CVPR 2024 论文列表

Deep learning models for semantic segmentation often experience performance degradation when deployed to unseen target domains unidentified during the training phase. This is mainly due to variations in image texture (i.e. style) from different data sources. To tackle this challenge existing domain generalized semantic segmentation (DGSS) methods attempt to remove style variations from the feature. However these approaches struggle with the entanglement of style and content which may lead to the unintentional removal of crucial content information causing performance degradation. This study addresses this limitation by proposing BlindNet a novel DGSS approach that blinds the style without external modules or datasets. The main idea behind our proposed approach is to alleviate the effect of style in the encoder whilst facilitating robust segmentation in the decoder. To achieve this BlindNet comprises two key components: covariance alignment and semantic consistency contrastive learning. Specifically the covariance alignment trains the encoder to uniformly recognize various styles and preserve the content information of the feature rather than removing the style-sensitive factor. Meanwhile semantic consistency contrastive learning enables the decoder to construct discriminative class embedding space and disentangles features that are vulnerable to misclassification. Through extensive experiments our approach outperforms existing DGSS methods exhibiting robustness and superior performance for semantic segmentation on unseen target domains.

The traditional frame-based cameras that rely on exposure windows for imaging experience motion blur in high-speed scenarios. Frame-based deblurring methods lack reliable motion cues to restore sharp images under extreme blur conditions. The spike camera is a novel neuromorphic visual sensor that outputs spike streams with ultra-high temporal resolution. It can supplement the temporal information lost in traditional cameras and guide motion deblurring. However in real-world scenarios aligning discrete RGB images and continuous spike streams along both temporal and spatial axes is challenging due to the complexity of calibrating their coordinates device displacements in vibrations and time deviations. Misalignment of pixels leads to severe degradation of deblurring. We introduce the first framework for spike-guided motion deblurring without knowing the spatiotemporal alignment between spikes and images. To address the problem we first propose a novel three-stage network containing a basic deblurring net a carefully designed bi-directional deformable aligning module and a flow-based multi-scale fusion net. Experimental results demonstrate that our approach can effectively guide the image deblurring with unknown alignment surpassing the performance of other methods. Public project page: https://github.com/Leozhangjiyuan/UaSDN.

Finding shortest paths on product spaces is a popular approach to tackle numerous variants of matching problems including the dynamic time warping method for matching signals the matching of curves or the matching of a curve to a 3D shape. While these approaches admit the computation of globally optimal solutions in polynomial time their natural generalisation to 3D shape matching is widely known to be intractable. In this work we address this issue by proposing a novel path-based formalism for 3D shape matching. More specifically we consider an alternative shape discretisation in which one of the 3D shapes (the source shape) is represented as a SpiderCurve i.e. a long self-intersecting curve that traces the 3D shape surface. We then tackle the 3D shape matching problem as finding a shortest path in the product graph of the SpiderCurve and the target 3D shape. Our approach introduces a set of novel constraints that ensure a globally geometrically consistent matching. Overall our formalism leads to an integer linear programming problem for which we experimentally show that it can efficiently be solved to global optimality. We demonstrate that our approach is competitive with recent state-of-the-art shape matching methods while in addition guaranteeing geometric consistency.

Although 3D shape matching and interpolation are highly interrelated they are often studied separately and applied sequentially to relate different 3D shapes thus resulting in sub-optimal performance. In this work we present a unified framework to predict both point-wise correspondences and shape interpolation between 3D shapes. To this end we combine the deep functional map framework with classical surface deformation models to map shapes in both spectral and spatial domains. On the one hand by incorporating spatial maps our method obtains more accurate and smooth point-wise correspondences compared to previous functional map methods for shape matching. On the other hand by introducing spectral maps our method gets rid of commonly used but computationally expensive geodesic distance constraints that are only valid for near-isometric shape deformations. Furthermore we propose a novel test-time adaptation scheme to capture both pose-dominant and shape-dominant deformations. Using different challenging datasets we demonstrate that our method outperforms previous state-of-the-art methods for both shape matching and interpolation even compared to supervised approaches.

A neural signed distance function (SDF) is a convenient shape representation for many tasks such as surface reconstruction editing and generation. However neural SDFs are difficult to fit to raw point clouds such as those sampled from the surface of a shape by a scanner. A major issue occurs when the shape's geometry is very different from the structural biases implicit in the network's initialization. In this case we observe that the standard loss formulation does not guide the network towards the correct SDF values. We circumvent this problem by introducing guiding points and use them to steer the optimization towards the true shape via small incremental changes for which the loss formulation has a good descent direction. We show that this point-guided homotopy-based optimization scheme facilitates a deformation from an easy problem to the difficult reconstruction problem. We also propose a metric to quantify the difference in surface geometry between a target shape and an initial surface which helps indicate whether the standard loss formulation is guiding towards the target shape. Our method outperforms previous state-of-the-art approaches with large improvements on shapes identified by this metric as particularly challenging.

Video Paragraph Grounding (VPG) is an emerging task in video-language understanding which aims at localizing multiple sentences with semantic relations and temporal order from an untrimmed video. However existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. In this work we introduce and explore Weakly-Supervised Video Paragraph Grounding (WSVPG) to eliminate the need of temporal annotations. Different from previous weakly-supervised grounding frameworks based on multiple instance learning or reconstruction learning for two-stage candidate ranking we propose a novel siamese learning framework that jointly learns the cross-modal feature alignment and temporal coordinate regression without timestamp labels to achieve concise one-stage localization for WSVPG. Specifically we devise a Siamese Grounding TRansformer (SiamGTR) consisting of two weight-sharing branches for learning complementary supervision. An Augmentation Branch is utilized for directly regressing the temporal boundaries of a complete paragraph within a pseudo video and an Inference Branch is designed to capture the order-guided feature correspondence for localizing multiple sentences in a normal video. We demonstrate by extensive experiments that our paradigm has superior practicability and flexibility to achieve efficient weakly-supervised or semi-supervised learning outperforming state-of-the-art methods trained with the same or stronger supervision.

In recent years there has been enormous interest in vision-language models trained using self-supervised objectives. However the use of large-scale datasets scraped from the web for training also makes these models vulnerable to potential security threats such as backdooring and poisoning attacks. In this paper we propose a method for mitigating such attacks on contrastively trained vision-language models. Our approach leverages external knowledge extracted from a language model to prevent models from learning correlations between image regions which lack strong alignment with external knowledge. We do this by imposing constraints to enforce that attention paid by the model to visual regions is proportional to the alignment of those regions with external knowledge. We conduct extensive experiments using a variety of recent backdooring and poisoning attacks on multiple datasets and architectures. Our results clearly demonstrate that our proposed approach is highly effective at defending against such attacks across multiple settings while maintaining model utility and without requiring any changes at inference time.

We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions. SAM presents strong generalizability to segment anything while is short for semantic understanding. By introducing a lightweight query-based feature mixer we align the region-specific features with the embedding space of language models for later caption generation. As the number of trainable parameters is small (typically in the order of tens of millions) it costs less computation less memory usage and less communication bandwidth resulting in both fast and scalable training. To address the scarcity problem of regional caption data we propose to first pre-train our model on objection detection and segmentation tasks. We call this step weak supervision pretraining since the pretraining data only contains category names instead of full-sentence descriptions. The weak supervision pretraining allows us to leverage many publicly available object detection and segmentation datasets. We conduct extensive experiments to demonstrate the superiority of our method and validate each design choice. This work serves as a stepping stone towards scaling up regional captioning data and sheds light on exploring efficient ways to augment SAM with regional semantics.

Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-vocabulary language grounding and segmentation but can suffer under false premises when queries imply the existence of something that is not actually present in the image. We observe that existing methods that fine-tune an LMM to segment images significantly degrade their ability to reliably determine ("see") if an object is present and to interact naturally with humans ("say") a form of catastrophic forgetting. In this work we propose a cascading and joint training approach for LMMs to solve this task avoiding catastrophic forgetting of previous skills. Our resulting model can "see" by detecting whether objects are present in an image "say" by telling the user if they are not proposing alternative queries or correcting semantic errors in the query and finally "segment" by outputting the mask of the desired objects if they exist. Additionally we introduce a novel False Premise Correction benchmark dataset an extension of existing RefCOCO(+/g) referring segmentation datasets (which we call FP-RefCOCO(+/g)). The results show that our method not only detects false premises up to 55% better than existing approaches but under false premise conditions produces relative cIOU improvements of more than 31% over baselines and produces natural language feedback judged helpful up to 67% of the time.

In recent years large-scale video-language pre-training (VidLP) has received considerable attention for its effectiveness in relevant tasks. In this paper we propose a novel action-centric VidLP framework that employs video tube features for temporal modeling and language features based on semantic role labeling (SRL). Our video encoder generates multiple tube features along object trajectories identifying action-related regions within videos to overcome the limitations of existing temporal attention mechanisms. Additionally our text encoder incorporates high-level action-related language knowledge previously underutilized in current VidLP models. The SRL captures action-verbs and related semantics among objects in sentences and enhances the ability to perform instance-level text matching thus enriching the cross-modal (CM) alignment process. We also introduce two novel pre-training objectives and a self-supervision strategy to produce a more faithful CM representation. Experimental results demonstrate that our method outperforms existing VidLP frameworks in various downstream tasks and datasets establishing our model a baseline in the modern VidLP framework.

We present SHINOBI an end-to-end framework for the reconstruction of shape material and illumination from object images captured with varying lighting pose and background. Inverse rendering of an object based on unconstrained image collections is a long-standing challenge in computer vision and graphics and requires a joint optimization over shape radiance and pose. We show that an implicit shape representation based on a multi-resolution hash encoding enables faster and robust shape reconstruction with joint camera alignment optimization that outperforms prior work. Further to enable the editing of illumination and object reflectance (i.e. material) we jointly optimize BRDF and illumination together with the object's shape. Our method is class-agnostic and works on in-the-wild image collections of objects to produce relightable 3D assets for several use cases such as AR/VR movies games etc.

Scene graphs have been recently introduced into 3D spatial understanding as a comprehensive representation of the scene. The alignment between 3D scene graphs is the first step of many downstream tasks such as scene graph aided point cloud registration mosaicking overlap checking and robot navigation. In this work we treat 3D scene graph alignment as a partial graph-matching problem and propose to solve it with a graph neural network. We reuse the geometric features learned by a point cloud registration method and associate the clustered point-level geometric features with the node-level semantic feature via our designed feature fusion module. Partial matching is enabled by using a learnable method to select the top-k similar node pairs. Subsequent downstream tasks such as point cloud registration are achieved by running a pre-trained registration network within the matched regions. We further propose a point-matching rescoring method that uses the node-wise alignment of the 3D scene graph to reweight the matching candidates from a pre-trained point cloud registration method. It reduces the false point correspondences estimated especially in low-overlapping cases. Experiments show that our method improves the alignment accuracy by 10 20% in low-overlap and random transformation scenarios and outperforms the existing work in multiple downstream tasks. Our code and models are available here (https://github.com/dfki-av/sg-pgm.git).

Adversarially robust knowledge distillation aims to compress large-scale models into lightweight models while preserving adversarial robustness and natural performance on a given dataset. Existing methods typically align probability distributions of natural and adversarial samples between teacher and student models but they overlook intermediate adversarial samples along the "adversarial path" formed by the multi-step gradient ascent of a sample towards the decision boundary. Such paths capture rich information about the decision boundary. In this paper we propose a novel adversarially robust knowledge distillation approach by incorporating such adversarial paths into the alignment process. Recognizing the diverse impacts of intermediate adversarial samples (ranging from benign to noisy) we propose an adaptive weighting strategy to selectively emphasize informative adversarial samples thus ensuring efficient utilization of lightweight model capacity. Moreover we propose a dual-branch mechanism exploiting two following insights: (i) complementary dynamics of adversarial paths obtained by targeted and untargeted adversarial learning and (ii) inherent differences between the gradient ascent path from class c_i towards the nearest class boundary and the gradient descent path from a specific class c_j towards the decision region of c_i (i \neq j). Comprehensive experiments demonstrate the effectiveness of our method on lightweight models under various settings.

Generalizable face anti-spoofing (FAS) approaches have drawn growing attention due to their robustness for diverse presentation attacks in unseen scenarios. Most previous methods always utilize domain generalization (DG) frameworks via directly aligning diverse source samples into a common feature space. However these methods neglect the hierarchical relations in FAS samples which may hinder the generalization ability by direct alignment. To address these issues we propose a novel Hierarchical Prototype-guided Distribution Refinement (HPDR) framework to learn embedding in hyperbolic space which facilitates the hierarchical relation construction. We also collaborate with prototype learning for hierarchical distribution refinement in hyperbolic space. In detail we propose the Hierarchical Prototype Learning to simultaneously guide domain alignment and improve the discriminative ability via constraining the multi-level relations between prototypes and instances in hyperbolic space. Moreover we design a Prototype-oriented Classifier which further considers relations between the sample and prototypes to improve the robustness of the final decision. Extensive experiments and visualizations demonstrate the effectiveness of our method against previous competitors.

This paper tackles the problem of creating relightable and animatable neural avatars from sparse-view (or monocular) videos of dynamic humans under unknown illumination. Previous neural human reconstruction methods produce animatable avatars from sparse views using deformed Signed Distance Fields (SDF) but are non-relightable. While differentiable inverse rendering methods have succeeded in the material recovery of static objects it is not straightforward to extend them to dynamic humans since it is computationally intensive to compute pixel-surface intersection and light visibility on deformed SDFs for relighting. To solve this challenge we propose a Hierarchical Distance Query (HDQ) algorithm to approximate the world space SDF under arbitrary human poses. Specifically we estimate coarse SDF based on a parametric human model and compute fine SDF by exploiting the invariance of SDF w.r.t. local deformation. Based on HDQ we leverage sphere tracing to efficiently estimate the surface intersection and light visibility. This allows us to develop the first system to recover relightable and animatable neural avatars from sparse or monocular inputs. Experiments show that our approach produces superior results compared to state-of-the-art methods. Our project page is available at https://zju3dv.github.io/relightable_avatar.

We propose a novel framework for real-time acquisition and reconstruction of temporally-varying 3D phenomena with high quality. The core of our framework is a deep neural network with an encoder that directly maps to the structured illumination during acquisition a decoder that predicts a 1D density distribution from single-pixel measurements under the optimized lighting and an aggregation module that combines the predicted densities for each camera into a single volume. It enables the automatic and joint optimization of physical acquisition and computational reconstruction and is flexible to adapt to different hardware configurations. The effectiveness of our framework is demonstrated on a lightweight setup with an off-the-shelf projector and one or multiple cameras achieving a performance of 40 volumes per second at a spatial resolution of 128^3. We compare favorably with state-of-the-art techniques in real and synthetic experiments and evaluate the impact of various factors over our pipeline.

Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding reasoning and interaction. However existing MLLMs prevalently suffer from serious hallucination problems generating text that is not factually grounded in associated images. The problem makes existing MLLMs untrustworthy and thus impractical in real-world (especially high-stakes) applications. To address the challenge we present RLHF-V which enhances MLLM trustworthiness via behavior alignment from fine-grained correctional human feedback. Specifically RLHF-V collects human preference in the form of segment-level corrections on hallucinations and performs dense direct preference optimization over the human feedback. Comprehensive experiments on five benchmarks in both automatic and human evaluation show that RLHF-V can enable substantially more trustworthy MLLM behaviors with promising data and computation efficiency. Remarkably using 1.4k annotated data samples RLHF-V significantly reduces the hallucination rate of the base MLLM by 34.8% outperforming the concurrent LLaVA-RLHF trained on 10k annotated data. The final model achieves state-of-the-art performance in trustworthiness among open-source MLLMs and shows better robustness than GPT-4V in preventing hallucinations aroused from over-generalization. All the data code and model weights will be released to facilitate future research.

Steganography is the art of hiding secret data into the cover media for covert communication. In recent years more and more deep neural network (DNN)-based steganographic schemes are proposed to train steganographic networks for secret embedding and recovery which are shown to be promising. Compared with the handcrafted steganographic tools steganographic networks tend to be large in size. It raises concerns on how to imperceptibly and effectively transmit these networks to the sender and receiver to facilitate the covert communication. To address this issue we propose in this paper a Purified and Unified Steganographic Network (PUSNet). It performs an ordinary machine learning task in a purified network which could be triggered into steganographic networks for secret embedding or recovery using different keys. We formulate the construction of the PUSNet into a sparse weight filling problem to flexibly switch between the purified and steganographic networks. We further instantiate our PUSNet as an image denoising network with two steganographic networks concealed for secret image embedding and recovery. Comprehensive experiments demonstrate that our PUSNet achieves good performance on secret image embedding secret image recovery and image denoising in a single architecture. It is also shown to be capable of imperceptibly carrying the steganographic networks in a purified network. Steganography is the art of hiding secret data into the cover media for covert communication. In recent years more and more deep neural network (DNN)-based steganographic schemes are proposed to train steganographic networks for secret embedding and recovery which are shown to be promising. Compared with the handcrafted steganographic tools steganographic networks tend to be large in size. It raises concerns on how to imperceptibly and effectively transmit these networks to the sender and receiver to facilitate the covert communication. To address this issue we propose in this paper a Purified and Unified Steganographic Network (PUSNet). It performs an ordinary machine learning task in a purified network which could be triggered into steganographic networks for secret embedding or recovery using different keys. We formulate the construction of the PUSNet into a sparse weight filling problem to flexibly switch between the purified and steganographic networks. We further instantiate our PUSNet as an image denoising network with two steganographic networks concealed for secret image embedding and recovery. Comprehensive experiments demonstrate that our PUSNet achieves good performance on secret image embedding secret image recovery and image denoising in a single architecture. It is also shown to be capable of imperceptibly carrying the steganographic networks in a purified network. Code is available at https://github.com/albblgb/PUSNet

Style transfer aims to render an image with the artistic features of a style image while maintaining the original structure. Various methods have been put forward for this task but some challenges still exist. For instance it is difficult for CNN-based methods to handle global information and long-range dependencies between input images for which transformer-based methods have been proposed. Although transformer can better model the relationship between content and style images they require high-cost hardware and time-consuming inference. To address these issues we design a novel transformer model that includes only encoders thus significantly reducing the computational cost. In addition we also find that existing style transfer methods may lead to images under-stylied or missing content. In order to achieve better stylization we design a content feature extractor and a style feature extractor. Then we can feed pure content and style images into the transformer. Finally we propose a network model termed Puff-Net i.e. efficient style transfer with pure content and style feature fusion network. Through qualitative and quantitative experiments we demonstrate the advantages of our model compared to state-of-the-art ones in the literature. The code is availabel at https://github.com/ZszYmy9/Puff-Net.