CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds further advancing downstream human-centric tasks and applications. Previous works usually focus on tackling one specific task and rely on huge labeled data which has poor generalization capability. Considering that human has specific characteristics including the structural semantics of human body and the dynamics of human motions we propose a unified framework to make full use of the prior knowledge and explore the inherent features in the data itself for generalized human-centric point cloud video understanding. Extensive experiments demonstrate that our method achieves state-of-the-art performance on various human-related tasks including action recognition and 3D pose estimation. All datasets and code will be released soon.
While recent 3D instance segmentation approaches show promising results based on transformer architectures they often fail to correctly identify instances with similar appearances. They also ambiguously determine edges leading to multiple misclassifications of adjacent edge points. In this work we introduce a novel framework called EASE to overcome these challenges and improve the perception of complex 3D instances. We first propose a semantic guidance network to leverage rich semantic knowledge from a language model as intelligent priors enhancing the functional understanding of real-world instances beyond relying solely on geometrical information. We explicitly instruct the basic instance queries using text embeddings of each instance to learn deep semantic details. Further we utilize the edge prediction module encouraging the segmentation network to be edge-aware. We extract voxel-wise edge maps from point features and use them as auxiliary information for learning edge cues. In our extensive experiments on large-scale benchmarks ScanNetV2 ScanNet200 S3DIS and STPLS3D our EASE outperforms existing state-of-the-art models demonstrating its superior performance.
Passive depth estimation based on stereo or defocus relies on the presence of the texture on an object to resolve its depth. Hence recovering the depth of a textureless object-- for example a large white wall--is not just hard but perhaps even impossible. Or is it? We show that spatial coherence a property of natural light sources can be used to resolve the depth of a scene point even when it is textureless. Our approach relies on the idea that natural light scattered off a scene point is locally coherent with itself while incoherent with the light scattered from other surface points; we use this insight to design an optical setup that uses self-interference as a texture feature for estimating depth. Our lab prototype is capable of resolving the depths of textureless objects in sunlight as well as indoor lights.
In this work we present a novel perspective on detecting out-of-distribution (OOD) samples and propose an algorithm for sample-aware model selection to enhance the effectiveness of OOD detection. Our algorithm determines for each test input which pre-trained models in the model zoo are capable of identifying the test input as an OOD sample. If no such models exist in the model zoo the test input is classified as an in-distribution (ID) sample. We theoretically demonstrate that our method maintains the true positive rate of ID samples and accurately identifies OOD samples with high probability when there are a sufficient number of diverse pre-trained models in the model zoo. Extensive experiments were conducted to validate our method demonstrating that it leverages the complementarity among single-model detectors to consistently improve the effectiveness of OOD sample identification. Compared to base-line methods our approach improved the relative performance by 65.40% and 37.25% on the CIFAR10 and ImageNet benchmarks respectively.
Collaborative perception in automated vehicles leverages the exchange of information between agents aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features and (ii) compressed orthogonal attention features shared between vehicles. Additionally due to the lack of a collaborative perception dataset designed for semantic occupancy prediction we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30% and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications showcasing enhanced accuracy and enriched semantic-awareness in road environments.
In class incremental learning (CIL) scenarios the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge. It is mainly caused by the characteristic of discriminative models. With the growing popularity of the generative multi-modal models we would explore replacing discriminative models with generative ones for CIL. However transitioning from discriminative to generative models requires addressing two key challenges. The primary challenge lies in transferring the generated textual information into the classification of distinct categories. Additionally it requires formulating the task of CIL within a generative framework. To this end we propose a novel generative multi-modal model (GMM) framework for class incremental learning. Our approach directly generates labels for images using an adapted generative model. After obtaining the detailed text we use a text encoder to extract text features and employ feature matching to determine the most similar label as the classification prediction. In the conventional CIL settings we achieve significantly better results in long-sequence task scenarios. Under the Few-shot CIL setting we have improved by at least 14% over the current state-of-the-art methods with significantly less forgetting.
Low-resource settings are well-established in natural lan- guage processing where many languages lack sufficient data for deep learning at scale. However low-resource problems are under-explored in computer vision. In this paper we address this gap and explore the challenges of low-resource image tasks with vision foundation models. We first collect a benchmark of genuinely low-resource image data covering historic maps circuit diagrams and mechanical drawings. These low-resource settings all share three challenges: data scarcity fine-grained differences and the distribution shift from natural images to the specialized domain of interest. While existing foundation models have shown impressive generalizability we find they cannot transfer well to our low-resource tasks. To begin to tackle the challenges of low-resource vision we introduce one simple baseline per challenge. Specifically we i) enlarge the data space by generative models ii) adopt the best sub-kernels to encode local regions for fine-grained difference discovery and iii) learn attention for specialized domains. Experiments on our three low-resource tasks demonstrate our proposals already provide a better baseline than transfer learning data aug- mentation and fine-grained methods. This highlights the unique characteristics and challenges of low-resource vision for foundation models that warrant further investigation. Project page: https://xiaobai1217.github.io/ Low-Resource-Vision/.
We introduce a new RGB-D object dataset captured in the wild called WildRGB-D. Unlike most existing real-world object-centric datasets which only come with RGB capturing the direct capture of the depth channel allows better 3D annotations and broader downstream applications. WildRGB-D comprises large-scale category-level RGB-D object videos which are taken using an iPhone to go around the objects in 360 degrees. It contains around 8500 recorded objects and nearly 20000 RGB-D videos across 46 common object categories. These videos are taken with diverse cluttered backgrounds with three setups to cover as many real-world scenarios as possible: (i) a single object in one video; (ii) multiple objects in one video; and (iii) an object with a static hand in one video. The dataset is annotated with object masks real-world scale camera poses and reconstructed aggregated point clouds from RGBD videos. We benchmark four tasks with WildRGB-D including novel view synthesis camera pose estimation object 6d pose estimation and object surface reconstruction. Our experiments show that the large-scale capture of RGB-D objects provides a large potential to advance 3D object learning. Our project page is https://wildrgbd.github.io/.
Conventional image outpainting methods usually treat unobserved areas as unknown and extend the scene only in terms of semantic consistency thus overlooking the hidden information in shadows cast by unobserved areas such as the invisible shapes and semantics. In this paper we propose to extract and utilize the hidden information of unobserved areas from their shadows to enhance image outpainting. To this end we propose an end-to-end deep approach that explicitly looks into the shadows within the image. Specifically we extract shadows from the input image and identify instance-level shadow regions cast by the unobserved areas. Then the instance-level shadow representations are concatenated to predict the scene layout of each unobserved instance and outpaint the unobserved areas. Finally two discriminators are implemented to enhance alignment between the extended semantics and their shadows. In the experiments we show that our proposed approach provides complementary cues for outpainting and achieves considerable improvement on all datasets by adopting our approach as a plug-in module.
Tumor synthesis enables the creation of artificial tumors in medical images facilitating the training of AI models for tumor detection and segmentation. However success in tumor synthesis hinges on creating visually realistic tumors that are generalizable across multiple organs and furthermore the resulting AI models being capable of detecting real tumors in images sourced from different domains (e.g. hospitals). This paper made a progressive stride toward generalizable tumor synthesis by leveraging a critical observation: early-stage tumors (< 2cm) tend to have similar imaging characteristics in computed tomography (CT) whether they originate in the liver pancreas or kidneys. We have ascertained that generative AI models e.g. Diffusion Models can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ. Moreover we have shown that AI models trained on these synthetic tumors can be generalized to detect and segment real tumors from CT volumes encompassing a broad spectrum of patient demographics imaging protocols and healthcare facilities.
For image super-resolution (SR) bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. This work introduces a novel "Low-Res Leads the Way" (LWay) training framework merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images. Our approach utilizes a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images merging them with super-resolved outputs for LR reconstruction. Leveraging unseen LR images for self-supervised learning guides the model to adapt its modeling space to the target domain facilitating fine-tuning of SR models without requiring paired high-resolution (HR) images. The integration of Discrete Wavelet Transform (DWT) further refines the focus on high-frequency details. Extensive evaluations show that our method significantly improves the generalization and detail restoration capabilities of SR models on unseen real-world datasets outperforming existing methods. Our training regime is universally compatible requiring no network architecture modifications making it a practical solution for real-world SR applications.
The recently emerging text-to-motion advances have spired numerous attempts for convenient and interactive human motion generation. Yet existing methods are largely limited to generating body motions only without considering the rich two-hand motions let alone handling various conditions like body dynamics or texts. To break the data bottleneck we propose BOTH57M a novel multi-modal dataset for two-hand motion generation. Our dataset includes accurate motion tracking for the human body and hands and provides pair-wised finger-level hand annotations and body descriptions. We further provide a strong baseline method BOTH2Hands for the novel task: generating vivid two-hand motions from both implicit body dynamics and explicit text prompts. We first warm up two parallel body-to-hand and text-to-hand diffusion models and then utilize the cross-attention transformer for motion blending. Extensive experiments and cross-validations demonstrate the effectiveness of our approach and dataset for generating convincing two-hand motions from the hybrid body-and-textual conditions. Our dataset and code will be disseminated to the community for future research which can be found at https://github.com/Godheritage/BOTH2Hands.
Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image. Recent methods that introduce 3D global representation into diffusion models have shown the potential to generate consistent multiviews but they have reduced generation speed and face challenges in maintaining generalizability and quality. To address this issue we propose EpiDiff a localized interactive multiview diffusion model. At the core of the proposed approach is to insert a lightweight epipolar attention block into the frozen diffusion model leveraging epipolar constraints to enable cross-view interaction among feature maps of neighboring views. The newly initialized 3D modeling module preserves the original feature distribution of the diffusion model exhibiting compatibility with a variety of base diffusion models. Experiments show that EpiDiff generates 16 multiview images in just 12 seconds and it surpasses previous methods in quality evaluation metrics including PSNR SSIM and LPIPS. Additionally EpiDiff can generate a more diverse distribution of views improving the reconstruction quality from generated multiviews. Please see the project page at https://huanngzh.github.io/EpiDiff/.
To interpret Vision Transformers post-hoc explanations assign salience scores to input pixels providing human-understandable heatmaps. However whether these interpretations reflect true rationales behind the model's output is still underexplored. To address this gap we study the faithfulness criterion of explanations: the assigned salience scores should represent the influence of the corresponding input pixels on the model's predictions. To evaluate faithfulness we introduce Salience-guided Faithfulness Coefficient (SaCo) a novel evaluation metric leveraging essential information of salience distribution. Specifically we conduct pair-wise comparisons among distinct pixel groups and then aggregate the differences in their salience scores resulting in a coefficient that indicates the explanation's degree of faithfulness. Our explorations reveal that current metrics struggle to differentiate between advanced explanation methods and Random Attribution thereby failing to capture the faithfulness property. In contrast our proposed SaCo offers a reliable faithfulness measurement establishing a robust metric for interpretations. Furthermore our SaCo demonstrates that the use of gradient and multi-layer aggregation can markedly enhance the faithfulness of attention-based explanation shedding light on potential paths for advancing Vision Transformer explainability.
Establishing precise semantic correspondence across object instances in different images is a fundamental and challenging task in computer vision. In this task difficulty arises often due to three challenges: confusing regions with similar appearance inconsistent object scale and indistinguishable nearby pixels. Recognizing these challenges our paper proposes a novel semantic matching pipeline named LPMFlow toward extracting fine-grained semantics and geometry layouts for building pixel-level semantic correspondences. LPMFlow consists of three modules each addressing one of the aforementioned challenges. The layout-aware representation learning module uniformly encodes source and target tokens to distinguish pixels or regions with similar appearances but different geometry semantics. The progressive feature superresolution module outputs four sets of 4D correlation tensors to generate accurate semantic flow between objects in different scales. Finally the matching flow integration and refinement module is exploited to fuse matching flow in different scales to give the final flow predictions. The whole pipeline can be trained end-to-end with a balance of computational cost and correspondence details. Extensive experiments based on benchmarks such as SPair-71K PF-PASCAL and PF-WILLOW have proved that the proposed method can well tackle the three challenges and outperform the previous methods especially in more stringent settings. Code is available at https://github.com/YXSUNMADMAX/LPMFlow.
We propose a self-supervised method for learning representations based on spatial audio-visual correspondences in egocentric videos. Our method uses a masked auto-encoding framework to synthesize masked binaural audio through the synergy of audio and vision thereby learning useful spatial relationships between the two modalities. We use our pretrained features to tackle two downstream video tasks requiring spatial understanding in social scenarios: active speaker detection and spatial audio denoising. Through extensive experiments we show that our features are generic enough to improve over multiple state-of-the-art baselines on both tasks on two challenging egocentric video datasets that offer binaural audio EgoCom and EasyCom. Project: http://vision.cs.utexas.edu/ projects/ego_av_corr.
We present DreamAvatar a text-and-shape guided framework for generating high-quality 3D human avatars with controllable poses. While encouraging results have been reported by recent methods on text-guided 3D common object generation generating high-quality human avatars remains an open challenge due to the complexity of the human body's shape pose and appearance. We propose DreamAvatar to tackle this challenge which utilizes a trainable NeRF for predicting density and color for 3D points and pretrained text-to-image diffusion models for providing 2D self-supervision. Specifically we leverage the SMPL model to provide shape and pose guidance for the generation. We introduce a dual-observation-space design that involves the joint optimization of a canonical space and a posed space that are related by a learnable deformation field. This facilitates the generation of more complete textures and geometry faithful to the target pose. We also jointly optimize the losses computed from the full body and from the zoomed-in 3D head to alleviate the common multi-face "Janus" problem and improve facial details in the generated avatars. Extensive evaluations demonstrate that DreamAvatar significantly outperforms existing methods establishing a new state-of-the-art for text-and-shape guided 3D human avatar generation.
Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches involve learning WSIs as instance-bag representations emphasizing significant instances but struggling to capture the interactions between instances. Additionally conventional graph representation methods utilize explicit spatial positions to construct topological structures but restrict the flexible interaction capabilities between instances at arbitrary locations particularly when spatially distant. In response we propose a novel dynamic graph representation algorithm that conceptualizes WSIs as a form of the knowledge graph structure. Specifically we dynamically construct neighbors and directed edge embeddings based on the head and tail relationships between instances. Then we devise a knowledge-aware attention mechanism that can update the head node features by learning the joint attention score of each neighbor and edge. Finally we obtain a graph-level embedding through the global pooling process of the updated head serving as an implicit representation for the WSI classification. Our end-to-end graph representation learning approach has outperformed the state-of-the-art WSI analysis methods on three TCGA benchmark datasets and in-house test sets. Our code is available at https://github.com/WonderLandxD/WiKG.
We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain thus exposing their hidden inside. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images. We report two findings. First explicit mapping between the brain and deep-network features across dimensions of space layers scales and channels is crucial. This mapping method FactorTopy is plug-and-play for any deep-network; with it one can paint a picture of the network onto the brain (literally!). Second our visualization shows how different training methods matter: they lead to remarkable differences in hierarchical organization and scaling behavior growing with more data or network capacity. It also provides insight into fine-tuning: how pre-trained models change when adapting to small datasets. We found brain-like hierarchically organized network suffer less from catastrophic forgetting after fine-tuned.
This paper addresses an interesting yet challenging problem-- source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation--given only a pinhole image-trained model (i.e. source) and unlabeled panoramic images (i.e. target). Tackling this problem is nontrivial due to the semantic mismatches style discrepancies and inevitable distortion of panoramic images. To this end we propose a novel method that utilizes Tangent Projection (TP) as it has less distortion and meanwhile slits the equirectangular projection (ERP) with a fixed FoV to mimic the pinhole images. Both projections are shown effective in extracting knowledge from the source model. However the distinct projection discrepancies between source and target domains impede the direct knowledge transfer; thus we propose a panoramic prototype adaptation module (PPAM) to integrate panoramic prototypes from the extracted knowledge for adaptation. We then impose the loss constraints on both predictions and prototypes and propose a cross-dual attention module (CDAM) at the feature level to better align the spatial and channel characteristics across the domains and projections. Both knowledge extraction and transfer processes are synchronously updated to reach the best performance.
We then impose the loss constraints on both predictions and prototypes and propose a cross-dual attention module (CDAM) at the feature level to better align the spatial and channel characteristics across the domains and projections. Both knowledge extraction and transfer processes are synchronously updated to reach the best performance. Extensive experiments on the synthetic and real-world benchmarks including outdoor and indoor scenarios demonstrate that our method achieves significantly better performance than prior SFUDA methods for pinhole-to-panoramic adaptation.