CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
The surge in multi-modal data has propelled cross-modal matching to the forefront of research interest. However the challenge lies in the laborious and expensive process of curating a large and accurately matched multimodal dataset. Commonly sourced from the Internet these datasets often suffer from a significant presence of mismatched data impairing the performance of matching models. To address this problem we introduce a novel regularization approach named Equivariant Similarity Consistency (ESC) which can facilitate robust clean and noisy data separation and improve the training for cross-modal matching. Intuitively our method posits that the semantic variations caused by image changes should be proportional to those caused by text changes for any two matched samples. Accordingly we first calculate the ESC by comparing image and text semantic variations between a set of elaborated anchor points and other undivided training data. Then pairs with high ESC are filtered out as noisy correspondence pairs. We implement our method by combining the ESC with a traditional hinge-based triplet loss. Extensive experiments on three widely used datasets including Flickr30K MS-COCO and Conceptual Captions verify the effectiveness of our method.
We train a set of open text-to-image (T2I) diffusion models on a dataset of curated Creative-Commons-licensed (CC) images which yields models that are competitive with Stable Diffusion 2 (SD2). This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train T2I models; (2) CC images are relatively scarce. To address these challenges we use an intuitive transfer learning technique to produce a set of high-quality synthetic captions paired with our assembled CC images. We then develop a data- and compute-efficient training recipe that requires as little as 3% of the LAION data (i.e. roughly 70 million examples) needed to train existing SD2 models but obtains the same quality. These results indicate that we have a sufficient number of CC images (also roughly 70 million) for training high-quality models. Our recipe also implements a variety of optimizations that achieve 2.71x training speed-ups enabling rapid model iteration. We leverage this recipe to train several high-quality T2I mod- els which we dub the CommonCanvas family.
Our recipe also implements a variety of optimizations that achieve 2.71x training speed-ups enabling rapid model iteration. We leverage this recipe to train several high-quality T2I mod- els which we dub the CommonCanvas family. Our largest model achieves comparable performance to SD2 on human evaluation even though we use a synthetically captioned CC-image dataset that is only <3% the size of LAION for training. We release our models data and code on GitHub.
Referring image segmentation (RIS) aims to segment the target referent described by natural language. Recently large-scale pre-trained models e.g. CLIP and SAM have been successfully applied in many downstream tasks but they are not well adapted to RIS task due to inter-task differences. In this paper we propose a new prompt-driven framework named Prompt-RIS which bridges CLIP and SAM end-to-end and transfers their rich knowledge and powerful capabilities to RIS task through prompt learning. To adapt CLIP to pixel-level task we first propose a Cross-Modal Prompting method which acquires more comprehensive vision-language interaction and fine-grained text-to-pixel alignment by performing bidirectional prompting. Then the prompt-tuned CLIP generates masks points and text prompts for SAM to generate more accurate mask predictions. Moreover we further propose Instance Contrastive Learning to improve the model's discriminability to different instances and robustness to diverse languages describing the same instance. Extensive experiments demonstrate that the performance of our method outperforms the state-of-the-art methods consistently in both general and open-vocabulary settings.
We present Image Sculpting a new framework for editing 2D images by incorporating tools from 3D geometry and graphics. This approach differs markedly from existing methods which are confined to 2D spaces and typically rely on textual instructions leading to ambiguity and limited control. Image Sculpting converts 2D objects into 3D enabling direct interaction with their 3D geometry. Post-editing these objects are re-rendered into 2D merging into the original image to produce high-fidelity results through a coarse-to-fine enhancement process. The framework supports precise quantifiable and physically-plausible editing options such as pose editing rotation translation 3D composition carving and serial addition. It marks an initial step towards combining the creative freedom of generative models with the precision of graphics pipelines.
In this paper we suggest a new novel method to understand complex semantic structures through long video inputs. Conventional methods for understanding videos have been focused on short-term clips and trained to get visual representations for the short clips using convolutional neural networks or transformer architectures. However most real-world videos are composed of long videos ranging from minutes to hours therefore it essentially brings limitations to understanding the overall semantic structures of the long videos by dividing them into small clips and learning the representations of them. We suggest a new algorithm to learn the multi-granular semantic structures of videos by defining spatiotemporal high-order relationships among object-based representations as semantic units. The proposed method includes a new transformer architecture capable of learning spatiotemporal graphs and a compositional learning method to learn disentangled features for each semantic unit. Using the suggested method we resolve the challenging video task which is compositional generalization understanding of unseen videos. In experiments we demonstrate new state-of-the-art performances for two challenging video datasets.
Building accurate maps is a key building block to enable reliable localization planning and navigation of autonomous vehicles. We propose a novel approach for building accurate 3D maps of dynamic environments utilizing a sequence of LiDAR scans. To this end we propose encoding the 4D scene into a novel spatio-temporal implicit neural map representation by fitting a time-dependent truncated signed distance function to each point. Using our representation we can extract the static map by filtering the dynamic parts. Our neural representation is based on sparse feature grids a globally shared decoder and time-dependent basis functions which can be jointly optimized in an unsupervised fashion. To learn this representation from a sequence of LiDAR scans we design a simple yet efficient loss function to supervise the map optimization in a piecewise way. We evaluate our approach on various scenes containing moving objects in terms of the reconstruction quality of static maps and the segmentation of dynamic point clouds. The experimental results demonstrate that our method is capable of removing the dynamic part of the input point clouds while reconstructing accurate and complete large-scale 3D maps outperforming several state-of-the-art methods for static map generation and scene reconstruction.
Spatio-temporal grounding describes the task of localizing events in space and time e.g. in video data based on verbal descriptions only. Models for this task are usually trained with human-annotated sentences and bounding box supervision. This work addresses this task from a multimodal supervision perspective proposing a framework for spatio-temporal action grounding trained on loose video and subtitle supervision only without human annotation. To this end we combine local representation learning which focuses on leveraging fine-grained spatial information with a global representation encoding that captures higher-level representations and incorporates both in a joint approach. To evaluate this challenging task in a real-life setting a new benchmark dataset is proposed providing dense spatio-temporal grounding annotations in long untrimmed multi-action instructional videos for over 5K events. We evaluate the proposed approach and other methods on the proposed and standard downstream tasks showing that our method improves over current baselines in various settings including spatial temporal and untrimmed multi-action spatio-temporal grounding.
We present FoundationPose a unified foundation model for 6D object pose estimation and tracking supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without finetuning as long as its CAD model is given or a small number of reference images are captured. Thanks to the unified framework the downstream pose estimation modules are the same in both setups with a neural implicit representation used for efficient novel view synthesis when no CAD model is available. Strong generalizability is achieved via large-scale synthetic training aided by a large language model (LLM) a novel transformer-based architecture and contrastive learning formulation. Extensive evaluation on multiple public datasets involving challenging scenarios and objects indicate our unified approach outperforms existing methods specialized for each task by a large margin. In addition it even achieves comparable results to instance-level methods despite the reduced assumptions. Project page: https://nvlabs.github.io/FoundationPose/
In recent years Neural Radiance Field (NeRF) has demonstrated remarkable capabilities in representing 3D scenes. To expedite the rendering process learnable explicit representations have been introduced for combination with implicit NeRF representation which however results in a large storage space requirement. In this paper we introduce the Context-based NeRF Compression (CNC) framework which leverages highly efficient context models to provide a storage-friendly NeRF representation. Specifically we excavate both level-wise and dimension-wise context dependencies to enable probability prediction for information entropy reduction. Additionally we exploit hash collision and occupancy grids as strong prior knowledge for better context modeling. To the best of our knowledge we are the first to construct and exploit context models for NeRF compression. We achieve a size reduction of 100X and 70X with improved fidelity against the baseline Instant-NGP on Synthesic-NeRF and Tanks and Temples datasets respectively. Additionally we attain 86.7% and 82.3% storage size reduction against the SOTA NeRF compression method BiRF. Our code is available here: https://github.com/YihangChen-ee/CNC.
Recent developments in face restoration have achieved remarkable results in producing high-quality and lifelike outputs. The stunning results however often fail to be faithful with respect to the identity of the person as the models lack necessary context. In this paper we explore the potential of personalized face restoration with diffusion models. In our approach a restoration model is personalized using a few images of the identity leading to tailored restoration with respect to the identity while retaining fine-grained details. By using independent trainable blocks for personalization the rich prior of a base restoration model can be exploited to its fullest. To avoid the model relying on parts of identity left in the conditioning low-quality images a generative regularizer is employed. With a learnable parameter the model learns to balance between the details generated based on the input image and the degree of personalization. Moreover we improve the training pipeline of face restoration models to enable an alignment-free approach. We showcase the robust capabilities of our approach in several real-world scenarios with multiple identities demonstrating our method's ability to generate fine-grained details with faithful restoration. In the user study we evaluate the perceptual quality and faithfulness of the generated details with our method being voted best 61% of the time compared to the second best with 25% of the votes.
We present TextureDreamer a novel image-guided texture synthesis method to transfer relightable textures from a small number of input images (3 to 5) to target 3D shapes across arbitrary categories. Texture creation is a pivotal challenge in vision and graphics. Industrial companies hire experienced artists to manually craft textures for 3D assets. Classical methods require densely sampled views and accurately aligned geometry while learning-based methods are confined to category-specific shapes within the dataset. In contrast TextureDreamer can transfer highly detailed intricate textures from real-world environments to arbitrary objects with only a few casually captured images potentially significantly democratizing texture creation. Our core idea personalized geometry-aware score distillation (PGSD) draws inspiration from recent advancements in diffuse models including personalized modeling for texture information extraction score distillation for detailed appearance synthesis and explicit geometry guidance with ControlNet. Our integration and several essential modifications substantially improve the texture quality. Experiments on real images spanning different categories show that TextureDreamer can successfully transfer highly realistic semantic meaningful texture to arbitrary objects surpassing the visual quality of previous state-of-the-art. Project page: https://texturedreamer.github.io
Image Quality Assessment (IQA) constitutes a fundamental task within the field of computer vision yet it remains an unresolved challenge owing to the intricate distortion conditions diverse image contents and limited availability of data. Recently the community has witnessed the emergence of numerous large-scale pretrained foundation models. However it remains an open problem whether the scaling law in high-level tasks is also applicable to IQA tasks which are closely related to low-level clues. In this paper we demonstrate that with a proper injection of local distortion features a larger pretrained vision transformer (ViT) foundation model performs better in IQA tasks. Specifically for the lack of local distortion structure and inductive bias of the large-scale pretrained ViT we use another pretrained convolution neural networks (CNNs) which is well known for capturing the local structure to extract multi-scale image features. Further we propose a local distortion extractor to obtain local distortion features from the pretrained CNNs and a local distortion injector to inject the local distortion features into ViT.
Further we propose a local distortion extractor to obtain local distortion features from the pretrained CNNs and a local distortion injector to inject the local distortion features into ViT. By only training the extractor and injector our method can benefit from the rich knowledge in the powerful foundation models and achieve state-of-the-art performance on popular IQA datasets indicating that IQA is not only a low-level problem but also benefits from stronger high-level features drawn from large-scale pretrained models. Codes are publicly available at: https://github.com/NeosXu/LoDa.
Anomaly detection is a challenging computer vision task in industrial scenario. Advancements in deep learning constantly revolutionize vision-based anomaly detection methods and considerable progress has been made in both supervised and self-supervised anomaly detection. The commonly-used pipeline is to optimize the model by constraining the feature embeddings using a distance-based loss function. However these methods work in Euclidean space and they cannot well exploit the data lied in non-Euclidean space. In this paper we are the first to explore anomaly detection task in hyperbolic space that is a representative of non-Euclidean space and propose a hyperbolic anomaly detection (HypAD) method. Specifically we first extract image features and then map them from Euclidean space to hyperbolic space where the hyperbolic distance metric is employed to optimize the proposed HypAD. Extensive experiments on the benchmarking datasets including MVTec AD and VisA show that our HypAD approach obtains the state-of-the-art performance demonstrating the effectiveness of our HypAD and the promise of investigating anomaly detection in hyperbolic space.
Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning. While vision-only autonomous driving methods have recently achieved notable performance through enhanced scene understanding several key issues including lack of reasoning low generalization performance and long-tail scenarios still need to be addressed. In this paper we present VLP a novel Vision-Language-Planning framework that exploits language models to bridge the gap between linguistic understanding and autonomous driving. VLP enhances autonomous driving systems by strengthening both the source memory foundation and the self-driving car's contextual understanding. VLP achieves state-of-the-art end-to-end planning performance on the challenging NuScenes dataset by achieving 35.9% and 60.5% reduction in terms of average L2 error and collision rates respectively compared to the previous best method. Moreover VLP shows improved performance in challenging long-tail scenarios and strong generalization capabilities when faced with new urban environments.
Recent thrilling progress in large-scale text-to-image (T2I) models has unlocked unprecedented synthesis quality of AI-generated content (AIGC) including image generation 3D and video composition. Further personalized techniques enable appealing customized production of a novel concept given only several images as reference. However an intriguing problem persists: Is it possible to capture multiple novel concepts from one single reference image? In this paper we identify that existing approaches fail to preserve visual consistency with the reference image and eliminate cross-influence from concepts. To alleviate this we propose an attention calibration mechanism to improve the concept-level understanding of the T2I model. Specifically we first introduce new learnable modifiers bound with classes to capture attributes of multiple concepts. Then the classes are separated and strengthened following the activation of the cross-attention operation ensuring comprehensive and self-contained concepts. Additionally we suppress the attention activation of different classes to mitigate mutual influence among concepts. Together our proposed method dubbed DisenDiff can learn disentangled multiple concepts from one single image and produce novel customized images with learned concepts. We demonstrate that our method outperforms the current state of the art in both qualitative and quantitative evaluations. More importantly our proposed techniques are compatible with LoRA and inpainting pipelines enabling more interactive experiences.
Generative AI (GenAI) is transforming creative workflows through the capability to synthesize and manipulate images via high-level prompts. Yet creatives are not well supported to receive recognition or reward for the use of their content in GenAI training. To this end we propose ProMark a causal attribution technique to attribute a synthetically generated image to its training data concepts like objects motifs templates artists or styles. The concept information is proactively embedded into the input training images using imperceptible watermarks and the diffusion models (unconditional or conditional) are trained to retain the corresponding watermarks in generated images. We show that we can embed as many as 2^ 16  unique watermarks into the training data and each training image can contain more than one watermark. ProMark can maintain image quality whilst outperforming correlation-based attribution. Finally several qualitative examples are presented providing the confidence that the presence of the watermark conveys a causative relationship between training data and synthetic images.
While GAN-based models have been successful in image stylization tasks they often struggle with structure preservation while stylizing a wide range of input images. Recently diffusion models have been adopted for image stylization but still lack the capability to maintain the original quality of input images. Building on this we propose OSASIS: a novel one-shot stylization method that is robust in structure preservation. We show that OSASIS is able to effectively disentangle the semantics from the structure of an image allowing it to control the level of content and style implemented to a given input. We apply OSASIS to various experimental settings including stylization with out-of-domain reference images and stylization with text-driven manipulation. Results show that OSASIS outperforms other stylization methods especially for input images that were rarely encountered during training providing a promising solution to stylization via diffusion models.
Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation but their understanding of the 3D world is notably deficient limiting progress in 3D language understanding and generation. To solve this problem we introduce GPT4Point an innovative groundbreaking point-language multimodal model designed specifically for unified 3D object understanding and generation within the MLLM framework. GPT4Point as a powerful 3D MLLM seamlessly can execute a variety of point-text reference tasks such as point-cloud captioning and Q&A. Additionally GPT4Point is equipped with advanced capabilities for controllable 3D generation it can get high-quality results through a low-quality point-text feature maintaining the geometric shapes and colors. To support the expansive needs of 3D object-text pairs we develop Pyramid-XL a point-language dataset annotation engine. It constructs a large-scale database over 1M objects of varied text granularity levels from the Objaverse-XL dataset essential for training GPT4Point. A comprehensive benchmark has been proposed to evaluate 3D point-language understanding capabilities. In extensive evaluations GPT4Point has demonstrated superior performance in understanding and generation.
We present "SemCity" a 3D diffusion model for semantic scene generation in real-world outdoor environments. Most 3D diffusion models focus on generating a single object synthetic indoor scenes or synthetic outdoor scenes while the generation of real-world outdoor scenes is rarely addressed. In this paper we concentrate on generating a real-outdoor scene through learning a diffusion model on a real-world outdoor dataset. In contrast to synthetic data real-outdoor datasets often contain more empty spaces due to sensor limitations causing challenges in learning real-outdoor distributions. To address this issue we exploit a triplane representation as a proxy form of scene distributions to be learned by our diffusion model. Furthermore we propose a triplane manipulation that integrates seamlessly with our triplane diffusion model. The manipulation improves our diffusion model's applicability in a variety of downstream tasks related to outdoor scene generation such as scene inpainting scene outpainting and semantic scene completion refinements. In experimental results we demonstrate that our triplane diffusion model shows meaningful generation results compared with existing work in a real-outdoor dataset SemanticKITTI. We also show our triplane manipulation facilitates seamlessly adding removing or modifying objects within a scene. Further it also enables the expansion of scenes toward a city-level scale.