ECCV 2024 论文列表

"Existing UV mapping algorithms are designed to operate on well-behaved meshes, instead of the geometry representations produced by state-of-the-art 3D reconstruction and generation techniques. As such, applying these methods to the volume densities recovered by neural radiance fields and related techniques (or meshes triangulated from such fields) results in texture atlases that are too fragmented to be useful for tasks such as view synthesis or appearance editing. We present a UV mapping method designed to operate on geometry produced by 3D reconstruction and generation techniques. Instead of computing a mapping defined on a mesh’s vertices, our method uses a neural field to represent a continuous UV mapping, and optimizes it to be a valid and well-behaved mapping for just the set of visible points, only points that affect the scene’s appearance. We show that our model is robust to the challenges posed by ill-behaved geometry, and that it produces editable UV mappings that can represent detailed appearance."

"In recent years, the Whole Slide Image (WSI) classification task has achieved great advancement due to the success of Multiple Instance Learning (MIL). However, the MIL-based studies usually consider instances within each bag as unordered, potentially resulting in the missing of local and global contextual information. To overcome this limitation, we propose a Noise Robust Memory-Augmented (Norma) framework for addressing the WSI classification task using a sequential approach. Norma serializes a WSI into a long sequence and adopts the Vision Transformer (ViT) to encode the local and global context information of the WSIs. Instead of processing long sequences at once, Norma splits the long sequence into multiple segments and sequentially trains these segments, with each segment being cached for future reuse. In addition, considering that segment-level labels are inherited from slide-level labels, which may introduce noise during training, Norma further introduces a cyclic method to reduce label noise. We achieve state-of-the-art performance on the CAMELYON-16, TCGA-BRAC and TCGA-LUNG datasets compared to recent studies. The code is available at https://github.com/weiaicunzai/Norma."

"Facial Expression Analysis remains a challenging task due to unexpected task-irrelevant noise, such as identity, head pose, and background. To address this issue, this paper proposes a novel framework, called Norface, that is unified for both Action Unit (AU) analysis and Facial Emotion Recognition (FER) tasks. Norface consists of a normalization network and a classification network. First, the carefully designed normalization network struggles to directly remove the above task-irrelevant noise, by maintaining facial expression consistency but normalizing all original images to a common identity with consistent pose, and background. Then, these additional normalized images are fed into the classification network. Due to consistent identity and other factors (e.g. head pose, background, etc.), the normalized images enable the classification network to extract useful expression information more effectively. Additionally, the classification network incorporates a Mixture of Experts to refine the latent representation, including handling the input of facial representations and the output of multiple (AU or emotion) labels. Extensive experiments validate the carefully designed framework with the insight of identity normalization. The proposed method outperforms existing SOTA methods in multiple facial expression analysis tasks, including AU detection, AU intensity estimation, and FER tasks, as well as their cross-dataset tasks. For the normalized datasets and code please visit project page."

"This work addresses a new challenge of understanding human nonverbal interaction in social contexts. Nonverbal signals pervade virtually every communicative act. Our gestures, facial expressions, postures, gaze, even physical appearance all convey messages, without anything being said. Despite their critical role in social life, nonverbal signals receive very limited attention as compared to the linguistic counterparts, and existing solutions typically examine nonverbal cues in isolation. Our study marks the first systematic effort to enhance the interpretation of multifaceted nonverbal signals. First, we contribute a novel large-scale dataset, called , which is meticulously annotated to include bounding boxes for humans and corresponding social groups, along with 22 atomic-level nonverbal behaviors under five broad interaction types. Second, we establish a new task for nonverbal interaction detection, which is formalized as identifying triplets in the form ⟨individual, group, interaction⟩ from images. Third, we propose a nonverbal interaction detection hypergraph (), a new approach that explicitly models high-order nonverbal interactions using hypergraphs. Central to the model is a dual multi-scale hypergraph that adeptly addresses individual-to-individual and group-to-group correlations across varying scales, facilitating interactional feature learning and eventually improving interaction prediction. Extensive experiments on show that improves various baselines significantly in . It also exhibits leading performance on HOI-DET, confirming its versatility in supporting related tasks and strong generalization ability. We hope that our study will offer the community new avenues to explore nonverbal signals in more depth."

"what is the usage of the first line? Generability of DNN is improved, and yet you would like to reduce it? Revise it, try to express that the model generability improves so that it will be vulnerable to be fine-tuned. Move your revised first sentence to line 14? Pretrained Deep Neural Networks (DNNs), developed from extensive datasets to integrate multifaceted knowledge, are increasingly recognized as valuable intellectual property (IP). To safeguard these models against IP infringement, strategies for ownership verification and usage authorization have emerged. Unlike most existing IP protection strategies that concentrate on restricting direct access to the model, our study addresses an extended DNN IP issue: applicability authorization, aiming to prevent the misuse of learned knowledge, particularly in unauthorized transfer learning scenarios. We propose Non-Transferable Pruning (NTP), a novel IP protection method that leverages model pruning to control a pretrained DNN’s transferability to unauthorized data domains. Selective pruning can deliberately diminish a model’s suitability on unauthorized domains, even with full fine-tuning. Specifically, our framework employs the alternating direction method of multipliers (ADMM) for optimizing both the model sparsity and an innovative non-transferable learning loss, augmented with fisher space discriminative regularization, to constrain the model’s generalizability to the target dataset. We also propose a novel effective metric to measure the model non-transferability: Area Under the Sample-wise Learning Curve (SLC-AUC). This metric facilitates consideration of full fine-tuning across various sample sizes. Experimental results demonstrate that NTP significantly surpasses the state-of-the-art non-transferable learning methods, with an average SLC-AUC at -0.54 across diverse pairs of source and target domains, indicating that models trained with NTP do not suit for transfer learning to unauthorized target domains. The efficacy of NTP is validated in both supervised and self-supervised learning contexts, confirming its applicability in real-world scenarios. Git Repo"

"We introduce a novel non-parametric sensor noise model that directly constructs probability mass functions per intensity level from captured images. We show that our noise model provides a more accurate fit to real sensor noise than existing models. We detail the capture procedure for deriving our non-parametric noise model and introduce an interpolation method that reduces the number of ISOs levels that need to be captured. In addition, we propose a method to synthesize noise on existing noisy images when noise-free images are not available. Our noise model is straightforward to calibrate and provides notable improvements over competing noise models on downstream tasks."

"Non-line-of-sight (NLoS) reconstruction, i.e., the task of imaging scenes beyond the camera’s field of view, is often implemented using source-and-sensor systems that scan the visible region and analyze secondary reflections of light that has interacted with the hidden static scene. Estimating human activity around the corner will be a task of major interest for emerging NLoS applications, and some attempts have been reported in the recent literature. However, due to the long exposure times and comprehensive scans needed for NLoS sensing, the reconstruction of continuous movement remains prone to artifacts and is unreliable. In this paper, we analyze the interplay between dynamic scenes and scanning hardware to identify possible failure cases for filtering and data-driven approaches. Our studies indicate that existing reconstruction methods are prone to systematic error due to the space-time skew introduced by scanning setups. To alleviate this issue, we propose an image formation model for dynamic scenes that explicitly integrates motion skew. Using this model, we construct a baseline method for human pose estimation that achieves high accuracy, even at very slow scan rates."

"Existing approaches to Domain Incremental Learning (DIL) address catastrophic forgetting by storing and rehearsing exemplars from old domains. However, exemplar-based solutions are not always viable due to data privacy concerns or storage limitations. Therefore, Non-Exemplar Domain Incremental Learning (NEDIL) has emerged as a significant paradigm for resolving DIL challenges. Current NEDIL solutions extend the classifier incrementally for new domains to learn new knowledge, but unrestricted extension within the same feature space leads to inter-class confusion. To tackle this issue, we propose a simple yet effective method through cross-domain concePt INtegrAtion (PINA). We train a Unified Classifier (UC) as a concept container across all domains. Then, a Domain Specific Alignment (DSA) module is proposed for each incremental domain, aligning the feature distribution to the base domain. During inference, we introduce a Patch Shuffle Selector (PSS) to select appropriate parameters of DSA for test images. Our developed patch shuffling technique disrupts class-dependent information, outperforming the domain selectors based on K-Nearest Neighbors or Nearest Mean Classifier. Extensive experiments demonstrate that our method achieves state-of-the-art performance while reducing the number of additional parameters. The source code will be released in https://github.com/qwangcv/PINA."

"We present CLIP-IFDL, a novel image forgery detection and localization (IFDL) model that harnesses the power of Contrastive Language Image Pre-Training (CLIP). However, directly incorporating CLIP in forgery detection poses challenges, given its lack of specific prompts and forgery consciousness. To overcome these challenges, we tailor the CLIP model for forgery detection and localization leveraging a noise-assisted prompt learning framework. This framework comprises instance-aware dual-stream prompt learning and a forgery-enhanced noise adapter. We initially create a pair of learnable prompts as negative-positive samples in place of discrete prompts, then fine-tune these prompts based on each image’s features and categories. Additionally, we constrain the text-image similarity between the prompts and their corresponding images to update the prompts. Moreover, We design a forgery-enhanced noise adapter that augments the image encoder’s forgery perceptual ability via multi-domain fusion and zero linear layers. By doing so, our method not only extracts pertinent features but also benefits from the generalizability of the open-world CLIP prior. Comprehensive tests indicate that our method outperforms existing ones in terms of accuracy and generalizability while effectively reducing false alarms."

"In order to improve the quality of synthesized videos, currently, one predominant method involves retraining an expert diffusion model and then implementing a noising-denoising process for refinement. Despite the significant training costs, maintaining consistency of content between the original and enhanced videos remains a major challenge. To tackle this challenge, we propose a novel formulation that considers both visual quality and consistency of content. Consistency of content is ensured by a proposed loss function that maintains the structure of the input, while visual quality is improved by utilizing the denoising process of pretrained diffusion models. To address the formulated optimization problem, we have developed a plug-and-play noise optimization strategy, referred to as Noise Calibration. By refining the initial random noise through a few iterations, the content of original video can be largely preserved, and the enhancement effect demonstrates a notable improvement. Extensive experiments have demonstrated the effectiveness of the proposed method. 0 Corresponding author"

"In this paper, we address the challenge of compressing generative adversarial networks (GANs) for deployment in resource-constrained environments by proposing two novel methods: Distribution Matching for Efficient compression (DiME) and Network Interactive Compression via Knowledge Exchange and Learning (NICKEL). DiME employs foundation models as embedding kernels for efficient distribution matching, leveraging maximum mean discrepancy to facilitate effective knowledge distillation. NICKEL employs an interactive compression method that enhances the communication between the student generator and discriminator, achieving a balanced and stable compression process. Our comprehensive evaluation on the StyleGAN2 architecture with the FFHQ dataset shows the effectiveness of our approach, with NICKEL & DiME achieving FID scores of 10.45 and 15.93 at compression rates of 95.73% and 98.92%, respectively. Remarkably, our methods sustain generative quality even at an extreme compression rate of 99.69%, surpassing the previous state-of-the-art performance by a large margin. These findings not only show our methodologies’ capacity to significantly lower GANs’ computational demands but also pave the way for deploying high-quality GAN models in settings with limited resources. Our code is available at Nickel & Dime."

"Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models. These approaches, while producing high-quality images, capture only a limited aspect of the complex information in fMRI signals and offer little detailed control over image creation. In contrast, this paper proposes to directly modulate the generation process of diffusion models using fMRI signals. Our approach, NeuroPictor, divides the fMRI-to-image process into three steps: i) fMRI calibrated-encoding, to tackle multi-individual pre-training for a shared latent space to minimize individual difference and enable the subsequent multi-subject training; ii) fMRI-to-image multi-subject pre-training, perceptually learning to guide diffusion model with high- and low-level conditions across different individuals; iii) fMRI-to-image single-subject refining, similar with step ii but focus on adapting to particular individual. NeuroPictor extracts high-level semantic features from fMRI signals that characterizing the visual stimulus and incrementally fine-tunes the diffusion model with a low-level manipulation network to provide precise structural instructions. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity, particularly in the within-subject setting, as evidenced in benchmark datasets. Our code and model are available at https://jingyanghuo.github.io/neuropictor/."

"We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios. The simulator learns from sequences of real-world driving sensor data and enables reconfigurations and renderings of new, unseen scenarios. In this work, we use our simulator to test the responses of AD models to safety-critical scenarios inspired by the European New Car Assessment Programme (Euro NCAP). Our evaluation reveals that, while state-of-the-art end-to-end planners excel in nominal driving scenarios in an open-loop setting, they exhibit critical flaws when navigating our safety-critical scenarios in a closed-loop setting. This highlights the need for advancements in the safety and real-world usability of end-to-end planners. By publicly releasing our simulator and scenarios as an easy-to-run evaluation suite, we invite the research community to explore, refine, and validate their AD models in controlled, yet highly configurable and challenging sensor-realistic environments."

"Advances in rendering have led to tremendous growth in texture assets, including resolution, complexity, and novel textures components, but this growth in data volume has not been matched by advances in its compression. Meanwhile Neural Image Compression (NIC) has advanced significantly and shown promising results, but the proposed methods cannot be directly adapted to neural texture compression. First, texture compression requires on-demand and real-time decoding with random access during parallel rendering (e.g. block texture decompression on GPUs). Additionally, NIC does not support multi-resolution reconstruction (mip-levels), nor does it have the ability to efficiently jointly compress different sets of texture channels. In this work, we introduce a novel approach to texture set compression that integrates traditional GPU texture representation and NIC techniques, designed to enable random access and support many-channel texture sets. To achieve this goal, we propose an asymmetric auto-encoder framework that employs a convolutional encoder to capture detailed information in a bottleneck-latent space, and at decoder side we utilize a fully connected network, whose inputs are sampled latent features plus positional information, for a given texture coordinate and mip level. This latent data is defined to enable simplified access to multi-resolution data by simply changing the scanning strides. Experimental results demonstrate that this approach provides much better results than conventional texture compression, and significant improvement over the latest method using neural networks."

"Effectively navigating a dynamic 3D world requires a comprehensive understanding of the 3D geometry and motion of surrounding objects and layouts. However, existing methods for perception and planning in autonomous driving primarily rely on a 2D spatial representation, based on a bird’s eye perspective of the scene, which is insufficient for modeling motion characteristics and decision-making in real-world 3D settings with occlusion, partial observability, subtle motions, and varying terrains. Motivated by this key insight, we present a novel framework for learning end-to-end autonomous driving based on volumetric representations. Our proposed neural volumetric world modeling approach, NeMo, can be trained in a self-supervised manner for image reconstruction and occupancy prediction tasks, benefiting scalable training and deployment paradigms such as imitation learning. Specifically, we demonstrate how the higher-fidelity modeling of 3D volumetric representations benefits vision-based motion planning. We further propose a motion flow module to model complex dynamic scenes, enabling additional robust spatiotemporal consistency supervision. Moreover, a temporal attention module is introduced to effectively integrate predicted future volumetric features for the planning task. Our proposed sensorimotor agent achieves state-of-the-art driving performance on nuScenes and CARLA, outperforming prior baseline methods by over 18%."

"Extracting surfaces from Signed Distance Fields (SDFs) can be accomplished using traditional algorithms, such as Marching Cubes. However, since they rely on sign flips across the surface, these algorithms cannot be used directly on Unsigned Distance Fields (UDFs). In this work, we introduce a deep-learning approach to taking a UDF and turning it locally into an SDF, so that it can be effectively triangulated using existing algorithms. We show that it achieves better accuracy in surface detection than existing methods. Furthermore it generalizes well to unseen shapes and datasets, while being parallelizable. We also demonstrate the flexibily of the method by using it in conjunction with DualMeshUDF, a state of the art dual meshing method that can operate on UDFs, improving its results and removing the need to tune its parameters."

"In this paper, we propose Neural Spectrum Decomposition, a generic decomposition framework for dataset distillation. Unlike previous methods, we consider the entire dataset as a high-dimensional observation that is low-rank across all dimensions. We aim to discover the low-rank representation of the entire dataset and perform distillation efficiently. Toward this end, we learn a set of spectrum tensors and transformation matrices, which, through simple matrix multiplication, reconstruct the data distribution. Specifically, a spectrum tensor can be mapped back to the image space by a transformation matrix, and efficient information sharing during the distillation learning process is achieved through pairwise combinations of different spectrum vectors and transformation matrices. Furthermore, we integrate a trajectory matching optimization method guided by a real distribution. Our experimental results demonstrate that our approach achieves state-of-the-art performance on benchmarks, including CIFAR10, CIFAR100, Tiny Imagenet, and ImageNet Subset. Our code are available at https://github.com/slyang2021/ NSD."

"Implicit Neural Representation (INR) has become a popular method for representing visual signals (, 2D images and 3D scenes), demonstrating promising results in various downstream applications. Given its potential as a medium for visual signals, exploring the development of a neural blending method that utilizes INRs is a natural progression. Neural blending involves merging two INRs to create a new INR that encapsulates information from both original representations. A direct approach involves applying traditional image editing methods to the INR rendering process. However, this method often results in blending distortions, artifacts, and color shifts, primarily due to the discretization of the underlying pixel grid and the introduction of boundary conditions for solving variational problems. To tackle this issue, we introduce the Neural Poisson Solver, a plug-and-play and universally applicable framework across different signal dimensions for blending visual signals represented by INRs. Our Neural Poisson Solver offers a variational problem-solving approach based on the continuous Poisson equation, demonstrating exceptional performance across various domains. Specifically, we propose a gradient-guided neural solver to represent the solution process of the variational problem, refining the target signal to achieve natural blending results. We also develop a Poisson equation-based loss and optimization scheme to train our solver, ensuring it effectively blends the input INR scenes while preserving their inherent structure and semantic content. The lack of dependence on additional prior knowledge makes our method easily adaptable to various task categories, highlighting its versatility. Comprehensive experimental results validate the robustness of our approach across multiple dimensions and blending tasks. Project: https://ep1phany05. github.io/NeuralPoissonSolver-website/"

"This paper introduces a new learning paradigm termed Neural Metamorphosis (NeuMeta), which aims to build self-morphable neural networks. Contrary to crafting separate models for different architectures or sizes, NeuMeta directly learns the continuous weight manifold of neural networks. Once trained, we can sample weights for any-sized network directly from the manifold, even for previously unseen configurations, without retraining. To achieve this ambitious goal, NeuMeta trains neural implicit functions as hypernetworks. They accept coordinates within the model space as input, and generate corresponding weight values on the manifold. In other words, the implicit function is learned in a way, that the predicted weights is well-performed across various models sizes. In training those models, we notice that, the final performance closely relates on smoothness of the learned manifold. In pursuit of enhancing this smoothness, we employ two strategies. First, we permute weight matrices to achieve intra-model smoothness, by solving the Shortest Hamiltonian Path problem. Besides, we add a noise on the input coordinates when training the implicit function, ensuring models with various sizes shows consistent outputs. As such, NeuMeta shows promising results in synthesizing parameters for various network configurations. Our extensive tests in image classification, semantic segmentation, and image generation reveal that NeuMeta sustains full-size performance even at a 75% compression rate."