CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data alongside a substantially larger pool of unlabeled data. Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting. Nevertheless SSL methods have considerable room for performance improvement when compared to fully-supervised DG training. To tackle this underexplored yet highly practical problem of SSDG we make the following core contributions. First we propose a feature-based conformity technique that matches the posterior distributions from the feature space with the pseudo-label from the model's output space. Second we develop a semantics alignment loss to learn semantically-compatible representations by regularizing the semantic structure in the feature space. Our method is plug-and-play and can be readily integrated with different SSL-based SSDG baselines without introducing any additional parameters. Extensive experimental results across five challenging DG benchmarks with four strong SSL baselines suggest that our method provides consistent and notable gains in two different SSDG settings.
With the success of large language models (LLMs) integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However existing LLM-based large multimodal models (e.g. Video-LLaMA VideoChat) can only take in a limited number of frames for short video understanding. In this study we mainly focus on designing an efficient and effective model for long-term video understanding. Instead of trying to process more frames simultaneously like most existing work we propose to process videos in an online manner and store past video information in a memory bank. This allows our model to reference historical video content for long-term analysis without exceeding LLMs' context length constraints or GPU memory limits. Our memory bank can be seamlessly integrated into current multimodal LLMs in an off-the-shelf manner. We conduct extensive experiments on various video understanding tasks such as long-video understanding video question answering and video captioning and our model can achieve state-of-the-art performances across multiple datasets.
Interactive motion synthesis is essential in creating immersive experiences in entertainment applications such as video games and virtual reality. However generating animations that are both high-quality and contextually responsive remains a challenge. Traditional techniques in the game industry can produce high-fidelity animations but suffer from high computational costs and poor scalability. Trained neural network models alleviate the memory and speed issues yet fall short on generating diverse motions. Diffusion models offer diverse motion synthesis with low memory usage but require expensive reverse diffusion processes. This paper introduces the Accelerated Auto-regressive Motion Diffusion Model (AAMDM) a novel motion synthesis framework designed to achieve quality diversity and efficiency all together. AAMDM integrates Denoising Diffusion GANs as a fast Generation Module and an Auto-regressive Diffusion Model as a Polishing Module. Furthermore AAMDM operates in a lower-dimensional embedded space rather than the full-dimensional pose space which reduces the training complexity as well as further improves the performance. We show that AAMDM outperforms existing methods in motion quality diversity and runtime efficiency through comprehensive quantitative analyses and visual comparisons. We also demonstrate the effectiveness of each algorithmic component through ablation studies.
Deep Text-to-Image Synthesis (TIS) models such as Stable Diffusion have recently gained significant popularity for creative text-to-image generation. However for domain-specific scenarios tuning-free Text-guided Image Editing (TIE) is of greater importance for application developers. This approach modifies objects or object properties in images by manipulating feature components in attention layers during the generation process. Nevertheless little is known about the semantic meanings that these attention layers have learned and which parts of the attention maps contribute to the success of image editing. In this paper we conduct an in-depth probing analysis and demonstrate that cross-attention maps in Stable Diffusion often contain object attribution information which can result in editing failures. In contrast self-attention maps play a crucial role in preserving the geometric and shape details of the source image during the transformation to the target image. Our analysis offers valuable insights into understanding cross and self-attention mechanisms in diffusion models. Furthermore based on our findings we propose a simplified yet more stable and efficient tuning-free procedure that modifies only the self-attention maps of specified attention layers during the denoising process. Experimental results show that our simplified method consistently surpasses the performance of popular approaches on multiple datasets.
Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning which is highly memory-intensive for tasks with high-resolution data e.g. video understanding small object detection and point cloud analysis. In this paper we propose Dynamic Reversible Dual-Residual Networks or Dr2Net a novel family of network architectures that acts as a surrogate network to finetune a pretrained model with substantially reduced memory consumption. Dr2Net contains two types of residual connections one maintaining the residual structure in the pretrained models and the other making the network reversible. Due to its reversibility intermediate activations which can be reconstructed from output are cleared from memory during training. We use two coefficients on either type of residual connections respectively and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision. We evaluate Dr2Net on various pretrained models and various tasks and show that it can reach comparable performance to conventional finetuning but with significantly less memory usage.
The primary focus of Neural Representation for Videos (NeRV) is to effectively model its spatiotemporal consistency. However current NeRV systems often face a significant issue of spatial inconsistency leading to decreased perceptual quality. To address this issue we introduce the Pyramidal Neural Representation for Videos (PNeRV) which is built on a multi-scale information connection and comprises a lightweight rescaling operator Kronecker Fully-connected layer (KFc) and a Benign Selective Memory (BSM) mechanism. The KFc inspired by the tensor decomposition of the vanilla Fully-connected layer facilitates low-cost rescaling and global correlation modeling. BSM merges high-level features with granular ones adaptively. Furthermore we provide an analysis based on the Universal Approximation Theory of the NeRV system and validate the effectiveness of the proposed PNeRV. We conducted comprehensive experiments to demonstrate that PNeRV surpasses the performance of contemporary NeRV models achieving the best results in video regression on UVG and DAVIS under various metrics (PSNR SSIM LPIPS and FVD).
We conducted comprehensive experiments to demonstrate that PNeRV surpasses the performance of contemporary NeRV models achieving the best results in video regression on UVG and DAVIS under various metrics (PSNR SSIM LPIPS and FVD). Compared to vanilla NeRV PNeRV achieves a +4.49 dB gain in PSNR and a 231% increase in FVD on UVG along with a +3.28 dB PSNR and 634% FVD increase on DAVIS.
Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories. In this paper we propose a novel generative and fine-tuning framework LTGC to handle long-tail recognition via leveraging generated content. Firstly inspired by the rich implicit knowledge in large-scale models (e.g. large language models LLMs) LTGC leverages the power of these models to parse and reason over the original tail data to produce diverse tail-class content. We then propose several novel designs for LTGC to ensure the quality of the generated data and to efficiently fine-tune the model using both the generated and original data. The visualization demonstrates the effectiveness of the generation module in LTGC which produces accurate and diverse tail data. Additionally the experimental results demonstrate that our LTGC outperforms existing state-of-the-art methods on popular long-tailed benchmarks.
Instance segmentation is data-hungry and as model capacity increases data scale becomes crucial for improving the accuracy. Most instance segmentation datasets today require costly manual annotation limiting their data scale. Models trained on such data are prone to overfitting on the training set especially for those rare categories. While recent works have delved into exploiting generative models to create synthetic datasets for data augmentation these approaches do not efficiently harness the full potential of generative models. To address these issues we introduce a more efficient strategy to construct generative datasets for data augmentation termed DiverGen. Firstly we provide an explanation of the role of generative data from the perspective of distribution discrepancy. We investigate the impact of different data on the distribution learned by the model. We argue that generative data can expand the data distribution that the model can learn thus mitigating overfitting. Additionally we find that the diversity of generative data is crucial for improving model performance and enhance it through various strategies including category diversity prompt diversity and generative model diversity. With these strategies we can scale the data to millions while maintaining the trend of model performance improvement.
Additionally we find that the diversity of generative data is crucial for improving model performance and enhance it through various strategies including category diversity prompt diversity and generative model diversity. With these strategies we can scale the data to millions while maintaining the trend of model performance improvement. On the LVIS dataset DiverGen significantly outperforms the strong model X-Paste achieving +1.1 box AP and +1.1 mask AP across all categories and +1.9 box AP and +2.5 mask AP for rare categories. Our codes are available at https://github.com/aim-uofa/DiverGen.
Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. However the predominant APR architectures only rely on 2D operations during inference resulting in limited accuracy of pose estimation due to the lack of 3D geometry constraints or priors. In this work we propose a test-time refinement pipeline that leverages implicit geometric constraints using a robust feature field to enhance the ability of APR methods to use 3D information during inference. We also introduce a novel Neural Feature Synthesizer (NeFeS) model which encodes 3D geometric features during training and directly renders dense novel view features at test time to refine APR methods. To enhance the robustness of our model we introduce a feature fusion module and a progressive training strategy. Our proposed method achieves state-of-the-art single-image APR accuracy on indoor and outdoor datasets. Code will be released at https://github.com/ActiveVisionLab/NeFeS.
This study focuses on a novel task in text-to-image (T2I) generation namely action customization. The objective of this task is to learn the co-existing action from limited data and generalize it to unseen humans or even animals. Experimental results show that existing subject-driven customization methods fail to learn the representative characteristics of actions and struggle in decoupling actions from context features including appearance. To overcome the preference for low-level features and the entanglement of high-level features we propose an inversion-based method Action-Disentangled Identifier (ADI) to learn action-specific identifiers from the exemplar images. ADI first expands the semantic conditioning space by introducing layer-wise identifier tokens thereby increasing the representational richness while distributing the inversion across different features. Then to block the inversion of action-agnostic features ADI extracts the gradient invariance from the constructed sample triples and masks the updates of irrelevant channels. To comprehensively evaluate the task we present an ActionBench that includes a variety of actions each accompanied by meticulously selected samples. Both quantitative and qualitative results show that our ADI outperforms existing baselines in action-customized T2I generation. Our project page is at https://adi-t2i.github.io/ADI.
We propose a framework for automatic colorization that allows for iterative editing and modifications. The core of our framework lies in an imagination module: by understanding the content within a grayscale image we utilize a pre-trained image generation model to generate multiple images that contain the same content. These images serve as references for coloring mimicking the process of human experts. As the synthesized images can be imperfect or different from the original grayscale image we propose a Reference Refinement Module to select the optimal reference composition. Unlike most previous end-to-end automatic colorization algorithms our framework allows for iterative and localized modifications of the colorization results because we explicitly model the coloring samples. Extensive experiments demonstrate the superiority of our framework over existing automatic colorization algorithms in editability and flexibility. Project page: https://xy-cong.github.io/imagine-colorization/.
This paper is not motivated to seek innovation within the attention mechanism. Instead it focuses on overcoming the existing trade-offs between accuracy and efficiency within the context of point cloud processing leveraging the power of scale. Drawing inspiration from recent advances in 3D large-scale representation learning we recognize that model performance is more influenced by scale than by intricate design. Therefore we present Point Transformer V3 (PTv3) which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the overall performance after scaling such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns. This principle enables significant scaling expanding the receptive field from 16 to 1024 points while remaining efficient (a 3x increase in processing speed and a 10x improvement in memory efficiency compared with its predecessor PTv2). PTv3 attains state-of-the-art results on over 20 downstream tasks that span both indoor and outdoor scenarios. Further enhanced with multi-dataset joint training PTv3 pushes these results to a higher level.
Precipitation nowcasting is an important spatio-temporal prediction task to predict the radar echoes sequences based on current observations which can serve both meteorological science and smart city applications. Due to the chaotic evolution nature of the precipitation systems it is a very challenging problem. Previous studies address the problem either from the perspectives of deterministic modeling or probabilistic modeling. However their predictions suffer from the blurry high-value echoes fading away and position inaccurate issues. The root reason of these issues is that the chaotic evolutionary precipitation systems are not appropriately modeled. Inspired by the nature of the systems we propose to decompose and model them from the perspective of global deterministic motion and local stochastic variations with residual mechanism. A unified and flexible framework that can equip any type of spatio-temporal models is proposed based on residual diffusion which effectively tackles the shortcomings of previous methods. Extensive experimental results on four publicly available radar datasets demonstrate the effectiveness and superiority of the proposed framework compared to state-of-the-art techniques. Our code is publicly available at https://github.com/DeminYu98/DiffCast.
We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g. sports music dance bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts yielding long-form captures from 1 to 42 minutes each and 1286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio eye gaze 3D point clouds camera poses IMU and multiple paired language descriptions---including a novel "expert commentary" done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity we also present a suite of benchmark tasks and their annotations including fine-grained activity understanding proficiency estimation cross-view translation and 3D hand/body pose. All resources are open sourced to fuel new research in the community.
Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However due to the unordered and non-uniform density characteristics of point clouds it is non-trivial to explore the prior knowledge of point clouds and pre-train a point cloud backbone. In this paper we propose a novel pre-training method called Point cloud Diffusion pre-training PointDif. We consider the point cloud pre-training task as a conditional point-to-point generation problem and introduce a conditional point generator. This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object. We also present a recurrent uniform sampling optimization strategy which enables the model to uniformly recover from various noise levels and learn from balanced supervision. Our PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification segmentation and detection.
We also present a recurrent uniform sampling optimization strategy which enables the model to uniformly recover from various noise levels and learn from balanced supervision. Our PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification segmentation and detection. Specifically PointDif attains 70.0% mIoU on S3DIS Area 5 for the segmentation task and achieves an average improvement of 2.4% on ScanObjectNN for the classification task compared to TAP. Furthermore our pre-training framework can be flexibly applied to diverse point cloud backbones and bring considerable gains. Code is available at https://github.com/zhengxiaozx/PointDif
In Visual Question Answering (VQA) recognizing and localizing entities pose significant challenges. Pretrained vision-and-language models have addressed this problem by providing a text description as the answer. However in visual scenes with multiple entities textual descriptions struggle to distinguish the entities from the same category effectively. Consequently the VQA dataset is limited by the limitations of text description and cannot adequately cover scenarios involving multiple entities. To address this challenge we introduce a Mask for Align (Mask4Align) method which can determine the entity's position in the given image that best matches the user-input question. This method incorporates colored masks into the image enabling the VQA model to handle discrimination and localization challenges associated with multiple entities. To process an arbitrary number of similar entities Mask4Align is designed hierarchically to discern subtle differences achieving precise localization. Since Mask4Align directly utilizes pre-trained models it does not introduce additional training overhead. Extensive experiments conducted on both the gaze target prediction task dataset and our proposed multi-entity localization dataset showcase the superiority of Mask4Align.
Deep neural networks are known to be overconfident for what they don't know in the wild which is undesirable for decision-making in high-stakes applications. Despite quantities of existing works most of them focus on detecting out-of-distribution (OOD) samples from unseen classes while ignoring large parts of relevant failure sources like misclassified samples from known classes. In particular recent studies reveal that prevalent OOD detection methods are actually harmful for misclassification detection (MisD) indicating that there seems to be a tradeoff between those two tasks. In this paper we study the critical yet under-explored problem of unified failure detection which aims to detect both misclassified and OOD examples. Concretely we identify the failure of simply integrating learning objectives of misclassification and OOD detection and show the potential of sequence learning. Inspired by this we propose a reliable continual learning paradigm whose spirit is to equip the model with MisD ability first and then improve the OOD detection ability without degrading the already adequate MisD performance. Extensive experiments demonstrate that our method achieves strong unified failure detection performance. The code is available at https://github.com/Impression2805/RCL.
Significant advancements have been made in image editing with the recent advance of the Diffusion model. However most of the current methods primarily focus on global or subject-level modifications and often face limitations when it comes to editing specific objects when there are other objects coexisting in the scene given solely textual prompts. In response to this challenge we introduce an object-level generative task called Referring Image Editing (RIE) which enables the identification and editing of specific source objects in an image using text prompts. To tackle this task effectively we propose a tailored framework called ReferDiffusion. It aims to disentangle input prompts into multiple embeddings and employs a mixed-supervised multi-stage training strategy. To facilitate further research in this domain we introduce the RefCOCO-Edit dataset comprising images editing prompts source object segmentation masks and reference edited images for training and evaluation. Our extensive experiments demonstrate the effectiveness of our approach in identifying and editing target objects while conventional general image editing and region-based image editing methods have difficulties in this challenging task.