CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail CLOUDS is a framework that integrates Foundation Models of various kinds: (i) CLIP backbone for its robust feature representation (ii) Diffusion Model to diversify the content thereby covering various modes of the possible target distribution and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions notably outperforming prior methods by 5.6% and 6.7% on averaged mIoU respectively. Our code is available at https://github.com/yasserben/CLOUDS
This paper addresses the problem of generating lifelike holistic co-speech motions for 3D avatars focusing on two key aspects: variability and coordination. Variability allows the avatar to exhibit a wide range of motions even with similar speech content while coordination ensures a harmonious alignment among facial expressions hand gestures and body poses. We aim to achieve both with ProbTalk a unified probabilistic framework designed to jointly model facial hand and body movements in speech. ProbTalk builds on the variational autoencoder (VAE) architecture and incorporates three core designs. First we introduce product quantization (PQ) to the VAE which enriches the representation of complex holistic motion. Second we devise a novel non-autoregressive model that embeds 2D positional encoding into the product-quantized representation thereby preserving essential structure information of the PQ codes. Last we employ a secondary stage to refine the preliminary prediction further sharpening the high-frequency details. Coupling these three designs enables ProbTalk to generate natural and diverse holistic co-speech motions outperforming several state-of-the-art methods in qualitative and quantitative evaluations particularly in terms of realism. Our code and model will be released for research purposes at https://feifeifeiliu.github.io/probtalk/.
Leveraging few-shot datasets in prompt learning for Vision-Language Models eliminates the need for manual prompt engineering while highlighting the necessity of accurate annotations for the labels. However high-level or complex label noise challenges prompt learning for Vision-Language Models. Aiming at this issue we propose a new framework for improving its robustness. Specifically we introduce the Joint Adaptive Partitioning for Label Refurbishment (JoAPR) a structured framework encompassing two key steps. 1) Data Partitioning where we differentiate between clean and noisy data using joint adaptive thresholds. 2) Label Refurbishment where we correct the labels based on the partition outcomes before retraining the network. Our comprehensive experiments confirm that JoAPR substantially enhances the robustness of prompt learning for Vision-Language Models against label noise offering a promising direction for future research.
Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods train the labeled data with ground truths and unlabeled data with pseudo labels. However the two training flows are separate which allows labeled data to dominate the training process resulting in low-quality pseudo labels and consequently sub-optimal results. To alleviate this issue we present AllSpark which reborns the labeled features from unlabeled ones with the channel-wise cross-attention mechanism. We further introduce a Semantic Memory along with a Channel Semantic Grouping strategy to ensure that unlabeled features adequately represent labeled features. The AllSpark shed new light on the architecture level designs of SSSS rather than framework level which avoids increasingly complicated training pipeline designs. It can also be regarded as a flexible bottleneck module that can be seamlessly integrated into a general transformer-based segmentation model. The proposed AllSpark outperforms existing methods across all evaluation protocols on Pascal Cityscapes and COCO benchmarks without bells-and-whistles. Code and model weights are available at: https://github.com/xmed-lab/AllSpark.
In dynamic 3D environments the ability to recognize a diverse range of objects without the constraints of predefined categories is indispensable for real-world applications. In response to this need we introduce OV3D an innovative framework designed for open-vocabulary 3D semantic segmentation. OV3D leverages the broad open-world knowledge embedded in vision and language foundation models to establish a fine-grained correspondence between 3D points and textual entity descriptions. These entity descriptions are enriched with contextual information enabling a more open and comprehensive understanding. By seamlessly aligning 3D point features with entity text features OV3D empowers open-vocabulary recognition in the 3D domain achieving state-of-the-art open-vocabulary semantic segmentation performance across multiple datasets including ScanNet Matterport3D and nuScenes.
Advances in image diffusion models have recently led to notable improvements in the generation of high-quality images. In combination with Neural Radiance Fields (NeRFs) they enabled new opportunities in 3D generation. However most generative 3D approaches are object-centric and applying them to editing existing photorealistic scenes is not trivial. We propose SIGNeRF a novel approach for fast and controllable NeRF scene editing and scene-integrated object generation. A new generative update strategy ensures 3D consistency across the edited images without requiring iterative optimization. We find that depth-conditioned diffusion models inherently possess the capability to generate 3D consistent views by requesting a grid of images instead of single views. Based on these insights we introduce a multi-view reference sheet of modified images. Our method updates an image collection consistently based on the reference sheet and refines the original NeRF with the newly generated image set in one go. By exploiting the depth conditioning mechanism of the image diffusion model we gain fine control over the spatial location of the edit and enforce shape guidance by a selected region or an external mesh.
While existing large vision-language multimodal models focus on whole image understanding there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge we introduce a novel multimodal model capable of decoding arbitrary (free-form) visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow'". Our simple design directly overlays visual markers onto the RGB image eliminating the need for complex region encodings yet achieves state-of-the-art performance on region-understanding tasks like Visual7W PointQA and Visual Commonsense Reasoning benchmark. Furthermore we present ViP-Bench a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions enabling future research in this domain. Code data and model are publicly available.
Recent advances in Iterative Vision-and-Language Navigation(IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task it poses more challenges on how to utilize the highly unstructured navigation memory with extremely sparse supervision. Towards this end we propose OVER-NAV which aims to go over and beyond the current arts of IVLN techniques. In particular we propose to incorporate LLMs and open-vocabulary detectors to distill key information and establish correspondence between multi-modal signals. Such a mechanism introduces reliable cross-modal supervision and enables on-the-fly generalization to unseen scenes without the need of extra annotation and re-training. To fully exploit the interpreted navigation data we further introduce a structured representation coded Omnigraph to effectively integrate multi-modal information along the tour. Accompanied with a novel omnigraph fusion mechanism OVER-NAV is able to extract the most relevant knowledge from omnigraph for a more accurate navigating action. In addition OVER-NAV seamlessly supports both discrete and continuous environments under a unified framework. We demonstrate the superiority of OVER-NAV in extensive experiments.
The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical systems. Recently the scientific community has focused on enhancing certifiable robustness guarantees by crafting \ols neural networks that leverage Lipschitz bounded dense and convolutional layers. Different methods have been proposed in the literature to achieve this goal however comparing the performance of such methods is not straightforward since different metrics can be relevant (e.g. training time memory usage accuracy certifiable robustness) for different applications. Therefore this work provides a thorough comparison between different methods covering theoretical aspects such as computational complexity and memory requirements as well as empirical measurements of time per epoch required memory accuracy and certifiable robust accuracy. The paper also provides some guidelines and recommendations to support the user in selecting the methods that work best depending on the available resources. We provide code at github.com/berndprach/1LipschitzLayersCompared
Data privacy is of great concern in cloud machine-learning service platforms when sensitive data are exposed to service providers. While private computing environments (e.g. secure enclaves) and cryptographic approaches (e.g. homomorphic encryption) provide strong privacy protection their computing performance still falls short compared to cloud GPUs. To achieve privacy protection with high computing performance we propose Delta a new private training and inference framework with comparable model performance as non-private centralized training. Delta features two asymmetric data flows: the main information-sensitive flow and the residual flow. The main part flows into a small model while the residuals are offloaded to a large model. Specifically Delta embeds the information-sensitive representations into a low-dimensional space while pushing the information-insensitive part into high-dimension residuals. To ensure privacy protection the low-dimensional information-sensitive part is secured and fed to a small model in a private environment. On the other hand the residual part is sent to fast cloud GPUs and processed by a large model. To further enhance privacy and reduce the communication cost Delta applies a random binary quantization technique along with a DP-based technique to the residuals before sharing them with the public platform.
On the other hand the residual part is sent to fast cloud GPUs and processed by a large model. To further enhance privacy and reduce the communication cost Delta applies a random binary quantization technique along with a DP-based technique to the residuals before sharing them with the public platform. We theoretically show that Delta guarantees differential privacy in the public environment and greatly reduces the complexity in the private environment. We conduct empirical analyses on CIFAR-10 CIFAR-100 and ImageNet datasets and ResNet-18 and ResNet-34 showing that Delta achieves strong privacy protection fast training and inference without significantly compromising the model utility.
We introduce a new task of generating "Illustrated Instructions" i.e. visual instructions customized to a user's needs. We identify desiderata unique to this task and formalize it through a suite of automatic and human evaluation metrics designed to measure the validity consistency and efficacy of the generations. We combine the power of large language models (LLMs) together with strong text-to-image generation diffusion models to propose a simple approach called StackedDiffusion which generates such illustrated instructions given text as input. The resulting model strongly outperforms baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases users even prefer it to human-generated articles. Most notably it enables various new and exciting applications far beyond what static articles on the web can provide such as personalized instructions complete with intermediate steps and pictures in response to a user's individual situation.
This paper tackles the domain adaptation problem in point cloud semantic segmentation which performs adaptation from a fully labeled domain (source domain) to an unlabeled target domain. Due to the unordered property of point clouds LiDAR scans typically show varying geometric structures across different regions in terms of density noises etc hence leading to increased dynamics on context. However such characteristics are not consistent across domains due to the difference in sensors environments etc thus hampering the effective scene comprehension across domains. To solve this we propose Cooperative Context Learning that performs context modeling and modulation from different aspects but in a cooperative manner. Specifically we first devise context embeddings to discover and model contextual relationships with close neighbors in a learnable manner. Then with the context embeddings from two domains we introduce a set of learnable prototypes to attend and associate them under the attention paradigm. As a result these prototypes naturally establish long-range dependency across regions and domains thereby encouraging the transfer of context knowledge and easing the adaptation. Moreover the attention in turn attunes and guides the local context modeling and urges them to focus on the domain-invariant context knowledge thus promoting the adaptation in a cooperative manner. Experiments on representative benchmarks verify that our method attains the new state-of-the-art.
Image denoising approaches based on deep neural networks often struggle with overfitting to specific noise distributions present in training data. This challenge persists in existing real-world denoising networks which are trained using a limited spectrum of real noise distributions and thus show poor robustness to out-of-distribution real noise types. To alleviate this issue we develop a novel training framework called Adversarial Frequency Mixup (AFM). AFM leverages mixup in the frequency domain to generate noisy images with distinctive and challenging noise characteristics all the while preserving the properties of authentic real-world noise. Subsequently incorporating these noisy images into the training pipeline enhances the denoising network's robustness to variations in noise distributions. Extensive experiments and analyses conducted on a wide range of real noise benchmarks demonstrate that denoising networks trained with our proposed framework exhibit significant improvements in robustness to unseen noise distributions. The code is available at https://github.com/dhryougit/AFM.
Reconstructing 3D hand mesh robustly from a single image is very challenging due to the lack of diversity in existing real-world datasets. While data synthesis helps relieve the issue the syn-to-real gap still hinders its usage. In this work we present HandBooster a new approach to uplift the data diversity and boost the 3D hand-mesh reconstruction performance by training a conditional generative space on hand-object interactions and purposely sampling the space to synthesize effective data samples. First we construct versatile content-aware conditions to guide a diffusion model to produce realistic images with diverse hand appearances poses views and backgrounds; favorably accurate 3D annotations are obtained for free. Then we design a novel condition creator based on our similarity-aware distribution sampling strategies to deliberately find novel and realistic interaction poses that are distinctive from the training set. Equipped with our method several baselines can be significantly improved beyond the SOTA on the HO3D and DexYCB benchmarks. Our code will be released on https://github.com/hxwork/HandBooster_Pytorch.
This work proposes the first online asymmetric semi-supervised framework namely A-Teacher for LiDAR-based 3D object detection. Our motivation stems from the observation that 1) existing symmetric teacher-student methods for semi-supervised 3D object detection have characterized simplicity but impede the distillation performance between teacher and student because of the demand for an identical model structure and input data format. 2) The offline asymmetric methods with a complex teacher model constructed differently can generate more precise pseudo-labels but is challenging to jointly optimize the teacher and student model. Consequently in this paper we devise a different path from the conventional paradigm which can harness the capacity of a strong teacher while preserving the advantages of online teacher model updates. The essence is the proposed attention-based refinement model that can be seamlessly integrated into a vanilla teacher. The refinement model works in the divide-and-conquer manner that respectively handles three challenging scenarios including 1) objects detected in the current timestamp but with suboptimal box quality 2) objects are missed in the current timestamp but are detected in past or future frames 3) objects are neglected in all frames.
The refinement model works in the divide-and-conquer manner that respectively handles three challenging scenarios including 1) objects detected in the current timestamp but with suboptimal box quality 2) objects are missed in the current timestamp but are detected in past or future frames 3) objects are neglected in all frames. It is worth noting that even while tackling these complex cases our model retains the efficiency of the online teacher-student semi-supervised framework. Experimental results on Waymo show that our method outperforms previous state-of-the-art HSSDA for 4.7 on mAP (L1) while consuming fewer training resources.
Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However directly aggregating adjacent costs can lead to suboptimal results due to local geometric inconsistency. Related methods either seek selective aggregation or improve aggregated depth in the 2D space both are unable to handle geometric inconsistency in the cost volume effectively. In this paper we propose GoMVS to aggregate geometrically consistent costs yielding better utilization of adjacent geometries. More specifically we correspond and propagate adjacent costs to the reference pixel by leveraging the local geometric smoothness in conjunction with surface normals. We achieve this by the geometric consistent propagation (GCP) module. It computes the correspondence from the adjacent depth hypothesis space to the reference depth space using surface normals then uses the correspondence to propagate adjacent costs to the reference geometry followed by a convolution for aggregation. Our method achieves new state-of-the-art performance on DTU Tanks & Temple and ETH3D datasets. Notably our method ranks 1st on the Tanks & Temple Advanced benchmark. Code is available at https://github.com/Wuuu3511/GoMVS.
Retrieval tasks play central roles in real-world machine learning systems such as search engines recommender systems and retrieval-augmented generation (RAG). Achieving decent performance in these tasks often requires fine-tuning various pre-trained models on specific datasets and selecting the best candidate a process that can be both time and resource-consuming. To tackle the problem we introduce a novel and efficient method called RetMMD that leverages Maximum Mean Discrepancy (MMD) and kernel methods to assess the transferability of pretrained models in retrieval tasks. RetMMD is calculated on pretrained model and target dataset without any fine-tuning involved. Specifically given some query we quantify the distribution discrepancy between relevant and irrelevant document embeddings by estimating the similarities within their mappings in the fine-tuned embedding space through kernel method. This discrepancy is averaged over multiple queries taking into account the distribution characteristics of the target dataset. Experiments suggest that the proposed metric calculated on pre-trained models closely aligns with retrieval performance post-fine-tuning. The observation holds across a variety of datasets including image text and multi-modal domains indicating the potential of using MMD and kernel methods for transfer learning evaluation in retrieval scenarios.
Experiments suggest that the proposed metric calculated on pre-trained models closely aligns with retrieval performance post-fine-tuning. The observation holds across a variety of datasets including image text and multi-modal domains indicating the potential of using MMD and kernel methods for transfer learning evaluation in retrieval scenarios. In addition we also design a way of evaluating dataset transferability for retrieval tasks with experimental results demonstrating the effectiveness of the proposed approach.
Recent advancements in text-to-image technology have significantly advanced the field of image customization. Among various applications the task of customizing diverse scenes for user-specified composited elements holds great application value but has not been extensively explored. Addressing this gap we propose AnyScene a specialized framework designed to create varied scenes from composited foreground using textual prompts. AnyScene addresses the primary challenges inherent in existing methods particularly scene disharmony due to a lack of foreground semantic understanding and distortion of foreground elements. Specifically we develop a foreground injection module that guides a pre-trained diffusion model to generate cohesive scenes in visual harmony with the provided foreground. To enhance robust generation we implement a layout control strategy that prevents distortions of foreground elements. Furthermore an efficient image blending mechanism seamlessly reintegrates foreground details into the generated scenes producing outputs with overall visual harmony and precise foreground details. In addition we propose a new benchmark and a series of quantitative metrics to evaluate this proposed image customization task. Extensive experimental results demonstrate the effectiveness of AnyScene which confirms its potential in various applications.