CVPR 2024 Papers

Current metrics for text-to-image models typically rely on statistical metrics which inadequately represent the real preference of humans. Although recent work attempts to learn these preferences via human annotated images they reduce the rich tapestry of human preference to a single overall score. However the preference results vary when humans evaluate images with different aspects. Therefore to learn the multi-dimensional human preferences we propose the Multi-dimensional Preference Score (MPS) the first multi-dimensional preference scoring model for the evaluation of text-to-image models. The MPS introduces the preference condition module upon CLIP model to learn these diverse preferences. It is trained based on our Multi-dimensional Human Preference (MHP) Dataset which comprises 918315 human preference choices across four dimensions (i.e. aesthetics semantic alignment detail quality and overall assessment) on 607541 images. The images are generated by a wide range of latest text-to-image models. The MPS outperforms existing scoring methods across 3 datasets in 4 dimensions enabling it a promising metric for evaluating and improving text-to-image generation. The model and dataset will be made publicly available to facilitate future research.

As the mainstream technique for capturing images of biological specimens at nanometer resolution electron microscopy (EM) is extremely time-consuming for scanning wide field-of-view (FOV) specimens. In this paper we investigate a challenging task of large-factor EM image super-resolution (EMSR) which holds great promise for reducing scanning time relaxing acquisition conditions and expanding imaging FOV. By exploiting the repetitive structures and volumetric coherence of EM images we propose the first generative learning-based framework for large-factor EMSR. Specifically motivated by the predictability of repetitive structures and textures in EM images we first learn a discrete codebook in the latent space to represent high-resolution (HR) cell-specific priors and a latent vector indexer to map low-resolution (LR) EM images to their corresponding latent vectors in a generative manner. By incorporating the generative cell-specific priors from HR EM images through a multi-scale prior fusion module we then deploy multi-image feature alignment and fusion to further exploit the inter-section coherence in the volumetric EM data. Extensive experiments demonstrate that our proposed framework outperforms advanced single-image and video super-resolution methods for 8x and 16x EMSR (i.e. with 64 times and 256 times less data acquired respectively) achieving superior visual reconstruction quality and downstream segmentation accuracy on benchmark EM datasets. Code is available at https://github.com/jtshou/GPEMSR.

Geometric knowledge has been shown to be beneficial for the stereo matching task. However prior attempts to integrate geometric insights into stereo matching algorithms have largely focused on geometric knowledge from single images while crucial cross-view factors such as occlusion and matching uniqueness have been overlooked. To address this gap we propose a novel Intra-view and Cross-view Geometric knowledge learning Network (ICGNet) specifically crafted to assimilate both intra-view and cross-view geometric knowledge. ICGNet harnesses the power of interest points to serve as a channel for intra-view geometric understanding. Simultaneously it employs the correspondences among these points to capture cross-view geometric relationships. This dual incorporation empowers the proposed ICGNet to leverage both intra-view and cross-view geometric knowledge in its learning process substantially improving its ability to estimate disparities. Our extensive experiments demonstrate the superiority of the ICGNet over contemporary leading models. The code will be available at https://github.com/DFSDDDDD1199/ICGNet.

Multi-instance point cloud registration estimates the poses of multiple instances of a model point cloud in a scene point cloud. Extracting accurate point correspondences is to the center of the problem. Existing approaches usually treat the scene point cloud as a whole overlooking the separation of instances. Therefore point features could be easily polluted by other points from the background or different instances leading to inaccurate correspondences oblivious to separate instances especially in cluttered scenes. In this work we propose MIRETR Multi-Instance REgistration TRansformer a coarse-to-fine approach to the extraction of instance-aware correspondences. At the coarse level it jointly learns instance-aware superpoint features and predicts per-instance masks. With instance masks the influence from outside of the instance being concerned is minimized such that highly reliable superpoint correspondences can be extracted. The superpoint correspondences are then extended to instance candidates at the fine level according to the instance masks. At last an efficient candidate selection and refinement algorithm is devised to obtain the final registrations. Extensive experiments on three public benchmarks demonstrate the efficacy of our approach. In particular MIRETR outperforms the state of the arts by 16.6 points on F1 score on the challenging ROBI benchmark. Code and models are available at https://github.com/zhiyuanYU134/MIRETR

Colorizing line art is a pivotal task in the production of hand-drawn cel animation. This typically involves digital painters using a paint bucket tool to manually color each segment enclosed by lines based on RGB values predetermined by a color designer. This frame-by-frame process is both arduous and time-intensive. Current automated methods mainly focus on segment matching. This technique migrates colors from a reference to the target frame by aligning features within line-enclosed segments across frames. However issues like occlusion and wrinkles in animations often disrupt these direct correspondences leading to mismatches. In this work we introduce a new learning-based inclusion matching pipeline which directs the network to comprehend the inclusion relationships between segments rather than relying solely on direct visual correspondences. Our method features a two-stage pipeline that integrates a coarse color warping module with an inclusion matching module enabling more nuanced and accurate colorization. To facilitate the training of our network we also develope a unique dataset referred to as PaintBucket-Character. This dataset includes rendered line arts alongside their colorized counterparts featuring various 3D characters. Extensive experiments demonstrate the effectiveness and superiority of our method over existing techniques.

This paper proposes Group Activity Feature (GAF) learning in which features of multi-person activity are learned as a compact latent vector. Unlike prior work in which the manual annotation of group activities is required for supervised learning our method learns the GAF through person attribute prediction without group activity annotations. By learning the whole network in an end-to-end manner so that the GAF is required for predicting the person attributes of people in a group the GAF is trained as the features of multi-person activity. As a person attribute we propose to use a person's action class and appearance features because the former is easy to annotate due to its simpleness and the latter requires no manual annotation. In addition we introduce a location-guided attribute prediction to disentangle the complex GAF for extracting the features of each target person properly. Various experimental results validate that our method outperforms SOTA methods quantitatively and qualitatively on two public datasets. Visualization of our GAF also demonstrates that our method learns the GAF representing fined-grained group activity classes. Code: https://github.com/chihina/GAFL-CVPR2024.

Online continual learning suffers from an underfitted solution due to insufficient training for prompt model updates (e.g. single-epoch training). To address the challenge we propose an efficient online continual learning method using the neural collapse phenomenon. In particular we induce neural collapse to form a simplex equiangular tight frame (ETF) structure in the representation space so that the continuously learned model with a single epoch can better fit to the streamed data by proposing preparatory data training and residual correction in the representation space. With an extensive set of empirical validations using CIFAR-10/100 TinyImageNet ImageNet-200 and ImageNet-1K we show that our proposed method outperforms state-of-the-art methods by a noticeable margin in various online continual learning scenarios such as disjoint and Gaussian scheduled continuous (i.e. boundary-free) data setups.

Recent works in implicit representations such as Neural Radiance Fields (NeRF) have advanced the generation of realistic and animatable head avatars from video sequences. These implicit methods are still confronted by visual artifacts and jitters since the lack of explicit geometric constraints poses a fundamental challenge in accurately modeling complex facial deformations. In this paper we introduce Dynamic Tetrahedra (DynTet) a novel hybrid representation that encodes explicit dynamic meshes by neural networks to ensure geometric consistency across various motions and viewpoints. DynTet is parameterized by the coordinate-based networks which learn signed distance deformation and material texture anchoring the training data into a predefined tetrahedra grid. Leveraging Marching Tetrahedra DynTet efficiently decodes textured meshes with a consistent topology enabling fast rendering through a differentiable rasterizer and supervision via a pixel loss. To enhance training efficiency we incorporate classical 3D Morphable Models to facilitate geometry learning and define a canonical space for simplifying texture learning. These advantages are readily achievable owing to the effective geometric representation employed in DynTet. Compared with prior works DynTet demonstrates significant improvements in fidelity lip synchronization and real-time performance according to various metrics. Beyond producing stable and visually appealing synthesis videos our method also outputs the dynamic meshes which is promising to enable many emerging applications. Code is available at https://github.com/zhangzc21/DynTet.

This study focuses on a novel task in text-to-image (T2I) generation namely action customization. The objective of this task is to learn the co-existing action from limited data and generalize it to unseen humans or even animals. Experimental results show that existing subject-driven customization methods fail to learn the representative characteristics of actions and struggle in decoupling actions from context features including appearance. To overcome the preference for low-level features and the entanglement of high-level features we propose an inversion-based method Action-Disentangled Identifier (ADI) to learn action-specific identifiers from the exemplar images. ADI first expands the semantic conditioning space by introducing layer-wise identifier tokens thereby increasing the representational richness while distributing the inversion across different features. Then to block the inversion of action-agnostic features ADI extracts the gradient invariance from the constructed sample triples and masks the updates of irrelevant channels. To comprehensively evaluate the task we present an ActionBench that includes a variety of actions each accompanied by meticulously selected samples. Both quantitative and qualitative results show that our ADI outperforms existing baselines in action-customized T2I generation. Our project page is at https://adi-t2i.github.io/ADI.

Label noise commonly found in real-world datasets has a detrimental impact on a model's generalization. To effectively detect incorrectly labeled instances previous works have mostly relied on distinguishable training signals such as training loss as indicators to differentiate between clean and noisy labels. However they have limitations in that the training signals incompletely reveal the model's behavior and are not effectively generalized to various noise types resulting in limited detection accuracy. In this paper we propose DynaCor framework that distinguishes incorrectly labeled instances from correctly labeled ones based on the dynamics of the training signals. To cope with the absence of supervision for clean and noisy labels DynaCor first introduces a label corruption strategy that augments the original dataset with intentionally corrupted labels enabling indirect simulation of the model's behavior on noisy labels. Then DynaCor learns to identify clean and noisy instances by inducing two clearly distinguishable clusters from the latent representations of training dynamics. Our comprehensive experiments show that DynaCor outperforms the state-of-the-art competitors and shows strong robustness to various noise types and noise rates.

Diffusion Models have shown remarkable performance in image generation tasks which are capable of generating diverse and realistic image content. When adopting diffusion models for image restoration the crucial challenge lies in how to preserve high-level image fidelity in the randomness diffusion process and generate accurate background structures and realistic texture details. In this paper we propose a general framework and develop a Diffusion Texture Prior Model (DTPM) for image restoration tasks. DTPM explicitly models high-quality texture details through the diffusion process rather than global contextual content. In phase one of the training stage we pre-train DTPM on approximately 55K high-quality image samples after which we freeze most of its parameters. In phase two we insert conditional guidance adapters into DTPM and equip it with an initial predictor thereby facilitating its rapid adaptation to downstream image restoration tasks. Our DTPM could mitigate the randomness of traditional diffusion models by utilizing encapsulated rich and diverse texture knowledge and background structural information provided by the initial predictor during the sampling process. Our comprehensive evaluations of five image restoration tasks demonstrate DTPM's superiority over existing regression and diffusion-based image restoration methods in perceptual quality and its exceptional generalization capabilities.

Blind face restoration focuses on restoring high-fidelity details from images subjected to complex and unknown degradations while preserving identity information. In this paper we present a Prior-based Latent Transformation approach (PLTrans) which is specifically designed to learn a degradation-unaware representation thereby allowing the restoration network to effectively generalize to real-world degradation. Toward this end PLTrans learns a degradation-unaware query via a latent diffusion-based regularization module. Furthermore conditioned on the features of a degraded face image a latent dictionary that captures the priors of HQ face images is leveraged to refine the features by mapping the top-d nearest elements. The refined version will be used to build key and value for the cross-attention computation which is tailored to each degraded image and exhibits reduced sensitivity to different degradation factors. Conditioned on the resulting representation we train a decoding network that synthesizes face images with authentic details and identity preservation. Through extensive experiments we verify the effectiveness of the design elements and demonstrate the generalization ability of our proposed approach for both synthetic and unknown degradations. We finally demonstrate the applicability of PLTrans in other vision tasks.

Image signal processing (ISP) pipeline plays a fundamental role in digital cameras which converts raw Bayer sensor data to RGB images. However ISP-generated images usually suffer from imperfections due to the compounded degradations that stem from sensor noises demosaicing noises compression artifacts and possibly adverse effects of erroneous ISP hyperparameter settings such as ISO and gamma values. In a general sense these ISP imperfections can be considered as degradations. The highly complex mechanisms of ISP degradations some of which are even unknown pose great challenges to the generalization capability of deep neural networks (DNN) for image restoration and to their adaptability to downstream tasks. To tackle the issues we propose a novel DNN approach to learn degradation-independent representations (DiR) through the refinement of a self-supervised learned baseline representation. The proposed DiR learning technique has remarkable domain generalization capability and consequently it outperforms state-of-the-art methods across various downstream tasks including blind image restoration object detection and instance segmentation as verified in our experiments.

The difficulty of acquiring high-resolution (HR) and low-resolution (LR) image pairs in real scenarios limits the performance of existing learning-based image super-resolution (SR) methods in the real world. To conduct training on real-world unpaired data current methods focus on synthesizing pseudo LR images to associate unpaired images. However the realness and diversity of pseudo LR images are vulnerable due to the large image space. In this paper we circumvent the difficulty of image generation and propose an alternative to build the connection between unpaired images in a compact proxy space. Specifically we first construct coupled HR and LR dictionaries and then encode HR and LR images into a common latent code space using these dictionaries. In addition we develop an autoencoder-based framework to couple these dictionaries during optimization by reconstructing input HR and LR images. The coupled dictionaries enable our method to employ a shallow network architecture with only 18 layers to achieve efficient image SR. Extensive experiments show that our method (DictSR) can effectively model the LR-to-HR mapping in coupled dictionaries and produces state-of-the-art performance on benchmark datasets.

We introduce a new attention mechanism dubbed structural self-attention (StructSA) that leverages rich correlation patterns naturally emerging in key-query interactions of attention. StructSA generates attention maps by recognizing space-time structures of key-query correlations via convolution and uses them to dynamically aggregate local contexts of value features. This effectively leverages rich structural patterns in images and videos such as scene layouts object motion and inter-object relations.Using StructSA as a main building block we develop the structural vision transformer (StructViT) and evaluate its effectiveness on both image and video classification tasks achieving state-of-the-art results on ImageNet-1K Kinetics-400 Something-Something V1 & V2 Diving-48 and FineGym.

Current controls over diffusion models (e.g. through text or ControlNet) for image generation fall short in recognizing abstract continuous attributes like illumination direction or non-rigid shape change. In this paper we present an approach for allowing users of text-to-image models to have fine-grained control of several attributes in an image. We do this by engineering special sets of input tokens that can be transformed in a continuous manner we call them Continuous 3D Words. These attributes can for example be represented as sliders and applied jointly with text prompts for fine-grained control over image generation. Given only a single mesh and a rendering engine we show that our approach can be adopted to provide continuous user control over several 3D-aware attributes including time-of-day illumination bird wing orientation dollyzoom effect and object poses. Our method is capable of conditioning image creation with multiple Continuous 3D Words and text descriptions simultaneously while adding no overhead to the generative process.

Lifelong Person Re-identification (L-ReID) aims to learn from sequentially collected data to match a person across different scenes. Once an L-ReID model is updated using new data all historical images in the gallery are required to be re-calculated to obtain new features for testing known as "re-indexing". However it is infeasible when raw images in the gallery are unavailable due to data privacy concerns resulting in incompatible retrieval between the query and the gallery features calculated by different models which causes significant performance degradation. In this paper we focus on a new task called Re-indexing Free Lifelong Person Re-identification (RFL-ReID) which requires achieving effective L-ReID without re-indexing raw images in the gallery. To this end we propose a Continual Compatible Representation (C2R) method which facilitates the query feature calculated by the continuously updated model to effectively retrieve the gallery feature calculated by the old model in a compatible manner. Specifically we design a Continual Compatible Transfer (CCT) network to continuously transfer and consolidate the old gallery feature into the new feature space. Besides a Balanced Compatible Distillation module is introduced to achieve compatibility by aligning the transferred feature space with the new feature space. Finally a Balanced Anti-forgetting Distillation module is proposed to eliminate the accumulated forgetting of old knowledge during the continual compatible transfer. Extensive experiments on several benchmark L-ReID datasets demonstrate the effectiveness of our method against state-of-the-art methods for both RFL-ReID and L-ReID tasks. The source code of this paper is available at https://github.com/PKU-ICST-MIPL/C2R_CVPR2024.

Most domain adaptation (DA) methods are based on either a convolutional neural networks (CNNs) or a vision transformers (ViTs). They align the distribution differences between domains as encoders without considering their unique characteristics. For instance ViT excels in accuracy due to its superior ability to capture global representations while CNN has an advantage in capturing local representations. This fact has led us to design a hybrid method to fully take advantage of both ViT and CNN called Explicitly Class-specific Boundaries (ECB). ECB learns CNN on ViT to combine their distinct strengths. In particular we leverage ViT's properties to explicitly find class-specific decision boundaries by maximizing the discrepancy between the outputs of the two classifiers to detect target samples far from the source support. In contrast the CNN encoder clusters target features based on the previously defined class-specific boundaries by minimizing the discrepancy between the probabilities of the two classifiers. Finally ViT and CNN mutually exchange knowledge to improve the quality of pseudo labels and reduce the knowledge discrepancies of these models. Compared to conventional DA methods our ECB achieves superior performance which verifies its effectiveness in this hybrid model. The project website can be found https://dotrannhattuong.github.io/ECB/website/.

Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection significantly generalizing the powerful capabilities of the detector to identify more unknown object categories. However these methods face significant challenges in background interpretation and model overfitting and thus often result in the loss of crucial background knowledge giving rise to sub-optimal inference performance of the detector. To mitigate these issues we present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge thus enhancing the detection performance w.r.t. base and novel categories. Specifically we devise three modules: Background Category-specific Prompt Background Object Discovery and Inference Probability Rectification to empower the detector to discover represent and leverage implicit object knowledge explored from background proposals. Evaluation on two benchmark datasets OV-COCO and OV-LVIS demonstrates the superiority of our proposed method over existing state-of-the-art approaches in handling the OVD tasks.