GenAI4ELab/papercli-papers huggingface dataset:
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange
Authors: Yanhao Wu; Tong Zhang; Wei Ke; Congpei Qiu; Sabine Süsstrunk; Mathieu Salzmann
Venue: CVPR 2024 (main)

Abstract: In the realm of point cloud scene understanding particularly in indoor scenes objects are arranged following human habits resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can create a tendency for neural networks to exploit these strong dependencies bypassing the individual object patterns. To address this challenge we introduce a novel self-supervised learning (SSL) strategy. Our approach leverages both object patterns and contextual cues to produce robust features. It begins with the formulation of an object-exchanging strategy where pairs of objects with comparable sizes are exchanged across different scenes effectively disentangling the strong contextual dependencies. Subsequently we introduce a context-aware feature learning strategy which encodes object patterns without relying on their specific context by aggregating object features across various scenes. Our extensive experiments demonstrate the superiority of our method over existing SSL techniques further showing its better robustness to environmental changes. Moreover we showcase the applicability of our approach by transferring pre-trained models to diverse point cloud datasets.
Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)
Authors: Tsu-Ching Hsiao; Hao-Wei Chen; Hsuan-Kung Yang; Chun-Yi Lee
Venue: CVPR 2024 (main)

Abstract: Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge particularly due to object symmetries or occlusions. In response we introduce a novel score-based diffusion method applied to the SE(3) group marking the first application of diffusion models to SE(3) within the image domain specifically tailored for pose estimation tasks. Extensive evaluations demonstrate the method's efficacy in handling pose ambiguity mitigating perspective-induced ambiguity and showcasing the robustness of our surrogate Stein score formulation on SE(3). This formulation not only improves the convergence of denoising process but also enhances computational efficiency. Thus we pioneer a promising strategy for 6D object pose estimation.
Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion Models
Authors: Daniel Geng; Inbum Park; Andrew Owens
Venue: CVPR 2024 (main)

Abstract: We address the problem of synthesizing multi-view optical illusions: images that change appearance upon a transformation such as a flip or rotation. We propose a simple zero-shot method for obtaining these illusions from off-the-shelf text-to-image diffusion models. During the reverse diffusion process we estimate the noise from different views of a noisy image and then combine these noise estimates together and denoise the image. A theoretical analysis suggests that this method works precisely for views that can be written as orthogonal transformations of which permutations are a subset. This leads to the idea of a   visual anagram ---an image that changes appearance under some rearrangement of pixels. This includes rotations and flips but also more exotic pixel permutations such as a jigsaw rearrangement. Our approach also naturally extends to illusions with more than two views. We provide both qualitative and quantitative results demonstrating the effectiveness and flexibility of our method. Please see our project webpage for additional visualizations and results: https://dangeng.github.io/visual_anagrams/
Unveiling Parts Beyond Objects: Towards Finer-Granularity Referring Expression Segmentation
Authors: Wenxuan Wang; Tongtian Yue; Yisi Zhang; Longteng Guo; Xingjian He; Xinlong Wang; Jing Liu
Venue: CVPR 2024 (main)

Abstract: Referring expression segmentation (RES) aims at segmenting the foreground masks of the entities that match the descriptive natural language expression. Previous datasets and methods for classic RES task heavily rely on the prior assumption that one expression must refer to object-level targets. In this paper we take a step further to finer-grained part-level RES task. To promote the object-level RES task towards finer-grained vision-language understanding we put forward a new multi-granularity referring expression segmentation (MRES) task and construct an evaluation benchmark called RefCOCOm by manual annotations. By employing our automatic model-assisted data engine we build the largest visual grounding dataset namely MRES-32M which comprises over 32.2M high-quality masks and captions on the provided 1M images. Besides a simple yet strong model named UniRES is designed to accomplish the unified object-level and part-level grounding task. Extensive experiments on our RefCOCOm for MRES and three datasets (i.e. RefCOCO(+/g)) for classic RES task demonstrate the superiority of our method over previous state-of-the-art methods. To foster future research into fine-grained visual grounding our benchmark RefCOCOm the MRES-32M dataset and model UniRES will be publicly available at https://github.com/Rubics-Xuan/MRES.
DiffInDScene: Diffusion-based High-Quality 3D Indoor Scene Generation
Authors: Xiaoliang Ju; Zhaoyang Huang; Yijin Li; Guofeng Zhang; Yu Qiao; Hongsheng Li
Venue: CVPR 2024 (main)

Abstract: We present DiffInDScene a novel framework for tackling the problem of high-quality 3D indoor scene generation which is challenging due to the complexity and diversity of the indoor scene geometry. Although diffusion-based generative models have previously demonstrated impressive performance in image generation and object-level 3D generation they have not yet been applied to room-level 3D generation due to their computationally intensive costs. In DiffInDScene we propose a cascaded 3D diffusion pipeline that is efficient and possesses strong generative performance for Truncated Signed Distance Function (TSDF). The whole pipeline is designed to run on a sparse occupancy space in a coarse-to-fine fashion. Inspired by KinectFusion's incremental alignment and fusion of local TSDF volumes we propose a diffusion-based SDF fusion approach that iteratively diffuses and fuses local TSDF volumes facilitating the generation of an entire room environment. The generated results demonstrate that our work is capable to achieve high-quality room generation directly in three-dimensional space starting from scratch. In addition to the scene generation the final part of DiffInDScene can be used as a post-processing module to refine the 3D reconstruction results from multi-view stereo. According to the user study the mesh quality generated by our DiffInDScene can even outperform the ground truth mesh provided by ScanNet.
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
Authors: Xuzhe Zhang; Yuhao Wu; Elsa Angelini; Ang Li; Jia Guo; Jerod M. Rasmussen; Thomas G. O'Connor; Pathik D. Wadhwa; Andrea Parolin Jackowski; Hai Li; Jonathan Posner; Andrew F. Laine; Yun Wang
Venue: CVPR 2024 (main)

Abstract: Robust segmentation is critical for deriving quantitative measures from large-scale multi-center and longitudinal medical scans. Manually annotating medical scans however is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a well-studied technique that alleviates this label-scarcity problem by leveraging available labels from another domain. In this study we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg) a unified UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation. To the best of our knowledge this is the first study that systematically reviews and develops a framework to tackle four different domain shifts in medical image segmentation. More importantly MAPSeg is the first framework that can be applied to centralized federated and test-time UDA while maintaining comparable performance. We compare MAPSeg with previous state-of-the-art methods on a private infant brain MRI dataset and a public cardiac CT-MRI dataset and MAPSeg outperforms others by a large margin (10.5 Dice improvement on the private MRI dataset and 5.7 on the public CT-MRI dataset). MAPSeg poses great practical value and can be applied to real-world problems. GitHub: https://github.com/XuzheZ/MAPSeg/.
Leveraging Predicate and Triplet Learning for Scene Graph Generation
Authors: Jiankai Li; Yunhong Wang; Xiefan Guo; Ruijie Yang; Weixin Li
Venue: CVPR 2024 (main)

Abstract: Scene Graph Generation (SGG) aims to identify entities and predict the relationship triplets <subject predicate object> in visual scenes. Given the prevalence of large visual variations of subject-object pairs even in the same predicate it can be quite challenging to model and refine predicate representations directly across such pairs which is however a common strategy adopted by most existing SGG methods. We observe that visual variations within the identical triplet are relatively small and certain relation cues are shared in the same type of triplet which can potentially facilitate the relation learning in SGG. Moreover for the long-tail problem widely studied in SGG task it is also crucial to deal with the limited types and quantity of triplets in tail predicates. Accordingly in this paper we propose a Dual-granularity Relation Modeling (DRM) network to leverage fine-grained triplet cues besides the coarse-grained predicate ones. DRM utilizes contexts and semantics of predicate and triplet with Dual-granularity Constraints generating compact and balanced representations from two perspectives to facilitate relation recognition. Furthermore a Dual-granularity Knowledge Transfer (DKT) strategy is introduced to transfer variation from head predicates/triplets to tail ones aiming to enrich the pattern diversity of tail classes to alleviate the long-tail problem. Extensive experiments demonstrate the effectiveness of our method which establishes new state-of-the-art performance on Visual Genome Open Image and GQA datasets. Our code is available at https://github.com/jkli1998/DRM.
DaReNeRF: Direction-aware Representation for Dynamic Scenes
Authors: Ange Lou; Benjamin Planche; Zhongpai Gao; Yamin Li; Tianyu Luan; Hao Ding; Terrence Chen; Jack Noble; Ziyan Wu
Venue: CVPR 2024 (main)

Abstract: Addressing the intricate challenge of modeling and re-rendering dynamic scenes most recent approaches have sought to simplify these complexities using plane-based explicit representations overcoming the slow training time issues associated with methods like Neural Radiance Fields (NeRF) and implicit representations. However the straightforward decomposition of 4D dynamic scenes into multiple 2D plane-based representations proves insufficient for re-rendering high-fidelity scenes with complex motions. In response we present a novel direction-aware representation (DaRe) approach that captures scene dynamics from six different directions. This learned representation undergoes an inverse dual-tree complex wavelet transformation (DTCWT) to recover plane-based information. DaReNeRF computes features for each space-time point by fusing vectors from these recovered planes. Combining DaReNeRF with a tiny MLP for color regression and leveraging volume rendering in training yield state-of-the-art performance in novel view synthesis for complex dynamic scenes. Notably to address redundancy introduced by the six real and six imaginary direction-aware wavelet coefficients we introduce a trainable masking approach mitigating storage issues without significant performance decline. Moreover DaReNeRF maintains a 2x reduction in training time compared to prior art while delivering superior performance.
SfmCAD: Unsupervised CAD Reconstruction by Learning Sketch-based Feature Modeling Operations
Authors: Pu Li; Jianwei Guo; Huibin Li; Bedrich Benes; Dong-Ming Yan
Venue: CVPR 2024 (main)

Abstract: This paper introduces SfmCAD a novel unsupervised network that reconstructs 3D shapes by learning the Sketch-based Feature Modeling operations commonly used in modern CAD workflows. Given a 3D shape represented as voxels SfmCAD learns a neural-typed sketch+path parameterized representation including 2D sketches of feature primitives and their 3D sweeping paths without supervision for inferring feature-based CAD programs. SfmCAD employs 2D sketches for local detail representation and 3D paths to capture the overall structure achieving a clear separation between shape details and structure. This conversion into parametric forms enables users to seamlessly adjust the shape's geometric and structural features thus enhancing interpretability and user control. We demonstrate the effectiveness of our method by applying SfmCAD to many different types of objects such as CAD parts ShapeNet objects and tree shapes. Extensive comparisons show that SfmCAD produces compact and faithful 3D reconstructions with superior quality compared to alternatives. The code is released at https://github.com/BunnySoCrazy/SfmCAD.
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation
Authors: Zineng Tang; Ziyi Yang; Mahmoud Khademi; Yang Liu; Chenguang Zhu; Mohit Bansal
Venue: CVPR 2024 (main)

Abstract: We present CoDi-2 a Multimodal Large Language Model (MLLM) for learning in-context interleaved multimodal representations. By aligning modalities with language for both encoding and generation CoDi-2 empowers Large Language Models (LLMs) to understand modality-interleaved instructions and in-context examples and autoregressively generate grounded and coherent multimodal outputs in an any-to-any input-output modality paradigm. To train CoDi-2 we build a large-scale generation dataset encompassing in-context multimodal instructions across text vision and audio. CoDi-2 demonstrates a wide range of zero-shot and few-shot capabilities for tasks like editing exemplar learning composition reasoning etc. CoDi-2 surpasses previous domain-specific models on tasks such as subject-driven image generation vision transformation and audio editing and showcases a significant advancement for integrating diverse multimodal tasks with sequential generation.
Tuning Stable Rank Shrinkage: Aiming at the Overlooked Structural Risk in Fine-tuning
Authors: Sicong Shen; Yang Zhou; Bingzheng Wei; Eric I-Chao Chang; Yan Xu
Venue: CVPR 2024 (main)

Abstract: Existing fine-tuning methods for computer vision tasks primarily focus on re-weighting the knowledge learned from the source domain during pre-training. They aim to retain beneficial knowledge for the target domain while suppressing unfavorable knowledge. During the pre-training and fine-tuning stages there is a notable disparity in the data scale. Consequently it is theoretically necessary to employ a model with reduced complexity to mitigate the potential structural risk. However our empirical investigation in this paper reveals that models fine-tuned using existing methods still manifest a high level of model complexity inherited from the pre-training stage leading to a suboptimal stability and generalization ability. This phenomenon indicates an issue that has been overlooked in fine-tuning: Structural Risk Minimization. To address this issue caused by data scale disparity during the fine-tuning stage we propose a simple yet effective approach called Tuning Stable Rank Shrinkage (TSRS). TSRS mitigates the structural risk during the fine-tuning stage by constraining the noise sensitivity of the target model based on stable rank theories. Through extensive experiments we demonstrate that incorporating TSRS into fine-tuning methods leads to improved generalization ability on various tasks regardless of whether the neural networks are based on convolution or transformer architectures. Additionally empirical analysis reveals that TSRS enhances the robustness convexity and smoothness of the loss landscapes in fine-tuned models. Code is available at https://github.com/WitGotFlg/TSRS.
Differentiable Display Photometric Stereo
Authors: Seokjun Choi; Seungwoo Yoon; Giljoo Nam; Seungyong Lee; Seung-Hwan Baek
Venue: CVPR 2024 (main)

Abstract: Photometric stereo leverages variations in illumination conditions to reconstruct surface normals. Display photometric stereo which employs a conventional monitor as an illumination source has the potential to overcome limitations often encountered in bulky and difficult-to-use conventional setups. In this paper we present differentiable display photometric stereo (DDPS) addressing an often overlooked challenge in display photometric stereo: the design of display patterns. Departing from using heuristic display patterns DDPS learns the display patterns that yield accurate normal reconstruction for a target system in an end-to-end manner. To this end we propose a differentiable framework that couples basis-illumination image formation with analytic photometric-stereo reconstruction. The differentiable framework facilitates the effective learning of display patterns via auto-differentiation. Also for training supervision we propose to use 3D printing for creating a real-world training dataset enabling accurate reconstruction on the target real-world setup. Finally we exploit that conventional LCD monitors emit polarized light which allows for the optical separation of diffuse and specular reflections when combined with a polarization camera leading to accurate normal reconstruction. Extensive evaluation of DDPS shows improved normal-reconstruction accuracy compared to heuristic patterns and demonstrates compelling properties such as robustness to pattern initialization calibration errors and simplifications in image formation and reconstruction.
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image Classification
Authors: Jinseong Park; Yujin Choi; Jaewook Lee
Venue: CVPR 2024 (main)

Abstract: To alleviate the utility degradation of deep learning image classification with differential privacy (DP) employing extra public data or pre-trained models has been widely explored. Recently the use of in-distribution public data has been investigated where tiny subsets of datasets are released publicly. In this paper we investigate a framework that leverages recent diffusion models to amplify the information of public data. Subsequently we identify data diversity and generalization gap between public and private data as critical factors addressing the limited public data. While assuming 4% of training data as public our method achieves 85.48% on CIFAR-10 with a privacy budget of ?=2 without employing extra public data for training.
Learning Degradation-unaware Representation with Prior-based Latent Transformations for Blind Face Restoration
Authors: Lianxin Xie; Csbingbing Zheng; Wen Xue; Le Jiang; Cheng Liu; Si Wu; Hau San Wong
Venue: CVPR 2024 (main)

Abstract: Blind face restoration focuses on restoring high-fidelity details from images subjected to complex and unknown degradations while preserving identity information. In this paper we present a Prior-based Latent Transformation approach (PLTrans) which is specifically designed to learn a degradation-unaware representation thereby allowing the restoration network to effectively generalize to real-world degradation. Toward this end PLTrans learns a degradation-unaware query via a latent diffusion-based regularization module. Furthermore conditioned on the features of a degraded face image a latent dictionary that captures the priors of HQ face images is leveraged to refine the features by mapping the top-d nearest elements. The refined version will be used to build key and value for the cross-attention computation which is tailored to each degraded image and exhibits reduced sensitivity to different degradation factors. Conditioned on the resulting representation we train a decoding network that synthesizes face images with authentic details and identity preservation. Through extensive experiments we verify the effectiveness of the design elements and demonstrate the generalization ability of our proposed approach for both synthetic and unknown degradations. We finally demonstrate the applicability of PLTrans in other vision tasks.
LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels
Authors: Tuo Feng; Wenguan Wang; Fan Ma; Yi Yang
Venue: CVPR 2024 (main)

Abstract: Autonomous systems need to process large-scale sparse and irregular point clouds with limited compute resources. Consequently it is essential to develop LiDAR perception methods that are both efficient and effective. Although naively enlarging 3D kernel size can enhance performance it will also lead to a cubically-increasing overhead. Therefore it is crucial to develop streamlined 3D large kernel designs that eliminate redundant weights and work effectively with larger kernels. In this paper we propose an efficient and effective Large Sparse Kernel 3D Neural Network (LSK3DNet) that leverages dynamic pruning to amplify the 3D kernel size. Our method comprises two core components: Spatial-wise Dynamic Sparsity (SDS) and Channel-wise Weight Selection (CWS). SDS dynamically prunes and regrows volumetric weights from the beginning to learn a large sparse 3D kernel. It not only boosts performance but also significantly reduces model size and computational cost. Moreover CWS selects the most important channels for 3D convolution during training and subsequently prunes the redundant channels to accelerate inference for 3D vision tasks. We demonstrate the effectiveness of LSK3DNet on three benchmark datasets and five tracks compared with classical models and large kernel designs. Notably LSK3DNet achieves the state-of-the-art performance on SemanticKITTI (i.e. 75.6% on single-scan and 63.4% on multi-scan) with roughly 40% model size reduction and 60% computing operations reduction compared to the naive large 3D kernel model.
Faces that Speak: Jointly Synthesising Talking Face and Speech from Text
Authors: Youngjoon Jang; Ji-Hoon Kim; Junseok Ahn; Doyeop Kwak; Hong-Sun Yang; Yoon-Cheol Ju; Il-Hwan Kim; Byeong-Yeol Kim; Joon Son Chung
Venue: CVPR 2024 (main)

Abstract: The goal of this work is to simultaneously generate natural talking faces and speech outputs from text. We achieve this by integrating Talking Face Generation (TFG) and Text-to-Speech (TTS) systems into a unified framework. We address the main challenges of each task: (1) generating a range of head poses representative of real-world scenarios and (2) ensuring voice consistency despite variations in facial motion for the same identity. To tackle these issues we introduce a motion sampler based on conditional flow matching which is capable of high-quality motion code generation in an efficient way. Moreover we introduce a novel conditioning method for the TTS system which utilises motion-removed features from the TFG model to yield uniform speech outputs. Our extensive experiments demonstrate that our method effectively creates natural-looking talking faces and speech that accurately match the input text. To our knowledge this is the first effort to build a multimodal synthesis system that can generalise to unseen identities.