CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Current state-of-the-art Video Object Segmentation (VOS) methods rely on dense per-object mask annotations both during training and testing. This requires time-consuming and costly video annotation mechanisms. We propose a novel Point-VOS task with a spatio-temporally sparse point-wise annotation scheme that substantially reduces the annotation effort. We apply our annotation scheme to two large-scale video datasets with text descriptions and annotate over 19M points across 133K objects in 32K videos. Based on our annotations we propose a new Point-VOS benchmark and a corresponding point-based training mechanism which we use to establish strong baseline results. We show that existing VOS methods can easily be adapted to leverage our point annotations during training and can achieve results close to the fully-supervised performance when trained on pseudo-masks generated from these points. In addition we show that our data can be used to improve models that connect vision and language by evaluating it on the Video Narrative Grounding (VNG) task. We will make our code and annotations available at https://pointvos.github.io.
Denoising probabilistic diffusion models have shown breakthrough performance to generate more photo-realistic images or human-level illustrations than the prior models such as GANs. This high image-generation capability has stimulated the creation of many downstream applications in various areas. However we find that this technology is actually a double-edged sword: We identify a new type of attack called the Natural Denoising Diffusion (NDD) attack based on the finding that state-of-the-art deep neural network (DNN) models still hold their prediction even if we intentionally remove their robust features which are essential to the human visual system (HVS) through text prompts. The NDD attack shows a significantly high capability to generate low-cost model-agnostic and transferable adversarial attacks by exploiting the natural attack capability in diffusion models. To systematically evaluate the risk of the NDD attack we perform a large-scale empirical study with our newly created dataset the Natural Denoising Diffusion Attack (NDDA) dataset. We evaluate the natural attack capability by answering 6 research questions.
To systematically evaluate the risk of the NDD attack we perform a large-scale empirical study with our newly created dataset the Natural Denoising Diffusion Attack (NDDA) dataset. We evaluate the natural attack capability by answering 6 research questions. Through a user study we find that it can achieve an 88% detection rate while being stealthy to 93% of human subjects; we also find that the non-robust features embedded by diffusion models contribute to the natural attack capability. To confirm the model-agnostic and transferable attack capability we perform the NDD attack against the Tesla Model 3 and find that 73% of the physically printed attacks can be detected as stop signs. Our hope is that the study and dataset can help our community be aware of the risks in diffusion models and facilitate further research toward robust DNN models.
Self-supervised 3D representation learning aims to learn effective representations from large-scale unlabeled point clouds. Most existing approaches adopt point discrimination as the pretext task which assigns matched points in two distinct views as positive pairs and unmatched points as negative pairs. However this approach often results in semantically identical points having dissimilar representations leading to a high number of false negatives and introducing a semantic conflict problem. To address this issue we propose GroupContrast a novel approach that combines segment grouping and semantic-aware contrastive learning. Segment grouping partitions points into semantically meaningful regions which enhances semantic coherence and provides semantic guidance for the subsequent contrastive representation learning. Semantic-aware contrastive learning augments the semantic information extracted from segment grouping and helps to alleviate the issue of semantic conflict. We conducted extensive experiments on multiple 3D scene understanding tasks. The results demonstrate that GroupContrast learns semantically meaningful representations and achieves promising transfer learning performance.
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches research is shifting towards category-level pose estimation for practical applications. Current category-level datasets however fall short in annotation quality and pose variety. Addressing this we introduce HouseCat6D a new category-level 6D pose dataset. It features 1) multi-modality with Polarimetric RGB and Depth (RGBD+P) 2) encompasses 194 diverse objects across 10 household categories including two photometrically challenging ones and 3) provides high-quality pose annotations with an error range of only 1.35 mm to 1.74 mm. The dataset also includes 4) 41 large-scale scenes with comprehensive viewpoint and occlusion coverage 5) a checkerboard-free environment and 6. dense 6D parallel-jaw robotic grasp annotations. Additionally we present benchmark results for leading category-level pose estimation networks.
The widespread adoption of face recognition has led to increasing privacy concerns as unauthorized access to face images can expose sensitive personal information. This paper explores face image protection against viewing and recovery attacks. Inspired by image compression we propose creating a visually uninformative face image through feature subtraction between an original face and its model-produced regeneration. Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation. To enhance privacy the high-dimensional representation is crafted through random channel shuffling resulting in randomized recognizable images devoid of attacker-leverageable texture details. We distill our methodologies into a novel privacy-preserving face recognition method MinusFace. Experiments demonstrate its high recognition accuracy and effective privacy protection. Its code is available at https://github.com/Tencent/TFace.
6DoF Pose estimation has been gaining increased importance in vision for over a decade however it does not yet meet the reliability and accuracy standards for mass deployment in industrial robotics. To this effect we present the Industrial Plenoptic Dataset (IPD): the first dataset for the co-evaluation of cameras HDR and algorithms targeted at reliable high-accuracy industrial automation. Specifically we capture 2300 physical scenes of 20 industrial parts covering a 1mx1mx0.5m working volume resulting in over 100000 distinct object views. Each scene is captured with 13 well-calibrated multi-modal cameras including polarization and high-resolution structured light. In terms of lighting we capture each scene at 4 exposures and in 3 challenging lighting conditions ranging from 100 lux to 100000 lux. We also present validate and analyze robot consistency an evaluation method targeted at scalable high accuracy evaluation. We hope that vision systems that succeed on this dataset will have direct industry impact. The dataset and evaluation code are available at https://github.com/intrinsic-ai/ipd.
Gait a prevalent and complex form of human motion plays a significant role in the field of long-range pedestrian retrieval due to the unique characteristics inherent in individual motion patterns. However gait recognition in real-world scenarios is challenging due to the limitations of capturing comprehensive cross-viewing and cross-clothing data. Additionally distractors such as occlusions directional changes and lingering movements further complicate the problem. The widespread application of deep learning techniques has led to the development of various potential gait recognition methods. However these methods utilize convolutional networks to extract shared information across different views and attire conditions. Once trained the parameters and non-linear function become constrained to fixed patterns limiting their adaptability to various distractors in real-world scenarios. In this paper we present a unified gait recognition framework to extract global motion patterns and develop a novel dynamic transformer to generate representative gait features. Specifically we develop a trainable part-based prompt pool with numerous key-value pairs that can dynamically select prompt templates to incorporate into the gait sequence thereby providing task-relevant shared knowledge information. Furthermore we specifically design dynamic attention to extract robust motion patterns and address the length generalization issue. Extensive experiments on four widely recognized gait datasets i.e.
Furthermore we specifically design dynamic attention to extract robust motion patterns and address the length generalization issue. Extensive experiments on four widely recognized gait datasets i.e. Gait3D GREW OUMVLP and CASIA-B reveal that the proposed method yields substantial improvements compared to current state-of-the-art approaches.
Self-attention mechanism is the key of the Transformer but often criticized for its computation demands. Previous token pruning works motivate their methods from the view of computation redundancy but still need to load the full network and require same memory costs. This paper introduces a novel strategy that simplifies vision transformers and reduces computational load through the selective removal of non-essential attention layers guided by entropy considerations. We identify that regarding the attention layer in bottom blocks their subsequent MLP layers i.e. two feed-forward layers can elicit the same entropy quantity. Meanwhile the accompanied MLPs are under-exploited since they exhibit smaller feature entropy compared to those MLPs in the top blocks. Therefore we propose to integrate the uninformative attention layers into their subsequent counterparts by degenerating them into identical mapping yielding only MLP in certain transformer blocks. Experimental results on ImageNet-1k show that the proposed method can remove 40% attention layer of DeiT-B improving throughput and memory bound without performance compromise.
As pretrained text-to-image diffusion models become increasingly powerful recent efforts have been made to distill knowledge from these text-to-image pretrained models for optimizing a text-guided 3D model. Most of the existing methods generate a holistic 3D model from a plain text input. This can be problematic when the text describes a complex scene with multiple objects because the vectorized text embeddings are inherently unable to capture a complex description with multiple entities and relationships. Holistic 3D modeling of the entire scene further prevents accurate grounding of text entities and concepts. To address this limitation we propose GraphDreamer a novel framework to generate compositional 3D scenes from scene graphs where objects are represented as nodes and their interactions as edges. By exploiting node and edge information in scene graphs our method makes better use of the pretrained text-to-image diffusion model and is able to fully disentangle different objects without image-level supervision. To facilitate modeling of object-wise relationships we use signed distance fields as representation and impose a constraint to avoid inter-penetration of objects. To avoid manual scene graph creation we design a text prompt for ChatGPT to generate scene graphs based on text inputs.
To facilitate modeling of object-wise relationships we use signed distance fields as representation and impose a constraint to avoid inter-penetration of objects. To avoid manual scene graph creation we design a text prompt for ChatGPT to generate scene graphs based on text inputs. We conduct both qualitative and quantitative experiments to validate the effectiveness of GraphDreamer in generating high-fidelity compositional 3D scenes with disentangled object entities.
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes which is an effective way to advance ZSL. However existing generative methods rely on the conditions of Gaussian noise and the predefined semantic prototype which limit the generator only optimized on specific seen classes rather than characterizing each visual instance resulting in poor generalizations (e.g. overfitting to seen classes). To address this issue we propose a novel Visual-Augmented Dynamic Semantic prototype method (termed VADS) to boost the generator to learn accurate semantic-visual mapping by fully exploiting the visual-augmented knowledge into semantic conditions. In detail VADS consists of two modules: (1) Visual-aware Domain Knowledge Learning module (VDKL) learns the local bias and global prior of the visual features (referred to as domain visual knowledge) which replace pure Gaussian noise to provide richer prior noise information; (2) Vision-Oriented Semantic Updation module (VOSU) updates the semantic prototype according to the visual representations of the samples. Ultimately we concatenate their output as a dynamic semantic prototype which serves as the condition of the generator.
Ultimately we concatenate their output as a dynamic semantic prototype which serves as the condition of the generator. Extensive experiments demonstrate that our VADS achieves superior CZSL and GZSL performances on three prominent datasets and outperforms other state-of-the-art methods with averaging increases by 6.4% 5.9% and 4.2% on SUN CUB and AWA2 respectively.
Text-to-image generative models specifically those based on diffusion models like Imagen and Stable Diffusion have made substantial advancements. Recently there has been a surge of interest in the delicate refinement of text prompts. Users assign weights or alter the injection time steps of certain words in the text prompts to improve the quality of generated images. However the success of fine-control prompts depends on the accuracy of the text prompts and the careful selection of weights and time steps which requires significant manual intervention. To address this we introduce the Prompt Auto-Editing (PAE) method. Besides refining the original prompts for image generation we further employ an online reinforcement learning strategy to explore the weights and injection time steps of each word leading to the dynamic fine-control prompts. The reward function during training encourages the model to consider aesthetic score semantic consistency and user preferences. Experimental results demonstrate that our proposed method effectively improves the original prompts generating visually more appealing images while maintaining semantic alignment. Code is available at \href https://github.com/Mowenyii/PAE  this https URL .
Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians respectively. Our key idea is to use sparse control points significantly fewer in number than the Gaussians to learn compact 6 DoF transformation bases which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a deformation MLP to predict time-varying 6 DoF transformations for each control point which reduces learning complexities enhances learning abilities and facilitates obtaining temporal and spatial coherent motion patterns. Then we jointly learn the 3D Gaussians the canonical space locations of control points and the deformation MLP to reconstruct the appearance geometry and dynamics of 3D scenes. During learning the location and number of control points are adaptively adjusted to accommodate varying motion complexities in different regions and an ARAP loss following the principle of as rigid as possible is developed to enforce spatial continuity and local rigidity of learned motions.
During learning the location and number of control points are adaptively adjusted to accommodate varying motion complexities in different regions and an ARAP loss following the principle of as rigid as possible is developed to enforce spatial continuity and local rigidity of learned motions. Finally thanks to the explicit sparse motion representation and its decomposition from appearance our method can enable user-controlled motion editing while retaining high-fidelity appearances. Extensive experiments demonstrate that our approach outperforms existing approaches on novel view synthesis with a high rendering speed and enables novel appearance-preserved motion editing applications.
Portable 360^\circ cameras are becoming a cheap and efficient tool to establish large visual databases. By capturing omnidirectional views of a scene these cameras could expedite building environment models that are essential for visual localization. However such an advantage is often overlooked due to the lack of valuable datasets. This paper introduces a new benchmark dataset 360Loc composed of 360^\circ images with ground truth poses for visual localization. We present a practical implementation of 360^\circ mapping combining 360^\circ images with lidar data to generate the ground truth 6DoF poses. 360Loc is the first dataset and benchmark that explores the challenge of cross-device visual positioning involving 360^\circ reference frames and query frames from pinhole ultra-wide FoV fisheye and 360^\circ cameras. We propose a virtual camera approach to generate lower-FoV query frames from 360^\circ images which ensures a fair comparison of performance among different query types in visual localization tasks. We also extend this virtual camera approach to feature matching-based and pose regression-based methods to alleviate the performance loss caused by the cross-device domain gap and evaluate its effectiveness against state-of-the-art baselines.
We also extend this virtual camera approach to feature matching-based and pose regression-based methods to alleviate the performance loss caused by the cross-device domain gap and evaluate its effectiveness against state-of-the-art baselines. We demonstrate that omnidirectional visual localization is more robust in challenging large-scale scenes with symmetries and repetitive structures. These results provide new insights into 360-camera mapping and omnidirectional visual localization with cross-device queries. Project Page and dataset: https://huajianup.github.io/research/360Loc/.
The performance of deep learning models is intrinsically tied to the quality volume and relevance of their training data. Gathering ample data for production scenarios often demands significant time and resources. Among various strategies data augmentation circumvents exhaustive data collection by generating new data points from existing ones. However traditional augmentation techniques can be less effective amidst a shift in training and testing distributions. This paper explores the potential of synthetic data by leveraging large pre-trained models for data augmentation especially when confronted with distribution shifts. Although recent advancements in generative models have enabled several prior works in cross-distribution data generation they require model fine-tuning and a complex setup. To bypass these shortcomings we introduce Domain Gap Embeddings (DoGE) a plug-and-play semantic data augmentation framework in a cross-distribution few-shot setting. Our method extracts disparities between source and desired data distributions in a latent form and subsequently steers a generative process to supplement the training set with endless diverse synthetic samples. Our evaluations conducted on a subpopulation shift and three domain adaptation scenarios under a few-shot paradigm reveal that our versatile method improves performance across tasks without needing hands-on intervention or intricate fine-tuning. DoGE paves the way to effortlessly generate realistic controllable synthetic datasets following the test distributions bolstering real-world efficacy for downstream task models.
Zero-shot 3D point cloud understanding can be achieved via 2D Vision-Language Models (VLMs). Existing strategies directly map VLM representations from 2D pixels of rendered or captured views to 3D points overlooking the inherent and expressible point cloud geometric structure. Geometrically similar or close regions can be exploited for bolstering point cloud understanding as they are likely to share semantic information. To this end we introduce the first training-free aggregation technique that leverages the point cloud's 3D geometric structure to improve the quality of the transferred VLM representation. Our approach operates iteratively performing local-to-global aggregation based on geometric and semantic point-level reasoning. We benchmark our approach on three downstream tasks including classification part segmentation and semantic segmentation with a variety of datasets representing both synthetic/real-world and indoor/outdoor scenarios. Our approach achieves new state-of-the-art results in all benchmarks.
Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However current leading methods rely on supervisory signals. They may compel models to preserve content that aligns with labeled categories and discard content belonging to unlabeled categories. This categorical inductive bias makes these methods less effective in real-world scenarios. To address this issue we propose a self-supervised framework for image redundancy reduction called Learning to Rank Patches (LTRP). We observe that image reconstruction of masked image modeling models is sensitive to the removal of visible patches when the masking ratio is high (e.g. 90%). Building upon it we implement LTRP via two steps: inferring the semantic density score of each patch by quantifying variation between reconstructions with and without this patch and learning to rank the patches with the pseudo score. The entire process is self-supervised thus getting out of the dilemma of categorical inductive bias. We design extensive experiments on different datasets and tasks. The results demonstrate that LTRP outperforms both supervised and other self-supervised methods due to the fair assessment of image content.
Multi-task visual scene understanding aims to leverage the relationships among a set of correlated tasks which are solved simultaneously by embedding them within a uni- fied network. However most existing methods give rise to two primary concerns from a task-level perspective: (1) the lack of task-independent correspondences for distinct tasks and (2) the neglect of explicit task-consensual dependencies among various tasks. To address these issues we propose a novel synergy embedding models (SEM) which goes be- yond multi-task dense prediction by leveraging two innova- tive designs: the intra-task hierarchy-adaptive module and the inter-task EM-interactive module. Specifically the con- structed intra-task module incorporates hierarchy-adaptive keys from multiple stages enabling the efficient learning of specialized visual patterns with an optimal trade-off. In ad- dition the developed inter-task module learns interactions from a compact set of mutual bases among various tasks benefiting from the expectation maximization (EM) algo- rithm. Extensive empirical evidence from two public bench- marks NYUD-v2 and PASCAL-Context demonstrates that SEM consistently outperforms state-of-the-art approaches across a range of metrics.