mtybilly/Instruct2DS huggingface dataset:
SpecNeRF: Gaussian Directional Encoding for Specular Reflections
Authors: Li Ma, Vasu Agrawal, Haithem Turki, Changil Kim, Chen Gao, Pedro Sander, Michael Zollhöfer, Christian Richardt
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However existing approaches still struggle with the view-dependent appearance of glossy surfaces especially under complex lighting of indoor environments. Unlike existing methods which typically assume distant lighting like an environment map we propose a learnable Gaussian directional encoding to better model the view-dependent effects under near-field lighting conditions. Importantly our new directional encoding captures the spatially-varying nature of near-field lighting and emulates the behavior of prefiltered environment maps. As a result it enables the efficient evaluation of preconvolved specular color at any 3D location with varying roughness coefficients. We further introduce a data-driven geometry prior that helps alleviate the shape radiance ambiguity in reflection modeling. We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields which helps decompose appearance into more physically meaningful components.
Improving Subject-Driven Image Synthesis with Subject-Agnostic Guidance
Authors: Kelvin C.K. Chan, Yang Zhao, Xuhui Jia, Ming-Hsuan Yang, Huisheng Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In subject-driven text-to-image synthesis the synthesis process tends to be heavily influenced by the reference images provided by users often overlooking crucial attributes detailed in the text prompt. In this work we propose Subject-Agnostic Guidance (SAG) a simple yet effective solution to remedy the problem. We show that through constructing a subject-agnostic condition and applying our proposed dual classifier-free guidance one could obtain outputs consistent with both the given subject and input text prompts. We validate the efficacy of our approach through both optimization-based and encoder-based methods. Additionally we demonstrate its applicability in second-order customization methods where an encoder-based model is fine-tuned with DreamBooth. Our approach is conceptually simple and requires only minimal code modifications but leads to substantial quality improvements as evidenced by our evaluations and user studies.
Diffusion Model Alignment Using Direct Preference Optimization
Authors: Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' preferences. In contrast to LLMs human preference learning has not been widely explored in text-to-image diffusion models; the best existing approach is to fine-tune a pretrained model using carefully curated high quality images and captions to improve visual appeal and text alignment. We propose Diffusion-DPO a method to align diffusion models to human preferences by directly optimizing on human comparison data. Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO) a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective. We re-formulate DPO to account for a diffusion model notion of likelihood utilizing the evidence lower bound to derive a differentiable objective. Using the Pick-a-pic dataset of 851K crowdsourced pairwise preferences we fine-tune the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1.0 model with Diffusion-DPO. Our fine-tuned base model significantly outperforms both base SDXL-1.0 and the larger SDXL-1.0 model consisting of an additional refinement model in human evaluation improving visual appeal and prompt alignment. We also develop a variant that uses AI feedback and has comparable performance to training on human preferences opening the door for scaling of diffusion model alignment methods.
Interactive Continual Learning: Fast and Slow Thinking
Authors: Biqing Qi, Xinquan Chen, Junqi Gao, Dong Li, Jianxing Liu, Ligang Wu, Bowen Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Advanced life forms sustained by the synergistic interaction of neural cognitive mechanisms continually acquire and transfer knowledge throughout their lifespan. In contrast contemporary machine learning paradigms exhibit limitations in emulating the facets of continual learning (CL). Nonetheless the emergence of large language models (LLMs) presents promising avenues for realizing CL via interactions with these models. Drawing on Complementary Learning System theory this paper presents a novel Interactive Continual Learning (ICL) framework enabled by collaborative interactions among models of various sizes. Specifically we assign the ViT model as System1 and multimodal LLM as System2. To enable the memory module to deduce tasks from class information and enhance Set2Set retrieval we propose the Class-Knowledge-Task Multi-Head Attention (CKT-MHA). Additionally to improve memory retrieval in System1 through enhanced geometric representation we introduce the CL-vMF mechanism based on the von Mises-Fisher (vMF) distribution. Meanwhile we introduce the von Mises-Fisher Outlier Detection and Interaction (vMF-ODI) strategy to identify hard examples thus enhancing collaboration between System1 and System2 for complex reasoning realization. Comprehensive evaluation of our proposed ICL demonstrates significant resistance to forgetting and superior performance relative to existing methods. Code is available at github.com/ICL.
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image
Authors: Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, Jiajun Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce a 3D-aware diffusion model ZeroNVS for single-image novel view synthesis for in-the-wild scenes. While existing methods are designed for single objects with masked backgrounds we propose new techniques to address challenges introduced by in-the-wild multi-object scenes with complex backgrounds. Specifically we train a generative prior on a mixture of data sources that capture object-centric indoor and outdoor scenes. To address issues from data mixture such as depth-scale ambiguity we propose a novel camera conditioning parameterization and normalization scheme. Further we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes and propose "SDS anchoring" to improve the diversity of synthesized novel views. Our model sets a new state-of-the-art result in LPIPS on the DTU dataset in the zero-shot setting even outperforming methods specifically trained on DTU. We further adapt the challenging Mip-NeRF 360 dataset as a new benchmark for single-image novel view synthesis and demonstrate strong performance in this setting. Code and models will be publicly available.
Restoration by Generation with Constrained Priors
Authors: Zheng Ding, Xuaner Zhang, Zhuowen Tu, Zhihao Xia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The inherent generative power of denoising diffusion models makes them well-suited for image restoration tasks where the objective is to find the optimal high-quality image within the generative space that closely resembles the input image. We propose a method to adapt a pretrained diffusion model for image restoration by simply adding noise to the input image to be restored and then denoise. Our method is based on the observation that the space of a generative model needs to be constrained. We impose this constraint by finetuning the generative model with a set of anchor images that capture the characteristics of the input image. With the constrained space we can then leverage the sampling strategy used for generation to do image restoration. We evaluate against previous methods and show superior performances on multiple real-world restoration datasets in preserving identity and image quality. We also demonstrate an important and practical application on personalized restoration where we use a personal album as the anchor images to constrain the generative space. This approach allows us to produce results that accurately preserve high-frequency details which previous works are unable to do. Project webpage: https://gen2res.github.io.
Snapshot Lidar: Fourier Embedding of Amplitude and Phase for Single-Image Depth Reconstruction
Authors: Sarah Friday, Yunzi Shi, Yaswanth Cherivirala, Vishwanath Saragadam, Adithya Pediredla
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Amplitude modulated continuous-wave time-of-flight (AMCW-ToF) cameras are finding applications as flash Lidars in autonomous navigation robotics and AR/VR applications. A conventional CW-ToF camera requires illuminating the scene with a temporally varying light source and demodulating a set of quadrature measurements to recover the scene's depth and intensity. Capturing the four measurements in sequence renders the system slow invariably causing inaccuracies in depth estimates due to motion in the scene or the camera. To mitigate this problem we propose a snapshot Lidar that captures amplitude and phase simultaneously as a single time-of-flight hologram. Uniquely our approach requires minimal changes to existing CW-ToF imaging hardware. To demonstrate the efficacy of the proposed system we design and build a lab prototype and evaluate it under varying scene geometries illumination conditions and compare the reconstructed depth measurements against conventional techniques. We rigorously evaluate the robustness of our system on diverse real-world scenes to show that our technique results in a significant reduction in data bandwidth with minimal loss in reconstruction accuracy. As high-resolution CW-ToF cameras are becoming ubiquitous increasing their temporal resolution by four times enables robust real-time capture of geometries of dynamic scenes.
Convolutional Prompting meets Language Models for Continual Learning
Authors: Anurag Roy, Riddhiman Moulick, Vinay K. Verma, Saptarshi Ghosh, Abir Das
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Continual Learning (CL) enables machine learning models to learn from continuously shifting new training data in absence of data from old tasks. Recently pre-trained vision transformers combined with prompt tuning have shown promise for overcoming catastrophic forgetting in CL. These approaches rely on a pool of learnable prompts which can be inefficient in sharing knowledge across tasks leading to inferior performance. In addition the lack of fine-grained layer specific prompts does not allow these to fully express the strength of the prompts for CL. We address these limitations by proposing ConvPrompt a novel convolutional prompt creation mechanism that maintains layer-wise shared embeddings enabling both layer-specific learning and better concept transfer across tasks. The intelligent use of convolution enables us to maintain a low parameter overhead without compromising performance. We further leverage Large Language Models to generate fine-grained text descriptions of each category which are used to get task similarity and dynamically decide the number of prompts to be learned. Extensive experiments demonstrate the superiority of ConvPrompt and improves SOTA by 3% with significantly less parameter overhead. We also perform strong ablation over various modules to disentangle the importance of different components.
Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring
Authors: Huicong Zhang, Haozhe Xie, Hongxun Yao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Video deblurring relies on leveraging information from other frames in the video sequence to restore the blurred regions in the current frame. Mainstream approaches employ bidirectional feature propagation spatio-temporal transformers or a combination of both to extract information from the video sequence. However limitations in memory and computational resources constraints the temporal window length of the spatio-temporal transformer preventing the extraction of longer temporal contextual information from the video sequence. Additionally bidirectional feature propagation is highly sensitive to inaccurate optical flow in blurry frames leading to error accumulation during the propagation process. To address these issues we propose BSSTNet Blur-aware Spatio-temporal Sparse Transformer Network. It introduces the blur map which converts the originally dense attention into a sparse form enabling a more extensive utilization of information throughout the entire video sequence. Specifically BSSTNet (1) uses a longer temporal window in the transformer leveraging information from more distant frames to restore the blurry pixels in the current frame. (2) introduces bidirectional feature propagation guided by blur maps which reduces error accumulation caused by the blur frame. The experimental results demonstrate the proposed BSSTNet outperforms the state-of-the-art methods on the GoPro and DVD datasets.
Towards Learning a Generalist Model for Embodied Navigation
Authors: Duo Zheng, Shijia Huang, Lin Zhao, Yiwu Zhong, Liwei Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Building a generalist agent that can interact with the world is an ultimate goal for humans thus spurring the research for embodied navigation where an agent is required to navigate according to instructions or respond to queries. Despite the major progress attained previous works primarily focus on task-specific agents and lack generalizability to unseen scenarios. Recently LLMs have presented remarkable capabilities across various fields and provided a promising opportunity for embodied navigation. Drawing on this we propose the first generalist model for embodied navigation NaviLLM. It adapts LLMs to embodied navigation by introducing schema-based instruction. The schema-based instruction flexibly casts various tasks into generation problems thereby unifying a wide range of tasks. This approach allows us to integrate diverse data sources from various datasets into the training equipping NaviLLM with a wide range of capabilities required by embodied navigation. We conduct extensive experiments to evaluate the performance and generalizability of our model. The experimental results demonstrate that our unified model achieves state-of-the-art performance on CVDN SOON and ScanQA. Specifically it surpasses the previous stats-of-the-art method by a significant margin of 29% in goal progress on CVDN. Moreover our model also demonstrates strong generalizability and presents impressive results on unseen tasks e.g. embodied question answering and 3D captioning.
DiffusionPoser: Real-time Human Motion Reconstruction From Arbitrary Sparse Sensors Using Autoregressive Diffusion
Authors: Tom Van Wouwe, Seunghwan Lee, Antoine Falisse, Scott Delp, C. Karen Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Motion capture from a limited number of body-worn sensors such as inertial measurement units (IMUs) and pressure insoles has important applications in health human performance and entertainment. Recent work has focused on accurately reconstructing whole-body motion from a specific sensor configuration using six IMUs. While a common goal across applications is to use the minimal number of sensors to achieve required accuracy the optimal arrangement of the sensors might differ from application to application. We propose a single diffusion model DiffusionPoser which reconstructs human motion in real-time from an arbitrary combination of sensors including IMUs placed at specified locations and pressure insoles. Unlike existing methods our model grants users the flexibility to determine the number and arrangement of sensors tailored to the specific activity of interest without the need for retraining. A novel autoregressive inferencing scheme ensures real-time motion reconstruction that closely aligns with measured sensor signals. The generative nature of DiffusionPoser ensures realistic behavior even for degrees-of-freedom not directly measured. Qualitative results can be found on our project website.
MANUS: Markerless Grasp Capture using Articulated 3D Gaussians
Authors: Chandradeep Pokhariya, Ishaan Nikhil Shah, Angela Xing, Zekun Li, Kefan Chen, Avinash Sharma, Srinath Sridhar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Understanding how we grasp objects with our hands has important applications in areas like robotics and mixed reality. However this challenging problem requires accurate modeling of the contact between hands and objects.To capture grasps existing methods use skeletons meshes or parametric models that does not represent hand shape accurately resulting in inaccurate contacts. We present MANUS a method for Markerless Hand-Object Grasp Capture using Articulated 3D Gaussians. We build a novel articulated 3D Gaussians representation that extends 3D Gaussian splatting for high-fidelity representation of articulating hands. Since our representation uses Gaussian primitives optimized from the multi-view pixel-aligned losses it enables us to efficiently and accurately estimate contacts between the hand and the object. For the most accurate results our method requires tens of camera views that current datasets do not provide. We therefore build MANUS-Grasps a new dataset that contains hand-object grasps viewed from 50+ cameras across 30+ scenes 3 subjects and comprising over 7M frames. In addition to extensive qualitative results we also show that our method outperforms others on a quantitative contact evaluation method that uses paint transfer from the object to the hand.
Distilling Semantic Priors from SAM to Efficient Image Restoration Models
Authors: Quan Zhang, Xiaoyu Liu, Wei Li, Hanting Chen, Junchao Liu, Jie Hu, Zhiwei Xiong, Chun Yuan, Yunhe Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In image restoration (IR) leveraging semantic priors from segmentation models has been a common approach to improve performance. The recent segment anything model (SAM) has emerged as a powerful tool for extracting advanced semantic priors to enhance IR tasks. However the computational cost of SAM is prohibitive for IR compared to existing smaller IR models. The incorporation of SAM for extracting semantic priors considerably hampers the model inference efficiency. To address this issue we propose a general framework to distill SAM's semantic knowledge to boost exiting IR models without interfering with their inference process. Specifically our proposed framework consists of the semantic priors fusion (SPF) scheme and the semantic priors distillation (SPD) scheme. SPF fuses two kinds of information between the restored image predicted by the original IR model and the semantic mask predicted by SAM for the refined restored image. SPD leverages a self-distillation manner to distill the fused semantic priors to boost the performance of original IR models. Additionallywe design a semantic-guided relation (SGR) module for SPD which ensures semantic feature representation space consistency to fully distill the priors. We demonstrate the effectiveness of our framework across multiple IR models and tasks including deraining deblurring and denoising.
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching
Authors: Rui Gong, Weide Liu, Zaiwang Gu, Xulei Yang, Jun Cheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Geometric knowledge has been shown to be beneficial for the stereo matching task. However prior attempts to integrate geometric insights into stereo matching algorithms have largely focused on geometric knowledge from single images while crucial cross-view factors such as occlusion and matching uniqueness have been overlooked. To address this gap we propose a novel Intra-view and Cross-view Geometric knowledge learning Network (ICGNet) specifically crafted to assimilate both intra-view and cross-view geometric knowledge. ICGNet harnesses the power of interest points to serve as a channel for intra-view geometric understanding. Simultaneously it employs the correspondences among these points to capture cross-view geometric relationships. This dual incorporation empowers the proposed ICGNet to leverage both intra-view and cross-view geometric knowledge in its learning process substantially improving its ability to estimate disparities. Our extensive experiments demonstrate the superiority of the ICGNet over contemporary leading models. The code will be available at https://github.com/DFSDDDDD1199/ICGNet.
Rethinking the Evaluation Protocol of Domain Generalization
Authors: Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Domain generalization aims to solve the challenge of Out-of-Distribution (OOD) generalization by leveraging common knowledge learned from multiple training domains to generalize to unseen test domains. To accurately evaluate the OOD generalization ability it is required that test data information is unavailable. However the current domain generalization protocol may still have potential test data information leakage. This paper examines the risks of test data information leakage from two aspects of the current evaluation protocol: supervised pretraining on ImageNet and oracle model selection. We propose modifications to the current protocol that we should employ self-supervised pretraining or train from scratch instead of employing the current supervised pretraining and we should use multiple test domains. These would result in a more precise evaluation of OOD generalization ability. We also rerun the algorithms with the modified protocol and introduce new leaderboards to encourage future research in domain generalization with a fairer comparison.
Aligning Logits Generatively for Principled Black-Box Knowledge Distillation
Authors: Jing Ma, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge model compression with invisible data and models hosted on the server. B2KD faces challenges such as limited Internet exchange and edge-cloud disparity of data distributions. In this paper we formalize a two-step workflow consisting of deprivatization and distillation and theoretically provide a new optimization direction from logits to cell boundary different from direct logits alignment. With its guidance we propose a new method Mapping-Emulation KD (MEKD) that distills a black-box cumbersome model into a lightweight one. Our method does not differentiate between treating soft or hard responses and consists of: 1) deprivatization: emulating the inverse mapping of the teacher function with a generator and 2) distillation: aligning low-dimensional logits of the teacher and student models by reducing the distance of high-dimensional image points. For different teacher-student pairs our method yields inspiring distillation performance on various benchmarks and outperforms the previous state-of-the-art approaches.
BerfScene: Bev-conditioned Equivariant Radiance Fields for Infinite 3D Scene Generation
Authors: Qihang Zhang, Yinghao Xu, Yujun Shen, Bo Dai, Bolei Zhou, Ceyuan Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generating large-scale 3D scenes cannot simply apply existing 3D object synthesis technique since 3D scenes usually hold complex spatial configurations and consist of a number of objects at varying scales. We thus propose a practical and efficient 3D representation that incorporates an equivariant radiance field with the guidance of a bird's-eye view (BEV) map. Concretely objects of synthesized 3D scenes could be easily manipulated through steering the corresponding BEV maps. Moreover by adequately incorporating positional encoding and low-pass filters into the generator the representation becomes equivariant to the given BEV map. Such equivariance allows us to produce large-scale even infinite-scale 3D scenes via synthesizing local scenes and then stitching them with smooth consistency. Extensive experiments on 3D scene datasets demonstrate the effectiveness of our approach. Our project website is at: https://https://zqh0253.github.io/BerfScene.
3D Facial Expressions through Analysis-by-Neural-Synthesis
Authors: George Retsinas, Panagiotis P. Filntisis, Radek Danecek, Victoria F. Abrevaya, Anastasios Roussos, Timo Bolkart, Petros Maragos
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: While existing methods for 3D face reconstruction from in-the-wild images excel at recovering the overall face shape they commonly miss subtle extreme asymmetric or rarely observed expressions. We improve upon these methods with SMIRK (Spatial Modeling for Image-based Reconstruction of Kinesics) which faithfully reconstructs expressive 3D faces from images. We identify two key limitations in existing methods: shortcomings in their self-supervised training formulation and a lack of expression diversity in the training images. For training most methods employ differentiable rendering to compare a predicted face mesh with the input image along with a plethora of additional loss functions. This differentiable rendering loss not only has to provide supervision to optimize for 3D face geometry camera albedo and lighting which is an ill-posed optimization problem but the domain gap between rendering and input image further hinders the learning process. Instead SMIRK replaces the differentiable rendering with a neural rendering module that given the rendered predicted mesh geometry and sparsely sampled pixels of the input image generates a face image. As the neural rendering gets color information from sampled image pixels supervising with neural rendering-based reconstruction loss can focus solely on the geometry. Further it enables us to generate images of the input identity with varying expressions while training. These are then utilized as input to the reconstruction model and used as supervision with ground truth geometry. This effectively augments the training data and enhances the generalization for diverse expressions. Our qualitative quantitative and particularly our perceptual evaluations demonstrate that SMIRK achieves the new state-of-the art performance on accurate expression reconstruction. For our method's source code demo video and more please visit our project webpage: https://georgeretsi.github.io/smirk/.
HoloVIC: Large-scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative
Authors: Cong Ma, Lei Qiao, Chengkai Zhu, Kai Liu, Zelong Kong, Qing Li, Xueqi Zhou, Yuheng Kan, Wei Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Vehicle-to-everything (V2X) is a popular topic in the field of Autonomous Driving in recent years. Vehicle-infrastructure cooperation (VIC) becomes one of the important research area. Due to the complexity of traffic conditions such as blind spots and occlusion it greatly limits the perception capabilities of single-view roadside sensing systems. To further enhance the accuracy of roadside perception and provide better information to the vehicle side in this paper we constructed holographic intersections with various layouts to build a large-scale multi-sensor holographic vehicle-infrastructure cooperation dataset called HoloVIC. Our dataset includes 3 different types of sensors (Camera Lidar Fisheye) and employs 4 sensor-layouts based on the different intersections. Each intersection is equipped with 6-18 sensors to capture synchronous data. While autonomous vehicles pass through these intersections for collecting VIC data. HoloVIC contains in total on 100k+ synchronous frames from different sensors. Additionally we annotated 3D bounding boxes based on Camera Fisheye and Lidar. We also associate the IDs of the same objects across different devices and consecutive frames in sequence. Based on HoloVIC we formulated four tasks to facilitate the development of related research. We also provide benchmarks for these tasks.
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
Authors: Zhiheng Cheng, Qingyue Wei, Hongru Zhu, Yan Wang, Liangqiong Qu, Wei Shao, Yuyin Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However its application in medical imaging presents challenges requiring either substantial training costs and extensive medical datasets for full model fine-tuning or high-quality prompts for optimal performance. This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure. In the initial stage H-SAM employs SAM's original decoder to generate a prior probabilistic mask guiding a more intricate decoding process in the second stage. Specifically we propose two key designs: 1) A class-balanced mask-guided self-attention mechanism addressing the unbalanced label distribution enhancing image embedding; 2) A learnable mask cross-attention mechanism spatially modulating the interplay among different image regions based on the prior mask. Moreover the inclusion of a hierarchical pixel decoder in H-SAM enhances its proficiency in capturing fine-grained and localized details. This approach enables SAM to effectively integrate learned medical priors facilitating enhanced adaptation for medical image segmentation with limited samples. Our H-SAM demonstrates a 4.78% improvement in average Dice compared to existing prompt-free SAM variants for multi-organ segmentation using only 10% of 2D slices. Notably without using any unlabeled data H-SAM even outperforms state-of-the-art semi-supervised models relying on extensive unlabeled training data across various medical datasets. Our code is available at https://github.com/Cccccczh404/H-SAM.
Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network
Authors: Sizhe Zheng, Pan Gao, Peng Zhou, Jie Qin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Style transfer aims to render an image with the artistic features of a style image while maintaining the original structure. Various methods have been put forward for this task but some challenges still exist. For instance it is difficult for CNN-based methods to handle global information and long-range dependencies between input images for which transformer-based methods have been proposed. Although transformer can better model the relationship between content and style images they require high-cost hardware and time-consuming inference. To address these issues we design a novel transformer model that includes only encoders thus significantly reducing the computational cost. In addition we also find that existing style transfer methods may lead to images under-stylied or missing content. In order to achieve better stylization we design a content feature extractor and a style feature extractor. Then we can feed pure content and style images into the transformer. Finally we propose a network model termed Puff-Net i.e. efficient style transfer with pure content and style feature fusion network. Through qualitative and quantitative experiments we demonstrate the advantages of our model compared to state-of-the-art ones in the literature. The code is availabel at https://github.com/ZszYmy9/Puff-Net.
Towards Progressive Multi-Frequency Representation for Image Warping
Authors: Jun Xiao, Zihang Lyu, Cong Zhang, Yakun Ju, Changjian Shui, Kin-Man Lam
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Image warping a classic task in computer vision aims to use geometric transformations to change the appearance of images. Recent methods learn the resampling kernels for warping through neural networks to estimate missing values in irregular grids which however fail to capture local variations in deformed content and produce images with distortion and less high-frequency details. To address this issue this paper proposes an effective method namely MFR to learn Multi-Frequency Representations from input images for image warping. Specifically we propose a progressive filtering network to learn image representations from different frequency subbands and generate deformable images in a coarse-to-fine manner. Furthermore we employ learnable Gabor wavelet filters to improve the model's capability to learn local spatial-frequency representations. Comprehensive experiments including homography transformation equirectangular to perspective projection and asymmetric image super-resolution demonstrate that the proposed MFR significantly outperforms state-of-the-art image warping methods. Our method also showcases superior generalization to out-of-distribution domains where the generated images are equipped with rich details and less distortion thereby high visual quality. The source code is available at https://github.com/junxiao01/MFR.
Learning to Control Camera Exposure via Reinforcement Learning
Authors: Kyunghyun Lee, Ukcheol Shin, Byeong-Uk Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Adjusting camera exposure in arbitrary lighting conditions is the first step to ensure the functionality of computer vision applications. Poorly adjusted camera exposure often leads to critical failure and performance degradation. Traditional camera exposure control methods require multiple convergence steps and time-consuming processes making them unsuitable for dynamic lighting conditions. In this paper we propose a new camera exposure control framework that rapidly controls camera exposure while performing real-time processing by exploiting deep reinforcement learning. The proposed framework consists of four contributions: 1) a simplified training ground to simulate real-world's diverse and dynamic lighting changes 2) flickering and image attribute-aware reward design along with lightweight state design for real-time processing 3) a static-to-dynamic lighting curriculum to gradually improve the agent's exposure-adjusting capability and 4) domain randomization techniques to alleviate the limitation of the training ground and achieve seamless generalization in the wild. As a result our proposed method rapidly reaches a desired exposure level within five steps with real-time processing (1 ms). Also the acquired images are well-exposed and show superiority in various computer vision tasks such as feature extraction and object detection.
Splatter Image: Ultra-Fast Single-View 3D Reconstruction
Authors: Stanislaw Szymanowicz, Chrisitian Rupprecht, Andrea Vedaldi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce the Splatter Image an ultra-efficient approach for monocular 3D object reconstruction. Splatter Image is based on Gaussian Splatting which allows fast and high-quality reconstruction of 3D scenes from multiple images. We apply Gaussian Splatting to monocular reconstruction by learning a neural network that at test time performs reconstruction in a feed-forward manner at 38 FPS. Our main innovation is the surprisingly straightforward design of this network which using 2D operators maps the input image to one 3D Gaussian per pixel. The resulting set of Gaussians thus has the form an image the Splatter Image. We further extend the method take several images as input via cross-view attention. Owning to the speed of the renderer (588 FPS) we use a single GPU for training while generating entire images at each iteration to optimize perceptual metrics like LPIPS. On several synthetic real multi-category and large-scale benchmark datasets we achieve better results in terms of PSNR LPIPS and other metrics while training and evaluating much faster than prior works. Code models and more results are available at https://szymanowiczs.github.io/ splatter-image.
Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use
Authors: Imad Eddine Toubal, Aditya Avinash, Neil Gordon Alldrin, Jan Dlabal, Wenlei Zhou, Enming Luo, Otilia Stretcu, Hao Xiong, Chun-Ta Lu, Howard Zhou, Ranjay Krishna, Ariel Fuxman, Tom Duerig
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: From content moderation to wildlife conservation the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally developing classifiers for such concepts requires substantial manual effort measured in hours days or even months to identify and annotate data needed for training. Even with recently proposed Agile Modeling techniques which enable rapid bootstrapping of image classifiers users are still required to spend 30 minutes or more of monotonous repetitive data labeling just to train a single classifier. Drawing on Fiske's Cognitive Miser theory we propose a new framework that alleviates manual effort by replacing human labeling with natural language interactions reducing the total effort required to define a concept by an order of magnitude: from labeling 2000 images to only 100 plus some natural language interactions. Our framework leverages recent advances in foundation models both large language models and vision-language models to carve out the concept space through conversation and by automatically labeling training data points. Most importantly our framework eliminates the need for crowd-sourced annotations. Moreover our framework ultimately produces lightweight classification models that are deployable in cost-sensitive scenarios. Across 15 subjective concepts and across 2 public image classification datasets our trained models outperform traditional Agile Modeling as well as state-of-the-art zero-shot classification models like ALIGN CLIP CuPL and large visual question answering models like PaLI-X.
RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction
Authors: Baptiste Brument, Robin Bruneau, Yvain Quéau, Jean Mélou, François Bernard Lauze, Jean-Denis Durou, Lilian Calvet
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper introduces a versatile paradigm for integrating multi-view reflectance (optional) and normal maps acquired through photometric stereo. Our approach employs a pixel-wise joint re-parameterization of reflectance and normal considering them as a vector of radiances rendered under simulated varying illumination. This re-parameterization enables the seamless integration of reflectance and normal maps as input data in neural volume rendering-based 3D reconstruction while preserving a single optimization objective. In contrast recent multi-view photometric stereo (MVPS) methods depend on multiple potentially conflicting objectives. Despite its apparent simplicity our proposed approach outperforms state-of-the-art approaches in MVPS benchmarks across F-score Chamfer distance and mean angular error metrics. Notably it significantly improves the detailed 3D reconstruction of areas with high curvature or low visibility.
LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning
Authors: Siyuan Cheng, Guanhong Tao, Yingqi Liu, Guangyu Shen, Shengwei An, Shiwei Feng, Xiangzhe Xu, Kaiyuan Zhang, Shiqing Ma, Xiangyu Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Backdoor attack poses a significant security threat to Deep Learning applications. Existing attacks are often not evasive to established backdoor detection techniques. This susceptibility primarily stems from the fact that these attacks typically leverage a universal trigger pattern or transformation function such that the trigger can cause misclassification for any input. In response to this recent papers have introduced attacks using sample-specific invisible triggers crafted through special transformation functions. While these approaches manage to evade detection to some extent they reveal vulnerability to existing backdoor mitigation techniques. To address and enhance both evasiveness and resilience we introduce a novel backdoor attack LOTUS. Specifically it leverages a secret function to separate samples in the victim class into a set of partitions and applies unique triggers to different partitions. Furthermore LOTUS incorporates an effective trigger focusing mechanism ensuring only the trigger corresponding to the partition can induce the backdoor behavior. Extensive experimental results show that LOTUS can achieve high attack success rate across 4 datasets and 7 model structures and effectively evading 13 backdoor detection and mitigation techniques. The code is available at https://github.com/Megum1/LOTUS.
GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement
Authors: Linfang Zheng, Tze Ho Elden Tse, Chen Wang, Yinghan Sun, Hua Chen, Ales Leonardis, Wei Zhang, Hyung Jin Chang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress towards instance-level object pose refinement. Yet category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrepancies between the target object and the shape prior. To address these challenges we introduce a novel architecture for category-level object pose refinement. Our approach integrates an HS-layer and learnable affine transformations which aims to enhance the extraction and alignment of geometric information. Additionally we introduce a cross-cloud transformation mechanism that efficiently merges diverse data sources. Finally we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive experiments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments we demonstrate significant improvement over the baseline method by a large margin across all metrics.
LAN: Learning to Adapt Noise for Image Denoising
Authors: Changjin Kim, Tae Hyun Kim, Sungyong Baik
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Removing noise from images a.k.a image denoising can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments. While there have been striking improvements in image denoising with the emergence of advanced deep learning architectures and real-world datasets recent denoising networks struggle to maintain performance on images with noise that has not been seen during training. One typical approach to address the challenge would be to adapt a denoising network to new noise distribution. Instead in this work we shift our attention to the input noise itself for adaptation rather than adapting a network. Thus we keep a pretrained network frozen and adapt an input noise to capture the fine-grained deviations. As such we propose a new denoising algorithm dubbed Learning-to-Adapt-Noise (LAN) where a learnable noise offset is directly added to a given noisy image to bring a given input noise closer towards the noise distribution a denoising network is trained to handle. Consequently the proposed framework exhibits performance improvement on images with unseen noise displaying the potential of the proposed research direction.
Scaling Up Dynamic Human-Scene Interaction Modeling
Authors: Nan Jiang, Zhiyuan Zhang, Hongjie Li, Xiaoxuan Ma, Zan Wang, Yixin Chen, Tengyu Liu, Yixin Zhu, Siyuan Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method. TRUMANS stands as the most comprehensive motion-captured HSI dataset currently available encompassing over 15 hours of human interactions across 100 indoor scenes. It intricately captures whole-body human motions and part-level object dynamics focusing on the realism of contact. This dataset is further scaled up by transforming physical environments into exact virtual models and applying extensive augmentations to appearance and motion for both humans and objects while maintaining interaction fidelity. Utilizing TRUMANS we devise a diffusion-based autoregressive model that efficiently generates HSI sequences of any length taking into account both scene context and intended actions. In experiments our approach shows remarkable zero-shot generalizability on a range of 3D scene datasets (e.g. PROX Replica ScanNet ScanNet++) producing motions that closely mimic original motion-captured sequences as confirmed by quantitative experiments and human studies.