GenAI4ELab/papercli-papers huggingface dataset:
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving
Authors: Junhao Zheng; Chenhao Lin; Jiahao Sun; Zhengyu Zhao; Qian Li; Chao Shen
Venue: CVPR 2024 (main)

Abstract: Deep learning-based monocular depth estimation (MDE) extensively applied in autonomous driving is known to be vulnerable to adversarial attacks. Previous physical attacks against MDE models rely on 2D adversarial patches so they only affect a small localized region in the MDE map but fail under various viewpoints. To address these limitations we propose 3D Depth Fool (3D^2Fool) the first 3D texture-based adversarial attack against MDE models. 3D^2Fool is specifically optimized to generate 3D adversarial textures agnostic to model types of vehicles and to have improved robustness in bad weather conditions such as rain and fog. Experimental results validate the superior performance of our 3D^2Fool across various scenarios including vehicles MDE models weather conditions and viewpoints. Real-world experiments with printed 3D textures on physical vehicle models further demonstrate that our 3D^2Fool can cause an MDE error of over 10 meters.
Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks
Authors: Shin'ya Yamaguchi; Sekitoshi Kanai; Kazuki Adachi; Daiki Chijiwa
Venue: CVPR 2024 (main)

Abstract: While fine-tuning is a de facto standard method for training deep neural networks it still suffers from overfitting when using small target datasets. Previous methods improve fine-tuning performance by maintaining knowledge of the source datasets or introducing regularization terms such as contrastive loss. However these methods require auxiliary source information (e.g. source labels or datasets) or heavy additional computations. In this paper we propose a simple method called adaptive random feature regularization (AdaRand). AdaRand helps the feature extractors of training models to adaptively change the distribution of feature vectors for downstream classification tasks without auxiliary source information and with reasonable computation costs. To this end AdaRand minimizes the gap between feature vectors and random reference vectors that are sampled from class conditional Gaussian distributions. Furthermore AdaRand dynamically updates the conditional distribution to follow the currently updated feature extractors and balance the distance between classes in feature spaces. Our experiments show that AdaRand outperforms the other fine-tuning regularization requiring auxiliary source information and heavy computation costs.
PH-Net: Semi-Supervised Breast Lesion Segmentation via Patch-wise Hardness
Authors: Siyao Jiang; Huisi Wu; Junyang Chen; Qin Zhang; Jing Qin
Venue: CVPR 2024 (main)

Abstract: We present a novel semi-supervised framework for breast ultrasound (BUS) image segmentation which is a very challenging task owing to (1) large scale and shape variations of breast lesions and (2) extremely ambiguous boundaries caused by massive speckle noise and artifacts in BUS images. While existing models achieved certain progress in this task we believe the main bottleneck nowadays for further improvement is that we still cannot deal with hard cases well. Our framework aims to break through this bottleneck which includes two innovative components: an adaptive patch augmentation scheme and a hard-patch contrastive learning module. We first identify hard patches by computing the average entropy of each patch and then shield hard patches to prevent them from being cropped out while performing random patch cutmix. Such a scheme is able to prevent hard regions from being inadequately trained under strong augmentation. We further develop a new hard-patch contrastive learning algorithm to direct model attention to hard regions by applying extra contrast to pixels in hard patches further improving segmentation performance on hard cases. We demonstrate the superiority of our framework to state-of-the-art approaches on two famous BUS datasets achieving better performance under different labeling conditions. The code is available at https://github.com/jjjsyyy/PH-Net.
Multimodal Prompt Perceiver: Empower Adaptiveness Generalizability and Fidelity for All-in-One Image Restoration
Authors: Yuang Ai; Huaibo Huang; Xiaoqiang Zhou; Jiexiang Wang; Ran He
Venue: CVPR 2024 (main)

Abstract: Despite substantial progress all-in-one image restoration (IR) grapples with persistent challenges in handling intricate real-world degradations. This paper introduces MPerceiver: a novel multimodal prompt learning approach that harnesses Stable Diffusion (SD) priors to enhance adaptiveness generalizability and fidelity for all-in-one image restoration. Specifically we develop a dual-branch module to master two types of SD prompts: textual for holistic representation and visual for multiscale detail representation. Both prompts are dynamically adjusted by degradation predictions from the CLIP image encoder enabling adaptive responses to diverse unknown degradations. Moreover a plug-in detail refinement module improves restoration fidelity via direct encoder-to-decoder information transformation. To assess our method MPerceiver is trained on 9 tasks for all-in-one IR and outperforms state-of-the-art task-specific methods across many tasks. Post multitask pre-training MPerceiver attains a generalized representation in low-level vision exhibiting remarkable zero-shot and few-shot capabilities in unseen tasks. Extensive experiments on 16 IR tasks underscore the superiority of MPerceiver in terms of adaptiveness generalizability and fidelity.
ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and More
Authors: Jiazhou Zhou; Xu Zheng; Yuanhuiyi Lyu; Lin Wang
Venue: CVPR 2024 (main)

Abstract: Event cameras have recently been shown beneficial for practical vision tasks such as action recognition thanks to their high temporal resolution power efficiency and reduced privacy concerns. However current research is hindered by 1) the difficulty in processing events because of their prolonged duration and dynamic actions with complex and ambiguous semantics and 2) the redundant action depiction of the event frame representation with fixed stacks. We find language naturally conveys abundant semantic information rendering it stunningly superior in reducing semantic uncertainty. In light of this we propose ExACT a novel approach that for the first time tackles event-based action recognition from a cross-modal conceptualizing perspective. Our ExACT brings two technical contributions. Firstly we propose an adaptive fine-grained event (AFE) representation to adaptively filter out the repeated events for the stationary objects while preserving dynamic ones. This subtly enhances the performance of ExACT without extra computational cost. Then we propose a conceptual reasoning-based uncertainty estimation module which simulates the recognition process to enrich the semantic representation. In particular conceptual reasoning builds the temporal relation based on the action semantics and uncertainty estimation tackles the semantic uncertainty of actions based on the distributional representation. Experiments show that our ExACT achieves superior recognition accuracy of 94.83%(+2.23%) 90.10%(+37.47%) and 67.24% on PAF HARDVS and our SeAct datasets respectively.
Color Shift Estimation-and-Correction for Image Enhancement
Authors: Yiyu Li; Ke Xu; Gerhard Petrus Hancke; Rynson W.H. Lau
Venue: CVPR 2024 (main)

Abstract: Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. We observe that over- and over-exposed regions display opposite color tone distribution shifts which may not be easily normalized in joint modeling as they usually do not have "normal-exposed" regions/pixels as reference. In this paper we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically we first derive the color feature maps of the brightened and darkened versions of the input image via a UNet-based network followed by a pseudo-normal feature generator to produce pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened (or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches.
Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
Authors: Hyeongjun Kwon; Jinhyun Jang; Jin Kim; Kwonyoung Kim; Kwanghoon Sohn
Venue: CVPR 2024 (main)

Abstract: Visual scenes are naturally organized in a hierarchy where a coarse semantic is recursively comprised of several fine details. Exploring such a visual hierarchy is crucial to recognize the complex relations of visual elements leading to a comprehensive scene understanding. In this paper we propose a Visual Hierarchy Mapper (Hi-Mapper) a novel approach for enhancing the structured understanding of the pre-trained Deep Neural Networks (DNNs). Hi-Mapper investigates the hierarchical organization of the visual scene by 1) pre-defining a hierarchy tree through the encapsulation of probability densities; and 2) learning the hierarchical relations in hyperbolic space with a novel hierarchical contrastive loss. The pre-defined hierarchy tree recursively interacts with the visual features of the pre-trained DNNs through hierarchy decomposition and encoding procedures thereby effectively identifying the visual hierarchy and enhancing the recognition of an entire scene. Extensive experiments demonstrate that Hi-Mapper significantly enhances the representation capability of DNNs leading to an improved performance on various tasks including image classification and dense prediction tasks.
ParameterNet: Parameters Are All You Need for Large-scale Visual Pretraining of Mobile Networks
Authors: Kai Han; Yunhe Wang; Jianyuan Guo; Enhua Wu
Venue: CVPR 2024 (main)

Abstract: The large-scale visual pretraining has significantly improve the performance of large vision models. However we observe the low FLOPs pitfall that the existing low-FLOPs models cannot benefit from large-scale pretraining. In this paper we introduce a novel design principle termed ParameterNet aimed at augmenting the number of parameters in large-scale visual pretraining models while minimizing the increase in FLOPs. We leverage dynamic convolutions to incorporate additional parameters into the networks with only a marginal rise in FLOPs. The ParameterNet approach allows low-FLOPs networks to take advantage of large-scale visual pretraining. Furthermore we extend the ParameterNet concept to the language domain to enhance inference results while preserving inference speed. Experiments on the large-scale ImageNet-22K have shown the superiority of our ParameterNet scheme. For example ParameterNet-600M can achieve higher accuracy than the widely-used Swin Transformer (81.6% vs. 80.9%) and has much lower FLOPs (0.6G vs. 4.5G). The code will be released at https://parameternet.github.io/.
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Authors: Bingxin Ke; Anton Obukhov; Shengyu Huang; Nando Metzger; Rodrigo Caye Daudt; Konrad Schindler
Venue: CVPR 2024 (main)

Abstract: Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the growth in model capacity from relatively modest CNNs to large Transformer architectures. Still monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout since their knowledge of the visual world is restricted by the data seen during training and challenged by zero-shot generalization to new domains. This motivates us to explore whether the extensive priors captured in recent generative diffusion models can enable better more generalizable depth estimation. We introduce Marigold a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic training data. It delivers state-of-the-art performance across a wide range of datasets including over 20% performance gains in specific cases. Project page: https://marigoldmonodepth.github.io.
Identifying Important Group of Pixels using Interactions
Authors: Kosuke Sumiyasu; Kazuhiko Kawamoto; Hiroshi Kera
Venue: CVPR 2024 (main)

Abstract: To better understand the behavior of image classifiers it is useful to visualize the contribution of individual pixels to the model prediction. In this study we propose a method MoXI(Model eXplanation by Interactions) that efficiently and accurately identifies a group of pixels with high prediction confidence. The proposed method employs game-theoretic concepts Shapley values and interactions taking into account the effects of individual pixels and the cooperative influence of pixels on model confidence. Theoretical analysis and experiments demonstrate that our method better identifies the pixels that are highly contributing to the model outputs than widely-used by Grad-CAM Attention rollout and Shapley value. While prior studies have suffered from the exponential computational cost in the computation of Shapley value and interactions we show that this can be reduced to quadratic cost for our task. The code is available at https://github.com/KosukeSumiyasu/MoXI.
Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network
Authors: Wenqiao Li; Xiaohao Xu; Yao Gu; Bozhong Zheng; Shenghua Gao; Yingna Wu
Venue: CVPR 2024 (main)

Abstract: Recently 3D anomaly detection a crucial problem involving fine-grained geometry discrimination is getting more attention. However the lack of abundant real 3D anomaly data limits the scalability of current models. To enable scalable anomaly data collection we propose a 3D anomaly synthesis pipeline to adapt existing large-scale 3D models for 3D anomaly detection. Specifically we construct a synthetic dataset i.e. Anomaly-ShapeNet based on ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40 categories which provides a rich and varied collection of data enabling efficient training and enhancing adaptability to industrial scenarios. Meanwhile to enable scalable representation learning for 3D anomaly localization we propose a self-supervised method i.e. Iterative Mask Reconstruction Network (IMRNet). During training we propose a geometry-aware sample module to preserve potentially anomalous local regions during point cloud down-sampling. Then we randomly mask out point patches and sent the visible patches to a transformer for reconstruction-based self-supervision. During testing the point cloud repeatedly goes through the Mask Reconstruction Network with each iteration's output becoming the next input. By merging and contrasting the final reconstructed point cloud with the initial input our method successfully locates anomalies. Experiments show that IMRNet outperforms previous state-of-the-art methods achieving 66.1% in I-AUC on our Anomaly-ShapeNet dataset and 72.5% in I-AUC on Real3D-AD dataset. Our benchmark will be released at https://github.com/Chopper233/Anomaly-ShapeNet.
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications
Authors: Junyi Ma; Xieyuanli Chen; Jiawei Huang; Jingyi Xu; Zhen Luo; Jintao Xu; Weihao Gu; Rui Ai; Hesheng Wang
Venue: CVPR 2024 (main)

Abstract: Understanding how the surrounding environment changes is crucial for performing downstream tasks safely and reliably in autonomous driving applications. Recent occupancy estimation techniques using only camera images as input can provide dense occupancy representations of large-scale scenes based on the current observation. However they are mostly limited to representing the current 3D space and do not consider the future state of surrounding objects along the time axis. To extend camera-only occupancy estimation into spatiotemporal prediction we propose Cam4DOcc a new benchmark for camera-only 4D occupancy forecasting evaluating the surrounding scene changes in a near future. We build our benchmark based on multiple publicly available datasets including nuScenes nuScenes-Occupancy and Lyft-Level5 which provides sequential occupancy states of general movable and static objects as well as their 3D backward centripetal flow. To establish this benchmark for future research with comprehensive comparisons we introduce four baseline types from diverse camera-based perception and prediction implementations including a static-world occupancy model voxelization of point cloud prediction 2D-3D instance-based prediction and our proposed novel end-to-end 4D occupancy forecasting network. Furthermore the standardized evaluation protocol for preset multiple tasks is also provided to compare the performance of all the proposed baselines on present and future occupancy estimation with respect to objects of interest in autonomous driving scenarios. The dataset and our implementation of all four baselines in the proposed Cam4DOcc benchmark are released as open source at https://github.com/haomo-ai/Cam4DOcc.
DIOD: Self-Distillation Meets Object Discovery
Authors: Sandra Kara; Hejer Ammar; Julien Denize; Florian Chabot; Quoc-Cuong Pham
Venue: CVPR 2024 (main)

Abstract: Instance segmentation demands substantial labeling resources. This has prompted increased interest to explore the object discovery task as an unsupervised alternative. In particular promising results were achieved in localizing instances using motion supervision only. However the motion signal introduces complexities due to its inherent noise and sparsity which constrains the effectiveness of current methodologies. In the present paper we propose DIOD (self DIstillation meets Object Discovery) the first method that places the motion-guided object discovery within a framework of continuous improvement through knowledge distillation providing solutions to existing limitations (i) DIOD robustly eliminates the noise present in the exploited motion maps providing accurate motion-supervision (ii) DIOD leverages the discovered objects within an iterative pseudo-labeling framework enriching the initial motion-supervision with static objects which results in a cost-efficient increase in performance. Through experiments on synthetic and real-world datasets we demonstrate the benefits of bridging the gap between object discovery and distillation by significantly improving the state-of-the-art. This enhancement is also sustained across other demanding metrics so far reserved for supervised tasks.
GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
Authors: Jing Wen; Xiaoming Zhao; Zhongzheng Ren; Alexander G. Schwing; Shenlong Wang
Venue: CVPR 2024 (main)

Abstract: We introduce GoMAvatar a novel approach for real-time memory-efficient high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rendering from novel viewpoints while seamlessly integrating with rasterization-based graphics pipelines. Central to our method is the Gaussians-on-Mesh (GoM) representation a hybrid 3D model combining rendering quality and speed of Gaussian splatting with geometry modeling and compatibility of deformable meshes. We assess GoMAvatar on ZJU-MoCap PeopleSnapshot and various YouTube videos. GoMAvatar matches or surpasses current monocular human modeling algorithms in rendering quality and significantly outperforms them in computational efficiency (43 FPS) while being memory-efficient (3.63 MB per subject).
Neural Redshift: Random Networks are not Random Functions
Authors: Damien Teney; Armand Mihai Nicolicioiu; Valentin Hartmann; Ehsan Abbasnejad
Venue: CVPR 2024 (main)

Abstract: Our understanding of the generalization capabilities of neural networks NNs is still incomplete. Prevailing explanations are based on implicit biases of gradient descent GD but they cannot account for the capabilities of models from gradientfree methods nor the simplicity bias recently observed in untrained networks This paper seeks other sources of generalization in NNs. To understand the inductive biases provided by architectures independently from GD we examine untrained randomweight networks Even simple MLPs show strong inductive biases uniform sampling in weight space yields a very biased distribution of functions in terms of complexity But unlike common wisdom NNs do not have an inherent simplicity bias This property depends on components such as ReLUs residual connections and layer normalizations Alternative architectures can be built with a bias for any level of complexity. Transformers also inherit all these properties from their building blocks. We provide a fresh explanation for the success of deep learning independent from gradientbased training It points at promising avenues for controlling the solutions implemented by trained models.
HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting
Authors: Xian Liu; Xiaohang Zhan; Jiaxiang Tang; Ying Shan; Gang Zeng; Dahua Lin; Xihui Liu; Ziwei Liu
Venue: CVPR 2024 (main)

Abstract: Realistic 3D human generation from text prompts is a desirable yet challenging task. Existing methods optimize 3D representations like mesh or neural fields via score distillation sampling (SDS) which suffers from inadequate fine details or excessive training time. In this paper we propose an efficient yet effective framework HumanGaussian that generates high-quality 3D humans with fine-grained geometry and realistic appearance. Our key insight is that 3D Gaussian Splatting is an efficient renderer with periodic Gaussian shrinkage or growing where such adaptive density control can be naturally guided by intrinsic human structures. Specifically 1) we first propose a Structure-Aware SDS that simultaneously optimizes human appearance and geometry. The multi-modal score function from both RGB and depth space is leveraged to distill the Gaussian densification and pruning process. 2) Moreover we devise an Annealed Negative Prompt Guidance by decomposing SDS into a noisier generative score and a cleaner classifier score which well addresses the over-saturation issue. The floating artifacts are further eliminated based on Gaussian size in a prune-only phase to enhance generation smoothness. Extensive experiments demonstrate the superior efficiency and competitive quality of our framework rendering vivid 3D humans under diverse scenarios.