CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Our approach is trained alternatingly between the text and image branches: in one optimization step we predict the mean and standard deviation from the text description and sample from a standard Gaussian and in the other we sample using a (image) conditional sampler. Once trained we directly predict depth from the encoded text using the conditional sampler. We demonstrate our approach on indoor (NYUv2) and outdoor (KITTI) scenarios where we show that language can consistently improve performance in both. Code: https://github.com/Adonis-galaxy/WorDepth.
Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy. A promising strategy to overcome this challenge is wavefront modulation which induces measurement diversity during image acquisition. Despite its importance designing optimal wavefront modulations to image through scattering remains under-explored. This paper introduces a novel learning-based framework to address the gap. Our approach jointly optimizes wavefront modulations and a computationally lightweight feedforward "proxy" reconstruction network. This network is trained to recover scenes obscured by scattering using measurements that are modified by these modulations. The learned modulations produced by our framework generalize effectively to unseen scattering scenarios and exhibit remarkable versatility. During deployment the learned modulations can be decoupled from the proxy network to augment other more computationally expensive restoration algorithms. Through extensive experiments we demonstrate our approach significantly advances the state of the art in imaging through scattering media. Our project webpage is at https://wavemo-2024.github.io/.
Humans constantly interact with their surrounding environments. Current human-centric generative models mainly focus on synthesizing humans plausibly interacting with static scenes and objects while the dynamic human action-reaction synthesis for ubiquitous causal human-human interactions is less explored. Human-human interactions can be regarded as asymmetric with actors and reactors in atomic interaction periods. In this paper we comprehensively analyze the asymmetric dynamic synchronous and detailed nature of human-human interactions and propose the first multi-setting human action-reaction synthesis benchmark to generate human reactions conditioned on given human actions. To begin with we propose to annotate the actor-reactor order of the interaction sequences for the NTU120 InterHuman and Chi3D datasets. Based on them a diffusion-based generative model with a Transformer decoder architecture called ReGenNet together with an explicit distance-based interaction loss is proposed to predict human reactions in an online manner where the future states of actors are unavailable to reactors. Quantitative and qualitative results show that our method can generate instant and plausible human reactions compared to the baselines and can generalize to unseen actor motions and viewpoint changes.
Hand mesh reconstruction has attracted considerable attention in recent years with various approaches and techniques being proposed. Some of these methods incorporate complex components and designs which while effective may complicate the model and hinder efficiency. In this paper we decompose the mesh decoder into token generator and mesh regressor. Through extensive ablation experiments we found that the token generator should select discriminating and representative points while the mesh regressor needs to upsample sparse keypoints into dense meshes in multiple stages. Given these functionalities we can achieve high performance with minimal computational resources. Based on this observation we propose a simple yet effective baseline that outperforms state-of-the-art methods by a large margin while maintaining real-time efficiency. Our method outperforms existing solutions achieving state-of-the-art (SOTA) results across multiple datasets. On the FreiHAND dataset our approach produced a PA-MPJPE of 5.8mm and a PA-MPVPE of 6.1mm. Similarly on the DexYCB dataset we observed a PA-MPJPE of 5.5mm and a PA-MPVPE of 5.5mm.
Similarly on the DexYCB dataset we observed a PA-MPJPE of 5.5mm and a PA-MPVPE of 5.5mm. As for performance speed our method reached up to 33 frames per second (fps) when using HRNet and up to 70 fps when employing FastViT-MA36. Code will be made available.
In the realm of computer vision and graphics accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking registration texture transfer and statistical shape analysis. Moving beyond traditional hand-crafted and data-driven feature learning methods we incorporate spectral methods with deep learning focusing on functional maps (FMs) and optimal transport (OT). Traditional OT-based approaches often reliant on entropy regularization OT in learning-based framework face computational challenges due to their quadratic cost. Our key contribution is to employ the sliced Wasserstein distance (SWD) for OT which is a valid fast optimal transport metric in an unsupervised shape matching framework. This unsupervised framework integrates functional map regularizers with a novel OT-based loss derived from SWD enhancing feature alignment between shapes treated as discrete probability measures. We also introduce an adaptive refinement process utilizing entropy regularized OT further refining feature alignments for accurate point-to-point correspondences. Our method demonstrates superior performance in non-rigid shape matching including near-isometric and non-isometric scenarios and excels in downstream tasks like segmentation transfer. The empirical results on diverse datasets highlight our framework's effectiveness and generalization capabilities setting new standards in non-rigid shape matching with efficient OT metrics and an adaptive refinement module.
Recent advances in text-to-image generation have made remarkable progress in synthesizing realistic human photos conditioned on given text prompts. However existing personalized generation methods cannot simultaneously satisfy the requirements of high efficiency promising identity (ID) fidelity and flexible text controllability. In this work we introduce PhotoMaker an efficient personalized text-to-image generation method which mainly encodes an arbitrary number of input ID images into a stack ID embedding for preserving ID information. Such an embedding also empowers our method to be applied in many interesting scenarios such as when replacing the corresponding class word and when combining the characteristics of different identities. Besides to better drive the training of our PhotoMaker we propose an ID-oriented data creation pipeline to assemble the training data. Under the nourishment of the dataset constructed through the proposed pipeline our PhotoMaker demonstrates comparable performance to test-time fine-tuning-based methods yet provides significant speed improvements strong generalization capabilities and a wide range of applications.
We present Score-Guided Human Mesh Recovery (ScoreHMR) an approach for solving inverse problems for 3D human pose and shape reconstruction. These inverse problems involve fitting a human body model to image observations traditionally solved through optimization techniques. ScoreHMR mimics model fitting approaches but alignment with the image observation is achieved through score guidance in the latent space of a diffusion model. The diffusion model is trained to capture the conditional distribution of the human model parameters given an input image. By guiding its denoising process with a task-specific score ScoreHMR effectively solves inverse problems for various applications without the need for retraining the task-agnostic diffusion model. We evaluate our approach on three settings/applications. These are: (i) single-frame model fitting; (ii) reconstruction from multiple uncalibrated views; (iii) reconstructing humans in video sequences. ScoreHMR consistently outperforms all optimization baselines on popular benchmarks across all settings. We make our code and models available on the project website: https://statho.github.io/ScoreHMR.
Diffusion models have recently achieved remarkable progress in generating realistic images. However challenges remain in accurately understanding and synthesizing the layout requirements in the textual prompts. To align the generated image with layout instructions we present a training-free layout calibration system SimM that intervenes in the generative process on the fly during inference time. Specifically following a "check-locate-rectify" pipeline the system first analyses the prompt to generate the target layout and compares it with the intermediate outputs to automatically detect errors. Then by moving the located activations and making intra- and inter-map adjustments the rectification process can be performed with negligible computational overhead. To evaluate SimM over a range of layout requirements we present a benchmark SimMBench that compensates for the lack of superlative spatial relations in existing datasets. And both quantitative and qualitative results demonstrate the effectiveness of the proposed SimM in calibrating the layout inconsistencies. Our project page is at https://simm-t2i.github.io/SimM.
Unpaired image dehazing (UID) holds significant research importance due to the challenges in acquiring haze/clear image pairs with identical backgrounds. This paper proposes a novel method for UID named Orthogonal Decoupling Contrastive Regularization (ODCR). Our method is grounded in the assumption that an image consists of both haze-related features which influence the degree of haze and haze-unrelated features such as texture and semantic information. ODCR aims to ensure that the haze-related features of the dehazing result closely resemble those of the clear image while the haze-unrelated features align with the input hazy image. To accomplish the motivation Orthogonal MLPs optimized geometrically on the Stiefel manifold are proposed which can project image features into an orthogonal space thereby reducing the relevance between different features. Furthermore a task-driven Depth-wise Feature Classifier (DWFC) is proposed which assigns weights to the orthogonal features based on the contribution of each channel's feature in predicting whether the feature source is hazy or clear in a self-supervised fashion.
Furthermore a task-driven Depth-wise Feature Classifier (DWFC) is proposed which assigns weights to the orthogonal features based on the contribution of each channel's feature in predicting whether the feature source is hazy or clear in a self-supervised fashion. Finally a Weighted PatchNCE (WPNCE) loss is introduced to achieve the pulling of haze-related features in the output image toward those of clear images while bringing haze-unrelated features close to those of the hazy input. Extensive experiments demonstrate the superior performance of our ODCR method on UID.
Predicting 3D point trajectory is a fundamental learning task which commonly should be equivariant under Euclidean transformation e.g. SE(3). The existing equivariant models are commonly based on the group equivariant convolution equivariant message passing vector neuron frame averaging etc. In this paper we propose a novel pose-transformed equivariant network in which the points are firstly uniquely normalized and then transformed by the learned pose transformations upon which the points after motion are predicted and aggregated. Under each transformed pose we design the point position predictor consisting of multiple Pose-Transformed Points Prediction blocks in which the global and local motions are estimated and aggregated. This framework can be proven to be equivariant to SE(3) transformation over 3D points. We evaluate the pose-transformed equivariant network on extensive datasets including human motion capture molecular dynamics modeling and dynamics simulation. Extensive experimental comparisons demonstrated our SOTA performance compared with the existing equivariant networks for 3D point trajectory prediction.
Towards holistic understanding of 3D scenes a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories while also reflecting the inherent hierarchical structure. To achieve this we propose OmniSeg3D an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework which is accomplished by two steps. Firstly we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly image features rendered from the 3D feature field are clustered at different levels which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations this framework yields a global consistent 3D feature field which further enables hierarchical segmentation multi-object selection and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation.
Many problems in computer vision can be formulated as geometric estimation problems i.e. given a collection of measurements (e.g. point correspondences) we wish to fit a model (e.g. an essential matrix) that agrees with our observations. This necessitates some measure of how much an observation "agrees" with a given model. A natural choice is to consider the smallest perturbation that makes the observation exactly satisfy the constraints. However for many problems this metric is expensive or otherwise intractable to compute. The so-called Sampson error approximates this geometric error through a linearization scheme. For epipolar geometry the Sampson error is a popular choice and in practice known to yield very tight approximations of the corresponding geometric residual (the reprojection error). In this paper we revisit the Sampson approximation and provide new theoretical insights as to why and when this approximation works as well as provide explicit bounds on the tightness under some mild assumptions. Our theoretical results are validated in several experiments on real data and in the context of different geometric estimation tasks.
We introduce the Fixed Point Diffusion Model (FPDM) a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based generative modeling. Our approach embeds an implicit fixed point solving layer into the denoising network of a diffusion model transforming the diffusion process into a sequence of closely-related fixed point problems. Combined with a new stochastic training method this approach significantly reduces model size reduces memory usage and accelerates training. Moreover it enables the development of two new techniques to improve sampling efficiency: reallocating computation across timesteps and reusing fixed point solutions between timesteps. We conduct extensive experiments with state-of-the-art models on ImageNet FFHQ CelebA-HQ and LSUN-Church demonstrating substantial improvements in performance and efficiency. Compared to the state-of-the-art DiT model FPDM contains 87% fewer parameters consumes 60% less memory during training and improves image generation quality in situations where sampling computation or time is limited.
Learning from a limited amount of data namely Few-Shot Learning stands out as a challenging computer vision task. Several works exploit semantics and design complicated semantic fusion mechanisms to compensate for rare representative features within restricted data. However relying on naive semantics such as class names introduces biases due to their brevity while acquiring extensive semantics from external knowledge takes a huge time and effort. This limitation severely constrains the potential of semantics in Few-Shot Learning. In this paper we design an automatic way called Semantic Evolution to generate high-quality semantics. The incorporation of high-quality semantics alleviates the need for complex network structures and learning algorithms used in previous works. Hence we employ a simple two-layer network termed Semantic Alignment Network to transform semantics and visual features into robust class prototypes with rich discriminative features for few-shot classification. The experimental results show our framework outperforms all previous methods on six benchmarks demonstrating a simple network with high-quality semantics can beat intricate multi-modal modules on few-shot classification tasks. Code is available at https://github.com/zhangdoudou123/SemFew.
Defocus blur is a persistent problem in microscope imaging that poses harm to pathology interpretation and medical intervention in cell microscopy and microscope surgery. To address this problem a unified framework including the multi-pyramid transformer (MPT) and extended frequency contrastive regularization (EFCR) is proposed to tackle two outstanding challenges in microscopy deblur: longer attention span and data deficiency. The MPT employs an explicit pyramid structure at each network stage that integrates the cross-scale window attention (CSWA) the intra-scale channel attention (ISCA) and the feature-enhancing feed-forward network (FEFN) to capture long-range cross-scale spatial interaction and global channel context. The EFCR addresses the data deficiency problem by exploring latent deblur signals from different frequency bands. It also enables deblur knowledge transfer to learn cross-domain information from extra data improving deblur performance for labeled and unlabeled data. Extensive experiments and downstream task validation show the framework achieves state-of-the-art performance across multiple datasets. Project page: https://github.com/PieceZhang/MPT-CataBlur.
Training a linear classifier or lightweight model on top of pretrained vision model outputs so-called 'frozen features' leads to impressive performance on a number of downstream few-shot tasks. Currently frozen features are not modified during training. On the other hand when networks are trained directly on images data augmentation is a standard recipe that improves performance with no substantial overhead. In this paper we conduct an extensive pilot study on few-shot image classification that explores applying data augmentations in the frozen feature space dubbed 'frozen feature augmentation (FroFA)' covering twenty augmentations in total. Our study demonstrates that adopting a deceptively simple pointwise FroFA such as brightness can improve few-shot performance consistently across three network architectures three large pretraining datasets and eight transfer datasets.
Diffusion models (DMs) have achieved remarkable generative performance particularly with the introduction of stochastic differential equations (SDEs). Nevertheless a gap emerges in the model sampling trajectory constructed by reverse-SDE due to the accumulation of score estimation and discretization errors. This gap results in a residual in the generated images adversely impacting the image quality. To remedy this we propose a novel residual learning framework built upon a correction function. The optimized function enables to improve image quality via rectifying the sampling trajectory effectively. Importantly our framework exhibits transferable residual correction ability i.e. a correction function optimized for one pre-trained DM can also enhance the sampling trajectory constructed by other different DMs on the same dataset. Experimental results on four widely-used datasets demonstrate the effectiveness and transferable capability of our framework.
CLIP showcases exceptional cross-modal matching capabilities due to its training on image-text contrastive learning tasks. However without specific optimization for unimodal scenarios its performance in single-modality feature extraction might be suboptimal. Despite this some studies have directly used CLIP's image encoder for tasks like few-shot classification introducing a misalignment between its pre-training objectives and feature extraction methods. This inconsistency can diminish the quality of the image's feature representation adversely affecting CLIP's effectiveness in target tasks. In this paper we view text features as precise neighbors of image features in CLIP's space and present a novel CrOss-moDal nEighbor Representation (CODER) based on the distance structure between images and their neighbor texts. This feature extraction method aligns better with CLIP's pre-training objectives thereby fully leveraging CLIP's robust cross-modal capabilities. The key to construct a high-quality CODER lies in how to create a vast amount of high-quality and diverse texts to match with images. We introduce the Auto Text Generator (ATG) to automatically produce the required text in a data-free and training-free manner. We apply CODER to CLIP's zero-shot and few-shot image classification tasks.
We introduce the Auto Text Generator (ATG) to automatically produce the required text in a data-free and training-free manner. We apply CODER to CLIP's zero-shot and few-shot image classification tasks. Experiment results across various datasets and models confirm CODER's effectiveness. Code is available at: https://github.com/YCaigogogo/CVPR24-CODER.
In this paper we delve into a novel aspect of learning novel diffusion conditions with datasets an order of magnitude smaller. The rationale behind our approach is the elimination of textual constraints during the few-shot learning process. To that end we implement two optimization strategies. The first prompt-free conditional learning utilizes a prompt-free encoder derived from a pre-trained Stable Diffusion model. This strategy is designed to adapt new conditions to the diffusion process by minimizing the textual-visual correlation thereby ensuring a more precise alignment between the generated content and the specified conditions. The second strategy entails condition-specific negative rectification which addresses the inconsistencies typically brought about by Classifier-free guidance in few-shot training contexts. Our extensive experiments across a variety of condition modalities demonstrate the effectiveness and efficiency of our framework yielding results comparable to those obtained with datasets a thousand times larger. Our codes are available at https://github.com/Yuyan9Yu/BeyondTextConstraint.
Existing object recognition models have been shown to lack robustness in diverse geographical scenarios due to domain shifts in design and context. Class representations need to be adapted to more accurately reflect an object concept under these shifts. In the absence of training data from target geographies we hypothesize that geographically diverse descriptive knowledge of categories can enhance robustness. For this purpose we explore the feasibility of probing a large language model for geography-based object knowledge and we examine the effects of integrating knowledge into zero-shot and learnable soft prompting with CLIP. Within this exploration we propose geography knowledge regularization to ensure that soft prompts trained on a source set of geographies generalize to an unseen target set. Accuracy gains over prompting baselines on DollarStreet while training only on Europe data are up to +2.8/1.2/1.6 on target data from Africa/Asia/Americas and +4.6 overall on the hardest classes. Competitive performance is shown vs. few-shot target training and analysis is provided to direct future study of geographical robustness.