mtybilly/Instruct2DS huggingface dataset:
ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images
Authors: Marius Schmidt-Mengin, Alexis Benichoux, Shibeshih Belachew, Nikos Komodakis, Nikos Paragios
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Annotating lots of 3D medical images for training segmentation models is time-consuming. The goal of weakly supervised semantic segmentation is to train segmentation models without using any ground truth segmentation masks. Our work addresses the case where only image-level categorical labels indicating the presence or absence of a particular region of interest (such as tumours or lesions) are available. Most existing methods rely on class activation mapping (CAM). We propose a novel approach ToNNO which is based on the Tomographic reconstruction of a Neural Network's Output. Our technique extracts stacks of slices with different angles from the input 3D volume feeds these slices to a 2D encoder and applies the inverse Radon transform in order to reconstruct a 3D heatmap of the encoder's predictions. This generic method allows to perform dense prediction tasks on 3D volumes using any 2D image encoder. We apply it to weakly supervised medical image segmentation by training the 2D encoder to output high values for slices containing the regions of interest. We test it on four large scale medical image datasets and outperform 2D CAM methods. We then extend ToNNO by combining tomographic reconstruction with CAM methods proposing Averaged CAM and Tomographic CAM which obtain even better results.
Learning to Navigate Efficiently and Precisely in Real Environments
Authors: Guillaume Bono, Hervé Poirier, Leonid Antsfeld, Gianluca Monaci, Boris Chidlovskii, Christian Wolf
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In the context of autonomous navigation of terrestrial robots the creation of realistic models for agent dynamics and sensing is a widespread habit in the robotics literature and in commercial applications where they are used for model based control and/or for localization and mapping. The more recent Embodied AI literature on the other hand focuses on modular or end-to-end agents trained in simulators like Habitat or AI-Thor where the emphasis is put on photo-realistic rendering and scene diversity but high-fidelity robot motion is assigned a less privileged role. The resulting sim2real gap significantly impacts transfer of the trained models to real robotic platforms. In this work we explore end-to-end training of agents in simulation in settings which minimize the sim2real gap both in sensing and in actuation. Our agent directly predicts (discretized) velocity commands which are maintained through closed-loop control in the real robot. The behavior of the real robot (including the underlying low-level controller) is identified and simulated in a modified Habitat simulator. Noise models for odometry and localization further contribute in lowering the sim2real gap. We evaluate on real navigation scenarios explore different localization and point goal calculation methods and report significant gains in performance and robustness compared to prior work.
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization Perspective
Authors: Shunsuke Yasuki, Masato Taki
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently convolutional neural networks (CNNs) with large size kernels have attracted much attention in the computer vision field following the success of the Vision Transformers. Large kernel CNNs have been reported to perform well in downstream vision tasks as well as in classification performance. The reason for the high-performance of large kernel CNNs in downstream tasks has been attributed to the large effective receptive field (ERF) produced by large size kernels but this view has not been fully tested. We therefore revisit the performance of large kernel CNNs in downstream task focusing on the weakly supervised object localization (WSOL) task. WSOL a difficult downstream task that is not fully supervised provides a new angle to explore the capabilities of the large kernel CNNs. Our study compares the modern large kernel CNNs ConvNeXt RepLKNet and SLaK to test the validity of the naive expectation that ERF size is important for improving downstream task performance. Our analysis of the factors contributing to high performance provides a different perspective in which the main factor is feature map improvement. Furthermore we find that modern CNNs are robust to the CAM problems of local regions of objects being activated which has long been discussed in WSOL. CAM is the most classic WSOL method but because of the above-mentioned problems it is often used as a baseline method for comparison. However experiments on the CUB-200-2011 dataset show that simply combining a large kernel CNN CAM and simple data augmentation methods can achieve performance (90.99% MaxBoxAcc) comparable to the latest WSOL method which is CNN-based and requires special training or complex post-processing.
VkD: Improving Knowledge Distillation using Orthogonal Projections
Authors: Roy Miles, Ismail Elezi, Jiankang Deng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Knowledge distillation is an effective method for training small and efficient deep learning models. However the efficacy of a single method can degenerate when transferring to other tasks modalities or even other architectures. To address this limitation we propose a novel constrained feature distillation method. This method is derived from a small set of core principles which results in two emerging components: an orthogonal projection and a task-specific normalisation. Equipped with both of these components our transformer models can outperform all previous methods on ImageNet and reach up to a 4.4% relative improvement over the previous state-of-the-art methods. To further demonstrate the generality of our method we apply it to object detection and image generation whereby we obtain consistent and substantial performance improvements over state-of-the-art. Code and models are publicly available.
Putting the Object Back into Video Object Segmentation
Authors: Ho Kei Cheng, Seoung Wug Oh, Brian Price, Joon-Young Lee, Alexander Schwing
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present Cutie a video object segmentation (VOS) network with object-level memory reading which puts the object representation from memory back into the video object segmentation result. Recent works on VOS employ bottom-up pixel-level memory reading which struggles due to matching noise especially in the presence of distractors resulting in lower performance in more challenging data. In contrast Cutie performs top-down object-level memory reading by adapting a small set of object queries. Via those it interacts with the bottom-up pixel features iteratively with a query-based object transformer (qt hence Cutie). The object queries act as a high-level summary of the target object while high-resolution feature maps are retained for accurate segmentation. Together with foreground-background masked attention Cutie cleanly separates the semantics of the foreground object from the background. On the challenging MOSE dataset Cutie improves by 8.7 J&F over XMem with a similar running time and improves by 4.2 J&F over DeAOT while being three times faster. Code is available at: hkchengrex.github.io/Cutie
Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models
Authors: Gihyun Kwon, Simon Jenni, Dingzeyu Li, Joon-Young Lee, Jong Chul Ye, Fabian Caba Heilbron
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: While there has been significant progress in customizing text-to-image generation models generating images that combine multiple personalized concepts remains challenging. In this work we introduce Concept Weaver a method for composing customized text-to-image diffusion models at inference time. Specifically the method breaks the process into two steps: creating a template image aligned with the semantics of input prompts and then personalizing the template using a concept fusion strategy. The fusion strategy incorporates the appearance of the target concepts into the template image while retaining its structural details. The results indicate that our method can generate multiple custom concepts with higher identity fidelity compared to alternative approaches. Furthermore the method is shown to seamlessly handle more than two concepts and closely follow the semantic meaning of the input prompt without blending appearances across different subjects.
PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling
Authors: Xiaoyun Zheng, Liwei Liao, Xufeng Li, Jianbo Jiao, Rongjie Wang, Feng Gao, Shiqi Wang, Ronggang Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: High-quality human reconstruction and photo-realistic rendering of a dynamic scene is a long-standing problem in computer vision and graphics. Despite considerable efforts invested in developing various capture systems and reconstruction algorithms recent advancements still struggle with loose or oversized clothing and overly complex poses. In part this is due to the challenges of acquiring high-quality human datasets. To facilitate the development of these fields in this paper we present PKU-DyMVHumans a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos. It comprises 8.2 million frames captured by more than 56 synchronized cameras across diverse scenarios. These sequences comprise 32 human subjects across 45 different scenarios each with a high-detailed appearance and realistic human motion. Inspired by recent advancements in neural radiance field (NeRF)-based scene representations we carefully set up an off-the-shelf framework that is easy to provide those state-of-the-art NeRF-based implementations and benchmark on PKU-DyMVHumans dataset. It is paving the way for various applications like fine-grained foreground/background decomposition high-quality human reconstruction and photo-realistic novel view synthesis of a dynamic scene. Extensive studies are performed on the benchmark demonstrating new observations and challenges that emerge from using such high-fidelity dynamic data. The project page and data is available at: https://pku-dymvhumans.github.io.
Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining
Authors: Jiahao Nie, Yun Xing, Gongjie Zhang, Pei Yan, Aoran Xiao, Yap-Peng Tan, Alex C. Kot, Shijian Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars. In this paper we undertake a comprehensive study of CD-FSS and uncover two crucial insights: (i) the necessity of a fine-tuning stage to effectively transfer the learned meta-knowledge across domains and (ii) the overfitting risk during the naive fine-tuning due to the scarcity of novel category examples. With these insights we propose a novel cross-domain fine-tuning strategy that addresses the challenging CD-FSS tasks. We first design Bi-directional Few-shot Prediction (BFP) which establishes support-query correspondence in a bi-directional manner crafting augmented supervision to reduce the overfitting risk. Then we further extend BFP into Iterative Few-shot Adaptor (IFA) which is a recursive framework to capture the support-query correspondence iteratively targeting maximal exploitation of supervisory signals from the sparse novel category samples. Extensive empirical evaluations show that our method significantly outperforms the state-of-the-arts (+7.8%) which verifies that IFA tackles the cross-domain challenges and mitigates the overfitting simultaneously. The code is available at: https://github.com/niejiahao1998/IFA.
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification
Authors: Yuanmin Huang, Mi Zhang, Daizong Ding, Erling Jiang, Zhaoxiang Wang, Min Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep neural networks have demonstrated remarkable performance in point cloud classification. However previous works show they are vulnerable to adversarial perturbations that can manipulate their predictions. Given the distinctive modality of point clouds various attack strategies have emerged posing challenges for existing defenses to achieve effective generalization. In this study we for the first time introduce causal modeling to enhance the robustness of point cloud classification models. Our insight is from the observation that adversarial examples closely resemble benign point clouds from the human perspective. In our causal modeling we incorporate two critical variables the structural information (standing for the key feature leading to the classification) and the hidden confounders (standing for the noise interfering with the classification). The resulting overall framework CausalPC consists of three sub-modules to identify the causal effect for robust classification. The framework is model-agnostic and adaptable for integration with various point cloud classifiers. Our approach significantly improves the adversarial robustness of three mainstream point cloud classification models on two benchmark datasets. For instance the classification accuracy for DGCNN on ModelNet40 increases from 29.2% to 72.0% with CausalPC whereas the best-performing baseline achieves only 42.4%.
LASA: Instance Reconstruction from Real Scans using A Large-scale Aligned Shape Annotation Dataset
Authors: Haolin Liu, Chongjie Ye, Yinyu Nie, Yingfan He, Xiaoguang Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Instance shape reconstruction from a 3D scene involves recovering the full geometries of multiple objects at the semantic instance level. Many methods leverage data-driven learning due to the intricacies of scene complexity and significant indoor occlusions. Training these methods often requires a large-scale high-quality dataset with aligned and paired shape annotations with real-world scans. Existing datasets are either synthetic or misaligned restricting the performance of data-driven methods on real data. To this end we introduce LASA a Large-scale Aligned Shape Annotation Dataset comprising 10412 high-quality CAD annotations aligned with 920 real-world scene scans from ArkitScenes created manually by professional artists. On this top we propose a novel Diffusion-based Cross-Modal Shape Reconstruction (DisCo) method. It is empowered by a hybrid feature aggregation design to fuse multi-modal inputs and recover high-fidelity object geometries. Besides we present an Occupancy-Guided 3D Object Detection (OccGOD) method and demonstrate that our shape annotations provide scene occupancy clues that can further improve 3D object detection. Supported by LASA extensive experiments show that our methods achieve state-of-the-art performance in both instance-level scene reconstruction and 3D object detection tasks.
LaRE^2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection
Authors: Yunpeng Luo, Junlong Du, Ke Yan, Shouhong Ding
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The evolution of Diffusion Models has dramatically improved image generation quality making it increasingly difficult to differentiate between real and generated images. This development while impressive also raises significant privacy and security concerns. In response to this we propose a novel Latent REconstruction error guided feature REfinement method (LaRE^2) for detecting the diffusion-generated images. We come up with the Latent Reconstruction Error (LaRE) the first reconstruction-error based feature in the latent space for generated image detection. LaRE surpasses existing methods in terms of feature extraction efficiency while preserving crucial cues required to differentiate between the real and the fake. To exploit LaRE we propose an Error-Guided feature REfinement module (EGRE) which can refine the image feature guided by LaRE to enhance the discriminativeness of the feature. Our EGRE utilizes an align-then-refine mechanism which effectively refines the image feature for generated-image detection from both spatial and channel perspectives. Extensive experiments on the large-scale GenImage benchmark demonstrate the superiority of our LaRE^2 which surpasses the best SoTA method by up to 11.9%/12.1% average ACC/AP across 8 different image generators. LaRE also surpasses existing methods in terms of feature extraction cost delivering an impressive speed enhancement of 8 times.
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model
Authors: Zhenghao Pan, Haijin Zeng, Jiezhang Cao, Kai Zhang, Yongyong Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI). To achieve this we integrate the advantageous attributes of established SCI techniques and an image generative model propose a novel structured zero-shot diffusion model dubbed DiffSCI. DiffSCI leverages the structural insights from the deep prior and optimization-based methodologies complemented by the generative capabilities offered by the contemporary denoising diffusion model. Specifically firstly we employ a pre-trained diffusion model which has been trained on a substantial corpus of RGB images as the generative denoiser within the Plug-and-Play framework for the first time. This integration allows for the successful completion of SCI reconstruction especially in the case that current methods struggle to address effectively. Secondly we systematically account for spectral band correlations and introduce a robust methodology to mitigate wavelength mismatch thus enabling seamless adaptation of the RGB diffusion model to MSIs.Thirdly an accelerated algorithm is implemented to expedite the resolution of the data subproblem. This augmentation not only accelerates the convergence rate but also elevates the quality of the reconstruction process.We present extensive testing to show that DiffSCI exhibits discernible performance enhancements over prevailing self-supervised and zero-shot approaches surpassing even supervised transformer counterparts across both simulated and real datasets. Code is at https://github.com/PAN083/DiffSCI.
DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation
Authors: Junming Chen, Yunfei Liu, Jianan Wang, Ailing Zeng, Yu Li, Qifeng Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose DiffSHEG a Diffusion-based approach for Speech-driven Holistic 3D Expression and Gesture generation. While previous works focused on co-speech gesture or expression generation individually the joint generation of synchronized expressions and gestures remains barely explored. To address this our diffusion-based co-speech motion generation Transformer enables uni-directional information flow from expression to gesture facilitating improved matching of joint expression-gesture distributions. Furthermore we introduce an outpainting-based sampling strategy for arbitrary long sequence generation in diffusion models offering flexibility and computational efficiency. Our method provides a practical solution that produces high-quality synchronized expression and gesture generation driven by speech. Evaluated on two public datasets our approach achieves state-of-the-art performance both quantitatively and qualitatively. Additionally a user study confirms the superiority of our method over prior approaches. By enabling the real-time generation of expressive and synchronized motions our method showcases its potential for various applications in the development of digital humans and embodied agents.
MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models
Authors: Sanjoy Chowdhury, Sayan Nag, K J Joseph, Balaji Vasan Srinivasan, Dinesh Manocha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Music is a universal language that can communicate emotions and feelings. It forms an essential part of the whole spectrum of creative media ranging from movies to social media posts. Machine learning models that can synthesize music are predominantly conditioned on textual descriptions of it. Inspired by how musicians compose music not just from a movie script but also through visualizations we propose MeLFusion a model that can effectively use cues from a textual description and the corresponding image to synthesize music. MeLFusion is a text-to-music diffusion model with a novel "visual synapse" which effectively infuses the semantics from the visual modality into the generated music. To facilitate research in this area we introduce a new dataset MeLBench and propose a new evaluation metric IMSM. Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music measured both objectively and subjectively with a relative gain of up to 67.98% on the FAD score. We hope that our work will gather attention to this pragmatic yet relatively under-explored research area.
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory
Authors: Daehee Park, Jaeseok Jeong, Sung-Hoon Yoon, Jaewoo Jeong, Kuk-Jin Yoon
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Trajectory prediction is a challenging problem that requires considering interactions among multiple actors and the surrounding environment. While data-driven approaches have been used to address this complex problem they suffer from unreliable predictions under distribution shifts during test time. Accordingly several online learning methods have been proposed using regression loss from the ground truth of observed data leveraging the auto-labeling nature of trajectory prediction task. We mainly tackle the following two issues. First previous works underfit and overfit as they only optimize the last layer of motion decoder. To this end we employ the masked autoencoder (MAE) for representation learning to encourage complex interaction modeling in shifted test distribution for updating deeper layers. Second utilizing the sequential nature of driving data we propose an actor-specific token memory that enables the test-time learning of actor-wise motion characteristics. Our proposed method has been validated across various challenging cross-dataset distribution shift scenarios including nuScenes Lyft Waymo and Interaction. Our method surpasses the performance of existing state-of-the-art online learning methods in terms of both prediction accuracy and computational efficiency. The code is available at https://github.com/daeheepark/T4P.
Noisy-Correspondence Learning for Text-to-Image Person Re-identification
Authors: Yang Qin, Yingke Chen, Dezhong Peng, Xi Peng, Joey Tianyi Zhou, Peng Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community which aims to retrieve the target person based on a textual query. Although numerous TIReID methods have been proposed and achieved promising performance they implicitly assume the training image-text pairs are correctly aligned which is not always the case in real-world scenarios. In practice the image-text pairs inevitably exist under-correlated or even false-correlated a.k.a noisy correspondence (NC) due to the low quality of the images and annotation errors. To address this problem we propose a novel Robust Dual Embedding method (RDE) that can learn robust visual-semantic associations even with NC. Specifically RDE consists of two main components: 1) A Confident Consensus Division (CCD) module that leverages the dual-grained decisions of dual embedding modules to obtain a consensus set of clean training data which enables the model to learn correct and reliable visual-semantic associations. 2) A Triplet Alignment Loss (TAL) relaxes the conventional Triplet Ranking loss with the hardest negative samples to a log-exponential upper bound over all negative ones thus preventing the model collapse under NC and can also focus on hard-negative samples for promising performance. We conduct extensive experiments on three public benchmarks namely CUHK-PEDES ICFG-PEDES and RSTPReID to evaluate the performance and robustness of our RDE. Our method achieves state-of-the-art results both with and without synthetic noisy correspondences on all three datasets. Code is available at https://github.com/QinYang79/RDE.
InstaGen: Enhancing Object Detection by Training on Synthetic Dataset
Authors: Chengjian Feng, Yujie Zhong, Zequn Jie, Weidi Xie, Lin Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we present a novel paradigm to enhance the ability of object detector e.g. expanding categories or improving detection performance by training on syn- thetic dataset generated from diffusion models. Specifically we integrate an instance-level grounding head into a pre- trained generative diffusion model to augment it with the ability of localising instances in the generated images. The grounding head is trained to align the text embedding of category names with the regional visual feature of the diffusion model using supervision from an off-the-shelf object detector and a novel self-training scheme on (novel) categories not covered by the detector. We conduct thorough experiments to show that this enhanced version of diffusion model termed as InstaGen can serve as a data synthesizer to enhance object detectors by training on its generated samples demonstrating superior performance over existing state-of-the-art methods in open-vocabulary (+4.5 AP) and data-sparse (+1.2 ? 5.2 AP) scenarios.
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular Video
Authors: Dong Wu, Zike Yan, Hongbin Zha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce the Panoptic 3D Reconstruction task a unified and holistic scene understanding task for a monocular video. And we present PanoRecon - a novel framework to address this new task which realizes an online geometry reconstruction alone with dense semantic and instance labeling. Specifically PanoRecon incrementally performs panoptic 3D reconstruction for each video fragment consisting of multiple consecutive key frames from a volumetric feature representation using feed-forward neural networks. We adopt a depth-guided back-projection strategy to sparse and purify the volumetric feature representation. We further introduce a voxel clustering module to get object instances in each local fragment and then design a tracking and fusion algorithm for the integration of instances from different fragments to ensure temporal coherence. Such design enables our PanoRecon to yield a coherent and accurate panoptic 3D reconstruction. Experiments on ScanNetV2 demonstrate a very competitive geometry reconstruction result compared with state-of-the-art reconstruction methods as well as promising 3D panoptic segmentation result with only RGB input while being real-time. Code is available at: https://github.com/Riser6/PanoRecon.
Animating General Image with Large Visual Motion Model
Authors: Dengsheng Chen, Xiaoming Wei, Xiaolin Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present the pioneering Large Visual Motion Model (LVMM) meticulously engineered to analyze the intrinsic dynamics encapsulated within real-world imagery. Our model fortified with a wealth of prior knowledge extracted from billions of image pairs demonstrates promising results in predicting a diverse spectrum of scene dynamics. As a result it can infuse any generic image with authentic dynamic effects enhancing its visual allure.
Visual Point Cloud Forecasting enables Scalable Autonomous Driving
Authors: Zetong Yang, Li Chen, Yanan Sun, Hongyang Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In contrast to extensive studies on general vision pre-training for scalable visual autonomous driving remains seldom explored. Visual autonomous driving applications require features encompassing semantics 3D geometry and temporal information simultaneously for joint perception prediction and planning posing dramatic challenges for pre-training. To resolve this we bring up a new pre-training task termed as visual point cloud forecasting - predicting future point clouds from historical visual input. The key merit of this task captures the synergic learning of semantics 3D structures and temporal dynamics. Hence it shows superiority in various downstream tasks. To cope with this new problem we present ViDAR a general model to pre-train downstream visual encoders. It first extracts historical embeddings by the encoder. These representations are then transformed to 3D geometric space via a novel Latent Rendering operator for future point cloud prediction. Experiments show significant gain in downstream tasks e.g. 3.1% NDS on 3D detection  10% error reduction on motion forecasting and  15% less collision rate on planning.
Towards Transferable Targeted 3D Adversarial Attack in the Physical World
Authors: Yao Huang, Yinpeng Dong, Shouwei Ruan, Xiao Yang, Hang Su, Xingxing Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Compared with transferable untargeted attacks transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples posing a greater threat to security-critical tasks. In the meanwhile 3D adversarial samples due to their potential of multi-view robustness can more comprehensively identify weaknesses in existing deep learning systems possessing great application value. However the field of transferable targeted 3D adversarial attacks remains vacant. The goal of this work is to develop a more effective technique that could generate transferable targeted 3D adversarial examples filling the gap in this field. To achieve this goal we design a novel framework named TT3D that could rapidly reconstruct from few multi-view images into Transferable Targeted 3D textured meshes. While existing mesh-based texture optimization methods compute gradients in the high-dimensional mesh space and easily fall into local optima leading to unsatisfactory transferability and distinct distortions TT3D innovatively performs dual optimization towards both feature grid and Multi-layer Perceptron (MLP) parameters in the grid-based NeRF space which significantly enhances black-box transferability while enjoying naturalness. Experimental results show that TT3D not only exhibits superior cross-model transferability but also maintains considerable adaptability across different renders and vision tasks. More importantly we produce 3D adversarial examples with 3D printing techniques in the real world and verify their robust performance under various scenarios.
SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting
Authors: Hoon Kim, Minje Jang, Wonjun Yoon, Jisoo Lee, Donghyun Na, Sanghyun Woo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore to overcome the limitation of scarce high-quality lightstage data we have developed a self-supervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism.
DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data
Authors: Qihao Liu, Yi Zhang, Song Bai, Adam Kortylewski, Alan Yuille
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present DIRECT-3D a diffusion-based 3D generative model for creating high-quality 3D assets (represented by Neural Radiance Fields) from text prompts. Unlike recent 3D generative models that rely on clean and well-aligned 3D data limiting them to single or few-class generation our model is directly trained on extensive noisy and unaligned `in-the-wild' 3D assets mitigating the key challenge (i.e. data scarcity) in large-scale 3D generation. In particular DIRECT-3D is a tri-plane diffusion model that integrates two innovations: 1) A novel learning framework where noisy data are filtered and aligned automatically during the training process. Specifically after an initial warm-up phase using a small set of clean data an iterative optimization is introduced in the diffusion process to explicitly estimate the 3D pose of objects and select beneficial data based on conditional density. 2) An efficient 3D representation that is achieved by disentangling object geometry and color features with two separate conditional diffusion models that are optimized hierarchically. Given a prompt input our model generates high-quality high-resolution realistic and complex 3D objects with accurate geometric details in seconds. We achieve state-of-the-art performance in both single-class generation and text-to-3D generation. We also demonstrate that DIRECT-3D can serve as a useful 3D geometric prior of objects for example to alleviate the well-known Janus problem in 2D-lifting methods such as DreamFusion.
Synthesize Step-by-Step: Tools Templates and LLMs as Data Generators for Reasoning-Based Chart VQA
Authors: Zhuowan Li, Bhavan Jasani, Peng Tang, Shabnam Ghadar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Understanding data visualizations like charts and plots requires reasoning about both visual elements and numerics. Although strong in extractive questions current chart visual question answering (chart VQA) models suffer on complex reasoning questions. In this work we address the lack of reasoning ability by data augmentation. We leverage Large Language Models (LLMs) which have shown to have strong reasoning ability as an automatic data annotator that generates question-answer annotations for chart images. The key innovation in our method lies in the Synthesize Step-by-Step strategy: our LLM-based data generator learns to decompose the complex question into step-by-step sub-questions (rationales) which are then used to derive the final answer using external tools i.e. Python. This step-wise generation procedure is trained on synthetic data generated using a template-based QA generation pipeline. Experimental results highlight the significance of the proposed step-by-step generation. By training with the LLM-augmented data (LAMENDA) we significantly enhance the chart VQA models achieving the state-of-the-art accuracy on the ChartQA and PlotQA datasets. In particular our approach improves the accuracy of the previous state-of-the-art approach from 38% to 54% on the human-written questions in the ChartQA dataset which needs strong reasoning. We hope our work underscores the potential of synthetic data and encourages further exploration of data augmentation using LLMs for reasoning-heavy tasks.
LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding
Authors: Chuwei Luo, Yufan Shen, Zhaoqing Zhu, Qi Zheng, Zhi Yu, Cong Yao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However previous works that employ LLMs/MLLMs for document understanding have not fully explored and utilized the document layout information which is vital for precise document understanding. In this paper we propose LayoutLLM an LLM/MLLM based method for document understanding. The core of LayoutLLM is a layout instruction tuning strategy which is specially designed to enhance the comprehension and utilization of document layouts. The proposed layout instruction tuning strategy consists of two components: Layout-aware Pre-training and Layout-aware Supervised Fine-tuning. To capture the characteristics of document layout in Layout-aware Pre-training three groups of pre-training tasks corresponding to document-level region-level and segment-level information are introduced. Furthermore a novel module called layout chain-of-thought (LayoutCoT) is devised to enable LayoutLLM to focus on regions relevant to the question and generate accurate answers. LayoutCoT is effective for boosting the performance of document understanding. Meanwhile it brings a certain degree of interpretability which could facilitate manual inspection and correction. Experiments on standard benchmarks show that the proposed LayoutLLM significantly outperforms existing methods that adopt open-source 7B LLMs/MLLMs for document understanding.
ProTeCt: Prompt Tuning for Taxonomic Open Set Classification
Authors: Tz-Ying Wu, Chih-Hui Ho, Nuno Vasconcelos
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Visual-language foundation models like CLIP learn generalized representations that enable zero-shot open-set classification. Few-shot adaptation methods based on prompt tuning have been shown to further improve performance on downstream datasets. However these methods do not fare well in the taxonomic open set (TOS) setting where the classifier is asked to make prediction from label set across different levels of semantic granularity. Frequently they infer incorrect labels at coarser taxonomic class levels even when the inference at the leaf level (original class labels) is correct. To address this problem we propose a prompt tuning technique that calibrates the hierarchical consistency of model predictions. A set of metrics of hierarchical consistency the Hierarchical Consistent Accuracy (HCA) and the Mean Treecut Accuracy (MTA) are first proposed to evaluate TOS model performance. A new Prompt Tuning for Hierarchical Consistency (ProTeCt) technique is then proposed to calibrate classification across label set granularities. Results show that ProTeCt can be combined with existing prompt tuning methods to significantly improve TOS classification without degrading the leaf level classification performance.
Adapters Strike Back
Authors: Jan-Martin O. Steitz, Stefan Roth
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Adapters provide an efficient and lightweight mechanism for adapting trained transformer models to a variety of different tasks. However they have often been found to be outperformed by other adaptation mechanisms including low-rank adaptation. In this paper we provide an in-depth study of adapters their internal structure as well as various implementation choices. We uncover pitfalls for using adapters and suggest a concrete improved adapter architecture called Adapter+ that not only outperforms previous adapter implementations but surpasses a number of other more complex adaptation mechanisms in several challenging settings. Despite this our suggested adapter is highly robust and unlike previous work requires little to no manual intervention when addressing a novel scenario. Adapter+ reaches state-of-the-art average accuracy on the VTAB benchmark even without a per-task hyperparameter optimization.
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Authors: Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah, Peter McLean, Jess Leung, Vasudev Sharma, Ayla Khan, Jia Balakrishnan, Safiye Celik, Dominique Beaini, Maciej Sypetkowski, Chi Vicky Cheng, Kristen Morse, Maureen Makes, Ben Mabey, Berton Earnshaw
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Featurizing microscopy images for use in biological research remains a significant challenge especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond.
OHTA: One-shot Hand Avatar via Data-driven Implicit Priors
Authors: Xiaozheng Zheng, Chao Wen, Zhuo Su, Zeran Xu, Zhaohu Li, Yang Zhao, Zhou Xue
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we delve into the creation of one-shot hand avatars attaining high-fidelity and drivable hand representations swiftly from a single image. With the burgeoning domains of the digital human the need for quick and personalized hand avatar creation has become increasingly critical. Existing techniques typically require extensive input data and may prove cumbersome or even impractical in certain scenarios. To enhance accessibility we present a novel method OHTA (One-shot Hand avaTAr) that enables the creation of detailed hand avatars from merely one image. OHTA tackles the inherent difficulties of this data-limited problem by learning and utilizing data-driven hand priors. Specifically we design a hand prior model initially employed for 1) learning various hand priors with available data and subsequently for 2) the inversion and fitting of the target identity with prior knowledge. OHTA demonstrates the capability to create high-fidelity hand avatars with consistent animatable quality solely relying on a single image. Furthermore we illustrate the versatility of OHTA through diverse applications encompassing text-to-avatar conversion hand editing and identity latent space manipulation.
Segment and Caption Anything
Authors: Xiaoke Huang, Jianfeng Wang, Yansong Tang, Zheng Zhang, Han Hu, Jiwen Lu, Lijuan Wang, Zicheng Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions. SAM presents strong generalizability to segment anything while is short for semantic understanding. By introducing a lightweight query-based feature mixer we align the region-specific features with the embedding space of language models for later caption generation. As the number of trainable parameters is small (typically in the order of tens of millions) it costs less computation less memory usage and less communication bandwidth resulting in both fast and scalable training. To address the scarcity problem of regional caption data we propose to first pre-train our model on objection detection and segmentation tasks. We call this step weak supervision pretraining since the pretraining data only contains category names instead of full-sentence descriptions. The weak supervision pretraining allows us to leverage many publicly available object detection and segmentation datasets. We conduct extensive experiments to demonstrate the superiority of our method and validate each design choice. This work serves as a stepping stone towards scaling up regional captioning data and sheds light on exploring efficient ways to augment SAM with regional semantics.
Human Motion Prediction Under Unexpected Perturbation
Authors: Jiangbei Yue, Baiyi Li, Julien Pettré, Armin Seyfried, He Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We investigate a new task in human motion prediction which is predicting motions under unexpected physical perturbation potentially involving multiple people. Compared with existing research this task involves predicting less controlled unpremeditated and pure reactive motions in response to external impact and how such motions can propagate through people. It brings new challenges such as data scarcity and predicting complex interactions. To this end we propose a new method capitalizing differentiable physics and deep neural networks leading to an explicit Latent Differentiable Physics (LDP) model. Through experiments we demonstrate that LDP has high data efficiency outstanding prediction accuracy strong generalizability and good explainability. Since there is no similar research a comprehensive comparison with 11 adapted baselines from several relevant domains is conducted showing LDP outperforming existing research both quantitatively and qualitatively improving prediction accuracy by as much as 70% and demonstrating significantly stronger generalization.