CVPR 2024 论文列表

How do two sets of images differ? Discerning set-level differences is crucial for understanding model behaviors and analyzing datasets yet manually sifting through thousands of images is impractical. To aid in this discovery process we explore the task of automatically describing the differences between two sets of images which we term Set Difference Captioning. This task takes in image sets \mathcal D _A and \mathcal D _B and outputs a description that is more often true on \mathcal D _A than \mathcal D _B. We outline a two-stage approach that first proposes candidate difference descriptions from image sets and then re-ranks the candidates by checking how well they can differentiate the two sets. We introduce VisDiff which first captions the images and prompts a language model to propose candidate descriptions then re-ranks these descriptions using CLIP. To evaluate VisDiff we collect VisDiffBench a dataset with 187 paired image sets with ground truth difference descriptions. We apply VisDiff to various domains such as comparing datasets (e.g. ImageNet vs. ImageNetV2) comparing classification models (e.g. zero-shot CLIP vs. supervised ResNet) characterizing differences between generative models (e.g. StableDiffusionV1 and V2) and discovering what makes images memorable. Using VisDiff we are able to find interesting and previously unknown differences in datasets and models demonstrating its utility in revealing nuanced insights.

Concealed Object Detection (COD) aims to identify objects visually embedded in their background. Existing COD datasets and methods predominantly focus on animals or humans ignoring the agricultural domain which often contains numerous small and concealed crops with severe occlusions. In this paper we introduce Concealed Crop Detection (CCD) which extends classic COD to agricultural domains. Experimental study shows that unimodal data provides insufficient information for CCD. To address this gap we first collect a large-scale RGB-D dataset ACOD-12K containing high-resolution crop images and depth maps. Then we propose a foundational framework named Recurrent Iterative Segmentation Network (RISNet). To tackle the challenge of dense objects we employ multi-scale receptive fields to capture objects of varying sizes thus enhancing the detection performance for dense objects. By fusing depth features our method can acquire spatial information about concealed objects to mitigate disturbances caused by intricate backgrounds and occlusions. Furthermore our model adopts a multi-stage iterative approach using predictions from each stage as gate attention to reinforce position information thereby improving the detection accuracy for small objects. Extensive experimental results demonstrate that our RISNet achieves new state-of-the-art performance on both newly proposed CCD and classic COD tasks. All resources will be available at https://github.com/Kki2Eve/RISNet.

Dense depth maps have been used as a key element of visual perception tasks. There have been tremendous efforts to enhance the depth quality ranging from optimization-based to learning-based methods. Despite the remarkable progress for a long time their applicability in the real world is limited due to systematic measurement biases such as density sensing pattern and scan range. It is well-known that the biases make it difficult for these methods to achieve their generalization. We observe that learning a joint representation for input modalities (e.g. images and depth) which most recent methods adopt is sensitive to the biases. In this work we disentangle those modalities to mitigate the biases with prompt engineering. For this we design a novel depth prompt module to allow the desirable feature representation according to new depth distributions from either sensor types or scene configurations. Our depth prompt can be embedded into foundation models for monocular depth estimation. Through this embedding process our method helps the pretrained model to be free from restraint of depth scan range and to provide absolute scale depth maps. We demonstrate the effectiveness of our method through extensive evaluations. Source code is publicly available at https://github.com/JinhwiPark/DepthPrompting.

Recovering a clear image from a single hazy image is an open inverse problem. Although significant research progress has been made most existing methods ignore the effect that downstream tasks play in promoting upstream dehazing. From the perspective of the haze generation mechanism there is a potential relationship between the depth information of the scene and the hazy image. Based on this we propose a dual-task collaborative mutual promotion framework to achieve the dehazing of a single image. This framework integrates depth estimation and dehazing by a dual-task interaction mechanism and achieves mutual enhancement of their performance. To realize the joint optimization of the two tasks an alternative implementation mechanism with the difference perception is developed. On the one hand the difference perception between the depth maps of the dehazing result and the ideal image is proposed to promote the dehazing network to pay attention to the non-ideal areas of the dehazing. On the other hand by improving the depth estimation performance in the difficult-to-recover areas of the hazy image the dehazing network can explicitly use the depth information of the hazy image to assist the clear image recovery. To promote the depth estimation we propose to use the difference between the dehazed image and the ground truth to guide the depth estimation network to focus on the dehazed unideal areas. It allows dehazing and depth estimation to leverage their strengths in a mutually reinforcing manner. Experimental results show that the proposed method can achieve better performance than that of the state-of-the-art approaches. The source code is released at https://github.com/zhoushen1/DCMPNet.

This work presents Depth Anything a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules we aim to build a simple yet powerful foundation model dealing with any images under any circumstances. To this end we scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data ( 62M) which significantly enlarges the data coverage and thus is able to reduce the generalization error. We investigate two simple yet effective strategies that make data scaling-up promising. First a more challenging optimization target is created by leveraging data augmentation tools. It compels the model to actively seek extra visual knowledge and acquire robust representations. Second an auxiliary supervision is developed to enforce the model to inherit rich semantic priors from pre-trained encoders. We evaluate its zero-shot capabilities extensively including six public datasets and randomly captured photos. It demonstrates impressive generalization ability. Further through fine-tuning it with metric depth information from NYUv2 and KITTI new SOTAs are set. Our better depth model also results in a better depth-conditioned ControlNet. Our models are released at https://github.com/LiheYoung/Depth-Anything.

3D synthetic-to-real unsupervised domain adaptive segmentation is crucial to annotating new domains. Self-training is a competitive approach for this task but its performance is limited by different sensor sampling patterns (i.e. variations in point density) and incomplete training strategies. In this work we propose a density-guided translator (DGT) which translates point density between domains and integrates it into a two-stage self-training pipeline named DGT-ST. First in contrast to existing works that simultaneously conduct data generation and feature/output alignment within unstable adversarial training we employ the non-learnable DGT to bridge the domain gap at the input level. Second to provide a well-initialized model for self-training we propose a category-level adversarial network in stage one that utilizes the prototype to prevent negative transfer. Finally by leveraging the designs above a domain-mixed self-training method with source-aware consistency loss is proposed in stage two to narrow the domain gap further. Experiments on two synthetic-to-real segmentation tasks (SynLiDAR ? semanticKITTI and SynLiDAR ? semanticPOSS) demonstrate that DGT-ST outperforms state-of-the-art methods achieving 9.4% and 4.3% mIoU improvements respectively. Code is available at https://github.com/yuan-zm/DGT-ST.

Densely annotating the large-scale point clouds is laborious. To alleviate the annotation burden contrastive learning has attracted increasing attention for tackling semi-supervised 3D semantic segmentation. However existing point-to-point contrastive learning techniques in literature are generally sensitive to outliers resulting in insufficient modeling of the point-wise representations. To address this problem we propose a method named DDSemi for semi-supervised 3D semantic segmentation where a density-guided contrastive learning technique is explored. This technique calculates the contrastive loss in a point-to-anchor manner by estimating an anchor for each class from the memory bank based on the finding that the cluster centers tend to be located in dense regions. In this technique an inter-contrast loss is derived from the perturbed unlabeled point cloud pairs while an intra-contrast loss is derived from a single unlabeled point cloud. The derived losses could enhance the discriminability of the features and implicitly constrain the semantic consistency between the perturbed unlabeled point cloud pairs. In addition we propose a dual-space hardness sampling strategy to pay more attention to the hard samples located in sparse regions of both the geometric space and feature space by reweighting the point-wise intra-contrast loss. Experimental results on both indoor-scene and outdoor-scene datasets demonstrate that the proposed method outperforms the comparative state-of-the-art semi-supervised methods.

Multi-agent trajectory prediction is essential in autonomous driving risk avoidance and traffic flow control. However the heterogeneous traffic density on interactions which caused by physical laws social norms and so on is often overlooked in existing methods. When the density varies the number of agents involved in interactions and the corresponding interaction probability change dynamically. To tackle this issue we propose a new method called \underline D ensity-\underline A daptive Model based on \underline M otif \underline M atrix for Multi-Agent Trajectory Prediction (DAMM) to gain insights into multi-agent systems. Here we leverage the motif matrix to represent dynamic connectivity in a higher-order pattern and distill the interaction information from the perspectives of the spatial and the temporal dimensions. Specifically in spatial dimension we utilize multi-scale feature fusion to adaptively select the optimal range of neighbors participating in interactions for each time slot. In temporal dimension we extract the temporal interaction features and adapt a pyramidal pooling layer to generate the interaction probability for each agent. Experimental results demonstrate that our approach surpasses state-of-the-art methods on autonomous driving dataset.

Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But few-shot compression for Vision Transformers (ViT) remains largely unexplored which presents a new challenge. In particular the issue of sparse compression exists in traditional CNN few-shot methods which can only produce very few compressed models of different model sizes. This paper proposes a novel framework for few-shot ViT compression named DC-ViT. Instead of dropping the entire block DC-ViT selectively eliminates the attention module while retaining and reusing portions of the MLP module. DC-ViT enables dense compression which outputs numerous compressed models that densely populate the range of model complexity. DC-ViT outperforms state-of-the-art few-shot compression methods by a significant margin of 10 percentage points along with lower latency in the compression of ViT and its variants.

High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but due to the enormous capital investment required for training it is increasingly centralised to a few large corporations and hidden behind paywalls. This paper aims to democratise high-resolution GenAI by advancing the frontier of high-resolution generation while remaining accessible to a broad audience. We demonstrate that existing Latent Diffusion Models (LDMs) possess untapped potential for higher-resolution image generation. Our novel DemoFusion framework seamlessly extends open-source GenAI models employing Progressive Upscaling Skip Residual and Dilated Sampling mechanisms to achieve higher-resolution image generation. The progressive nature of DemoFusion requires more passes but the intermediate results can serve as "previews" facilitating rapid prompt iteration.

In this paper we democratise caricature generation empowering individuals to effortlessly craft personalised caricatures with just a photo and a conceptual sketch. Our objective is to strike a delicate balance between abstraction and identity while preserving the creativity and subjectivity inherent in a sketch. To achieve this we present Explicit Rank-1 Model Editing alongside single-image personalisation selectively applying nuanced edits to cross-attention layers for a seamless merge of identity and style. Additionally we propose Random Mask Reconstruction to enhance robustness directing the model to focus on distinctive identity and style features. Crucially our aim is not to replace artists but to eliminate accessibility barriers allowing enthusiasts to engage in the artistry.

Multiple Object Tracking (MOT) is a critical area within computer vision with a broad spectrum of practical implementations. Current research has primarily focused on the development of tracking algorithms and enhancement of post-processing techniques. Yet there has been a lack of thorough examination concerning the nature of tracking data it self. In this study we pioneer an exploration into the distribution patterns of tracking data and identify a pronounced long-tail distribution issue within existing MOT datasets. We note a significant imbalance in the distribution of trajectory lengths across different pedestrians a phenomenon we refer to as "pedestrians trajectory long-tail distribution". Addressing this challenge we introduce a bespoke strategy designed to mitigate the effects of this skewed distribution. Specifically we propose two data augmentation strategies including Stationary Camera View Data Augmentation (SVA) and Dynamic Camera View Data Augmentation (DVA) designed for viewpoint states and the Group Softmax (GS) module for Re-ID. SVA is to backtrack and predict the pedestrian trajectory of tail classes and DVA is to use diffusion model to change the background of the scene. GS divides the pedestrians into unrelated groups and performs softmax operation on each group individually. Our proposed strategies can be integrated into numerous existing tracking systems and extensive experimentation validates the efficacy of our method in reducing the influence of long-tail distribution on multi-object tracking performance. The code is available at https://github.com/chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT.

Vision Transformer (ViT) has emerged as a prominent architecture for various computer vision tasks. In ViT we divide the input image into patch tokens and process them through a stack of self-attention blocks. However unlike Convolutional Neural Network (CNN) ViT's simple architecture has no informative inductive bias (e.g. locality etc.). Due to this ViT requires a large amount of data for pre-training. Various data-efficient approaches (DeiT) have been proposed to train ViT on balanced datasets effectively. However limited literature discusses the use of ViT for datasets with long-tailed imbalances. In this work we introduce DeiT-LT to tackle the problem of training ViTs from scratch on long-tailed datasets. In DeiT-LT we introduce an efficient and effective way of distillation from CNN via distillation \texttt DIST token by using out-of-distribution images and re-weighting the distillation loss to enhance focus on tail classes. This leads to the learning of local CNN-like features in early ViT blocks improving generalization for tail classes. Further to mitigate overfitting we propose distilling from a flat CNN teacher which leads to learning low-rank generalizable features for DIST tokens across all ViT blocks. With the proposed DeiT-LT scheme the distillation DIST token becomes an expert on the tail classes and the classifier CLS token becomes an expert on the head classes. The experts help to effectively learn features corresponding to both the majority and minority classes using a distinct set of tokens within the same ViT architecture. We show the effectiveness of DeiT-LT for training ViT from scratch on datasets ranging from small-scale CIFAR-10 LT to large-scale iNaturalist-2018. Project Page: https://rangwani-harsh.github.io/DeiT-LT.

Understanding the dynamics of generic 3D scenes is fundamentally challenging in computer vision essential in enhancing applications related to scene reconstruction motion tracking and avatar creation. In this work we address the task as the problem of inferring dense long-range motion of 3D points. By observing a set of point trajectories we aim to learn an implicit motion field parameterized by a neural network to predict the movement of novel points within the same domain without relying on any data-driven or scene-specific priors. To achieve this our approach builds upon the recently introduced dynamic point field model that learns smooth deformation fields between the canonical frame and individual observation frames. However temporal consistency between consecutive frames is neglected and the number of required parameters increases linearly with the sequence length due to per-frame modeling. To address these shortcomings we exploit the intrinsic regularization provided by SIREN and modify the input layer to produce a spatiotemporally smooth motion field. Additionally we analyze the motion field Jacobian matrix and discover that the motion degrees of freedom (DOFs) in an infinitesimal area around a point and the network hidden variables have different behaviors to affect the model's representational power. This enables us to improve the model representation capability while retaining the model compactness. Furthermore to reduce the risk of overfitting we introduce a regularization term based on the assumption of piece-wise motion smoothness. Our experiments assess the model's performance in predicting unseen point trajectories and its application in temporal mesh alignment with guidance. The results demonstrate its superiority and effectiveness. The code and data for the project are publicly available at https://yz-cnsdqz.github.io/eigenmotion/DOMA.

This paper addresses the complex issue of one-shot face stylization focusing on the simultaneous consideration of appearance and structure where previous methods have fallen short. We explore deformation-aware face stylization that diverges from traditional single-image style reference opting for a real-style image pair instead. The cornerstone of our method is the utilization of a self-supervised vision transformer specifically DINO-ViT to establish a robust and consistent facial structure representation across both real and style domains. Our stylization process begins by adapting the StyleGAN generator to be deformation-aware through the integration of spatial transformers (STN). We then introduce two innovative constraints for generator fine-tuning under the guidance of DINO semantics: i) a directional deformation loss that regulates directional vectors in DINO space and ii) a relative structural consistency constraint based on DINO token self-similarities ensuring diverse generation. Additionally style-mixing is employed to align the color generation with the reference minimizing inconsistent correspondences. This framework delivers enhanced deformability for general one-shot face stylization achieving notable efficiency with a fine-tuning duration of approximately 10 minutes. Extensive qualitative and quantitative comparisons demonstrate our superiority over state-of-the-art one-shot face stylization methods. Code is available at https://github.com/zichongc/DoesFS

Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction. Nonetheless cutting-edge dynamic neural rendering methods rely heavily on these implicit representations which frequently struggle to capture the intricate details of objects in the scene. Furthermore implicit methods have difficulty achieving real-time rendering in general dynamic scenes limiting their use in a variety of tasks. To address the issues we propose a deformable 3D Gaussians splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space with a deformation field to model monocular dynamic scenes. We also introduce an annealing smoothing training mechanism with no extra overhead which can mitigate the impact of inaccurate poses on the smoothness of time interpolation tasks in real-world scenes. Through a differential Gaussian rasterizer the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed. Experiments show that our method outperforms existing methods significantly in terms of both rendering quality and speed making it well-suited for tasks such as novel-view synthesis time interpolation and real-time rendering.