Scene coordinate regression (SCR) methods are a family of visual localization methods that directly regress 2D-3D matches for camera pose estimation. They are effective in small-scale scenes but face significant challenges in large-scale scenes that are further amplified in the absence of ground truth 3D point clouds for supervision. Here the model can only rely on reprojection constraints and needs to implicitly triangulate the points. The challenges stem from a fundamental dilemma: The network has to be invariant to observations of the same landmark at different viewpoints and lighting conditions etc. but at the same time discriminate unrelated but similar observations. The latter becomes more relevant and severe in larger scenes. In this work we tackle this problem by introducing the concept of co-visibility to the network. We propose GLACE which integrates pre-trained global and local encodings and enables SCR to scale to large scenes with only a single small-sized network. Specifically we propose a novel feature diffusion technique that implicitly groups the reprojection constraints with co-visibility and avoids overfitting to trivial solutions. Additionally our position decoder parameterizes the output positions for large-scale scenes more effectively. Without using 3D models or depth maps for supervision our method achieves state-of-the-art results on large-scale scenes with a low-map-size model. On Cambridge landmarks with a single model we achieve a 17% lower median position error than Poker the ensemble variant of the state-of-the-art SCR method ACE. Code is available at: https://github.com/cvg/glace.
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Referring Remote Sensing Image Segmentation (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image Segmentation (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery leading to suboptimal segmentation results. To address these challenges we introduce the Rotated Multi-Scale Interaction Network (RMSIN) an innovative approach designed for the unique demands of RRSIS. RMSIN incorporates an Intra-scale Interaction Module (IIM) to effectively address the fine-grained detail required at multiple scales and a Cross-scale Interaction Module (CIM) for integrating these details coherently across the network. Furthermore RMSIN employs an Adaptive Rotated Convolution (ARC) to account for the diverse orientations of objects a novel contribution that significantly enhances segmentation accuracy. To assess the efficacy of RMSIN we have curated an expansive dataset comprising 17402 image-caption-mask triplets which is unparalleled in terms of scale and variety. This dataset not only presents the model with a wide range of spatial and rotational scenarios but also establishes a stringent benchmark for the RRSIS task ensuring a rigorous evaluation of performance. Experimental evaluations demonstrate the exceptional performance of RMSIN surpassing existing state-of-the-art models by a significant margin. Datasets and code are available at https://github.com/Lsan2401/RMSIN.
In this work we investigate the potential of a large language model (LLM) to directly comprehend visual signals without the necessity of fine-tuning on multi-modal datasets. The foundational concept of our method views an image as a linguistic entity and translates it to a set of discrete words derived from the LLM's vocabulary. To achieve this we present the Vision-to-Language Tokenizer abbreviated as V2T Tokenizer which transforms an image into a "foreign language" with the combined aid of an encoder-decoder the LLM vocabulary and a CLIP model. With this innovative image encoding the LLM gains the ability not only for visual comprehension but also for image denoising and restoration in an auto-regressive fashion--crucially without any fine-tuning. We undertake rigorous experiments to validate our method encompassing understanding tasks like image recognition image captioning and visual question answering as well as image denoising tasks like inpainting outpainting deblurring and shift restoration. Code and models are available at https://github.com/zh460045050/V2L-Tokenizer.
We propose NViST a transformer-based model for efficient and generalizable novel-view synthesis from a single image for real-world scenes. In contrast to many methods that are trained on synthetic data object-centred scenarios or in a category-specific manner NViST is trained on MVImgNet a large-scale dataset of casually-captured real-world videos of hundreds of object categories with diverse backgrounds. NViST transforms image inputs directly into a radiance field conditioned on camera parameters via adaptive layer normalisation. In practice NViST exploits fine-tuned masked autoencoder (MAE) features and translates them to 3D output tokens via cross-attention while addressing occlusions with self-attention. To move away from object-centred datasets and enable full scene synthesis NViST adopts a 6-DOF camera pose model and only requires relative pose dropping the need for canonicalization of the training data which removes a substantial barrier to it being used on casually captured datasets. We show results on unseen objects and categories from MVImgNet and even generalization to casual phone captures. We conduct qualitative and quantitative evaluations on MVImgNet and ShapeNet to show that our model represents a step forward towards enabling true in-the-wild generalizable novel-view synthesis from a single image. Project webpage: https://wbjang.github.io/nvist_webpage.
Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions enabling vast applications in content generation. While recent advancements have introduced control over factors such as object localization posture and image contours a crucial gap remains in our ability to control the interactions between objects in the generated content. Well-controlling interactions in generated images could yield meaningful applications such as creating realistic scenes with interacting characters. In this work we study the problems of conditioning T2I diffusion models with Human-Object Interaction (HOI) information consisting of a triplet label (person action object) and corresponding bounding boxes. We propose a pluggable interaction control model called InteractDiffusion that extends existing pre-trained T2I diffusion models to enable them being better conditioned on interactions. Specifically we tokenize the HOI information and learn their relationships via interaction embeddings. A conditioning self-attention layer is trained to map HOI tokens to visual tokens thereby conditioning the visual tokens better in existing T2I diffusion models. Our model attains the ability to control the interaction and location on existing T2I diffusion models which outperforms existing baselines by a large margin in HOI detection score as well as fidelity in FID and KID. Project page: https://jiuntian.github.io/interactdiffusion.
On the Test-Time Zero-Shot Generalization of Vision-Language Models: Do We Really Need Prompt Learning?
PDF ↗The development of large vision-language models notably CLIP has catalyzed research into effective adaptation techniques with a particular focus on soft prompt tuning. Conjointly test-time augmentation which utilizes multiple augmented views of a single image to enhance zero-shot generalization is emerging as a significant area of interest. This has predominantly directed research efforts towards test-time prompt tuning. In contrast we introduce a robust MeanShift for Test-time Augmentation (MTA) which surpasses prompt-based methods without requiring this intensive training procedure. This positions MTA as an ideal solution for both standalone and API-based applications. Additionally our method does not rely on ad hoc rules (e.g. confidence threshold) used in some previous test-time augmentation techniques to filter the augmented views. Instead MTA incorporates a quality assessment variable for each view directly into its optimization process termed as the inlierness score. This score is jointly optimized with a density mode seeking process leading to an efficient training- and hyperparameter-free approach. We extensively benchmark our method on 15 datasets and demonstrate MTA's superiority and computational efficiency. Deployed easily as plug-and-play module on top of zero-shot models and state-of-the-art few-shot methods MTA shows systematic and consistent improvements.
Many face anti-spoofing (FAS) methods have focused on learning discriminative features from both live and spoof training data to strengthen the security of face recognition systems. However since not every possible attack type is available in the training stage these FAS methods usually fail to detect unseen attacks in the inference stage. In comparison one-class FAS where the training data are from only live faces aims to detect whether a test face image belongs to the live class or not. In this paper we propose a novel One-Class Spoof Cue Map estimation Network (OC-SCMNet) to address the one-class FAS detection problem. Our first goal is to learn to extract latent spoof features from live images so that their estimated Spoof Cue Maps (SCMs) should have zero responses. To avoid trapping to a trivial solution we devise a novel SCM-guided feature learning by combining many SCMs as pseudo ground-truths to guide a conditional generator to generate latent spoof features for spoof data. Our second goal is to approximately simulate the potential out-of-distribution spoof attacks. To this end we propose using a memory bank to dynamically preserve a set of sufficiently "independent" latent spoof features to encourage the generator to probe the latent spoof feature space. Extensive experiments conducted on eight FAS benchmark datasets demonstrate that the proposed OC-SCMNet not only outperforms previous one-class FAS methods but also achieves comparable performances to state-of-the-art two-class FAS method. The codes are available at https://github.com/Pei-KaiHuang/CVPR24_OC_SCMNet.
We present an approach to modeling an image-space prior on scene motion. Our prior is learned from a collection of motion trajectories extracted from real video sequences depicting natural oscillatory dynamics of objects such as treesflowers candles and clothes swaying in the wind. We model dense long-term motion in the Fourier domain as spectral volumes which we find are well-suited to prediction with diffusion models. Given a single image our trained model uses a frequency-coordinated diffusion sampling process to predict a spectral volume which can be converted into a motion texture that spans an entire video. Along with an image-based rendering module the predicted motion representation can be used for a number of downstream applications such as turning still images into seamlessly looping videos or allowing users to interact with objects in real images producing realistic simulated dynamics (by interpreting the spectral volumes as image-space modal bases). See our project page for more results: generative-dynamics.github.io
Comprehensive capturing of human motions requires both accurate captures of complex poses and precise localization of the human within scenes. Most of the HPE datasets and methods primarily rely on RGB LiDAR or IMU data. However solely using these modalities or a combination of them may not be adequate for HPE particularly for complex and fast movements. For holistic human motion understanding we present RELI11D a high-quality multimodal human motion dataset involves LiDAR IMU system RGB camera and Event camera. It records the motions of 10 actors performing 5 sports in 7 scenes including 3.32 hours of synchronized LiDAR point clouds IMU measurement data RGB videos and Event steams. Through extensive experiments we demonstrate that the RELI11D presents considerable challenges and opportunities as it contains many rapid and complex motions that require precise location. To address the challenge of integrating different modalities we propose LEIR a multimodal baseline that effectively utilizes LiDAR Point Cloud Event stream and RGB through our cross-attention fusion strategy. We show that LEIR exhibits promising results for rapid motions and daily motions and that utilizing the characteristics of multiple modalities can indeed improve HPE performance. Both the dataset and source code will be released publicly to the research community fostering collaboration and enabling further exploration in this field.
The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models especially in 3D semantic segmentation. However sparse CNNs are still valuable networks due to their efficiency treasure and ease of application. In this work we reexamine the design distinctions and test the limits of what a sparse CNN can achieve. We discover that the key credit to the performance difference is adaptivity. Specifically we propose two key components i.e. adaptive receptive fields (spatially) and adaptive relation to bridge the gap. This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs) a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost. Without any self-attention modules OA-CNNs favorably surpass point transformers in terms of accuracy in both indoor and outdoor scenes with much less latency and memory cost. Notably it achieves 76.1% 78.9% and 70.6% mIoU on ScanNet v2 nuScenes and SemanticKITTI validation benchmarks respectively while maintaining at most 5x better speed than transformer counterparts. This revelation highlights the potential of pure sparse CNNs to outperform transformer-related networks. Our code is built upon Pointcept which is available at https://github.com/Pointcept/Pointcept.
Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation
PDF ↗We contribute the Habitat Synthetic Scene Dataset a dataset of 211 high-quality 3D scenes and use it to test navigation agent generalization to realistic 3D environments. Our dataset represents real interiors and contains a diverse set of 18656 models of real-world objects. We investigate the impact of synthetic 3D scene dataset scale and realism on the task of training embodied agents to find and navigate to objects (ObjectGoal navigation). By comparing to synthetic 3D scene datasets from prior work we find that scale helps in generalization but the benefits quickly saturate making visual fidelity and correlation to real-world scenes more important. Our experiments show that agents trained on our smaller-scale dataset can match or outperform agents trained on much larger datasets. Surprisingly we observe that agents trained on just 122 scenes from our dataset outperform agents trained on 10000 scenes from the ProcTHOR-10K dataset in terms of zero-shot generalization in real-world scanned environments.
OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
PDF ↗Due to the resource-intensive nature of training vision-language models on expansive video data a majority of studies have centered on adapting pre-trained image-language models to the video domain. Dominant pipelines propose to tackle the visual discrepancies with additional temporal learners while overlooking the substantial discrepancy for web-scaled descriptive narratives and concise action category names leading to less distinct semantic space and potential performance limitations. In this work we prioritize the refinement of text knowledge to facilitate generalizable video recognition. To address the limitations of the less distinct semantic space of category names we prompt a large language model (LLM) to augment action class names into Spatio-Temporal Descriptors thus bridging the textual discrepancy and serving as a knowledge base for general recognition. Moreover to assign the best descriptors with different video instances we propose Optimal Descriptor Solver forming the video recognition problem as solving the optimal matching flow across frame-level representations and descriptors. Comprehensive evaluations in zero-shot few-shot and fully supervised video recognition highlight the effectiveness of our approach. Our best model achieves a state-of-the-art zero-shot accuracy of 75.1% on Kinetics-600.
Vision-Language (VL) models have gained significant research focus enabling remarkable advances in multimodal reasoning. These architectures typically comprise a vision encoder a Large Language Model (LLM) and a projection module that aligns visual features with the LLM's representation space. Despite their success a critical limitation persists: the vision encoding process remains decoupled from user queries often in the form of image-related questions. Consequently the resulting visual features may not be optimally attuned to the query-specific elements of the image. To address this we introduce QA-ViT a Question Aware Vision Transformer approach for multimodal reasoning which embeds question awareness directly within the vision encoder. This integration results in dynamic visual features focusing on relevant image aspects to the posed question. QA-ViT is model-agnostic and can be incorporated efficiently into any VL architecture. Extensive experiments demonstrate the effectiveness of applying our method to various multimodal architectures leading to consistent improvement across diverse tasks and showcasing its potential for enhancing visual and scene-text understanding.
Blind video quality assessment (BVQA) plays a pivotal role in evaluating and improving the viewing experience of end-users across a wide range of video-based platforms and services. Contemporary deep learning-based models primarily analyze video content in its aggressively subsampled format while being blind to the impact of the actual spatial resolution and frame rate on video quality. In this paper we propose a modular BVQA model and a method of training it to improve its modularity. Our model comprises a base quality predictor a spatial rectifier and a temporal rectifier responding to the visual content and distortion spatial resolution and frame rate changes on video quality respectively. During training spatial and temporal rectifiers are dropped out with some probabilities to render the base quality predictor a standalone BVQA model which should work better with the rectifiers. Extensive experiments on both professionally-generated content and user-generated content video databases show that our quality model achieves superior or comparable performance to current methods. Additionally the modularity of our model offers an opportunity to analyze existing video quality databases in terms of their spatial and temporal complexity.
Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes
PDF ↗Neural implicit representation of geometric shapes has witnessed considerable advancements in recent years. However common distance field based implicit representations specifically signed distance field (SDF) for watertight shapes or unsigned distance field (UDF) for arbitrary shapes routinely suffer from degradation of reconstruction accuracy when converting to explicit surface points and meshes. In this paper we introduce a novel neural implicit representation based on unsigned orthogonal distance fields (UODFs). In UODFs the minimal unsigned distance from any spatial point to the shape surface is defined solely in one orthogonal direction contrasting with the multi-directional determination made by SDF and UDF. Consequently every point in the 3D UODFs can directly access its closest surface points along three orthogonal directions. This distinctive feature leverages the accurate reconstruction of surface points without interpolation errors. We verify the effectiveness of UODFs through a range of reconstruction examples extending from simple watertight or non-watertight shapes to complex shapes that include hollows internal or assembling structures.
Learning 3D scene flow from LiDAR point clouds presents significant difficulties including poor generalization from synthetic datasets to real scenes scarcity of real-world 3D labels and poor performance on real sparse LiDAR point clouds. We present a novel approach from the perspective of auto-labelling aiming to generate a large number of 3D scene flow pseudo labels for real-world LiDAR point clouds. Specifically we employ the assumption of rigid body motion to simulate potential object-level rigid movements in autonomous driving scenarios. By updating different motion attributes for multiple anchor boxes the rigid motion decomposition is obtained for the whole scene. Furthermore we developed a novel 3D scene flow data augmentation method for global and local motion. By perfectly synthesizing target point clouds based on augmented motion parameters we easily obtain lots of 3D scene flow labels in point clouds highly consistent with real scenarios. On multiple real-world datasets including LiDAR KITTI nuScenes and Argoverse our method outperforms all previous supervised and unsupervised methods without requiring manual labelling. Impressively our method achieves a tenfold reduction in EPE3D metric on the LiDAR KITTI dataset reducing it from 0.190m to a mere 0.008m error.
Generative models e.g. Stable Diffusion have enabled the creation of photorealistic images from text prompts. Yet the generation of 360-degree panorama images from text remains a challenge particularly due to the dearth of paired text-panorama data and the domain gap between panorama and perspective images. In this paper we introduce a novel dual-branch diffusion model named PanFusion to generate a 360-degree image from a text prompt. We leverage the stable diffusion model as one branch to provide prior knowledge in natural image generation and register it to another panorama branch for holistic image generation. We propose a unique cross-attention mechanism with projection awareness to minimize distortion during the collaborative denoising process. Our experiments validate that PanFusion surpasses existing methods and thanks to its dual-branch structure can integrate additional constraints like room layout for customized panorama outputs.
Recently the advancement of self-supervised learning techniques like masked autoencoders (MAE) has greatly influenced visual representation learning for images and videos. Nevertheless it is worth noting that the predominant approaches in existing masked image / video modeling rely excessively on resource-intensive vision transformers (ViTs) as the feature encoder. In this paper we propose a new approach termed as VideoMAC which combines video masked autoencoders with resource-friendly ConvNets. Specifically VideoMAC employs symmetric masking on randomly sampled pairs of video frames. To prevent the issue of mask pattern dissipation we utilize ConvNets which are implemented with sparse convolutional operators as encoders. Simultaneously we present a simple yet effective masked video modeling (MVM) approach a dual encoder architecture comprising an online encoder and an exponential moving average target encoder aimed to facilitate inter-frame reconstruction consistency in videos. Additionally we demonstrate that VideoMAC empowering classical (ResNet) / modern (ConvNeXt) convolutional encoders to harness the benefits of MVM outperforms ViT-based approaches on downstream tasks including video object segmentation (+5.2% / 6.4% \mathcal J &\mathcal F ) body part propagation (+6.3% / 3.1% mIoU) and human pose tracking (+10.2% / 11.1% PCK@0.1).
The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems. In the context of classification the task of detecting images outside of a model's training domain is known as out-of-distribution (OOD) detection. While there has been a growing research interest in developing post-hoc OOD detection methods there has been comparably little discussion around how these methods perform when the underlying classifier is not trained on a clean carefully curated dataset. In this work we take a closer look at 20 state-of-the-art OOD detection methods in the (more realistic) scenario where the labels used to train the underlying classifier are unreliable (e.g. crowd-sourced or web-scraped labels). Extensive experiments across different datasets noise types & levels architectures and checkpointing strategies provide insights into the effect of class label noise on OOD detection and show that poor separation between incorrectly classified ID samples vs. OOD samples is an overlooked yet important limitation of existing methods. Code: https://github.com/glhr/ood-labelnoise
Existing multi-person human reconstruction approaches mainly focus on recovering accurate poses or avoiding penetration but overlook the modeling of close interactions. In this work we tackle the task of reconstructing closely interactive humans from a monocular video. The main challenge of this task comes from insufficient visual information caused by depth ambiguity and severe inter-person occlusion. In view of this we propose to leverage knowledge from proxemic behavior and physics to compensate the lack of visual information. This is based on the observation that human interaction has specific patterns following the social proxemics. Specifically we first design a latent representation based on Vector Quantised-Variational AutoEncoder (VQ-VAE) to model human interaction. A proxemics and physics guided diffusion model is then introduced to denoise the initial distribution. We design the diffusion model as dual branch with each branch representing one individual such that the interaction can be modeled via cross attention. With the learned priors of VQ-VAE and physical constraint as the additional information our proposed approach is capable of estimating accurate poses that are also proxemics and physics plausible. Experimental results on Hi4D 3DPW and CHI3D demonstrate that our method outperforms existing approaches. The code is available at https://github.com/boycehbz/HumanInteraction.