CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Diffusion models trained on large-scale text-image datasets have demonstrated a strong capability of controllable high-quality image generation from arbitrary text prompts. However the generation quality and generalization ability of 3D diffusion models is hindered by the scarcity of high-quality and large-scale 3D datasets. In this paper we present PI3D a framework that fully leverages the pre-trained text-to-image diffusion models' ability to generate high-quality 3D shapes from text prompts in minutes. The core idea is to connect the 2D and 3D domains by representing a 3D shape as a set of Pseudo RGB Images. We fine-tune an existing text-to-image diffusion model to produce such pseudo-images using a small number of text-3D pairs. Surprisingly we find that it can already generate meaningful and consistent 3D shapes given complex text descriptions. We further take the generated shapes as the starting point for a lightweight iterative refinement using score distillation sampling to achieve high-quality generation under a low budget. PI3D generates a single 3D shape from text in only 3 minutes and the quality is validated to outperform existing 3D generative models by a large margin.
Customization techniques for text-to-image models have paved the way for a wide range of previously unattainable applications enabling the generation of specific concepts across diverse contexts and styles. While existing methods facilitate high-fidelity customization for individual concepts or a limited pre-defined set of them they fall short of achieving scalability where a single model can seamlessly render countless concepts. In this paper we address a new problem called Modular Customization with the goal of efficiently merging customized models that were fine-tuned independently for individual concepts. This allows the merged model to jointly synthesize concepts in one image without compromising fidelity or incurring any additional computational costs. To address this problem we introduce Orthogonal Adaptation a method designed to encourage the customized models which do not have access to each other during fine-tuning to have orthogonal residual weights. This ensures that during inference time the customized models can be summed with minimal interference. Our proposed method is both simple and versatile applicable to nearly all optimizable weights in the model architecture. Through an extensive set of quantitative and qualitative evaluations our method consistently outperforms relevant baselines in terms of efficiency and identity preservation demonstrating a significant leap toward scalable customization of diffusion models.
We introduce pixelSplat a feed-forward model that learns to reconstruct 3D radiance fields parameterized by 3D Gaussian primitives from pairs of images. Our model features real-time and memory-efficient rendering for scalable training as well as fast 3D reconstruction at inference time. To overcome local minima inherent to sparse and locally supported representations we predict a dense probability distribution over 3D and sample Gaussian means from that probability distribution. We make this sampling operation differentiable via a reparameterization trick allowing us to back-propagate gradients through the Gaussian splatting representation. We benchmark our method on wide-baseline novel view synthesis on the real-world RealEstate10k and ACID datasets where we outperform state-of-the-art light field transformers and accelerate rendering by 2.5 orders of magnitude while reconstructing an interpretable and editable 3D radiance field. Additional materials can be found on the anonymous project website (pixelsplat.github.io).
Video generation has witnessed significant advancements yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal evaluation system should provide insights to inform future developments of video generation. To this end we present VBench a comprehensive benchmark suite that dissects "video generation quality" into specific hierarchical and disentangled dimensions each with tailored prompts and evaluation methods. VBench has three appealing properties: 1) Comprehensive Dimensions: VBench comprises 16 dimensions in video generation (e.g. subject identity inconsistency motion smoothness temporal flickering and spatial relationship etc). The evaluation metrics with fine-grained levels reveal individual models' strengths and weaknesses. 2) Human Alignment: We also provide a dataset of human preference annotations to validate our benchmarks' alignment with human perception for each evaluation dimension respectively. 3) Valuable Insights: We look into current models' ability across various evaluation dimensions and various content types. We also investigate the gaps between video and image generation models. We will open-source VBench including all prompts evaluation methods generated videos and human preference annotations and also include more video generation models in VBench to drive forward the field of video generation.
We present a new open-vocabulary detection framework. Our framework uses both image-level labels and detailed detection annotations when available. Our framework proceeds in three steps. We first train a language-conditioned object detector on fully-supervised detection data. This detector gets to see the presence or absence of ground truth classes during training and conditions prediction on the set of present classes. We use this detector to pseudo-label images with image-level labels. Our detector provides much more accurate pseudo-labels than prior approaches with its conditioning mechanism. Finally we train an unconditioned open-vocabulary detector on the pseudo-annotated images. The resulting detector named DECOLA shows strong zero-shot performance in open-vocabulary LVIS benchmark as well as direct zero-shot transfer benchmarks on LVIS COCO Object365 and OpenImages. DECOLA outperforms the prior arts by 17.1 AP-rare and 9.4 mAP on zero-shot LVIS benchmark. DECOLA achieves state-of-the-art results in various model sizes architectures and datasets by only training on open-sourced data and academic-scale computing. Code is available at https://github.com/janghyuncho/DECOLA.
We propose Diffusion Noise Optimization (DNO) a new method that effectively leverages existing motion diffusion models as motion priors for a wide range of motion-related tasks. Instead of training a task-specific diffusion model for each new task DNO operates by optimizing the diffusion latent noise of an existing pre-trained text-to-motion model. Given the corresponding latent noise of a human motion it propagates the gradient from the target criteria defined on the motion space through the whole denoising process to update the diffusion latent noise. As a result DNO supports any use cases where criteria can be defined as a function of motion. In particular we show that for motion editing and control DNO outperforms existing methods in both achieving the objective and preserving the motion content. DNO accommodates a diverse range of editing modes including changing trajectory pose joint locations or avoiding newly added obstacles. In addition DNO is effective in motion denoising and completion producing smooth and realistic motion from noisy and partial inputs. DNO achieves these results at inference time without the need for model retraining offering great versatility for any defined reward or loss function on the motion representation.
Deep learning has achieved remarkable progress in various applications heightening the importance of safeguarding the intellectual property (IP) of well-trained models. It entails not only authorizing usage but also ensuring the deployment of models in authorized data domains i.e. making models exclusive to certain target domains. Previous methods necessitate concurrent access to source training data and target unauthorized data when performing IP protection making them risky and inefficient for decentralized private data. In this paper we target a practical setting where only a well-trained source model is available and investigate how we can realize IP protection. To achieve this we propose a novel MAsk Pruning (MAP) framework. MAP stems from an intuitive hypothesis i.e. there are target-related parameters in a well-trained model locating and pruning them is the key to IP protection. Technically MAP freezes the source model and learns a target-specific binary mask to prevent unauthorized data usage while minimizing performance degradation on authorized data. Moreover we introduce a new metric aimed at achieving a better balance between source and target performance degradation. To verify the effectiveness and versatility we have evaluated MAP in a variety of scenarios including vanilla source-available practical source-free and challenging data-free. Extensive experiments indicate that MAP yields new state-of-the-art performance.
In this work we tackle the problem of domain generalization for object detection specifically focusing on the scenario where only a single source domain is available. We propose an effective approach that involves two key steps: diversifying the source domain and aligning detections based on class prediction confidence and localization. Firstly we demonstrate that by carefully selecting a set of augmentations a base detector can outperform existing methods for single domain generalization by a good margin. This highlights the importance of domain diversification in improving the performance of object detectors. Secondly we introduce a method to align detections from multiple views considering both classification and localization outputs. This alignment procedure leads to better generalized and well-calibrated object detector models which are crucial for accurate decision-making in safety-critical applications. Our approach is detector-agnostic and can be seamlessly applied to both single-stage and two-stage detectors. To validate the effectiveness of our proposed methods we conduct extensive experiments and ablations on challenging domain-shift scenarios. The results consistently demonstrate the superiority of our approach compared to existing methods.
In the realm of food computing segmenting ingredients from images poses substantial challenges due to the large intra-class variance among the same ingredients the emergence of new ingredients and the high annotation costs associated with large food segmentation datasets. Existing approaches primarily utilize a closed-vocabulary and static text embeddings setting. These methods often fall short in effectively handling the ingredients particularly new and diverse ones. In response to these limitations we introduce OVFoodSeg a framework that adopts an open-vocabulary setting and enhances text embeddings with visual context. By integrating vision-language models (VLMs) our approach enriches text embedding with image-specific information through two innovative modules e.g. an image-to-text learner FoodLearner and an Image-Informed Text Encoder. The training process of OVFoodSeg is divided into two stages: the pre-training of FoodLearner and the subsequent learning phase for segmentation. The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task.
The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task. By addressing the deficiencies of previous models OVFoodSeg demonstrates a significant improvement achieving an 4.9% increase in mean Intersection over Union (mIoU) on the FoodSeg103 dataset setting a new milestone for food image segmentation.
We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method dubbed XFeat (Accelerated Features) revisits fundamental design choices in convolutional neural networks for detecting extracting and matching local features. Our new model satisfies a critical need for fast and robust algorithms suitable to resource-limited devices. In particular accurate image matching requires sufficiently large image resolutions -- for this reason we keep the resolution as large as possible while limiting the number of channels in the network. Besides our model is designed to offer the choice of matching at the sparse or semi-dense levels each of which may be more suitable for different downstream applications such as visual navigation and augmented reality. Our model is the first to offer semi-dense matching efficiently leveraging a novel match refinement module that relies on coarse local descriptors. XFeat is versatile and hardware-independent surpassing current deep learning-based local features in speed (up to 5x faster) with comparable or better accuracy proven in pose estimation and visual localization. We showcase it running in real-time on an inexpensive laptop CPU without specialized hardware optimizations. Code and weights are available at verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24.
The emergence of attention-based transformer models has led to their extensive use in various tasks due to their superior generalization and transfer properties. Recent research has demonstrated that such models when prompted appropriately are excellent for few-shot inference. However such techniques are under-explored for dense prediction tasks like semantic segmentation. In this work we examine the effectiveness of prompting a transformer-decoder with learned visual prompts for the generalized few-shot segmentation (GFSS) task. Our goal is to achieve strong performance not only on novel categories with limited examples but also to retain performance on base categories. We propose an approach to learn visual prompts with limited examples. These learned visual prompts are used to prompt a multiscale transformer decoder to facilitate accurate dense predictions. Additionally we introduce a unidirectional causal attention mechanism between the novel prompts learned with limited examples and the base prompts learned with abundant data. This mechanism enriches the novel prompts without deteriorating the base class performance. Overall this form of prompting helps us achieve state-of-the-art performance for GFSS on two different benchmark datasets: COCO-20^i and Pascal-5^i without the need for test-time optimization (or transduction).
This mechanism enriches the novel prompts without deteriorating the base class performance. Overall this form of prompting helps us achieve state-of-the-art performance for GFSS on two different benchmark datasets: COCO-20^i and Pascal-5^i without the need for test-time optimization (or transduction). Furthermore test-time optimization leveraging unlabelled test data can be used to improve the prompts which we refer to as transductive prompt tuning.
We present ARTrackV2 which integrates two pivotal aspects of tracking: determining where to look (localization) and how to describe (appearance analysis) the target object across video frames. Building on the foundation of its predecessor ARTrackV2 extends the concept by introducing a unified generative framework to "read out" object's trajectory and "retell" its appearance in an autoregressive manner. This approach fosters a time-continuous methodology that models the joint evolution of motion and visual features guided by previous estimates. Furthermore ARTrackV2 stands out for its efficiency and simplicity obviating the less efficient intra-frame autoregression and hand-tuned parameters for appearance updates. Despite its simplicity ARTrackV2 achieves state-of-the-art performance on prevailing benchmark datasets while demonstrating a remarkable efficiency improvement. In particular ARTrackV2 achieves an AO score of 79. 5% on GOT-10k and an AUC of 86. 1% on TrackingNet while being 3.6 xfaster than ARTrack.
What does learning to model relationships between strings teach Large Language Models (LLMs) about the visual world? We systematically evaluate LLMs' abilities to generate and recognize an assortment of visual concepts of increasing complexity and then demonstrate how a preliminary visual representation learning system can be trained using models of text. As language models lack the ability to consume or output visual information as pixels we use code to represent images in our study. Although LLM-generated images do not look like natural images results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world. Furthermore experiments on self-supervised visual representation learning utilizing images generated with text models highlight the potential to train vision models capable of making semantic assessments of natural images using just LLMs.
In this paper we propose a new framework for online 3D scene perception. Conventional 3D scene perception methods are offline i.e. take an already reconstructed 3D scene geometry as input which is not applicable in robotic applications where the input data is streaming RGB-D videos rather than a complete 3D scene reconstructed from pre- collected RGB-D videos. To deal with online 3D scene per- ception tasks where data collection and perception should be performed simultaneously the model should be able to process 3D scenes frame by frame and make use of the temporal information. To this end we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability. Specifically we propose a queued memory mechanism to cache the supporting point cloud and image features. Then we devise aggregation modules which directly perform on the memory and pass temporal information to current frame. We further propose 3D-to-2D adapter to enhance image features with strong global context. Our adapters can be easily inserted into mainstream offline architectures of different tasks and significantly boost their performance on online tasks.
Then we devise aggregation modules which directly perform on the memory and pass temporal information to current frame. We further propose 3D-to-2D adapter to enhance image features with strong global context. Our adapters can be easily inserted into mainstream offline architectures of different tasks and significantly boost their performance on online tasks. Extensive experiments on ScanNet and SceneNN datasets demonstrate our approach achieves leading performance on three 3D scene perception tasks compared with state-of-the-art online methods by simply finetuning existing offline models without any model and task-specific designs.
Vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However in fashion domain datasets often exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text leading to cases where some textual details are not visible in individual images. This mismatch particularly when non-co-occurring elements are masked undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem we propose Synchronized attentional Masking (SyncMask) which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model ensuring a precise alignment between the two modalities. Additionally we enhance grouped batch sampling with semi-hard negatives effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach outperforming existing methods in three downstream tasks.
Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames performing even worse than single-modality models. While applying the common dropout techniques to the video modality enhances robustness to missing frames it simultaneously results in a performance loss when dealing with complete data input. In this study we delve into this contrasting phenomenon through the lens of modality bias and uncover that an excessive modality bias towards the audio modality induced by dropout constitutes the fundamental cause. Next we present the Modality Bias Hypothesis (MBH) to systematically describe the relationship between the modality bias and the robustness against missing modality in multimodal systems. Building on these findings we propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality maintaining performance and robustness simultaneously. Finally to address an entirely missing modality we adopt adapters to dynamically switch decision strategies. The effectiveness of our proposed approach is evaluated through comprehensive experiments on the MISP2021 and MISP2022 datasets. Our code is available at https://github.com/dalision/ModalBiasAVSR.
Point cloud upsampling (PCU) enriches the representation of raw point clouds significantly improving the performance in downstream tasks such as classification and reconstruction. Most of the existing point cloud upsampling methods focus on sparse point cloud feature extraction and upsampling module design. In a different way we dive deeper into directly modelling the gradient of data distribution from dense point clouds. In this paper we proposed a conditional denoising diffusion probabilistic model (DDPM) for point cloud upsampling called PUDM. Specifically PUDM treats the sparse point cloud as a condition and iteratively learns the transformation relationship between the dense point cloud and the noise. Simultaneously PUDM aligns with a dual mapping paradigm to further improve the discernment of point features. In this context PUDM enables learning complex geometry details in the ground truth through the dominant features while avoiding an additional upsampling module design. Furthermore to generate high-quality arbitrary-scale point clouds during inference PUDM exploits the prior knowledge of the scale between sparse point clouds and dense point clouds during training by parameterizing a rate factor. Moreover PUDM exhibits strong noise robustness in experimental results.
Furthermore to generate high-quality arbitrary-scale point clouds during inference PUDM exploits the prior knowledge of the scale between sparse point clouds and dense point clouds during training by parameterizing a rate factor. Moreover PUDM exhibits strong noise robustness in experimental results. In the quantitative and qualitative evaluations on PU1K and PUGAN PUDM significantly outperformed existing methods in terms of Chamfer Distance (CD) and Hausdorff Distance (HD) achieving state of the art (SOTA) performance.