The Laplace-Beltrami operator (LBO) emerges from studying manifolds equipped with a Riemannian metric. It is often called the swiss army knife of geometry processing as it allows to capture intrinsic shape information and gives rise to heat diffusion geodesic distances and a multitude of shape descriptors. It also plays a central role in geometric deep learning. In this work we explore Finsler manifolds as a generalization of Riemannian manifolds. We revisit the Finsler heat equation and derive a Finsler heat kernel and a Finsler-Laplace-Beltrami Operator (FLBO): a novel theoretically justified anisotropic Laplace-Beltrami operator (ALBO). In experimental evaluations we demonstrate that the proposed FLBO is a valuable alternative to the traditional Riemannian-based LBO and ALBOs for spatial filtering and shape correspondence estimation. We hope that the proposed Finsler heat kernel and the FLBO will inspire further exploration of Finsler geometry in the computer vision community.
论文检索
输入标题、作者或关键词，从 2,715 篇学术成果中精准定位
Visual perception evolves over time. This is particularly the case of oracle bone scripts where visual glyphs seem intuitive to people from distant past prove difficult to be understood in contemporary eyes. While semantic correspondence of an oracle can be found via a dictionary lookup this proves to be not enough for public viewers to connect the dots i.e. why does this oracle mean that? Common solution relies on a laborious curation process to collect visual guide for each oracle (Fig.1) which hinges on the case-by-case effort and taste of curators. This paper delves into one natural follow-up question: can AI take over?Begin with a comprehensive human study we show participants could indeed make better sense of an oracle glyph subjected to a proper visual guide and its efficacy can be approximated via a novel metric termed TransOV (Transferable Oracle Visuals). We then define a new conditional visual generation task based on an oracle glyph and its semantic meaning and importantly approach it by circumventing any form of model training in the presence of fatal lack of oracle data. At its heart is to leverage foundation model like GPT-4V to reason about the visual cues hidden inside an oracle and take advantage of an existing text-to-image model for final visual guide generation. Extensive empirical evidence shows our AI-enabled visual guides achieve significantly comparable TransOV performance compared with those collected under manual efforts. Finally we demonstrate the versatility of our system under a more complex setting where it is required to work alongside an AI image denoiser to cope with raw oracle scan image inputs (cf. processed clean oracle glyphs). Code is available at https://github.com/RQ-Lab/OBS-Visual.
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion
PDF ↗Diffusion Models (DMs) have evolved into advanced image generation tools especially for few-shot generation where a pre-trained model is fine-tuned on a small set of images to capture a specific style or object. Despite their success concerns exist about potential copyright violations stemming from the use of unauthorized data in this process. In response we present Contrasting Gradient Inversion for Diffusion Models (CGI-DM) a novel method featuring vivid visual representations for digital copyright authentication. Our approach involves removing partial information of an image and recovering missing details by exploiting conceptual differences between the pre-trained and fine-tuned models. We formulate the differences as KL divergence between latent variables of the two models when given the same input image which can be maximized through Monte Carlo sampling and Projected Gradient Descent (PGD). The similarity between original and recovered images serves as a strong indicator of potential infringements. Extensive experiments on the WikiArt and Dreambooth datasets demonstrate the high accuracy of CGI-DM in digital copyright authentication surpassing alternative validation techniques. Code implementation is available at https://github.com/Nicholas0228/Revelio.
Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between source and target point clouds. Existing methods obtain correspondences directly by computing point-wise feature similarity between point clouds. However non-rigid objects possess strong deformability and unusual shapes making it a longstanding challenge to directly establish correspondences between point clouds with unconventional shapes. To address this challenge we propose an unsupervised Template-Assisted point cloud shape correspondence Network termed TANet including a template generation module and a template assistance module. The proposed TANet enjoys several merits. Firstly the template generation module establishes a set of learnable templates with explicit structures. Secondly we introduce a template assistance module that extensively leverages the generated templates to establish more accurate shape correspondences from multiple perspectives. Extensive experiments on four human and animal datasets demonstrate that TANet achieves favorable performance against state-of-the-art methods.
End-to-end autonomous driving recently emerged as a promising research direction to target autonomy from a full-stack perspective. Along this line many of the latest works follow an open-loop evaluation setting on nuScenes to study the planning behavior. In this paper we delve deeper into the problem by conducting thorough analyses and demystifying more devils in the details. We initially observed that the nuScenes dataset characterized by relatively simple driving scenarios leads to an under-utilization of perception information in end-to-end models incorporating ego status such as the ego vehicle's velocity. These models tend to rely predominantly on the ego vehicle's status for future path planning. Beyond the limitations of the dataset we also note that current metrics do not comprehensively assess the planning quality leading to potentially biased conclusions drawn from existing benchmarks. To address this issue we introduce a new metric to evaluate whether the predicted trajectories adhere to the road. We further propose a simple baseline able to achieve competitive results without relying on perception annotations. Given the current limitations on the benchmark and metrics we suggest the community reassess relevant prevailing research and be cautious about whether the continued pursuit of state-of-the-art would yield convincing and universal conclusions. Code and models are available at https://github.com/NVlabs/BEV-Planner.
Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-vocabulary language grounding and segmentation but can suffer under false premises when queries imply the existence of something that is not actually present in the image. We observe that existing methods that fine-tune an LMM to segment images significantly degrade their ability to reliably determine ("see") if an object is present and to interact naturally with humans ("say") a form of catastrophic forgetting. In this work we propose a cascading and joint training approach for LMMs to solve this task avoiding catastrophic forgetting of previous skills. Our resulting model can "see" by detecting whether objects are present in an image "say" by telling the user if they are not proposing alternative queries or correcting semantic errors in the query and finally "segment" by outputting the mask of the desired objects if they exist. Additionally we introduce a novel False Premise Correction benchmark dataset an extension of existing RefCOCO(+/g) referring segmentation datasets (which we call FP-RefCOCO(+/g)). The results show that our method not only detects false premises up to 55% better than existing approaches but under false premise conditions produces relative cIOU improvements of more than 31% over baselines and produces natural language feedback judged helpful up to 67% of the time.
Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis but their sampling efficiency is still to be desired due to the typically large number of sampling steps. Recent advancements in high-order numerical ODE solvers for DPMs have enabled the generation of high-quality images with much fewer sampling steps. While this is a significant development most sampling methods still employ uniform time steps which is not optimal when using a small number of steps. To address this issue we propose a general framework for designing an optimization problem that seeks more appropriate time steps for a specific numerical ODE solver for DPMs. This optimization problem aims to minimize the distance between the ground-truth solution to the ODE and an approximate solution corresponding to the numerical solver. It can be efficiently solved using the constrained trust region method taking less than 15 seconds. Our extensive experiments on both unconditional and conditional sampling using pixel- and latent-space DPMs demonstrate that when combined with the state-of-the-art sampling method UniPC our optimized time steps significantly improve image generation performance in terms of FID scores for datasets such as CIFAR-10 and ImageNet compared to using uniform time steps.
The autonomous driving community has shown significant interest in 3D occupancy prediction driven by its exceptional geometric perception and general object recognition capabilities. To achieve this current works try to construct a Tri-Perspective View (TPV) or Occupancy (OCC) representation extending from the Bird-Eye-View perception. However compressed views like TPV representation lose 3D geometry information while raw and sparse OCC representation requires heavy but redundant computational costs. To address the above limitations we propose Compact Occupancy TRansformer (COTR) with a geometry-aware occupancy encoder and a semantic-aware group decoder to reconstruct a compact 3D OCC representation. The occupancy encoder first generates a compact geometrical OCC feature through efficient explicit-implicit view transformation. Then the occupancy decoder further enhances the semantic discriminability of the compact OCC representation by a coarse-to-fine semantic grouping strategy. Empirical experiments show that there are evident performance gains across multiple baselines e.g. COTR outperforms baselines with a relative improvement of 8%-15% demonstrating the superiority of our method.
In the context of computer vision and human-robot interaction forecasting 3D human poses is crucial for understanding human behavior and enhancing the predictive capabilities of intelligent systems. While existing methods have made significant progress they often focus on predicting major body joints overlooking fine-grained gestures and their interaction with objects. Human hand movements particularly during object interactions play a pivotal role and provide more precise expressions of human poses. This work fills this gap and introduces a novel paradigm: forecasting 3D whole-body human poses with a focus on grasping objects. This task involves predicting activities across all joints in the body and hands encompassing the complexities of internal heterogeneity and external interactivity. To tackle these challenges we also propose a novel approach: C^3HOST cross-context cross-modal consolidation for 3D whole-body pose forecasting effectively handles the complexities of internal heterogeneity and external interactivity. C^3HOST involves distinct steps including the heterogeneous content encoding and alignment and cross-modal feature learning and interaction. These enable us to predict activities across all body and hand joints ensuring high-precision whole-body human pose prediction even during object grasping. Extensive experiments on two benchmarks demonstrate that our model significantly enhances the accuracy of whole-body human motion prediction. The project page is available at https://sites.google.com/view/c3host.
Although neural networks excel in video action recognition tasks their "black-box" nature makes it challenging to understand the rationale behind their decisions. Recent approaches used inherently interpretable models to analyze video actions in a manner akin to human reasoning. However it has been observed that these interpretable models tend to underperform when compared to their black-box counterparts. In this work we present a new framework called Language-guided Interpretable Action Recognition framework (LaIAR). This framework leverages knowledge from language models to enhance both the recognition capabilities and the interpretability of video models. In essence we reframe the challenge of understanding video model decisions as a task of aligning video and language models. Using the logical reasoning captured by the language model we steer the training of the video model. This integrated approach not only improves the video model's adaptability to different domains but also boosts its overall performance. Extensive experiments on Charades and CAD-120 datasets demonstrate the superior performance and interpretability of our proposed method. The code of LaIAR is available at https://github.com/NingWang2049/LaIAR.
Manual annotation of every point in a point cloud is a costly and labor-intensive process. While weakly supervised point cloud semantic segmentation (WSPCSS) with sparse annotation shows promise the limited information from initial sparse labels can place an upper bound on performance. As a new research direction for WSPCSS we propose a novel Region Exploration via Artificial Labeling (REAL) framework. It leverages a foundational image model as an artificial oracle within the active learning context eliminating the need for manual annotation by a human oracle. To integrate the 2D model into the 3D domain we first introduce a Projection-based Point-toSegment (PP2S) module designed to enable prompt segmentation of 3D data without additional training. The REAL framework samples query points based on model predictions and requests annotations from PP2S dynamically refining labels and improving model training. Furthermore to overcome several challenges of employing an artificial model as an oracle we formulate effective query sampling and label updating strategies. Our comprehensive experiments and comparisons demonstrate that the REAL framework significantly outperforms existing methods across various benchmarks. The code is available at https://github.com/jihun1998/AO.
We introduce pix2gestalt a framework for zero-shot amodal segmentation which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions. By capitalizing on large-scale diffusion models and transferring their representations to this task we learn a conditional diffusion model for reconstructing whole objects in challenging zero-shot cases including examples that break natural and physical priors such as art. As training data we use a synthetically curated dataset containing occluded objects paired with their whole counterparts. Experiments show that our approach outperforms supervised baselines on established benchmarks. Our model can furthermore be used to significantly improve the performance of existing object recognition and 3D reconstruction methods in the presence of occlusions.
StreamingFlow: Streaming Occupancy Forecasting with Asynchronous Multi-modal Data Streams via Neural Ordinary Differential Equation
PDF ↗Predicting the future occupancy states of the surrounding environment is a vital task for autonomous driving. However current best-performing single-modality methods or multi-modality fusion perception methods are only able to predict uniform snapshots of future occupancy states and require strictly synchronized sensory data for sensor fusion. We propose a novel framework StreamingFlow to lift these strong limitations. StreamingFlow is a novel BEV occupancy predictor that ingests asynchronous multi-sensor data streams for fusion and performs streaming forecasting of the future occupancy map at any future timestamps. By integrating neural ordinary differential equations (N-ODE) into recurrent neural networks StreamingFlow learns derivatives of BEV features over temporal horizons updates the implicit sensor's BEV features as part of the fusion process and propagates BEV states to the desired future time point. It shows good zero-shot generalization ability of prediction reflected in the interpolation of the observed prediction time horizon and the reasonable inference of the unseen farther future period. Extensive experiments on two large-scale datasets nuScenes and Lyft L5 demonstrate that StreamingFlow significantly outperforms previous vision-based LiDAR-based methods and shows superior performance compared to state-of-the-art fusion-based methods.
Fairness is a critical concern in deep learning especially in healthcare where these models influence diagnoses and treatment decisions. Although fairness has been investigated in the vision-only domain the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of medical VL datasets for studying fairness. To bridge this research gap we introduce the first fair vision-language medical dataset (Harvard-FairVLMed) that provides detailed demographic attributes ground-truth labels and clinical notes to facilitate an in-depth examination of fairness within VL foundation models. Using Harvard-FairVLMed we conduct a comprehensive fairness analysis of two widely-used VL models (CLIP and BLIP2) pre-trained on both natural and medical domains across four different protected attributes. Our results highlight significant biases in all VL models with Asian Male Non-Hispanic and Spanish being the preferred subgroups across the protected attributes of race gender ethnicity and language respectively. In order to alleviate these biases we propose FairCLIP an optimal-transport-based approach that achieves a favorable trade-off between performance and fairness by reducing the Sinkhorn distance between the overall sample distribution and the distributions corresponding to each demographic group. As the first VL dataset of its kind Harvard-FairVLMed holds the potential to catalyze advancements in the development of machine learning models that are both ethically aware and clinically effective. Our dataset and code are available at https://ophai.hms.harvard.edu/datasets/harvard-fairvlmed10k.
Spike cameras a novel neuromorphic visual sensor can capture full-time spatial information through spike stream offering ultra-high temporal resolution and an extensive dynamic range. Autofocus control (AC) plays a pivotal role in a camera to efficiently capture information in challenging real-world scenarios. Nevertheless due to disparities in data modality and information characteristics compared to frame stream and event stream the current lack of efficient AC methods has made it challenging for spike cameras to adapt to intricate real-world conditions. To address this challenge we introduce a spike-based autofocus framework that includes a spike-specific focus measure called spike dispersion (SD) which effectively mitigates the influence of variations in scene light intensity during the focusing process by leveraging the spike camera's ability to record full-time spatial light intensity. Additionally the framework integrates a fast search strategy called spike-based golden fast search (SGFS) allowing rapid focal positioning without the need for a complete focus range traversal. To validate the performance of our method we have collected a spike-based autofocus dataset (SAD) containing synthetic data and real-world data under varying scene brightness and motion scenarios. Experimental results on these datasets demonstrate that our method offers state-of-the-art accuracy and efficiency. Furthermore experiments with data captured under varying scene brightness levels illustrate the robustness of our method to changes in light intensity during the focusing process.
We present a scene representation that brings vision and touch into a shared 3D space which we call a tactile-augmented radiance field. This representation capitalizes on two key insights: (i) ubiquitous vision-based touch sensors are built on perspective cameras and (ii) visually and structurally similar regions of a scene share the same tactile features. We use these insights to train a conditional diffusion model that provided with an RGB image and a depth map rendered from a neural radiance field generates its corresponding tactile "image". To train this diffusion model we collect the largest collection of spatially-aligned visual and tactile data. Through qualitative and quantitative experiments we demonstrate the accuracy of our cross-modal generative model and the utility of collected and rendered visual-tactile pairs across a range of downstream tasks. Project page: https://dou-yiming.github.io/TaRF
The advent of neural 3D Gaussians has recently brought about a revolution in the field of neural rendering facilitating the generation of high-quality renderings at real-time speeds. However the explicit and discrete representation encounters challenges when applied to scenes featuring reflective surfaces. In this paper we present GaussianShader a novel method that applies a simplified shading function on 3D Gaussians to enhance the neural rendering in scenes with reflective surfaces while preserving the training and rendering efficiency. The main challenge in applying the shading function lies in the accurate normal estimation on discrete 3D Gaussians. Specifically we proposed a novel normal estimation framework based on the shortest axis directions of 3D Gaussians with a delicately designed loss to make the consistency between the normals and the geometries of Gaussian spheres. Experiments show that GaussianShader strikes a commendable balance between efficiency and visual quality. Our method surpasses Gaussian Splatting in PSNR on specular object datasets exhibiting an improvement of 1.57dB. When compared to prior works handling reflective surfaces such as Ref-NeRF our optimization time is significantly accelerated (23h vs. 0.58h).
Deep neural networks have shown great success in representation learning. Deep neural networks have shown great success in representation learning. However when learning with noisy labels (LNL) they can easily overfit and fail to generalize to new data. This paper introduces a simple and effective method named Learning to Bootstrap (L2B) which enables models to bootstrap themselves using their own predictions without being adversely affected by erroneous pseudo-labels. It achieves this by dynamically adjusting the importance weight between real observed and generated labels as well as between different samples through meta-learning. Unlike existing instance reweighting methods the key to our method lies in a new versatile objective that enables implicit relabeling concurrently leading to significant improvements without incurring additional costs. L2B offers several benefits over the baseline methods. It yields more robust models that are less susceptible to the impact of noisy labels by guiding the bootstrapping procedure more effectively. It better exploits the valuable information contained in corrupted instances by adapting the weights of both instances and labels. Furthermore L2B is compatible with existing LNL methods and delivers competitive results spanning natural and medical imaging tasks including classification and segmentation under both synthetic and real-world noise. Extensive experiments demonstrate that our method effectively mitigates the challenges of noisy labels often necessitating few to no validation samples and is well generalized to other tasks such as image segmentation. This not only positions it as a robust complement to existing LNL techniques but also underscores its practical applicability. The code and models are available at https://github.com/yuyinzhou/l2b.
Generative AI has made significant strides in computer vision particularly in text-driven image/video synthesis (T2I/T2V). Despite the notable advancements it remains challenging in human-centric content synthesis such as realistic dance generation. Current methodologies primarily tailored for human motion transfer encounter difficulties when confronted with real-world dance scenarios (e.g. social media dance) which require to generalize across a wide spectrum of poses and intricate human details. In this paper we depart from the traditional paradigm of human motion transfer and emphasize two additional critical attributes for the synthesis of human dance content in social media contexts: (i) Generalizability: the model should be able to generalize beyond generic human viewpoints as well as unseen human subjects backgrounds and poses; (ii) Compositionality: it should allow for the seamless composition of seen/unseen subjects backgrounds and poses from different sources. To address these challenges we introduce DISCO which includes a novel model architecture with disentangled control to improve the compositionality of dance synthesis and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions. Code is available at https://disco-dance.github.io/.
In the realm of image composition generating realistic shadow for the inserted foreground remains a formidable challenge. Previous works have developed image-to-image translation models which are trained on paired training data. However they are struggling to generate shadows with accurate shapes and intensities hindered by data scarcity and inherent task complexity. In this paper we resort to foundation model with rich prior knowledge of natural shadow images. Specifically we first adapt ControlNet to our task and then propose intensity modulation modules to improve the shadow intensity. Moreover we extend the small-scale DESOBA dataset to DESOBAv2 using a novel data acquisition pipeline. Experimental results on both DESOBA and DESOBAv2 datasets as well as real composite images demonstrate the superior capability of our model for shadow generation task. The dataset code and model are released at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2.