CVPR 2024 Papers

While image diffusion models have made significant progress in text-driven 3D content creation they often fail to accurately capture the intended meaning of text prompts especially for view information. This limitation leads to the Janus problem where multi-faced 3D models are generated under the guidance of such diffusion models. In this paper we propose a robust high-quality 3D content generation pipeline by exploiting orthogonal-view image guidance. First we introduce a novel 2D diffusion model that generates an image consisting of four orthogonal-view sub-images based on the given text prompt. Then the 3D content is created using this diffusion model. Notably the generated orthogonal-view image provides strong geometric structure priors and thus improves 3D consistency. As a result it effectively resolves the Janus problem and significantly enhances the quality of 3D content creation. Additionally we present a 3D synthesis fusion network that can further improve the details of the generated 3D contents. Both quantitative and qualitative evaluations demonstrate that our method surpasses previous text-to-3D techniques. Project page: https://efficientdreamer.github.io.

Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript which is time-consuming and hard to be parallelized while training. In this work we aim to escape from this inefficient alignment with massive but redundant frames and instead to directly localize a few action transitions for pseudo segmentation generation where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed.

We propose a simple strategy for masking image patches during visual-language contrastive learning that improves the quality of the learned representations and the training speed. During each iteration of training we randomly mask clusters of visually similar image patches as measured by their raw pixel intensities. This provides an extra learning signal beyond the contrastive training itself since it forces a model to predict words for masked visual structures solely from context. It also speeds up training by reducing the amount of data used in each image. We evaluate the effectiveness of our model by pre-training on a number of benchmarks finding that it outperforms other masking strategies such as FLIP on the quality of the learned representation.

Test-time adaptation with pre-trained vision-language models has attracted increasing attention for tackling distribution shifts during the test time. Though prior studies have achieved very promising performance they involve intensive computation which is severely unaligned with test-time adaptation. We design TDA a training-free dynamic adapter that enables effective and efficient test-time adaptation with vision-language models. TDA works with a lightweight key-value cache that maintains a dynamic queue with few-shot pseudo labels as values and the corresponding test-sample features as keys. Leveraging the key-value cache TDA allows adapting to test data gradually via progressive pseudo label refinement which is super-efficient without incurring any backpropagation. In addition we introduce negative pseudo labeling that alleviates the adverse impact of pseudo label noises by assigning pseudo labels to certain negative classes when the model is uncertain about its pseudo label predictions. Extensive experiments over two benchmarks demonstrate TDA's superior effectiveness and efficiency as compared with the state-of-the-art. The code has been released in https://kdiaaa.github.io/tda/.

The paradigm of pre-training and fine-tuning has laid the foundation for deploying deep learning models. However most fine-tuning methods are designed to meet a specific resource budget. Recently considering diverse deployment scenarios with various resource budgets SN-Net is introduced to quickly obtain numerous new networks (stitches) from the pre-trained models (anchors) in a model family via model stitching. Although promising SN-Net confronts new challenges when adapting it to new target domains including huge memory and storage requirements and a long and sub-optimal multistage adaptation process. In this work we present a novel framework Efficient Stitchable Task Adaptation (ESTA) to efficiently produce a palette of fine-tuned models that adhere to diverse resource constraints. Specifically we first tailor parameter-efficient fine-tuning to share low-rank updates among the stitches while maintaining independent bias terms. In this way we largely reduce fine-tuning memory burdens and mitigate the interference among stitches that arises in task adaptation. Furthermore we streamline a simple yet effective one-stage deployment pipeline which estimates the important stitches to deploy with training-time gradient statistics. By assigning higher sampling probabilities to important stitches we also get a boosted Pareto frontier. Extensive experiments on 25 downstream visual recognition tasks demonstrate that our ESTA is capable of generating stitches with smooth accuracy-efficiency trade-offs and surpasses the direct SN-Net adaptation by remarkable margins with significantly lower training time and fewer trainable parameters. Furthermore we demonstrate the flexibility and scalability of our ESTA framework by stitching LLMs from LLaMA family obtaining chatbot stitches of assorted sizes.

We revisit certain problems of pose estimation based on 3D--2D correspondences between features which may be points or lines. Specifically we address the two previously-studied minimal problems of estimating camera extrinsics from p \in \ 1 2 \ point--point correspondences and l=3-p line--line correspondences. To the best of our knowledge all of the previously-known practical solutions to these problems required computing the roots of degree \ge 4 (univariate) polynomials when p=2 or degree \ge 8 polynomials when p=1. We describe and implement two elementary solutions which reduce the degrees of the needed polynomials from 4 to 2 and from 8 to 4 respectively. We show experimentally that the resulting solvers are numerically stable and fast: when compared to the previous state-of-the art we may obtain nearly an order of magnitude speedup. The code is available at https://github.com/petrhruby97/efficient_absolute

Scene recovery the restoration of images degraded by adverse weather conditions presents significant challenges for existing methods. Physical models constrained by their inherent assumptions often fail when these assumptions are not met; Deep learning models are powerful they are limited by the diversity of their training datasets leading to poor generalization and high computational demands. To address these limitations we propose the Luminous Flux Prior (LFP) to recover degraded images under diverse adverse weather without learning. Luminous flux a physical measure that reflects image brightness has a rate of change that demonstrates a significant correlation with transmission. Consequently we leverage this rate of change in luminous flux as prior knowledge to estimate transmission which in turn assists in image recovery. This approach reduces dependency on physical parameters and enhances adaptability to various weather. Experimental validation under diverse conditions such as sandstorms underwater environments and haze attests to the robustness of LFP in restoring clear images. With a time complexity of \mathcal O (N\log N) LFP enables real-time recovery making it a suitable for devices with limited computational resources.

The recent success in revealing scene details from sparse 3D point clouds obtained via structure-from-motion has raised significant privacy concerns in visual localization. One prominent approach for mitigating this issue is to lift 3D points to 3D lines thereby reducing the effectiveness of the scene inversion attacks but this comes at the cost of increased algorithmic complexity for camera localization due to weaker geometric constraints induced by line clouds. To overcome this limitation we propose a new lifting approach called "ray cloud" whereby each lifted 3D line intersects at one of two predefined locations depicting omnidirectional rays from two cameras. This yields two benefits i) camera localization can now be cast as relative pose estimation between the query image and the calibrated rig of two perspective cameras which can be efficiently solved using a variant of the 5-point algorithm and ii) the ray cloud introduces erroneous estimations for the density-based inversion attack degrading the quality of scene recovery. Moreover we explore possible modifications of the inversion attack to better recover scenes from the ray clouds and propose a ray sampling technique to reduce the effectiveness of the modified attack. Experimental results on two public datasets show real-time localization speed as well as enhanced privacy-preserving capability over the state-of-the-art without overly sacrificing the localization accuracy.

Multi-task learning for dense prediction has emerged as a pivotal area in computer vision enabling simultaneous processing of diverse yet interrelated pixel-wise prediction tasks. However the substantial computational demands of state-of-the-art (SoTA) models often limit their widespread deployment. This paper addresses this challenge by introducing network binarization to compress resource-intensive multi-task dense predictors. Specifically our goal is to significantly accelerate multi-task dense prediction models via Binary Neural Networks (BNNs) while maintaining and even improving model performance at the same time. To reach this goal we propose a Binary Multi-task Dense Predictor Bi-MTDP and several variants of \bimtdp in which a multi-task dense predictor is constructed via specified binarized modules. Our systematical analysis of this predictor reveals that performance drop from binarization is primarily caused by severe information degradation. To address this issue we introduce a deep information bottleneck layer that enforces representations for downstream tasks satisfying Gaussian distribution in forward propagation. Moreover we introduce a knowledge distillation mechanism to correct the direction of information flow in backward propagation. Intriguingly one variant of Bi-MTDP outperforms full-precision (FP) multi-task dense prediction SoTAs ARTC (CNN-based) and InvPT (ViT-based). This result indicates that Bi-MTDP is not merely a naive trade-off between performance and efficiency but is rather a benefit of the redundant information flow thanks to the multi-task architecture.

Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however in the context of deep learning existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and deep semantics but also manually generate low-resolution pairs of images that do not have sufficient confidence. In this work we propose a multi-scale network based on single-input and multiple-outputs(SIMO) for motion deblurring. This simplifies the complexity of algorithms based on a coarse-to-fine scheme. To alleviate restoration defects impacting detail information brought about by using a multi-scale architecture we combine the characteristics of real-world blurring trajectories with a learnable wavelet transform module to focus on the directional continuity and frequency features of the step-by-step transitions between blurred images to sharp images. In conclusion we propose a multi-scale network with a learnable discrete wavelet transform (MLWNet) which exhibits state-of-the-art performance on multiple real-world deblurred datasets in terms of both subjective and objective quality as well as computational efficiency.

With the escalating complexity and investment cost of training deep neural networks safeguarding them from unauthorized usage and intellectual property theft has become imperative. Especially the rampant misuse of prediction APIs to replicate models without access to the original data or architecture poses grave security threats. Diverse defense strategies have emerged to address these vulnerabilities yet these defenses either incur heavy inference overheads or assume idealized attack scenarios. To address these challenges we revisit the utilization of noise transition matrix as an efficient perturbation technique which injects noise into predicted posteriors in a linear manner and integrates seamlessly into existing systems with minimal overhead for model stealing defense. Provably with such perturbed posteriors the attacker's cloning process degrades into learning from noisy data. Toward optimizing the noise transition matrix we proposed a novel bi-level optimization training framework which performs fidelity on the victim model while the surrogate model adversarially. Comprehensive experimental results demonstrate that our method effectively thwarts model stealing attacks and achieves minimal utility trade-offs outperforming existing state-of-the-art defenses.

In this paper we explore the problem of event-based meshflow estimation a novel task that involves predicting a spatially smooth sparse motion field from event cameras. To start we generate a large-scale High-Resolution Event Meshflow (HREM) dataset which showcases its superiority by encompassing the merits of high resolution at 1280x720 handling dynamic objects and complex motion patterns and offering both optical flow and meshflow labels. These aspects have not been fully explored in previous works. Besides we propose Efficient Event-based MeshFlow (EEMFlow) network a lightweight model featuring a specially crafted encoder-decoder architecture to facilitate swift and accurate meshflow estimation. Furthermore we upgrade EEMFlow network to support dense event optical flow in which a Confidence-induced Detail Completion (CDC) module is proposed to preserve sharp motion boundaries. We conduct comprehensive experiments to show the exceptional performance and runtime efficiency (39x faster) of our EEMFlow model compared to recent state-of-the-art flow methods. Our code is available at https://github.com/boomluo02/EEMFlow.

We present a novel method for efficiently producing semi-dense matches across images. Previous detector-free matcher LoFTR has shown remarkable matching capability in handling large-viewpoint change and texture-poor scenarios but suffers from low efficiency. We revisit its design choices and derive multiple improvements for both efficiency and accuracy. One key observation is that performing the transformer over the entire feature map is redundant due to shared local information therefore we propose an aggregated attention mechanism with adaptive token selection for efficiency. Furthermore we find spatial variance exists in LoFTR's fine correlation module which is adverse to matching accuracy. A novel two-stage correlation layer is proposed to achieve accurate subpixel correspondences for accuracy improvement. Our efficiency optimized model is ~ 2.5xfaster than LoFTR which can even surpass state-of-the-art efficient sparse matching pipeline SuperPoint + LightGlue. Moreover extensive experiments show that our method can achieve higher accuracy compared with competitive semi-dense matchers with considerable efficiency benefits. This opens up exciting prospects for large-scale or latency-sensitive applications such as image retrieval and 3D reconstruction. Project page: https://zju3dv.github.io/efficientloftr/.

Hyperparameter Optimization and Neural Architecture Search are powerful in attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing out as a mainstream method. Extending BO into the multi-fidelity setting has been an emerging research topic in this field but faces the challenge of determining an appropriate fidelity for each hyperparameter configuration to fit the surrogate model. To tackle the challenge we propose a multi-fidelity BO method named FastBO which excels in adaptively deciding the fidelity for each configuration and providing strong performance while ensuring efficient resource usage. These advantages are achieved through our proposed techniques based on the concepts of efficient point and saturation point for each configuration which can be obtained from the empirical learning curve of the configuration estimated from early observations. Extensive experiments demonstrate FastBO's superior anytime performance and efficiency in identifying high-quality configurations and architectures. We also show that our method provides a way to extend any single-fidelity method to the multi-fidelity setting highlighting the wide applicability of our approach.

Group synchronization plays a crucial role in global pipelines for Structure from Motion (SfM). Its formulation is nonconvex and it is faced with highly corrupted measurements. Cycle consistency has been effective in addressing these challenges. However computationally efficient solutions are needed for cycles longer than three especially in practical scenarios where 3-cycles are unavailable. To overcome this computational bottleneck we propose an algorithm for group synchronization that leverages information from cycles of lengths ranging from three to six with a complexity of O(n^3) (or O(n^ 2.373 ) when using a faster matrix multiplication algorithm). We establish non-trivial theory for this and related methods that achieves competitive sample complexity assuming the uniform corruption model. To advocate the practical need for our method we consider distributed group synchronization which requires at least 4-cycles and we illustrate state-of-the-art performance by our method in this context.

We introduce Deformable Convolution v4 (DCNv4) a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of its predecessor DCNv3 with two key enhancements: 1. removing softmax normalization in spatial aggregation to enhance its dynamic property and expressive power and 2. optimizing memory access to minimize redundant operations for speedup. These improvements result in a significantly faster convergence compared to DCNv3 and a substantial increase in processing speed with DCNv4 achieving more than three times the forward speed. DCNv4 demonstrates exceptional performance across various tasks including image classification instance and semantic segmentation and notably image generation. When integrated into generative models like U-Net in the latent diffusion model DCNv4 outperforms its baseline underscoring its possibility to enhance generative models. In practical applications replacing DCNv3 with DCNv4 in the InternImage model to create FlashInternImage results in up to 80% speed increase and further performance improvement without further modifications. The advancements in speed and efficiency of DCNv4 combined with its robust performance across diverse vision tasks show its potential as a foundational building block for future vision models.

Dataset distillation reduces the storage and computational consumption of training a network by generating a small surrogate dataset that encapsulates rich information of the original large-scale one. However previous distillation methods heavily rely on the sample-wise iterative optimization scheme. As the images-per-class (IPC) setting or image resolution grows larger the necessary computation will demand overwhelming time and resources. In this work we intend to incorporate generative diffusion techniques for computing the surrogate dataset. Observing that key factors for constructing an effective surrogate dataset are representativeness and diversity we design additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models. We present a theoretical model of the process as hierarchical diffusion control demonstrating the flexibility of the diffusion process to target these criteria without jeopardizing the faithfulness of the sample to the desired distribution. The proposed method achieves state-of-the-art validation performance while demanding much less computational resources. Under the 100-IPC setting on ImageWoof our method requires less than one-twentieth the distillation time of previous methods yet yields even better performance. Source code and generated data are available in https://github.com/vimar-gu/MinimaxDiffusion.

3D head avatars built with neural implicit volumetric representations have achieved unprecedented levels of photorealism. However the computational cost of these methods remains a significant barrier to their widespread adoption particularly in real-time applications such as virtual reality and teleconferencing. While attempts have been made to develop fast neural rendering approaches for static scenes these methods cannot be simply employed to support realistic facial expressions such as in the case of a dynamic facial performance. To address these challenges we propose a novel fast 3D neural implicit head avatar model that achieves real-time rendering while maintaining fine-grained controllability and high rendering quality. Our key idea lies in the introduction of local hash table blendshapes which are learned and attached to the vertices of an underlying face parametric model. These per-vertex hash-tables are linearly merged with weights predicted via a CNN resulting in expression dependent embeddings. Our novel representation enables efficient density and color predictions using a lightweight MLP which is further accelerated by a hierarchical nearest neighbor search method. Extensive experiments show that our approach runs in real-time while achieving comparable rendering quality to state-of-the-arts and decent results on challenging expressions.

Image-based mirror detection has recently undergone rapid research due to its significance in applications such as robotic navigation semantic segmentation and scene reconstruction. Recently VMD-Net was proposed as the first video mirror detection technique by modeling dual correspondences between the inside and outside of the mirror both spatially and temporally. However this approach is not reliable as correspondences can occur completely inside or outside of the mirrors. In addition the proposed dataset VMD-D contains many small mirrors limiting its applicability to real-world scenarios. To address these problems we developed a more challenging dataset that includes mirrors of various shapes and sizes at different locations of the frames providing a better reflection of real-world scenarios. Next we observed that the motions between the inside and outside of the mirror are often inconsistent. For instance when moving in front of a mirror the motion inside the mirror is often much smaller than the motion outside due to increased depth perception. With these observations we propose modeling inconsistent motion cues to detect mirrors and a new network with two novel modules. The Motion Attention Module (MAM) explicitly models inconsistent motions around mirrors via optical flow and the Motion-Guided Edge Detection Module (MEDM) uses motions to guide mirror edge feature learning. Experimental results on our proposed dataset show that our method outperforms state-of-the-arts. The code and dataset are available at https://github.com/AlexAnthonyWarren/MG-VMD.