CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
This paper presents GenH2R a framework for learning generalizable vision-based human-to-robot (H2R) handover skills. The goal is to equip robots with the ability to reliably receive objects with unseen geometry handed over by humans in various complex trajectories. We acquire such generalizability by learning H2R handover at scale with a comprehensive solution including procedural simulation assets creation automated demonstration generation and effective imitation learning. We leverage large-scale 3D model repositories dexterous grasp generation methods and curve-based 3D animation to create an H2R handover simulation environment named GenH2R-Sim surpassing the number of scenes in existing simulators by three orders of magnitude. We further introduce a distillation-friendly demonstration generation method that automatically generates a million high-quality demonstrations suitable for learning. Finally we present a 4D imitation learning method augmented by a future forecasting objective to distill demonstrations into a visuo-motor handover policy. Experimental evaluations in both simulators and the real world demonstrate significant improvements (at least +10% success rate) over baselines in all cases.
Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-guided radiotherapy. Existing multi-modality image registration algorithms rely on statistical-based similarity measures or local structural image representations. However the former is sensitive to locally varying noise while the latter is not discriminative enough to cope with complex anatomical structures in multimodal scans causing ambiguity in determining the anatomical correspondence across scans with different modalities. In this paper we propose a modality-agnostic structural representation learning method which leverages Deep Neighbourhood Self-similarity (DNS) and anatomy-aware contrastive learning to learn discriminative and contrast-invariance deep structural image representations (DSIR) without the need for anatomical delineations or pre-aligned training images. We evaluate our method on multiphase CT abdomen MR-CT and brain MR T1w-T2w registration. Comprehensive results demonstrate that our method is superior to the conventional local structural representation and statistical-based similarity measures in terms of discriminability and accuracy.
Image-language models with prompt learning have shown remarkable advances in numerous downstream vision tasks. Nevertheless conventional prompt learning methods overfit the training distribution and lose the generalization ability on the test distributions. To improve the generalization across various distribution shifts we propose any-shift prompting: a general probabilistic inference framework that considers the relationship between training and test distributions during prompt learning. We explicitly connect training and test distributions in the latent space by constructing training and test prompts in a hierarchical architecture. Within this framework the test prompt exploits the distribution relationships to guide the generalization of the CLIP image-language model from training to any test distribution. To effectively encode the distribution information and their relationships we further introduce a transformer inference network with a pseudo-shift training mechanism. The network generates the tailored test prompt with both training and test information in a feedforward pass avoiding extra training costs at test time. Extensive experiments on twenty-three datasets demonstrate the effectiveness of any-shift prompting on the generalization over various distribution shifts.
We present InterHandGen a novel framework that learns the generative prior of two-hand interaction. Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object. Our prior can be incorporated into any optimization or learning methods to reduce ambiguity in an ill-posed setup. Our key observation is that directly modeling the joint distribution of multiple instances imposes high learning complexity due to its combinatorial nature. Thus we propose to decompose the modeling of joint distribution into the modeling of factored unconditional and conditional single instance distribution. In particular we introduce a diffusion model that learns the single-hand distribution unconditional and conditional to another hand via conditioning dropout. For sampling we combine anti-penetration and classifier-free guidance to enable plausible generation. Furthermore we establish the rigorous evaluation protocol of two-hand synthesis where our method significantly outperforms baseline generative models in terms of plausibility and diversity. We also demonstrate that our diffusion prior can boost the performance of two-hand reconstruction from monocular in-the-wild images achieving new state-of-the-art accuracy.
Fine-grained medical action analysis plays a vital role in improving medical skill training efficiency but it faces the problems of data and algorithm shortage. Cardiopulmonary Resuscitation (CPR) is an essential skill in emergency treatment. Currently the assessment of CPR skills mainly depends on dummies and trainers leading to high training costs and low efficiency. For the first time this paper constructs a vision-based system to complete error action recognition and skill assessment in CPR. Specifically we define 13 types of single-error actions and 74 types of composite error actions during external cardiac compression and then develop a video dataset named CPR-Coach. By taking the CPR-Coach as a benchmark this paper investigates and compares the performance of existing action recognition models based on different data modalities. To solve the unavoidable "Single-class Training & Multi-class Testing" problem we propose a human-cognition-inspired framework named ImagineNet to improve the model's multi-error recognition performance under restricted supervision. Extensive comparison and actual deployment experiments verify the effectiveness of the framework. We hope this work could bring new inspiration to the computer vision and medical skills training communities simultaneously. The dataset and the code are publicly available on https://github.com/Shunli-Wang/CPR-Coach.
Creating high-quality and interactive virtual environments such as games and simulators often involves complex and costly manual modeling processes. In this paper we present Video2Game a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the heart of our system are three core components: (i) a neural radiance fields (NeRF) module that effectively captures the geometry and visual appearance of the scene; (ii) a mesh module that distills the knowledge from NeRF for faster rendering; and (iii) a physics module that models the interactions and physical dynamics among the objects. By following the carefully designed pipeline one can construct an interactable and actionable digital replica of the real world. We benchmark our system on both indoor and large-scale outdoor scenes. We show that we can not only produce highly-realistic renderings in real-time but also build interactive games on top.
Most diffusion models assume that the reverse process adheres to a Gaussian distribution. However this approximation has not been rigorously validated especially at singularities where t=0 and t=1. Improperly dealing with such singularities leads to an average brightness issue in applications and limits the generation of images with extreme brightness or darkness. We primarily focus on tackling singularities from both theoretical and practical perspectives. Initially we establish the error bounds for the reverse process approximation and showcase its Gaussian characteristics at singularity time steps. Based on this theoretical insight we confirm the singularity at t=1 is conditionally removable while it at t=0 is an inherent property. Upon these significant conclusions we propose a novel plug-and-play method SingDiffusion to address the initial singular time step sampling which not only effectively resolves the average brightness issue for a wide range of diffusion models without extra training efforts but also enhances their generation capability in achieving notable lower FID scores.
We introduce MatSynth a dataset of 4000+ CC0 ultra-high resolution PBR materials. Materials are crucial components of virtual relightable assets defining the interaction of light at the surface of geometries. Given their importance significant research effort was dedicated to their representation creation and acquisition. However in the past 6 years most research in material acquisition or generation relied either on the same unique dataset or on company-owned huge library of procedural materials. With this dataset we propose a significantly larger more diverse and higher resolution set of materials than previously publicly available. We carefully discuss the data collection process and demonstrate the benefits of this dataset for material acquisition and generation applications. The complete data further contains metadata with each material's origin license category tags creation method and when available descriptions and physical size as well as 3M+ renderings of the augmented materials in 1K under various environment lightings. The MatSynth dataset is released through the project page at: https://www.gvecchio.com/matsynth.
Generative Adversarial Networks (GANs) significantly advanced image generation but their performance heavily depends on abundant training data. In scenarios with limited data GANs often struggle with discriminator overfitting and unstable training. Batch Normalization (BN) despite being known for enhancing generalization and training stability has rarely been used in the discriminator of Data-Efficient GANs. Our work addresses this gap by identifying a critical flaw in BN: the tendency for gradient explosion during the centering and scaling steps. To tackle this issue we present CHAIN (lipsCHitz continuity constrAIned Normalization) which replaces the conventional centering step with zero-mean regularization and integrates a Lipschitz continuity constraint in the scaling step. CHAIN further enhances GAN training by adaptively interpolating the normalized and unnormalized features effectively avoiding discriminator overfitting. Our theoretical analyses firmly establishes CHAIN's effectiveness in reducing gradients in latent features and weights improving stability and generalization in GAN training. Empirical evidence supports our theory. CHAIN achieves state-of-the-art results in data-limited scenarios on CIFAR-10/100 ImageNet five low-shot and seven high-resolution few-shot image datasets.
Existing tracking methods mainly focus on learning better target representation or developing more robust prediction models to improve tracking performance. While tracking performance has significantly improved the target loss issue occurs frequently due to tracking failures complete occlusion or out-of-view situations. However considerably less attention is paid to the self-recovery issue of tracking methods which is crucial for practical applications. To this end we propose a recoverable tracking framework \ourmethod that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery ability. Specifically we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Upon the PN tree memory we develop corresponding walking rules for determining the state of the target and define a set of control flows to unite the tracker and the detector in different tracking scenarios. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss. The favorable performance in comparison against the state-of-the-art methods on numerous challenging benchmarks demonstrates the effectiveness of the proposed algorithm. All the source code and trained models will be released at https://github.com/NorahGreen/RTracker.
Facial geometry and appearance capture have demonstrated tremendous success in 3D scanning real humans in studios. Recent works propose to democratize this technique while keeping the results high quality. However they are still inconvenient for daily usage. In addition they focus on an easier problem of only capturing facial skin. This paper proposes a novel method for high-quality face capture featuring an easy-to-use system and the capability to model the complete face with skin mouth interior hair and eyes. We reconstruct facial geometry and appearance from a single co-located smartphone flashlight sequence captured in a dim room where the flashlight is the dominant light source (e.g. rooms with curtains or at night). To model the complete face we propose a novel hybrid representation to effectively model both eyes and other facial regions along with novel techniques to learn it from images. We apply a combined lighting model to compactly represent real illuminations and exploit a morphable face albedo model as a reflectance prior to disentangle diffuse and specular. Experiments show that our method can capture high-quality 3D relightable scans. Our code will be released.
State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by passing latent representations between the different modules. In contrast to previous approaches that rely on a unified grid to represent the belief state of the scene we propose dedicated representations to disentangle dynamic agents and static scene elements. This allows us to explicitly compensate for the effect of both ego and object motion between consecutive time steps and to flexibly propagate the belief state through time. Furthermore dynamic objects can not only attend to the input camera images but also directly benefit from the inferred static scene structure via a novel dynamic-static cross-attention. Extensive experiments on the challenging nuScenes benchmark demonstrate the benefits of the proposed dual-stream design especially for modelling highly dynamic agents in the scene and highlight the improved temporal consistency of our approach. Our method titled DualAD not only outperforms independently trained single-task networks but also improves over previous state-of-the-art end-to-end models by a large margin on all tasks along the functional chain of driving.
In this paper we propose a novel framework to fully exploit the potential of a single vector for scene text recognition (STR). Different from previous sequence-to-sequence methods that rely on a sequence of visual tokens to represent scene text images we prove that just one token is enough to characterize the entire text image and achieve accurate text recognition. Based on this insight we introduce a new paradigm for STR called One Token rEcognizer (OTE). Specifically we implement an image-to-vector encoder to extract the fine-grained global semantics eliminating the need for sequential features. Furthermore an elegant yet potent vector-to-sequence decoder is designed to adaptively diffuse global semantics to corresponding character locations enabling both autoregressive and non-autoregressive decoding schemes. By executing decoding within a high-level representational space our vector-to-sequence (V2S) approach avoids the alignment issues between visual tokens and character embeddings prevalent in traditional sequence-to-sequence methods. Remarkably due to introducing character-wise fine-grained information such global tokens also boost the performance of scene text retrieval tasks. Extensive experiments on synthetic and real datasets demonstrate the effectiveness of our method by achieving new state-of-the-art results on various public STR benchmarks. Our code is available at https://github.com/Xu-Jianjun/OTE.
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this distribution with a neural network. This lets us estimate the likelihood of test videos and detect video anomalies by thresholding the likelihood estimates. We train our video anomaly detector using a modification of denoising score matching a method that injects training data with noise to facilitate modeling its distribution. To eliminate hyperparameter selection we model the distribution of noisy video features across a range of noise levels and introduce a regularizer that tends to align the models for different levels of noise. At test time we combine anomaly indications at multiple noise scales with a Gaussian mixture model. Running our video anomaly detector induces minimal delays as inference requires merely extracting the features and forward-propagating them through a shallow neural network and a Gaussian mixture model. Our experiments on five popular video anomaly detection benchmarks demonstrate state-of-the-art performance both in the object-centric and in the frame-centric setup.
The landscape of deep learning research is moving towards innovative strategies to harness the true potential of data. Traditionally emphasis has been on scaling model architectures resulting in large and complex neural networks which can be difficult to train with limited computational resources. However independently of the model size data quality (i.e. amount and variability) is still a major factor that affects model generalization. In this work we propose a novel technique to exploit available data through the use of automatic data augmentation for the tasks of image classification and semantic segmentation. We introduce the first Differentiable Augmentation Search method (DAS) to generate variations of images that can be processed as videos. Compared to previous approaches DAS is extremely fast and flexible allowing the search on very large search spaces in less than a GPU day. Our intuition is that the increased receptive field in the temporal dimension provided by DAS could lead to benefits also to the spatial receptive field. More specifically we leverage DAS to guide the reshaping of the spatial receptive field by selecting task-dependant transformations.
Our intuition is that the increased receptive field in the temporal dimension provided by DAS could lead to benefits also to the spatial receptive field. More specifically we leverage DAS to guide the reshaping of the spatial receptive field by selecting task-dependant transformations. As a result compared to standard augmentation alternatives we improve in terms of accuracy on ImageNet Cifar10 Cifar100 Tiny-ImageNet Pascal-VOC-2012 and CityScapes datasets when plugging-in our DAS over different light-weight video backbones.
Segment Anything Model (SAM) has achieved impressive performance in many computer vision tasks. However as a large-scale model the immense memory and computation costs hinder its practical deployment. In this paper we propose a post-training quantization (PTQ) framework for Segment Anything Model namely PTQ4SAM. First we investigate the inherent bottleneck of SAM quantization attributed to the bimodal distribution in \cls post-Key-Linear  activations. We analyze its characteristics from both per-tensor and per-channel perspectives and propose a Bimodal Integration strategy which utilizes a mathematically equivalent sign operation to transform the bimodal distribution into a relatively easy-quantized normal distribution offline. Second SAM encompasses diverse attention mechanisms (i.e. self-attention and two-way cross-attention) resulting in substantial variations in the post-Softmax distributions. Therefore we introduce an Adaptive Granularity Quantization for Softmax through searching the optimal power-of-two base which is hardware-friendly. Extensive experimental results across various vision tasks (instance segmentation semantic segmentation and object detection) datasets and model variants show the superiority of PTQ4SAM.
Therefore we introduce an Adaptive Granularity Quantization for Softmax through searching the optimal power-of-two base which is hardware-friendly. Extensive experimental results across various vision tasks (instance segmentation semantic segmentation and object detection) datasets and model variants show the superiority of PTQ4SAM. For example when quantizing SAM-L to 6-bit we achieve lossless accuracy for instance segmentation about 0.5% drop with theoretical 3.9xacceleration. The code is available at https://github.com/chengtao-lv/PTQ4SAM.
Semantic segmentation in bird's eye view (BEV) plays a crucial role in autonomous driving. Previous methods usually follow an end-to-end pipeline directly predicting the BEV segmentation map from monocular RGB inputs. However the challenge arises when the RGB inputs and BEV targets from distinct perspectives making the direct point-to-point predicting hard to optimize. In this paper we decompose the original BEV segmentation task into two stages namely BEV map reconstruction and RGB-BEV feature alignment. In the first stage we train a BEV autoencoder to reconstruct the BEV segmentation maps given corrupted noisy latent representation which urges the decoder to learn fundamental knowledge of typical BEV patterns. The second stage involves mapping RGB input images into the BEV latent space of the first stage directly optimizing the correlations between the two views at the feature level. Our approach simplifies the complexity of combining perception and generation into distinct steps equipping the model to handle intricate and challenging scenes effectively. Besides we propose to transform the BEV segmentation map from the Cartesian to the polar coordinate system to establish the column-wise correspondence between RGB images and BEV maps. Moreover our method requires neither multi-scale features nor camera intrinsic parameters for depth estimation and saves computational overhead.
Besides we propose to transform the BEV segmentation map from the Cartesian to the polar coordinate system to establish the column-wise correspondence between RGB images and BEV maps. Moreover our method requires neither multi-scale features nor camera intrinsic parameters for depth estimation and saves computational overhead. Extensive experiments on nuScenes and Argoverse show the effectiveness and efficiency of our method. Code is available at https://github.com/happytianhao/TaDe.
The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing methods propose spiking self-attention mechanisms that are compatible with SNNs they lack reasonable scaling methods and the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting local features. To address these challenges we propose a novel spiking self-attention mechanism named Dual Spike Self-Attention (DSSA) with a reasonable scaling method. Based on DSSA we propose a novel spiking Vision Transformer architecture called SpikingResformer which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters. Experimental results show that SpikingResformer achieves higher accuracy with fewer parameters and lower energy consumption than other spiking Vision Transformer counterparts. Notably our SpikingResformer-L achieves 79.40% top-1 accuracy on ImageNet with 4 time-steps which is the state-of-the-art result in the SNN field.