CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Neural Radiance Fields (NeRF) have been successfully applied in various aerial scenes yet they face challenges with sparse views due to limited supervision. The acquisition of dense aerial views is often prohibitive as unmanned aerial vehicles (UAVs) may encounter constraints in perspective range and energy constraints. In this work we introduce Multiplane Prior guided NeRF (MPNeRF) a novel approach tailored for few-shot aerial scene rendering--marking a pioneering effort in this domain. Our key insight is that the intrinsic geometric regularities specific to aerial imagery could be leveraged to enhance NeRF in sparse aerial scenes. By investigating NeRF's and Multiplane Image (MPI)'s behavior we propose to guide the training process of NeRF with a Multiplane Prior. The proposed Multiplane Prior draws upon MPI's benefits and incorporates advanced image comprehension through a SwinV2 Transformer pre-trained via SimMIM. Our extensive experiments demonstrate that MPNeRF outperforms existing state-of-the-art methods applied in non-aerial contexts by tripling the performance in SSIM and LPIPS even with three views available. We hope our work offers insights into the development of NeRF-based applications in aerial scenes with limited data.
We introduce Multi-view Ancestral Sampling (MAS) a method for 3D motion generation using 2D diffusion models that were trained on motions obtained from in-the-wild videos. As such MAS opens opportunities to exciting and diverse fields of motion previously under-explored as 3D data is scarce and hard to collect. MAS works by simultaneously denoising multiple 2D motion sequences representing different views of the same 3D motion. It ensures consistency across all views at each diffusion step by combining the individual generations into a unified 3D sequence and projecting it back to the original views. We demonstrate MAS on 2D pose data acquired from videos depicting professional basketball maneuvers rhythmic gymnastic performances featuring a ball apparatus and horse races. In each of these domains 3D motion capture is arduous and yet MAS generates diverse and realistic 3D sequences. Unlike the Score Distillation approach which optimizes each sample by repeatedly applying small fixes our method uses a sampling process that was constructed for the diffusion framework. As we demonstrate MAS avoids common issues such as out-of-domain sampling and mode-collapse. https://guytevet.github.io/mas-page/
Despite the significant demand for assistive technology among vulnerable groups (e.g. the elderly children and the disabled) in daily tasks research into advanced AI-driven assistive solutions that genuinely accommodate their diverse needs remains sparse. Traditional human-machine interaction tasks often require machines to simply help without nuanced consideration of human abilities and feelings such as their opportunity for practice and learning sense of self-improvement and self-esteem. Addressing this gap we define a pivotal and novel challenge Smart Help which aims to provide proactive yet adaptive support to human agents with diverse disabilities and dynamic goals in various tasks and environments. To establish this challenge we leverage AI2-THOR to build a new interactive 3D realistic household environment for the Smart Help task. We introduce an innovative opponent modeling module that provides a nuanced understanding of the main agent's capabilities and goals in order to optimize the assisting agent's helping policy. Rigorous experiments validate the efficacy of our model components and show the superiority of our holistic approach against established baselines. Our findings illustrate the potential of AI-imbued assistive robots in improving the well-being of vulnerable groups.
Event Stream Super-Resolution (ESR) aims to address the challenge of insufficient spatial resolution in event streams which holds great significance for the application of event cameras in complex scenarios. Previous works for ESR often process positive and negative events in a mixed paradigm. This paradigm limits their ability to effectively model the unique characteristics of each event and mutually refine each other by considering their correlations. In this paper we propose a bilateral event mining and complementary network (BMCNet) to fully leverage the potential of each event and capture the shared information to complement each other simultaneously. Specifically we resort to a two-stream network to accomplish comprehensive mining of each type of events individually. To facilitate the exchange of information between two streams we propose a bilateral information exchange (BIE) module. This module is layer-wisely embedded between two streams enabling the effective propagation of hierarchical global information while alleviating the impact of invalid information brought by inherent characteristics of events. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods in ESR achieving performance improvements of over 11% on both real and synthetic datasets. Moreover our method significantly enhances the performance of event-based downstream tasks such as object recognition and video reconstruction.
The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods in ESR achieving performance improvements of over 11% on both real and synthetic datasets. Moreover our method significantly enhances the performance of event-based downstream tasks such as object recognition and video reconstruction. Our code is available at https://github.com/Lqm26/BMCNet-ESR.
Online Task-Free Continual Learning (OTFCL) aims to learn novel concepts from streaming data without accessing task information. Most memory-based approaches used in OTFCL are not suitable for unsupervised learning because they require accessing supervised signals to implement their sample selection mechanisms. In this study we address this issue by proposing a novel memory management approach namely the Dynamic Cluster Memory (DCM) which builds new memory clusters to capture distribution shifts over time without accessing any supervised signals. DCM introduces a novel memory expansion mechanism based on the knowledge discrepancy criterion which evaluates the novelty of the incoming data as the signal for the memory expansion ensuring a compact memory capacity. We also propose a new sample selection approach that automatically stores incoming data samples with similar semantic information in the same memory cluster while also facilitating the knowledge diversity among memory clusters. Furthermore a novel memory pruning approach is proposed to automatically remove overlapping memory clusters through a graph relation evaluation ensuring a fixed memory capacity while maintaining the diversity among the samples stored in the memory. The proposed DCM is model-free plug-and-play and can be used in both supervised and unsupervised learning without modifications.
Furthermore a novel memory pruning approach is proposed to automatically remove overlapping memory clusters through a graph relation evaluation ensuring a fixed memory capacity while maintaining the diversity among the samples stored in the memory. The proposed DCM is model-free plug-and-play and can be used in both supervised and unsupervised learning without modifications. Empirical results on OTFCL experiments show that the proposed DCM outperforms the state-of-the-art while requiring fewer data samples to be stored. The source code is available at https://github.com/dtuzi123/DCM.
Developing generalizable manipulation skills is a core challenge in embodied AI. This includes generalization across diverse task configurations encompassing variations in object shape density friction coefficient and external disturbances such as forces applied to the robot. Rapid Motor Adaptation (RMA) offers a promising solution to this challenge. It posits that essential hidden variables influencing an agent's task performance such as object mass and shape can be effectively inferred from the agent's action and proprioceptive history. Drawing inspiration from RMA in locomotion and in-hand rotation we use depth perception to develop agents tailored for rapid motor adaptation in a variety of manipulation tasks. We evaluated our agents on four challenging tasks from the Maniskill2 benchmark namely pick-and-place operations with hundreds of objects from the YCB and EGAD datasets peg insertion with precise position and orientation and operating a variety of faucets and handles with customized environment variations. Empirical results demonstrate that our agents surpass state-of-the-art methods like automatic domain randomization and vision-based policies obtaining better generalization performance and sample efficiency.
Recently the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation while NeRF (Neural Radiance Fields) has gained popularity as a method for various 3D problems beyond novel view synthesis. Though there exist initial attempts to incorporate these two methods into 3D segmentation they face the challenge of accurately and consistently segmenting objects in complex scenarios. In this paper we introduce the Segment Anything for NeRF in High Quality (SANeRF-HQ) to achieve high-quality 3D segmentation of any target object in a given scene. SANeRF-HQ utilizes SAM for open-world object segmentation guided by user-supplied prompts while leveraging NeRF to aggregate information from different viewpoints. To overcome the aforementioned challenges we employ density field and RGB similarity to enhance the accuracy of segmentation boundary during the aggregation. Emphasizing on segmentation accuracy we evaluate our method on multiple NeRF datasets where high-quality ground-truths are available or manually annotated. SANeRF-HQ shows a significant quality improvement over state-of-the-art methods in NeRF object segmentation provides higher flexibility for object localization and enables more consistent object segmentation across multiple views.
Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image which is challenging due to incomplete labeling long-tailed relationship categories and relational semantic overlap. Existing Transformer-based methods either employ distinct queries for objects and predicates or utilize holistic queries for relation triplets and hence often suffer from limited capacity in learning low-frequency relationships. In this paper we present a new Transformer-based method called DSGG that views scene graph detection as a direct graph prediction problem based on a unique set of graph-aware queries. In particular each graph-aware query encodes a compact representation of both the node and all of its relations in the graph acquired through the utilization of a relaxed sub-graph matching during the training process. Moreover to address the problem of relational semantic overlap we utilize a strategy for relation distillation aiming to efficiently learn multiple instances of semantic relationships.
Moreover to address the problem of relational semantic overlap we utilize a strategy for relation distillation aiming to efficiently learn multiple instances of semantic relationships. Extensive experiments on the VG and the PSG datasets show that our model achieves state-of-the-art results showing a significant improvement of 3.5% and 6.7% in mR@50 and mR@100 for the scene-graph generation task and achieves an even more substantial improvement of 8.5% and 10.3% in mR@50 and mR@100 for the panoptic scene graph generation task. Code is available at https://github.com/zeeshanhayder/DSGG.
Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks (DNNs) especially Transformers for super-resolution have seen significant advancements in recent years challenges still remain particularly in limited receptive field caused by window-based self-attention. To address these issues we introduce a group of auxiliary Adaptive Token Dictionary to SR Transformer and establish an ATD-SR method. The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step. The refinement strategy could not only provide global information to all input tokens but also group image tokens into categories. Based on category partitions we further propose a category-based self-attention mechanism designed to leverage distant but similar tokens for enhancing input features. The experimental results show that our method achieves the best performance on various single image super-resolution benchmarks.
Modeling object dynamics with a neural network is an important problem with numerous applications. Most recent work has been based on graph neural networks. However physics happens in 3D space where geometric information potentially plays an important role in modeling physical phenomena. In this work we propose a novel U-net architecture based on continuous point convolution which naturally embeds information from 3D coordinates and allows for multi-scale feature representations with established downsampling and upsampling procedures. Bottleneck layers in the downsampled point clouds lead to better long-range interaction modeling. Besides the flexibility of point convolutions allows our approach to generalize to sparsely sampled points from mesh vertices and dynamically generate features on important interaction points on mesh faces. Experimental results demonstrate that our approach significantly improves the state-of-the-art especially in scenarios that require accurate gravity or collision reasoning.
Recent advancements in neural networks have showcased their remarkable capabilities across various domains. Despite these successes the "black box" problem still remains. To address this we propose a novel framework WWW that offers the 'what' 'where' and 'why' of the neural network decisions in human-understandable terms. Specifically WWW utilizes an adaptive selection for concept discovery employing adaptive cosine similarity and thresholding techniques to effectively explain 'what'. To address the 'where' and 'why' we proposed a novel combination of neuron activation maps (NAMs) with Shapley values generating localized concept maps and heatmaps for individual inputs. Furthermore WWW introduces a method for predicting uncertainty leveraging heatmap similarities to estimate the prediction's reliability. Experimental evaluations of WWW demonstrate superior performance in both quantitative and qualitative metrics outperforming existing methods in interpretability. WWW provides a unified solution for explaining 'what' 'where' and 'why' introducing a method for localized explanations from global interpretations and offering a plug-and-play solution adaptable to various architectures. The code is available at: https://github.com/ailab-kyunghee/WWW
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation. Nevertheless these works primarily focus on a single modality without temporal and geo-context modeling hampering their capabilities for diverse tasks. In this study we present SkySense a generic billion-scale model pre-trained on a curated multi-modal Remote Sensing Imagery (RSI) dataset with 21.5 million temporal sequences. SkySense incorporates a factorized multi-modal spatiotemporal encoder taking temporal sequences of optical and Synthetic Aperture Radar (SAR) data as input. This encoder is pre-trained by our proposed Multi-Granularity Contrastive Learning to learn representations across different modal and spatial granularities. To further enhance the RSI representations by the geo-context clue we introduce Geo-Context Prototype Learning to learn region-aware prototypes upon RSI's multi-modal spatiotemporal features. To our best knowledge SkySense is the largest Multi-Modal RSFM to date whose modules can be flexibly combined or used individually to accommodate various tasks. It demonstrates remarkable generalization capabilities on a thorough evaluation encompassing 16 datasets over 7 tasks from single- to multi-modal static to temporal and classification to localization.
It demonstrates remarkable generalization capabilities on a thorough evaluation encompassing 16 datasets over 7 tasks from single- to multi-modal static to temporal and classification to localization. SkySense surpasses 18 recent RSFMs in all test scenarios. Specifically it outperforms the latest models such as GFM SatLas and Scale-MAE by a large margin i.e. 2.76% 3.67% and 3.61% on average respectively. We will release the pre-trained weights to facilitate future research and Earth Observation applications.
Knowledge distillation (KD) possesses immense potential to accelerate the deep neural networks (DNNs) for LiDAR-based 3D detection. However in most of prevailing approaches the suboptimal teacher models and insufficient student architecture investigations limit the performance gains. To address these issues we propose a simple yet effective Category-aware Knowledge Distillation and Pruning (CaKDP) framework for compressing 3D detectors. Firstly CaKDP transfers the knowledge of two-stage detector to one-stage student one mitigating the impact of inadequate teacher models. To bridge the gap between the heterogeneous detectors we investigate their differences and then introduce the student-motivated category-aware KD to align the category prediction between distillation pairs. Secondly we propose a category-aware pruning scheme to obtain the customizable architecture of compact student model. The method calculates the category prediction gap before and after removing each filter to evaluate the importance of filters and retains the important filters. Finally to further improve the student performance a modified IOU-aware refinement module with negligible computations is leveraged to remove the redundant false positive predictions. Experiments demonstrate that CaKDP achieves the compact detector with high performance.
Finally to further improve the student performance a modified IOU-aware refinement module with negligible computations is leveraged to remove the redundant false positive predictions. Experiments demonstrate that CaKDP achieves the compact detector with high performance. For example on WOD CaKDP accelerates CenterPoint by half while boosting L2 mAPH by 1.61%. The code is available at https://github.com/zhnxjtu/CaKDP.
While federated learning (FL) systems often utilize quantization to battle communication and computational bottlenecks they have heretofore been limited to deploying fixed-precision quantization schemes. Meanwhile the concept of mixed-precision quantization (MPQ) where different layers of a deep learning model are assigned varying bit-width remains unexplored in the FL settings. We present a novel FL algorithm FedMPQ which introduces mixed-precision quantization to resource-heterogeneous FL systems. Specifically local models quantized so as to satisfy bit-width constraint are trained by optimizing an objective function that includes a regularization term which promotes reduction of precision in some of the layers without significant performance degradation. The server collects local model updates de-quantizes them into full-precision models and then aggregates them into a global model. To initialize the next round of local training the server relies on the information learned in the previous training round to customize bit-width assignments of the models delivered to different clients. In extensive benchmarking experiments on several model architectures and different datasets in both iid and non-iid settings FedMPQ outperformed the baseline FL schemes that utilize fixed-precision quantization while incurring only a minor computational overhead on the participating devices.
Transformer-based models have revolutionized the field of image super-resolution (SR) by harnessing their inherent ability to capture complex contextual features. The overlapping rectangular shifted window technique used in transformer architecture nowadays is a common practice in super-resolution models to improve the quality and robustness of image upscaling. However it suffers from distortion at the boundaries and has limited unique shifting modes. To overcome these weaknesses we propose a non-overlapping triangular window technique that synchronously works with the rectangular one to mitigate boundary-level distortion and allows the model to access more unique sifting modes. In this paper we propose a Composite Fusion Attention Transformer (CFAT) that incorporates triangular-rectangular window-based local attention with a channel-based global attention technique in image super-resolution. As a result CFAT enables attention mechanisms to be activated on more image pixels and captures long-range multi-scale features to improve SR performance. The extensive experimental results and ablation study demonstrate the effectiveness of CFAT in the SR domain. Our proposed model shows a significant 0.7 dB performance improvement over other state-of-the-art SR architectures.
Scene flow characterizes the 3D motion between two LiDAR scans captured by an autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point-wise unconstrained flow vectors that can be learned by either large-scale training beforehand or time-consuming optimization at inference. However these methods do not take into account that objects in autonomous driving often move rigidly. We incorporate this rigid-motion assumption into our design where the goal is to associate objects over scans and then estimate the locally rigid transformations. We propose ICP-Flow a learning-free flow estimator. The core of our design is the conventional Iterative Closest Point (ICP) algorithm which aligns the objects over time and outputs the corresponding rigid transformations. Crucially to aid ICP we propose a histogram-based initialization that discovers the most likely translation thus providing a good starting point for ICP. The complete scene flow is then recovered from the rigid transformations. We outperform state-of-the-art baselines including supervised models on the Waymo dataset and perform competitively on Argoverse-v2 and nuScenes. Further we train a feedforward neural network supervised by the pseudo labels from our model and achieve top performance among all models capable of real-time inference.
We outperform state-of-the-art baselines including supervised models on the Waymo dataset and perform competitively on Argoverse-v2 and nuScenes. Further we train a feedforward neural network supervised by the pseudo labels from our model and achieve top performance among all models capable of real-time inference. We validate the advantage of our model on scene flow estimation with longer temporal gaps up to 0.4 seconds where other models fail to deliver meaningful results.