mtybilly/Instruct2DS huggingface dataset:
HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation
Authors: Xin Huang, Ruizhi Shao, Qi Zhang, Hongwen Zhang, Ying Feng, Yebin Liu, Qing Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent text-to-3D methods employing diffusion models have made significant advancements in 3D human generation. However these approaches face challenges due to the limitations of text-to-image diffusion models which lack an understanding of 3D structures. Consequently these methods struggle to achieve high-quality human generation resulting in smooth geometry and cartoon-like appearances. In this paper we propose HumanNorm a novel approach for high-quality and realistic 3D human generation. The main idea is to enhance the model's 2D perception of 3D geometry by learning a normal-adapted diffusion model and a normal-aligned diffusion model. The normal-adapted diffusion model can generate high-fidelity normal maps corresponding to user prompts with view-dependent and body-aware text. The normal-aligned diffusion model learns to generate color images aligned with the normal maps thereby transforming physical geometry details into realistic appearance. Leveraging the proposed normal diffusion model we devise a progressive geometry generation strategy and a multi-step Score Distillation Sampling (SDS) loss to enhance the performance of 3D human generation. Comprehensive experiments substantiate HumanNorm's ability to generate 3D humans with intricate geometry and realistic appearances. HumanNorm outperforms existing text-to-3D methods in both geometry and texture quality. The project page of HumanNorm is https://humannorm.github.io/.
Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization
Authors: Guopeng Li, Ming Qian, Gui-Song Xia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper investigates the effective utilization of unlabeled data for large-area cross-view geo-localization (CVGL) encompassing both unsupervised and semi-supervised settings. Common approaches to CVGL rely on ground-satellite image pairs and employ label-driven supervised training. However the cost of collecting precise cross-view image pairs hinders the deployment of CVGL in real-life scenarios. Without the pairs CVGL will be more challenging to handle the significant imaging and spatial gaps between ground and satellite images. To this end we propose an unsupervised framework including a cross-view projection to guide the model for retrieving initial pseudo-labels and a fast re-ranking mechanism to refine the pseudo-labels by leveraging the fact that "the perfectly paired ground-satellite image is located in a unique and identical scene". The framework exhibits competitive performance compared with supervised works on three open-source benchmarks. Our code and models will be released on https://github.com/liguopeng0923/UCVGL.
Global Latent Neural Rendering
Authors: Thomas Tanay, Matteo Maggioni
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: A recent trend among generalizable novel view synthesis methods is to learn a rendering operator acting over single camera rays. This approach is promising because it removes the need for explicit volumetric rendering but it effectively treats target images as collections of independent pixels. Here we propose to learn a global rendering operator acting over all camera rays jointly. We show that the right representation to enable such rendering is a 5-dimensional plane sweep volume consisting of the projection of the input images on a set of planes facing the target camera. Based on this understanding we introduce our Convolutional Global Latent Renderer (ConvGLR) an efficient convolutional architecture that performs the rendering operation globally in a low-resolution latent space. Experiments on various datasets under sparse and generalizable setups show that our approach consistently outperforms existing methods by significant margins.
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation
Authors: Yuqi Wang, Yuntao Chen, Xingyu Liao, Lue Fan, Zhaoxiang Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Comprehensive modeling of the surrounding 3D world is crucial for the success of autonomous driving. However existing perception tasks like object detection road structure segmentation depth & elevation estimation and open-set object localization each only focus on a small facet of the holistic 3D scene understanding task. This divide-and-conquer strategy simplifies the algorithm development process but comes at the cost of losing an end-to-end unified solution to the problem. In this work we address this limitation by studying camera-based 3D panoptic segmentation aiming to achieve a unified occupancy representation for camera-only 3D scene understanding. To achieve this we introduce a novel method called PanoOcc which utilizes voxel queries to aggregate spatiotemporal information from multi-frame and multi-view images in a coarse-to-fine scheme integrating feature learning and scene representation into a unified occupancy representation. We have conducted extensive ablation studies to validate the effectiveness and efficiency of the proposed method. Our approach achieves new state-of-the-art results for camera-based semantic segmentation and panoptic segmentation on the nuScenes dataset. Furthermore our method can be easily extended to dense occupancy prediction and has demonstrated promising performance on the Occ3D benchmark. The code will be made available at https://github.com/Robertwyq/PanoOcc.
Sparse Views Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo
Authors: Mohammed Brahimi, Bjoern Haefner, Zhenzhang Ye, Bastian Goldluecke, Daniel Cremers
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Neural approaches have shown a significant progress on camera-based reconstruction. But they require either a fairly dense sampling of the viewing sphere or pre-training on an existing dataset thereby limiting their generalizability. In contrast photometric stereo (PS) approaches have shown great potential for achieving high-quality reconstruction under sparse viewpoints. Yet they are impractical because they typically require tedious laboratory conditions are restricted to dark rooms and often multi-staged making them subject to accumulated errors. To address these shortcomings we propose an end-to-end uncalibrated multi-view PS framework for reconstructing high-resolution shapes acquired from sparse viewpoints in a real-world environment. We relax the dark room assumption and allow a combination of static ambient lighting and dynamic near LED lighting thereby enabling easy data capture outside the lab. Experimental validation confirms that it outperforms existing baseline approaches in the regime of sparse viewpoints by a large margin. This allows to bring high accuracy 3D reconstruction from the dark room to the real world while maintaining a reasonable data capture complexity.
Meta-Point Learning and Refining for Category-Agnostic Pose Estimation
Authors: Junjie Chen, Jiebin Yan, Yuming Fang, Li Niu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary classes given a few support images annotated with keypoints. Existing methods only rely on the features extracted at support keypoints to predict or refine the keypoints on query image but a few support feature vectors are local and inadequate for CAPE. Considering that human can quickly perceive potential keypoints of arbitrary objects we propose a novel framework for CAPE based on such potential keypoints (named as meta-points). Specifically we maintain learnable embeddings to capture inherent information of various keypoints which interact with image feature maps to produce meta-points without any support. The produced meta-points could serve as meaningful potential keypoints for CAPE. Due to the inevitable gap between inherency and annotation we finally utilize the identities and details offered by support keypoints to assign and refine meta-points to desired keypoints in query image. In addition we propose a progressive deformable point decoder and a slacked regression loss for better prediction and supervision. Our novel framework not only reveals the inherency of keypoints but also outperforms existing methods of CAPE. Comprehensive experiments and in-depth studies on large-scale MP-100 dataset demonstrate the effectiveness of our framework.
Cross-view and Cross-pose Completion for 3D Human Understanding
Authors: Matthieu Armando, Salma Galaaoui, Fabien Baradel, Thomas Lucas, Vincent Leroy, Romain Brégier, Philippe Weinzaepfel, Grégory Rogez
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human perception and understanding is a major domain of computer vision which like many other vision subdomains recently stands to gain from the use of large models pre-trained on large datasets. We hypothesize that the most common pre-training strategy of relying on general purpose object-centric image datasets such as ImageNet is limited by an important domain shift. On the other hand collecting domain-specific ground truth such as 2D or 3D labels does not scale well. Therefore we propose a pre-training approach based on self-supervised learning that works on human-centric data using only images. Our method uses pairs of images of humans: the first is partially masked and the model is trained to reconstruct the masked parts given the visible ones and a second image. It relies on both stereoscopic (cross-view) pairs and temporal (cross-pose) pairs taken from videos in order to learn priors about 3D as well as human motion. We pre-train a model for body-centric tasks and one for hand-centric tasks. With a generic transformer architecture these models outperform existing self-supervised pre-training methods on a wide set of human-centric downstream tasks and obtain state-of-the-art performance for instance when fine-tuning for model-based and model-free human mesh recovery.
Batch Normalization Alleviates the Spectral Bias in Coordinate Networks
Authors: Zhicheng Cai, Hao Zhu, Qiu Shen, Xinran Wang, Xun Cao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Representing signals using coordinate networks dominates the area of inverse problems recently and is widely applied in various scientific computing tasks. Still there exists an issue of spectral bias in coordinate networks limiting the capacity to learn high-frequency components. This problem is caused by the pathological distribution of the neural tangent kernel's (NTK's) eigenvalues of coordinate networks. We find that this pathological distribution could be improved using the classical batch normalization (BN) which is a common deep learning technique but rarely used in coordinate networks. BN greatly reduces the maximum and variance of NTK's eigenvalues while slightly modifies the mean value considering the max eigenvalue is much larger than the most this variance change results in a shift of eigenvalues' distribution from a lower one to a higher one therefore the spectral bias could be alleviated (see Fig. 1). This observation is substantiated by the significant improvements of applying BN-based coordinate networks to various tasks including the image compression computed tomography reconstruction shape representation magnetic resonance imaging and novel view synthesis.
Efficient Scene Recovery Using Luminous Flux Prior
Authors: Zhongyu Li, Lei Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Scene recovery the restoration of images degraded by adverse weather conditions presents significant challenges for existing methods. Physical models constrained by their inherent assumptions often fail when these assumptions are not met; Deep learning models are powerful they are limited by the diversity of their training datasets leading to poor generalization and high computational demands. To address these limitations we propose the Luminous Flux Prior (LFP) to recover degraded images under diverse adverse weather without learning. Luminous flux a physical measure that reflects image brightness has a rate of change that demonstrates a significant correlation with transmission. Consequently we leverage this rate of change in luminous flux as prior knowledge to estimate transmission which in turn assists in image recovery. This approach reduces dependency on physical parameters and enhances adaptability to various weather. Experimental validation under diverse conditions such as sandstorms underwater environments and haze attests to the robustness of LFP in restoring clear images. With a time complexity of \mathcal O (N\log N) LFP enables real-time recovery making it a suitable for devices with limited computational resources.
LQMFormer: Language-aware Query Mask Transformer for Referring Image Segmentation
Authors: Nisarg A. Shah, Vibashan VS, Vishal M. Patel
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Referring Image Segmentation (RIS) aims to segment objects from an image based on a language description. Recent advancements have introduced transformer-based methods that leverage cross-modal dependencies significantly enhancing performance in referring segmentation tasks. These methods are designed such that each query predicts different masks. However RIS inherently requires a single-mask prediction leading to a phenomenon known as Query Collapse where all queries yield the same mask prediction. This reduces the generalization capability of the RIS model for complex or novel scenarios. To address this issue we propose a Multi-modal Query Feature Fusion technique characterized by two innovative designs: (1) Gaussian enhanced Multi-Modal Fusion a novel visual grounding mechanism that enhances overall representation by extracting rich local visual information and global visual-linguistic relationships and (2) A Dynamic Query Module that produces a diverse set of queries through a scoring network where the network selectively focuses on queries for objects referred to in the language description. Moreover we show that including an auxiliary loss to increase the distance between mask representations of different queries further enhances performance and mitigates query collapse. Extensive experiments conducted on four benchmark datasets validate the effectiveness of our framework.
Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training
Authors: Runze He, Shaofei Huang, Xuecheng Nie, Tianrui Hui, Luoqi Liu, Jiao Dai, Jizhong Han, Guanbin Li, Si Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we target the adaptive source driven 3D scene editing task by proposing a CustomNeRF model that unifies a text description or a reference image as the editing prompt. However obtaining desired editing results conformed with the editing prompt is nontrivial since there exist two significant challenges including accurate editing of only foreground regions and multi-view consistency given a single-view reference image. To tackle the first challenge we propose a Local-Global Iterative Editing (LGIE) training scheme that alternates between foreground region editing and full-image editing aimed at foreground-only manipulation while preserving the background. For the second challenge we also design a class-guided regularization that exploits class priors within the generation model to alleviate the inconsistency problem among different views in image-driven editing. Extensive experiments show that our CustomNeRF produces precise editing results under various real scenes for both text- and image-driven settings. The code is available at: https://github. com/hrz2000/CustomNeRF.
SplaTAM: Splat Track & Map 3D Gaussians for Dense RGB-D SLAM
Authors: Nikhil Keetha, Jay Karhade, Krishna Murthy Jatavallabhula, Gengshan Yang, Sebastian Scherer, Deva Ramanan, Jonathon Luiten
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications. However current methods are often hampered by the non-volumetric or implicit way they represent a scene. This work introduces SplaTAM an approach that for the first time leverages explicit volumetric representations i.e. 3D Gaussians to enable high-fidelity reconstruction from a single unposed RGB-D camera surpassing the capabilities of existing methods. SplaTAM employs a simple online tracking and mapping system tailored to the underlying Gaussian representation. It utilizes a silhouette mask to elegantly capture the presence of scene density. This combination enables several benefits over prior representations including fast rendering and dense optimization quickly determining if areas have been previously mapped and structured map expansion by adding more Gaussians. Extensive experiments show that SplaTAM achieves up to 2x superior performance in camera pose estimation map construction and novel-view synthesis over existing methods paving the way for more immersive high-fidelity SLAM applications.
Instance-based Max-margin for Practical Few-shot Recognition
Authors: Minghao Fu, Ke Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In order to mimic the human few-shot learning (FSL) ability better and to make FSL closer to real-world applications this paper proposes a practical FSL (pFSL) setting. pFSL is based on unsupervised pre-trained models (analogous to human prior knowledge) and recognizes many novel classes simultaneously. Compared to traditional FSL pFSL is simpler in its formulation easier to evaluate more challenging and more practical. To cope with the rarity of training examples this paper proposes IbM2 an instance-based max-margin method not only for the new pFSL setting but also works well in traditional FSL scenarios. Based on the Gaussian Annulus Theorem IbM2 converts random noise applied to the instances into a mechanism to achieve maximum margin in the many-way pFSL (or traditional FSL) recognition task. Experiments with various self-supervised pre-training methods and diverse many- or few-way FSL tasks show that IbM2 almost always leads to improvements compared to its respective baseline methods and in most cases the improvements are significant. With both the new pFSL setting and novel IbM2 method this paper shows that practical few-shot learning is both viable and promising.
Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation
Authors: Sangyun Shin, Kaichen Zhou, Madhu Vankadari, Andrew Markham, Niki Trigoni
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Coarse-to-fine 3D instance segmentation methods show weak performances compared to recent Grouping-based Kernel-based and Transformer-based methods. We argue that this is due to two limitations: 1) Instance size overestimation by axis-aligned bounding box(AABB) 2) False negative error accumulation from inaccurate box to the refinement phase. In this work we introduce Spherical Mask a novel coarse-to-fine approach based on spherical representation overcoming those two limitations with several benefits. Specifically our coarse detection estimates each instance with a 3D polygon using a center and radial distance predictions which avoids excessive size estimation of AABB. To cut the error propagation in the existing coarse-to-fine approaches we virtually migrate points based on the polygon allowing all foreground points including false negatives to be refined. During inference the proposal and point migration modules run in parallel and are assembled to form binary masks of instances. We also introduce two margin-based losses for the point migration to enforce corrections for the false positives/negatives and cohesion of foreground points significantly improving the performance. Experimental results from three datasets such as ScanNetV2 S3DIS and STPLS3D show that our proposed method outperforms existing works demonstrating the effectiveness of the new instance representation with spherical coordinates. The code is available at: https://github.com/yunshin/SphericalMask
Omni-Q: Omni-Directional Scene Understanding for Unsupervised Visual Grounding
Authors: Sai Wang, Yutian Lin, Yu Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Unsupervised visual grounding methods alleviate the issue of expensive manual annotation of image-query pairs by generating pseudo-queries. However existing methods are prone to confusing the spatial relationships between objects and rely on designing complex prompt modules to generate query texts which severely impedes the ability to generate accurate and comprehensive queries due to ambiguous spatial relationships and manually-defined fixed templates. To tackle these challenges we propose a omni-directional language query generation approach for unsupervised visual grounding named Omni-Q. Specifically we develop a 3D spatial relation module to extend the 2D spatial representation to 3D thereby utilizing 3D location information to accurately determine the spatial position among objects. Besides we introduce a spatial graph module leveraging the power of graph structures to establish accurate and diverse object relationships and thus enhancing the flexibility of query generation. Extensive experiments on five public benchmark datasets demonstrate that our method significantly outperforms existing state-of-the-art unsupervised methods by up to 16.17%. In addition when applied in the supervised setting our method can freely save up to 60% human annotations without a loss of performance.
VISTA-LLAMA: Reducing Hallucination in Video Language Models via Equal Distance to Visual Tokens
Authors: Fan Ma, Xiaojie Jin, Heng Wang, Yuchen Xian, Jiashi Feng, Yi Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advances in large video-language models have displayed promising outcomes in video comprehension. Current approaches straightforwardly convert video into language tokens and employ large language models for multi-modal tasks. However this method often leads to the generation of irrelevant content commonly known as "hallucination" as the length of the text increases and the impact of the video diminishes. To address this problem we propose Vista-LLaMA a novel framework that maintains the consistent distance between all visual tokens and any language tokens irrespective of the generated text length. Vista-LLaMA omits relative position encoding when determining attention weights between visual and text tokens retaining the position encoding for text and text tokens. This amplifies the effect of visual tokens on text generation especially when the relative distance is longer between visual and text tokens. The proposed attention mechanism significantly reduces the chance of producing irrelevant text related to the video content. Furthermore we present a sequential visual projector that projects the current video frame into tokens of language space with the assistance of the previous frame. This approach not only captures the temporal relationship within the video but also allows less visual tokens to encompass the entire video. Our approach significantly outperforms various previous methods (e.g. Video-ChatGPT MovieChat) on four challenging open-ended video question answering benchmarks. We reach an accuracy of 60.7 on the zero-shot NExT-QA and 60.5 on the zero-shot MSRVTT-QA setting a new state-of-the-art performance.
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance Head-pose and Facial Expression Features
Authors: Andre Rochow, Max Schwarz, Sven Behnke
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The task of face reenactment is to transfer the head motion and facial expressions from a driving video to the appearance of a source image which may be of a different person (cross-reenactment). Most existing methods are CNN-based and estimate optical flow from the source image to the current driving frame which is then inpainted and refined to produce the output animation. We propose a transformer-based encoder for computing a set-latent representation of the source image(s). We then predict the output color of a query pixel using a transformer-based decoder which is conditioned with keypoints and a facial expression vector extracted from the driving frame. Latent representations of the source person are learned in a self-supervised manner that factorize their appearance head pose and facial expressions. Thus they are perfectly suited for cross-reenactment. In contrast to most related work our method naturally extends to multiple source images and can thus adapt to person-specific facial dynamics. We also propose data augmentation and regularization schemes that are necessary to prevent overfitting and support generalizability of the learned representations. We evaluated our approach in a randomized user study. The results indicate superior performance compared to the state-of-the-art in terms of motion transfer quality and temporal consistency.
Efficient Multitask Dense Predictor via Binarization
Authors: Yuzhang Shang, Dan Xu, Gaowen Liu, Ramana Rao Kompella, Yan Yan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multi-task learning for dense prediction has emerged as a pivotal area in computer vision enabling simultaneous processing of diverse yet interrelated pixel-wise prediction tasks. However the substantial computational demands of state-of-the-art (SoTA) models often limit their widespread deployment. This paper addresses this challenge by introducing network binarization to compress resource-intensive multi-task dense predictors. Specifically our goal is to significantly accelerate multi-task dense prediction models via Binary Neural Networks (BNNs) while maintaining and even improving model performance at the same time. To reach this goal we propose a Binary Multi-task Dense Predictor Bi-MTDP and several variants of \bimtdp in which a multi-task dense predictor is constructed via specified binarized modules. Our systematical analysis of this predictor reveals that performance drop from binarization is primarily caused by severe information degradation. To address this issue we introduce a deep information bottleneck layer that enforces representations for downstream tasks satisfying Gaussian distribution in forward propagation. Moreover we introduce a knowledge distillation mechanism to correct the direction of information flow in backward propagation. Intriguingly one variant of Bi-MTDP outperforms full-precision (FP) multi-task dense prediction SoTAs ARTC (CNN-based) and InvPT (ViT-based). This result indicates that Bi-MTDP is not merely a naive trade-off between performance and efficiency but is rather a benefit of the redundant information flow thanks to the multi-task architecture.
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis
Authors: Pavlo Melnyk, Andreas Robinson, Michael Felsberg, Mårten Wadenbäck
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In many practical applications 3D point cloud analysis requires rotation invariance. In this paper we present a learnable descriptor invariant under 3D rotations and reflections i.e. the O(3) actions utilizing the recently introduced steerable 3D spherical neurons and vector neurons. Specifically we propose an embedding of the 3D spherical neurons into 4D vector neurons which leverages end-to-end training of the model. In our approach we perform TetraTransform---an equivariant embedding of the 3D input into 4D constructed from the steerable neurons---and extract deeper O(3)-equivariant features using vector neurons. This integration of the TetraTransform into the VN-DGCNN framework termed TetraSphere negligibly increases the number of parameters by less than 0.0002%. TetraSphere sets a new state-of-the-art performance classifying randomly rotated real-world object scans of the challenging subsets of ScanObjectNN. Additionally TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes. Thus our results reveal the practical value of steerable 3D spherical neurons for learning in 3D Euclidean space. The code is available at https://github.com/pavlo-melnyk/tetrasphere.
ZeroRF: Fast Sparse View 360deg Reconstruction with Zero Pretraining
Authors: Ruoxi Shi, Xinyue Wei, Cheng Wang, Hao Su
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present ZeroRF a novel per-scene optimization method addressing the challenge of sparse view 360deg reconstruction in neural field representations. Current breakthroughs like Neural Radiance Fields (NeRF) have demonstrated high-fidelity image synthesis but struggle with sparse input views. Existing methods such as Generalizable NeRFs and per-scene optimization approaches face limitations in data dependency computational cost and generalization across diverse scenarios. To overcome these challenges we propose ZeroRF whose key idea is to integrate a tailored Deep Image Prior into a factorized NeRF representation. Unlike traditional methods ZeroRF parametrizes feature grids with a neural network generator enabling efficient sparse view 360deg reconstruction without any pretraining or additional regularization. Extensive experiments showcase ZeroRF's versatility and superiority in terms of both quality and speed achieving state-of-the-art results on benchmark datasets. ZeroRF's significance extends to applications in 3D content generation and editing. Project page: https://sarahweiii.github.io/zerorf/
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception
Authors: Ruiyang Hao, Siqi Fan, Yingru Dai, Zhenlin Zhang, Chenxi Li, Yuntian Wang, Haibao Yu, Wenxian Yang, Jirui Yuan, Zaiqing Nie
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The value of roadside perception which could extend the boundaries of autonomous driving and traffic management has gradually become more prominent and acknowledged in recent years. However existing roadside perception approaches only focus on the single-infrastructure sensor system which cannot realize a comprehensive understanding of a traffic area because of the limited sensing range and blind spots. Orienting high-quality roadside perception we need Roadside Cooperative Perception (RCooper) to achieve practical area-coverage roadside perception for restricted traffic areas. Rcooper has its own domain-specific challenges but further exploration is hindered due to the lack of datasets. We hence release the first real-world large-scale RCooper dataset to bloom the research on practical roadside cooperative perception including detection and tracking. The manually annotated dataset comprises 50k images and 30k point clouds including two representative traffic scenes (i.e. intersection and corridor). The constructed benchmarks prove the effectiveness of roadside cooperation perception and demonstrate the direction of further research. Codes and dataset can be accessed at: https://github.com/AIR-THU/DAIR-RCooper.
TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformations
Authors: Bo Sun, Thibault Groueix, Chen Song, Qixing Huang, Noam Aigerman
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This work proposes a novel representation of injective deformations of 3D space which overcomes existing limitations of injective methods namely inaccuracy lack of robustness and incompatibility with general learning and optimization frameworks. Our core idea is to reduce the problem to a "deep" composition of multiple 2D mesh-based piecewise-linear maps. Namely we build differentiable layers that produce mesh deformations through Tutte's embedding (guaranteed to be injective in 2D) and compose these layers over different planes to create complex 3D injective deformations of the 3D volume. We show our method provides the ability to ef?ciently and accurately optimize and learn complex deformations outperforming other injective approaches. As a main application we produce complex and artifact-free NeRF and SDF deformations.
WANDR: Intention-guided Human Motion Generation
Authors: Markos Diomataris, Nikos Athanasiou, Omid Taheri, Xi Wang, Otmar Hilliges, Michael J. Black
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Synthesizing natural human motions that enable a 3D human avatar to walk and reach for arbitrary goals in 3D space remains an unsolved problem with many applications. Existing methods (data-driven or using reinforcement learning) are limited in terms of generalization and motion naturalness. A primary obstacle is the scarcity of training data that combines locomotion with goal reaching. To address this we introduce WANDR a data-driven model that takes an avatar's initial pose and a goal's 3D position and generates natural human motions that place the end effector (wrist) on the goal location. To solve this we introduce novel intention features that drive rich goal-oriented movement. Intention guides the agent to the goal and interactively adapts the generation to novel situations without needing to define sub-goals or the entire motion path. Crucially intention allows training on datasets that have goal-oriented motions as well as those that do not. WANDR is a conditional Variational Auto-Encoder (c-VAE) which we train using the AMASS and CIRCLE datasets. We evaluate our method extensively and demonstrate its ability to generate natural and long-term motions that reach 3D goals and generalize to unseen goal locations. Our models and code are available for research purposes at wandr.is.tue.mpg.de
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment
Authors: Alireza Ganjdanesh, Shangqian Gao, Heng Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Structural model pruning is a prominent approach used for reducing the computational cost of Convolutional Neural Networks (CNNs) before their deployment on resource-constrained devices. Yet the majority of proposed ideas require a pretrained model before pruning which is costly to secure. In this paper we propose a novel structural pruning approach to jointly learn the weights and structurally prune architectures of CNN models. The core element of our method is a Reinforcement Learning (RL) agent whose actions determine the pruning ratios of the CNN model's layers and the resulting model's accuracy serves as its reward. We conduct the joint training and pruning by iteratively training the model's weights and the agent's policy and we regularize the model's weights to align with the selected structure by the agent. The evolving model's weights result in a dynamic reward function for the agent which prevents using prominent episodic RL methods with stationary environment assumption for our purpose. We address this challenge by designing a mechanism to model the complex changing dynamics of the reward function and provide a representation of it to the RL agent. To do so we take a learnable embedding for each training epoch and employ a recurrent model to calculate a representation of the changing environment. We train the recurrent model and embeddings using a decoder model to reconstruct observed rewards. Such a design empowers our agent to effectively leverage episodic observations along with the environment representations to learn a proper policy to determine performant sub-networks of the CNN model. Our extensive experiments on CIFAR-10 and ImageNet using ResNets and MobileNets demonstrate the effectiveness of our method.
Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
Authors: Rui Zhao, Bin Shi, Jianfei Ruan, Tianze Pan, Bo Dong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In noisy label learning estimating noisy class posteriors plays a fundamental role for developing consistent classifiers as it forms the basis for estimating clean class posteriors and the transition matrix. Existing methods typically learn noisy class posteriors by training a classification model with noisy labels. However when labels are incorrect these models may be misled to overemphasize the feature parts that do not reflect the instance characteristics resulting in significant errors in estimating noisy class posteriors. To address this issue this paper proposes to augment the supervised information with part-level labels encouraging the model to focus on and integrate richer information from various parts. Specifically our method first partitions features into distinct parts by cropping instances yielding part-level labels associated with these various parts. Subsequently we introduce a novel single-to-multiple transition matrix to model the relationship between the noisy and part-level labels which incorporates part-level labels into a classifier-consistent framework. Utilizing this framework with part-level labels we can learn the noisy class posteriors more precisely by guiding the model to integrate information from various parts ultimately improving the classification performance. Our method is theoretically sound while experiments show that it is empirically effective in synthetic and real-world noisy benchmarks.
Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification
Authors: Sravanti Addepalli, Ashish Ramayee Asokan, Lakshay Sharma, R. Venkatesh Babu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Vision-Language Models (VLMs) such as CLIP are trained on large amounts of image-text pairs resulting in remarkable generalization across several data distributions. However in several cases their expensive training and data collection/curation costs do not justify the end application. This motivates a vendor-client paradigm where a vendor trains a large-scale VLM and grants only input-output access to clients on a pay-per-query basis in a black-box setting. The client aims to minimize inference cost by distilling the VLM to a student model using the limited available task-specific data and further deploying this student model in the downstream application. While naive distillation largely improves the In-Domain (ID) accuracy of the student it fails to transfer the superior out-of-distribution (OOD) generalization of the VLM teacher using the limited available labeled images. To mitigate this we propose Vision-Language to Vision - Align Distill Predict (VL2V-ADiP) which first aligns the vision and language modalities of the teacher model with the vision modality of a pre-trained student model and further distills the aligned VLM representations to the student. This maximally retains the pre-trained features of the student while also incorporating the rich representations of the VLM image encoder and the superior generalization of the text embeddings. The proposed approach achieves state-of-the-art results on the standard Domain Generalization benchmarks in a black-box teacher setting as well as a white-box setting where the weights of the VLM are accessible.
Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation
Authors: Hyunwoo Ryu, Jiwoo Kim, Hyunseok An, Junwoo Chang, Joohwan Seo, Taehan Kim, Yubin Kim, Chaewon Hwang, Jongeun Choi, Roberto Horowitz
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion generative modeling has become a promising approach for learning robotic manipulation tasks from stochastic human demonstrations. In this paper we present Diffusion-EDFs a novel SE(3)-equivariant diffusion-based approach for visual robotic manipulation tasks. We show that our proposed method achieves remarkable data efficiency requiring only 5 to 10 human demonstrations for effective end-to-end training in less than an hour. Furthermore our benchmark experiments demonstrate that our approach has superior generalizability and robustness compared to state-of-the-art methods. Lastly we validate our methods with real hardware experiments.
Prompt Learning via Meta-Regularization
Authors: Jinyoung Park, Juyeon Ko, Hyunwoo J. Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently prompt learning approaches have been explored to efficiently and effectively adapt the vision-language models to a variety of downstream tasks. However most existing prompt learning methods suffer from task overfitting since the general knowledge of the pre-trained vision language models is forgotten while the prompts are finetuned on a small data set from a specific target task. To address this issue we propose a Prompt Meta-Regularization (ProMetaR) to improve the generalizability of prompt learning for vision-language models. Specifically ProMetaR meta-learns both the regularizer and the soft prompts to harness the task-specific knowledge from the downstream tasks and task-agnostic general knowledge from the vision-language models. Further ProMetaR augments the task to generate multiple virtual tasks to alleviate the meta-overfitting. In addition we provide the analysis to comprehend how ProMetaR improves the generalizability of prompt tuning in the perspective of the gradient alignment. Our extensive experiments demonstrate that our ProMetaR improves the generalizability of conventional prompt learning methods under base-to-base/base-to-new and domain generalization settings. The code of ProMetaR is available at https://github.com/mlvlab/ProMetaR.
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional Understanding
Authors: Le Zhang, Rabiul Awal, Aishwarya Agrawal
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Vision-Language Models (VLMs) such as CLIP exhibit strong image-text comprehension abilities facilitating advances in several downstream tasks such as zero-shot image classification image-text retrieval and text-to-image generation. However the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally the current contrastive learning objective fails to focus on fine-grained grounding components like relations actions and attributes resulting in "bag-of-words" representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks.
CMA: A Chromaticity Map Adapter for Robust Detection of Screen-Recapture Document Images
Authors: Changsheng Chen, Liangwei Lin, Yongqi Chen, Bin Li, Jishen Zeng, Jiwu Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The rebroadcasting of screen-recaptured document images introduces a significant risk to the confidential documents processed in government departments and commercial companies. However detecting recaptured document images subjected to distortions from online social networks (OSNs) is challenging since the common forensics cues such as moir ?e pattern are weakened during transmission. In this work we first devise a pixel-level distortion model of the screen-recaptured document image to identify the robust features of color artifacts. Then we extract a chromaticity map from the recaptured image to highlight the presence of color artifacts even under low-quality samples. Based on the prior understanding we design a chromaticity map adapter (CMA) to efficiently extract the chromaticity map and feed it into the transformer backbone as multi-modal prompt tokens. To evaluate the performance of the proposed method we collect a recaptured office document image dataset with over 10K diverse samples. Experimental results demonstrate that the proposed CMA method outperforms a SOTA approach (with RGB modality only) reducing the average EER from 26.82% to 16.78%. Robustness evaluation shows that our method achieves 0.8688 and 0.7554 AUCs under samples with JPEG compression (QF=70) and resolution as low as 534x503 pixels.
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld
Authors: Yijun Yang, Tianyi Zhou, Kanxue Li, Dapeng Tao, Lusong Li, Li Shen, Xiaodong He, Jing Jiang, Yuhui Shi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: While large language models (LLMs) excel in a simulated world of texts they struggle to interact with the more realistic world without perceptions of other modalities such as visual or audio signals. Although vision-language models (VLMs) integrate LLM modules (1) aligned with static image features and (2) may possess prior knowledge of world dynamics (as demonstrated in the text world) they have not been trained in an embodied visual world and thus cannot align with its dynamics. On the other hand training an embodied agent in a noisy visual world without expert guidance is often challenging and inefficient. In this paper we train a VLM agent living in a visual world using an LLM agent excelling in a parallel text world. Specifically we distill LLM's reflection outcomes (improved actions by analyzing mistakes) in a text world's tasks to finetune the VLM on the same tasks of the visual world resulting in an Embodied Multi-Modal Agent (EMMA) quickly adapting to the visual world dynamics. Such cross-modality imitation learning between the two parallel worlds is achieved by a novel DAgger-DPO algorithm enabling EMMA to generalize to a broad scope of new tasks without any further guidance from the LLM expert. Extensive evaluations on the ALFWorld benchmark's diverse tasks highlight EMMA's superior performance to SOTA VLM-based agents e.g. 20%-70% improvement in the success rate.