results to produce modern-looking images. Meanwhile, the synthetic data generation scheme trains the network to effectively utilize multiple references to perform modernization. To evaluate the performance, we propose a new old photos benchmark dataset (CHD) consisting of diverse natural indoor and outdoor scenes. Extensive experiments show that the proposed method outperforms other baselines in performing modernization on real old photos, even though no old photos were used during training. Moreover, our method can appropriately select styles from multiple references for each semantic region in the old photo to further improve the modernization performance.
Interactive Cartoonization With Controllable Perceptual Factors
Authors: Namhyuk Ahn; Patrick Kwon; Jihye Back; Kibeom Hong; Seungkwon Kim
Venue: CVPR 2023 (main)

Abstract: Cartoonization is a task that renders natural photos into cartoon styles. Previous deep methods only have focused on end-to-end translation, disabling artists from manipulating results. To tackle this, in this work, we propose a novel solution with editing features of texture and color based on the cartoon creation process. To do that, we design a model architecture to have separate decoders, texture and color, to decouple these attributes. In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures. We also introduce an HSV color augmentation to induce the networks to generate consistent color translation. To the best of our knowledge, our work is the first method to control the cartoonization during the inferences step, generating high-quality results compared to baselines.
Curvature-Balanced Feature Manifold Learning for Long-Tailed Classification
Authors: Yanbiao Ma; Licheng Jiao; Fang Liu; Shuyuan Yang; Xu Liu; Lingling Li
Venue: CVPR 2023 (main)

Abstract: To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes. However, recent studies have shown that tail classes are not always hard to learn, and model bias has been observed on sample-balanced datasets, suggesting the existence of other factors that affect model bias. In this work, we systematically propose a series of geometric measures for perceptual manifolds in deep neural networks, and then explore the effect of the geometric characteristics of perceptual manifolds on classification difficulty and how learning shapes the geometric characteristics of perceptual manifolds. An unanticipated finding is that the correlation between the class accuracy and the separation degree of perceptual manifolds gradually decreases during training, while the negative correlation with the curvature gradually increases, implying that curvature imbalance leads to model bias. Therefore, we propose curvature regularization to facilitate the model to learn curvature-balanced and flatter perceptual manifolds. Evaluations on multiple long-tailed and non-long-tailed datasets show the excellent performance and exciting generality of our approach, especially in achieving significant performance improvements based on current state-of-the-art techniques. Our work reminds researchers to pay attention to model bias not only on long-tailed datasets but also on non-long-tailed and even data-balanced datasets, which can improve model performance from another perspective.
Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising
Authors: Haijin Zeng; Jiezhang Cao; Kai Zhang; Yongyong Chen; Hiep Luong; Wilfried Philips
Venue: CVPR 2024 (main)

Abstract: Hyperspectral images (HSIs) have extensive applications in various fields such as medicine agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a challenge due to narrow-band spectral filtering. Consequently the importance of HSI denoising is substantial especially for snapshot hyperspectral imaging technology. While most previous HSI denoising methods are supervised creating supervised training datasets for the diverse scenes hyperspectral cameras and scan parameters is impractical. In this work we present Diff-Unmix a self-supervised denoising method for HSI using diffusion denoising generative models. Specifically Diff-Unmix addresses the challenge of recovering noise-degraded HSI through a fusion of Spectral Unmixing and conditional abundance generation. Firstly it employs a learnable block-based spectral unmixing strategy complemented by a pure transformer-based backbone. Then we introduce a self-supervised generative diffusion network to enhance abundance maps from the spectral unmixing block. This network reconstructs noise-free Unmixing probability distributions effectively mitigating noise-induced degradations within these components. Finally the reconstructed HSI is reconstructed through unmixing reconstruction by blending the diffusion-adjusted abundance map with the spectral endmembers. Experimental results on both simulated and real-world noisy datasets show that Diff-Unmix achieves state-of-the-art performance.
Seeing the World through Your Eyes
Authors: Hadi Alzayer; Kevin Zhang; Brandon Feng; Christopher A. Metzler; Jia-Bin Huang
Venue: CVPR 2024 (main)

Abstract: The reflective nature of the human eye is an under-appreciated source of information about what the world around us looks like. By imaging the eyes of a moving person we capture multiple views of a scene outside the camera's direct line of sight through the reflections in the eyes. In this paper we reconstruct a radiance field beyond the camera's line of sight using portrait images containing eye reflections. This task is challenging due to 1) the difficulty of accurately estimating eye poses and 2) the entangled appearance of the iris textures and the scene reflections. To address these our method jointly optimizes the cornea poses the radiance field depicting the scene and the observer's eye iris texture. We further present a regularization prior on the iris texture to improve scene reconstruction quality. Through various experiments on synthetic and real-world captures featuring people with varied eye colors and lighting conditions we demonstrate the feasibility of our approach to recover the radiance field using cornea reflections.
DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh Recovery
Authors: Yixuan Zhu; Ao Li; Yansong Tang; Wenliang Zhao; Jie Zhou; Jiwen Lu
Venue: CVPR 2024 (main)

Abstract: The recovery of occluded human meshes poses challenges for current methods due to the difficulty in extracting effective image features under severe occlusion. In this paper we introduce DPMesh an innovative framework for occluded human mesh recovery that capitalizes on the profound knowledge about object structure and spatial relationships embedded in a pre-trained text-to-image diffusion model. Unlike previous methods reliant on conventional backbones for vanilla feature extraction DPMesh seamlessly integrates the pre-trained denoising U-Net with potent priors as its image backbone and performs a single-step inference to provide occlusion-aware information. To enhance the perception capability for occluded poses DPMesh incorporates judicious guidance via condition injection which produces effective controls from 2D observations for the denoising U-Net. Furthermore we explore a dedicated noisy key-point reasoning approach to mitigate disturbances arising from occlusion and crowded scenarios. This strategy fully unleashes the perceptual capability of the diffusion prior thereby enhancing accuracy. Extensive quantitative and qualitative experiments affirm the efficacy of our framework as we outperform state-of-the-art methods on both occlusion-specific and standard datasets underscoring its ability to achieve precise and robust 3D human mesh recovery particularly in challenging scenarios involving occlusion and crowded scenes. Code is available at https://github.com/EternalEvan/DPMesh.
Ungeneralizable Examples
Authors: Jingwen Ye; Xinchao Wang
Venue: CVPR 2024 (main)

Abstract: The training of contemporary deep learning models heavily relies on publicly available data posing a risk of unauthorized access to online data and raising concerns about data privacy. Current approaches to creating unlearnable data involve incorporating small specially designed noises but these methods strictly limit data usability overlooking its potential usage in authorized scenarios. In this paper we extend the concept of unlearnable data to conditional data learnability and introduce UnGeneralizable Examples (UGEs). UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers. The protector defines the authorized network and optimizes UGEs to match the gradients of the original data and its ungeneralizable version ensuring learnability. To prevent unauthorized learning UGEs are trained by maximizing a designated distance loss in a common feature space. Additionally to further safeguard the authorized side from potential attacks we introduce additional undistillation optimization. Experimental results on multiple datasets and various networks demonstrate that the proposed UGEs framework preserves data usability while reducing training performance on hacker networks even under different types of attacks.
LaneCPP: Continuous 3D Lane Detection using Physical Priors
Authors: Maximilian Pittner; Joel Janai; Alexandru P. Condurache
Venue: CVPR 2024 (main)

Abstract: Monocular 3D lane detection has become a fundamental problem in the context of autonomous driving which comprises the tasks of finding the road surface and locating lane markings. One major challenge lies in a flexible but robust line representation capable of modeling complex lane structures while still avoiding unpredictable behavior. While previous methods rely on fully data-driven approaches we instead introduce a novel approach LaneCPP that uses a continuous 3D lane detection model leveraging physical prior knowledge about the lane structure and road geometry. While our sophisticated lane model is capable of modeling complex road structures it also shows robust behavior since physical constraints are incorporated by means of a regularization scheme that can be analytically applied to our parametric representation. Moreover we incorporate prior knowledge about the road geometry into the 3D feature space by modeling geometry-aware spatial features guiding the network to learn an internal road surface representation. In our experiments we show the benefits of our contributions and prove the meaningfulness of using priors to make 3D lane detection more robust. The results show that LaneCPP achieves state-of-the-art performance in terms of F-Score and geometric errors.
CityDreamer: Compositional Generative Model of Unbounded 3D Cities
Authors: Haozhe Xie; Zhaoxi Chen; Fangzhou Hong; Ziwei Liu
Venue: CVPR 2024 (main)

Abstract: 3D city generation is a desirable yet challenging task since humans are more sensitive to structural distortions in urban environments. Additionally generating 3D cities is more complex than 3D natural scenes since buildings as objects of the same class exhibit a wider range of appearances compared to the relatively consistent appearance of objects like trees in natural scenes. To address these challenges we propose CityDreamer a compositional generative model designed specifically for unbounded 3D cities. Our key insight is that 3D city generation should be a composition of different types of neural fields: 1) various building instances and 2) background stuff such as roads and green lands. Specifically we adopt the bird's eye view scene representation and employ a volumetric render for both instance-oriented and stuff-oriented neural fields. The generative hash grid and periodic positional embedding are tailored as scene parameterization to suit the distinct characteristics of building instances and background stuff. Furthermore we contribute a suite of CityGen Datasets including OSM and GoogleEarth which comprises a vast amount of real-world city imagery to enhance the realism of the generated 3D cities both in their layouts and appearances. CityDreamer achieves state-of-the-art performance not only in generating realistic 3D cities but also in localized editing within the generated cities.
HEAL-SWIN: A Vision Transformer On The Sphere
Authors: Oscar Carlsson; Jan E. Gerken; Hampus Linander; Heiner Spieß; Fredrik Ohlsson; Christoffer Petersson; Daniel Persson
Venue: CVPR 2024 (main)

Abstract: High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution distortion-free spherical data. In HEAL-SWIN the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer enabling the network to process spherical representations with minimal computational overhead. We demonstrate the superior performance of our model on both synthetic and real automotive datasets as well as a selection of other image datasets for semantic segmentation depth regression and classification tasks. Our code is publicly available.
3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation
Authors: Dale Decatur; Itai Lang; Kfir Aberman; Rana Hanocka
Venue: CVPR 2024 (main)

Abstract: We present 3D Paintbrush a technique for automatically texturing local semantic regions on meshes via text descriptions. Our method is designed to operate directly on meshes producing texture maps which seamlessly integrate into standard graphics pipelines. We opt to simultaneously produce a localization map (to specify the edit region) and a texture map which conforms to it. This approach improves the quality of both the localization and the stylization. To enhance the details and resolution of the textured area we leverage multiple stages of a cascaded diffusion model to supervise our local editing technique with generative priors learned from images at different resolutions. Our technique referred to as Cascaded Score Distillation (CSD) simultaneously distills scores at multiple resolutions in a cascaded fashion enabling control over both the granularity and global understanding of the supervision. We demonstrate the effectiveness of 3D Paintbrush to locally texture different semantic regions on a variety of shapes.
Test-Time Linear Out-of-Distribution Detection
Authors: Ke Fan; Tong Liu; Xingyu Qiu; Yikai Wang; Lian Huai; Zeyu Shangguan; Shuang Gou; Fengjian Liu; Yuqian Fu; Yanwei Fu; Xingqun Jiang
Venue: CVPR 2024 (main)

Abstract: Out-of-Distribution (OOD) detection aims to address the excessive confidence prediction by neural networks by triggering an alert when the input sample deviates significantly from the training distribution (in-distribution) indicating that the output may not be reliable. Current OOD detection approaches explore all kinds of cues to identify OOD data such as finding irregular patterns in the feature space logit space gradient space or the raw image space. Surprisingly we observe a linear trend between the OOD score produced by current OOD detection algorithms and the network features on several datasets. We conduct a thorough investigation theoretically and empirically to analyze and understand the meaning of such a linear trend in OOD detection. This paper proposes a Robust Test-time Linear method (RTL) to utilize such linear trends like a `free lunch' when we have a batch of data to perform OOD detection. By using a simple linear regression as a test time adaptation we can make a more precise OOD prediction. We further propose an online variant of the proposed method which achieves promising performance and is more practical for real applications. Theoretical analysis is given to prove the effectiveness of our methods. Extensive experiments on several OOD datasets show the efficacy of RTL for OOD detection tasks significantly improving the results of base OOD detectors. Project will be available at https://github.com/kfan21/RTL.
Guided Slot Attention for Unsupervised Video Object Segmentation
Authors: Minhyeok Lee; Suhwan Cho; Dogyoon Lee; Chaewon Park; Jungho Lee; Sangyoun Lee
Venue: CVPR 2024 (main)

Abstract: Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue we propose a guided slot attention network to reinforce spatial structural information and obtain better foreground-background separation. The foreground and background slots which are initialized with query guidance are iteratively refined based on interactions with template information. Furthermore to improve slot-template interaction and effectively fuse global and local features in the target and reference frames K-nearest neighbors filtering and a feature aggregation transformer are introduced. The proposed model achieves state-of-the-art performance on two popular datasets. Additionally we demonstrate the robustness of the proposed model in challenging scenes through various comparative experiments.