mtybilly/Instruct2DS huggingface dataset:
MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model
Authors: Jianfeng Zhang, Jun Hao Liew, Hanshu Yan, Jia-Wei Liu, Chenxu Zhang, Jiashi Feng, Mike Zheng Shou Zhongcong Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper studies the human image animation task which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work we introduce MagicAnimate a diffusion-based framework that aims at enhancing temporal consistency preserving reference image faithfully and improving animation fidelity. To achieve this we first develop a video diffusion model to encode temporal information. Second to maintain the appearance coherence across frames we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available at https://showlab.github.io/magicanimate.
Dense Vision Transformer Compression with Few Samples
Authors: Yifan Zhou, Guo-Hua Wang Hanxiao Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But few-shot compression for Vision Transformers (ViT) remains largely unexplored which presents a new challenge. In particular the issue of sparse compression exists in traditional CNN few-shot methods which can only produce very few compressed models of different model sizes. This paper proposes a novel framework for few-shot ViT compression named DC-ViT. Instead of dropping the entire block DC-ViT selectively eliminates the attention module while retaining and reusing portions of the MLP module. DC-ViT enables dense compression which outputs numerous compressed models that densely populate the range of model complexity. DC-ViT outperforms state-of-the-art few-shot compression methods by a significant margin of 10 percentage points along with lower latency in the compression of ViT and its variants.
Masked AutoDecoder is Effective Multi-Task Vision Generalist
Authors: Jiaxing Huang, Peng Gao, Lewei Lu, Xiaoqin Zhang, Shijian Lu Han Qiu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Inspired by the success of general-purpose models in NLP recent studies attempt to unify different vision tasks in the same sequence format and employ autoregressive Transformers for sequence prediction. They apply uni-directional attention to capture sequential dependencies and generate task sequences recursively. However such autoregressive Transformers may not fit vision tasks well as vision task sequences usually lack the sequential dependencies typically observed in natural languages. In this work we design Masked AutoDecoder (MAD) an effective multi-task vision generalist. MAD consists of two core designs. First we develop a parallel decoding framework that introduces bi-directional attention to capture contextual dependencies comprehensively and decode vision task sequences in parallel. Second we design a masked sequence modeling approach that learns rich task contexts by masking and reconstructing task sequences. In this way MAD handles all the tasks by a single network branch and a simple cross-entropy loss with minimal task-specific designs. Extensive experiments demonstrate the great potential of MAD as a new paradigm for unifying various vision tasks. MAD achieves superior performance and inference efficiency compared to autoregressive counterparts while obtaining competitive accuracy with task-specific models. Code will be released at https://github.com/hanqiu-hq/MAD.
Weakly Misalignment-free Adaptive Feature Alignment for UAVs-based Multimodal Object Detection
Authors: Jiahao Qi, Xingyue Liu, Kangcheng Bin, Ruigang Fu, Xikun Hu, Ping Zhong Chen Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Visible-infrared (RGB-IR) image fusion has shown great potentials in object detection based on unmanned aerial vehicles (UAVs). However the weakly misalignment problem between multimodal image pairs limits its performance in object detection. Most existing methods often ignore the modality gap and emphasize a strict alignment resulting in an upper bound of alignment quality and an increase of implementation costs. To address these challenges we propose a novel method named Offset-guided Adaptive Feature Alignment (OAFA) which could adaptively adjust the relative positions between multimodal features. Considering the impact of modality gap on the cross-modality spatial matching a Cross-modality Spatial Offset Modeling (CSOM) module is designed to establish a common subspace to estimate the precise feature-level offsets. Then an Offset-guided Deformable Alignment and Fusion (ODAF) module is utilized to implicitly capture optimal fusion positions for detection task rather than conducting a strict alignment. Comprehensive experiments demonstrate that our method not only achieves state-of-the-art performance in the UAVs-based object detection task but also shows strong robustness to the weakly misalignment problem.
From Correspondences to Pose: Non-minimal Certifiably Optimal Relative Pose without Disambiguation
Authors: Javier Civera Javier Tirado-Garín
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Estimating the relative camera pose from n \geq 5 correspondences between two calibrated views is a fundamental task in computer vision. This process typically involves two stages: 1) estimating the essential matrix between the views and 2) disambiguating among the four candidate relative poses that satisfy the epipolar geometry. In this paper we demonstrate a novel approach that for the first time bypasses the second stage. Specifically we show that it is possible to directly estimate the correct relative camera pose from correspondences without needing a post-processing step to enforce the cheirality constraint on the correspondences. Building on recent advances in certifiable non-minimal optimization we frame the relative pose estimation as a Quadratically Constrained Quadratic Program (QCQP). By applying the appropriate constraints we ensure the estimation of a camera pose that corresponds to a valid 3D geometry and that is globally optimal when certified. We validate our method through exhaustive synthetic and real-world experiments confirming the efficacy efficiency and accuracy of the proposed approach. Code is available at https://github.com/javrtg/C2P.
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging
Authors: Salman Siddique Khan, Pranav Sharma, Shreyas Singh, Vivek Boominathan, Kaushik Mitra, Ashok Veeraraghavan Bhargav Ghanekar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Passive compact single-shot 3D sensing is useful in many application areas such as microscopy medical imaging surgical navigation and autonomous driving where form factor time and power constraints can exist. Obtaining RGB-D scene information over a short imaging distance in an ultra-compact form factor and in a passive snapshot manner is challenging. Dual-pixel (DP) sensors are a potential solution to achieve the same. DP sensors collect light rays from two different halves of the lens in two interleaved pixel arrays thus capturing two slightly different views of the scene like a stereo camera system. However imaging with a DP sensor implies that the defocus blur size is directly proportional to the disparity seen between the views. This creates a trade-off between disparity estimation vs. deblurring accuracy. To improve this trade-off effect we propose CADS (Coded Aperture Dual-Pixel Sensing) in which we use a coded aperture in the imaging lens along with a DP sensor. In our approach we jointly learn an optimal coded pattern and the reconstruction algorithm in an end-to-end optimization setting. Our resulting CADS imaging system demonstrates improvement of >1.5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings. Furthermore we build the proposed CADS prototypes for DSLR photography settings and in an endoscope and a dermoscope form factor. Our novel coded dual-pixel sensing approach demonstrates accurate RGB-D reconstruction results in simulations and real-world experiments in a passive snapshot and compact manner.
Loose Inertial Poser: Motion Capture with IMU-attached Loose-Wear Jacket
Authors: Yiming Wang, Lishuang Zhan, Shihui Guo, Xinyu Yi, Feng Xu, Yipeng Qin Chengxu Zuo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing wearable motion capture methods typically demand tight on-body fixation (often using straps) for reliable sensing limiting their application in everyday life. In this paper we introduce Loose Inertial Poser a novel motion capture solution with high wearing comfortableness by integrating four Inertial Measurement Units (IMUs) into a loose-wear jacket. Specifically we address the challenge of scarce loose-wear IMU training data by proposing a Secondary Motion AutoEncoder (SeMo-AE) that learns to model and synthesize the effects of secondary motion between the skin and loose clothing on IMU data. SeMo-AE is leveraged to generate a diverse synthetic dataset of loose-wear IMU data to augment training for the pose estimation network and significantly improve its accuracy. For validation we collected a dataset with various subjects and 2 wearing styles (zipped and unzipped). Experimental results demonstrate that our approach maintains high-quality real-time posture estimation even in loose-wear scenarios.
Instance Tracking in 3D Scenes from Egocentric Videos
Authors: Haoyu Ma, Shu Kong, Charless Fowlkes Yunhan Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Egocentric sensors such as AR/VR devices capture human-object interactions and offer the potential to provide task-assistance by recalling 3D locations of objects of interest in the surrounding environment. This capability requires instance tracking in real-world 3D scenes from egocentric videos (IT3DEgo). We explore this problem by first introducing a new benchmark dataset consisting of RGB and depth videos per-frame camera pose and instance-level annotations in both 2D camera and 3D world coordinates. We present an evaluation protocol which evaluates tracking performance in 3D coordinates with two settings for enrolling instances to track: (1) single-view online enrollment where an instance is specified on-the-fly based on the human wearer's interactions. and (2) multi-view pre-enrollment where images of an instance to be tracked are stored in memory ahead of time. To address IT3DEgo we first re-purpose methods from relevant areas e.g. single object tracking (SOT) -- running SOT methods to track instances in 2D frames and lifting them to 3D using camera pose and depth. We also present a simple method that leverages pretrained segmentation and detection models to generate proposals from RGB frames and match proposals with enrolled instance images. Our experiments show that our method (with no finetuning) significantly outperforms SOT-based approaches in the egocentric setting. We conclude by arguing that the problem of egocentric instance tracking is made easier by leveraging camera pose and using a 3D allocentric (world) coordinate representation.
Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration
Authors: Dagan Feng, Lei Bi, Jinman Kim Mingyuan Meng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deformable image registration is a fundamental step for medical image analysis. Recently transformers have been used for registration and outperformed Convolutional Neural Networks (CNNs). Transformers can capture long-range dependence among image features which have been shown beneficial for registration. However due to the high computation/memory loads of self-attention transformers are typically used at downsampled feature resolutions and cannot capture fine-grained long-range dependence at the full image resolution. This limits deformable registration as it necessitates precise dense correspondence between each image pixel. Multi-layer Perceptrons (MLPs) without self-attention are efficient in computation/memory usage enabling the feasibility of capturing fine-grained long-range dependence at full resolution. Nevertheless MLPs have not been extensively explored for image registration and are lacking the consideration of inductive bias crucial for medical registration tasks. In this study we propose the first correlation-aware MLP-based registration network (CorrMLP) for deformable medical image registration. Our CorrMLP introduces a correlation-aware multi-window MLP block in a novel coarse-to-fine registration architecture which captures fine-grained multi-range dependence to perform correlation-aware coarse-to-fine registration. Extensive experiments with seven public medical datasets show that our CorrMLP outperforms state-of-the-art deformable registration methods.
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts
Authors: Guansong Pang Jiawen Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data. Some recent studies have shown that large pre-trained Visual-Language Models (VLMs) like CLIP have strong generalization capabilities on detecting industrial defects from various datasets but their methods rely heavily on handcrafted text prompts about defects making them difficult to generalize to anomalies in other applications e.g. medical image anomalies or semantic anomalies in natural images. In this work we propose to train a GAD model with few-shot normal images as sample prompts for AD on diverse datasets on the fly. To this end we introduce a novel approach that learns an in-context residual learning model for GAD termed InCTRL. It is trained on an auxiliary dataset to discriminate anomalies from normal samples based on a holistic evaluation of the residuals between query images and few-shot normal sample prompts. Regardless of the datasets per definition of anomaly larger residuals are expected for anomalies than normal samples thereby enabling InCTRL to generalize across different domains without further training. Comprehensive experiments on nine AD datasets are performed to establish a GAD benchmark that encapsulate the detection of industrial defect anomalies medical anomalies and semantic anomalies in both one-vs-all and multi-class setting on which InCTRL is the best performer and significantly outperforms state-of-the-art competing methods. Code is available at https://github.com/mala-lab/InCTRL.
Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification
Authors: Shunxin Wang, Nicola Strisciuglio Puru Vaish
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Computer vision models normally witness degraded performance when deployed in real-world scenarios due to unexpected changes in inputs that were not accounted for during training. Data augmentation is commonly used to address this issue as it aims to increase data variety and reduce the distribution gap between training and test data. However common visual augmentations might not guarantee extensive robustness of computer vision models. In this paper we propose Auxiliary Fourier-basis Augmentation (AFA) a complementary technique targeting augmentation in the frequency domain and filling the robustness gap left by visual augmentations. We demonstrate the utility of augmentation via Fourier-basis additive noise in a straightforward and efficient adversarial setting. Our results show that AFA benefits the robustness of models against common corruptions OOD generalization and consistency of performance of models against increasing perturbations with negligible deficit to the standard performance of models. It can be seamlessly integrated with other augmentation techniques to further boost performance. Codes and models are available at \href https://github.com/nis-research/afa-augment  https://github.com/nis-research/afa-augment .
Learning to Transform Dynamically for Better Adversarial Transferability
Authors: Zeliang Zhang, Susan Liang, Zhuo Liu, Chenliang Xu Rongyi Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Adversarial examples crafted by adding perturbations imperceptible to humans can deceive neural networks. Recent studies identify the adversarial transferability across various models i.e. the cross-model attack ability of adversarial samples. To enhance such adversarial transferability existing input transformation-based methods diversify input data with transformation augmentation. However their effectiveness is limited by the finite number of available transformations. In our study we introduce a novel approach named Learning to Transform (L2T). L2T increases the diversity of transformed images by selecting the optimal combination of operations from a pool of candidates consequently improving adversarial transferability. We conceptualize the selection of optimal transformation combinations as a trajectory optimization problem and employ a reinforcement learning strategy to effectively solve the problem. Comprehensive experiments on the ImageNet dataset as well as practical tests with Google Vision and GPT-4V reveal that L2T surpasses current methodologies in enhancing adversarial transferability thereby confirming its effectiveness and practical significance.
PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar
Authors: Xiaoyu Xiang, Siddharth Somasundaram, Yuchen Fan, Christian Richardt, Ramesh Raskar, Rakesh Ranjan Tzofi Klinghoffer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions. Neural radiance fields (NeRF) while popular for view synthesis and 3D reconstruction are typically reliant on multi-view images. Existing methods for single-view 3D reconstruction with NeRF rely on either data priors to hallucinate views of occluded regions which may not be physically accurate or shadows observed by RGB cameras which are difficult to detect in ambient light and low albedo backgrounds. We propose using time-of-flight data captured by a single-photon avalanche diode to overcome these limitations. Our method models two-bounce optical paths with NeRF using lidar transient data for supervision. By leveraging the advantages of both NeRF and two-bounce light measured by lidar we demonstrate that we can reconstruct visible and occluded geometry without data priors or reliance on controlled ambient lighting or scene albedo. In addition we demonstrate improved generalization under practical constraints on sensor spatial- and temporal-resolution. We believe our method is a promising direction as single-photon lidars become ubiquitous on consumer devices such as phones tablets and headsets.
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer
Authors: Chuan Fang, Liefeng Bo, Zilong Dong, Ping Tan Yuan Dong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Panoramic images enable deeper understanding and more holistic perception of 360 surrounding environment which can naturally encode enriched scene context information compared to standard perspective image. Previous work has made lots of effort to solve the scene understanding task in a hybrid solution based on 2D-3D geometric reasoning thus each sub-task is processed separately and few correlations are explored in this procedure. In this paper we propose a fully 3D method for holistic indoor scene understanding which recovers the objects' shapes oriented bounding boxes and the 3D room layout simultaneously from a single panorama. To maximize the exploration of the rich context information we design a transformer-based context module to predict the representation and relationship among each component of the scene. In addition we introduce a new dataset for scene understanding including photo-realistic panoramas high-fidelity depth images accurately annotated room layouts oriented object bounding boxes and shapes. Experiments on the synthetic and new datasets demonstrate that our method outperforms previous panoramic scene understanding methods in terms of both layout estimation and 3D object detection.
Training-Free Pretrained Model Merging
Authors: Ke Yuan, Huiqiong Wang, Yong Wang, Mingli Song, Jie Song Zhengqi Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently model merging techniques have surfaced as a solution to combine multiple single-talent models into a single multi-talent model. However previous endeavors in this field have either necessitated additional training or fine-tuning processes or require that the models possess the same pre-trained initialization. In this work we identify a common drawback in prior works w.r.t. the inconsistency of unit similarity in the weight space and the activation space. To address this inconsistency we propose an innovative model merging framework coined as merging under dual-space constraints (MuDSC). Specifically instead of solely maximizing the objective of a single space we advocate for the exploration of permutation matrices situated in a region with a unified high similarity in the dual space achieved through the linear combination of activation and weight similarity matrices. In order to enhance usability we have also incorporated adaptations for group structure including Multi-Head Attention and Group Normalization. Comprehensive experimental comparisons demonstrate that MuDSC can significantly boost the performance of merged models with various task combinations and architectures. Furthermore the visualization of the merged model within the multi-task loss landscape reveals that MuDSC enables the merged model to reside in the overlapping segment featuring a unified lower loss for each task. Our code is publicly available at https://github.com/zju-vipa/training_free_model_merging.
NC-SDF: Enhancing Indoor Scene Reconstruction Using Neural SDFs with View-Dependent Normal Compensation
Authors: Xiaolong Wu, Yu Zhang Ziyi Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: State-of-the-art neural implicit surface representations have achieved impressive results in indoor scene reconstruction by incorporating monocular geometric priors as additional supervision. However we have observed that multi-view inconsistency between such priors poses a challenge for high-quality reconstructions. In response we present NC-SDF a neural signed distance field (SDF) 3D reconstruction framework with view-dependent normal compensation (NC). Specifically we integrate view-dependent biases in monocular normal priors into the neural implicit representation of the scene. By adaptively learning and correcting the biases our NC-SDF effectively mitigates the adverse impact of inconsistent supervision enhancing both the global consistency and local details in the reconstructions. To further refine the details we introduce an informative pixel sampling strategy to pay more attention to intricate geometry with higher information content. Additionally we design a hybrid geometry modeling approach to improve the neural implicit representation. Experiments on synthetic and real-world datasets demonstrate that NC-SDF outperforms existing approaches in terms of reconstruction quality.
An Interactive Navigation Method with Effect-oriented Affordance
Authors: Yuehu Liu, Xinhang Song, Yuyi Liu, Sixian Zhang, Shuqiang Jiang Xiaohan Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Visual navigation is to let the agent reach the target according to the continuous visual input. In most previous works visual navigation is usually assumed to be done in a static and ideal environment: the target is always reachable with no need to alter the environment. However the "messy" environments are more general and practical in our daily lives where the agent may get blocked by obstacles. Thus Interactive Navigation (InterNav) is introduced to navigate to the objects in more realistic "messy" environments according to the object interaction. Prior work on InterNav learns short-term interaction through extensive trials with reinforcement learning. However interaction does not guarantee efficient navigation that is planning obstacle interactions that make shorter paths and consume less effort is also crucial. In this paper we introduce an effect-oriented affordance map to enable long-term interactive navigation extending the existing map-based navigation framework to the domain of dynamic environment. We train a set of affordance functions predicting available interactions and the time cost of removing obstacles which informatively support an interactive modular system to address interaction and long-term planning. Experiments on the ProcTHOR simulator demonstrate the capability of our affordance-driven system in long-term navigation in complex dynamic environments.
Person in Place: Generating Associative Skeleton-Guidance Maps for Human-Object Interaction Image Editing
Authors: ChanHee Kang, Kyeongbo Kong, Hanni Oh, Suk-Ju Kang ChangHee Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently there were remarkable advances in image editing tasks in various ways. Nevertheless existing image editing models are not designed for Human-Object Interaction (HOI) image editing. One of these approaches (e.g. ControlNet) employs the skeleton guidance to offer precise representations of human showing better results in HOI image editing. However using conventional methods manually creating HOI skeleton guidance is necessary. This paper proposes the object interactive diffuser with associative attention that considers both the interaction with objects and the joint graph structure automating the generation of HOI skeleton guidance. Additionally we propose the HOI loss with novel scaling parameter demonstrating its effectiveness in generating skeletons that interact better. To evaluate generated object-interactive skeletons we propose two metrics top-N accuracy and skeleton probabilistic distance. Our framework integrates object interactive diffuser that generates object-interactive skeletons with previous methods demonstrating the outstanding results in HOI image editing. Finally we present potentials of our framework beyond HOI image editing as applications to human-to-human interaction skeleton editing and 3D mesh optimization. The code is available at https://github.com/YangChangHee/CVPR2024_Person-In-Place_RELEASE
PREGO: Online Mistake Detection in PRocedural EGOcentric Videos
Authors: Guido Maria D'Amely di Melendugno, Leonardo Plini, Luca Scofano, Edoardo De Matteis, Antonino Furnari, Giovanni Maria Farinella, Fabio Galasso Alessandro Flaborea
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Promptly identifying procedural errors from egocentric videos in an online setting is highly challenging and valuable for detecting mistakes as soon as they happen. This capability has a wide range of applications across various fields such as manufacturing and healthcare. The nature of procedural mistakes is open-set since novel types of failures might occur which calls for one-class classifiers trained on correctly executed procedures. However no technique can currently detect open-set procedural mistakes online. We propose PREGO the first online one-class classification model for mistake detection in PRocedural EGOcentric videos. PREGO is based on an online action recognition component to model the current action and a symbolic reasoning module to predict the next actions. Mistake detection is performed by comparing the recognized current action with the expected future one. We evaluate PREGO on two procedural egocentric video datasets Assembly101 and Epic-tent which we adapt for online benchmarking of procedural mistake detection to establish suitable benchmarks thus defining the Assembly101-O and Epic-tent-O datasets respectively. The code is available at https://github.com/aleflabo/PREGO
ChatPose: Chatting about 3D Human Pose
Authors: Jing Lin, Sai Kumar Dwivedi, Yu Sun, Priyanka Patel, Michael J. Black Yao Feng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce ChatPose a framework employing Large Language Models (LLMs) to understand and reason about 3D human poses from images or textual descriptions. Our work is motivated by the human ability to intuitively understand postures from a single image or a brief description a process that intertwines image interpretation world knowledge and an understanding of body language. Traditional human pose estimation and generation methods often operate in isolation lacking semantic understanding and reasoning abilities. ChatPose addresses these limitations by embedding SMPL poses as distinct signal tokens within a multimodal LLM enabling the direct generation of 3D body poses from both textual and visual inputs. Leveraging the powerful capabilities of multimodal LLMs ChatPose unifies classical 3D human pose and generation tasks while offering user interactions. Additionally ChatPose empowers LLMs to apply their extensive world knowledge in reasoning about human poses leading to two advanced tasks: speculative pose generation and reasoning about pose estimation. These tasks involve reasoning about humans to generate 3D poses from subtle text queries possibly accompanied by images. We establish benchmarks for these tasks moving beyond traditional 3D pose generation and estimation methods. Our results show that ChatPose out-performs existing multimodal LLMs and task-specific methods on these newly proposed tasks. Furthermore ChatPose's ability to understand and generate 3D human poses based on complex reasoning opens new directions in human pose analysis. Code and data are available for research at https://yfeng95.github.io/ChatPose.
Prompt3D: Random Prompt Assisted Weakly-Supervised 3D Object Detection
Authors: Huisheng Ye, Jingwen Li, Qinyu Tang, Yuanqi Li, Yanwen Guo, Jie Guo Xiaohong Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The prohibitive cost of annotations for fully supervised 3D indoor object detection limits its practicality. In this work we propose Random Prompt Assisted Weakly-supervised 3D Object Detection termed as Prompt3D a weakly-supervised approach that leverages position-level labels to overcome this challenge. Explicitly our method focuses on enhancing labeling using synthetic scenes crafted from 3D shapes generated via random prompts. First a Synthetic Scene Generation (SSG) module is introduced to assemble synthetic scenes with a curated collection of 3D shapes created via random prompts for each category. These scenes are enriched with automatically generated point-level annotations providing a robust supervisory framework for training the detection algorithm. To enhance the transfer of knowledge from virtual to real datasets we then introduce a Prototypical Proposal Feature Alignment (PPFA) module. This module effectively alleviates the domain gap by directly minimizing the distance between feature prototypes of the same class proposals across two domains. Compared with sota BR our method improves by 5.4% and 8.7% on mAP with VoteNet and GroupFree3D serving as detectors respectively demonstrating the effectiveness of our proposed method. Code is available at: https://github.com/huishengye/prompt3d.
Logit Standardization in Knowledge Distillation
Authors: Wenqi Ren, Jingzhi Li, Rui Wang, Xiaochun Cao Shangquan Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Knowledge distillation involves transferring soft labels from a teacher to a student using a shared temperature-based softmax function. However the assumption of a shared temperature between teacher and student implies a mandatory exact match between their logits in terms of logit range and variance. This side-effect limits the performance of student considering the capacity discrepancy between them and the finding that the innate logit relations of teacher are sufficient for student to learn. To address this issue we propose setting the temperature as the weighted standard deviation of logit and performing a plug-and-play Z-score pre-process of logit standardization before applying softmax and Kullback-Leibler divergence. Our pre-process enables student to focus on essential logit relations from teacher rather than requiring a magnitude match and can improve the performance of existing logit-based distillation methods. We also show a typical case where the conventional setting of sharing temperature between teacher and student cannot reliably yield the authentic distillation evaluation; nonetheless this challenge is successfully alleviated by our Z-score. We extensively evaluate our method for various student and teacher models on CIFAR-100 and ImageNet showing its significant superiority. The vanilla knowledge distillation powered by our pre-process can achieve favorable performance against state-of-the-art methods and other distillation variants can obtain considerable gain with the assistance of our pre-process. The codes pre-trained models and logs are released on Github.
Fine-grained Prototypical Voting with Heterogeneous Mixup for Semi-supervised 2D-3D Cross-modal Retrieval
Authors: Xian-Sheng Hua, Chong Chen, Xiao Luo Fan Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper studies the problem of semi-supervised 2D-3D retrieval which aims to align both labeled and unlabeled 2D and 3D data into the same embedding space. The problem is challenging due to the complicated heterogeneous relationships between 2D and 3D data. Moreover label scarcity in real-world applications hinders from generating discriminative representations. In this paper we propose a semi-supervised approach named Fine-grained Prototypcical Voting with Heterogeneous Mixup (FIVE) which maps both 2D and 3D data into a common embedding space for cross-modal retrieval. Specifically we generate fine-grained prototypes to model inter-class variation for both 2D and 3D data. Then considering each unlabeled sample as a query we retrieve relevant prototypes to vote for reliable and robust pseudo-labels which serve as guidance for discriminative learning under label scarcity. Furthermore to bridge the semantic gap between two modalities we mix cross-modal pairs with similar semantics in the embedding space and then perform similarity learning for cross-modal discrepancy reduction in a soft manner. The whole FIVE is optimized with the consideration of sharpness to mitigate the impact of potential label noise. Extensive experiments on benchmark datasets validate the superiority of FIVE compared with a range of baselines in different settings. On average FIVE outperforms the second-best approach by 4.74% on 3D MNIST 12.94% on ModelNet10 and 22.10% on ModelNet40.
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
Authors: Ahaan Dabholkar, Atul Sharma, Saurabh Bagchi Joshua C. Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Federated learning is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this prior work has shown that an attacker at the server can still reconstruct the private training data using only the client updates. These attacks are known as data reconstruction attacks and fall into two major categories: gradient inversion (GI) and linear layer leakage attacks (LLL). However despite demonstrating the effectiveness of these attacks in breaching privacy prior work has not investigated the usefulness of the reconstructed data for downstream tasks. In this work we explore data reconstruction attacks through the lens of training and improving models with leaked data. We demonstrate the effectiveness of both GI and LLL attacks in maliciously training models using the leaked data more accurately than a benign federated learning strategy. Counter-intuitively this bump in training quality can occur despite limited reconstruction quality or a small total number of leaked images. Finally we show the limitations of these attacks for downstream training individually for GI attacks and for LLL attacks.
OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation
Authors: Hong Cai, Risheek Garrepalli, Jamie Menjay Lin, Munawar Hayat, Fatih Porikli Jisoo Jeong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The scarcity of ground-truth labels poses one major challenge in developing optical flow estimation models that are both generalizable and robust. While current methods rely on data augmentation they have yet to fully exploit the rich information available in labeled video sequences. We propose OCAI a method that supports robust frame interpolation by generating intermediate video frames alongside optical flows in between. Utilizing a forward warping approach OCAI employs occlusion awareness to resolve ambiguities in pixel values and fills in missing values by leveraging the forward-backward consistency of optical flows. Additionally we introduce a teacher-student style semi-supervised learning method on top of the interpolated frames. Using a pair of unlabeled frames and the teacher model's predicted optical flow we generate interpolated frames and flows to train a student model. The teacher's weights are maintained using Exponential Moving Averaging of the student. Our evaluations demonstrate perceptually superior interpolation quality and enhanced optical flow accuracy on established benchmarks such as Sintel and KITTI.
Distilling ODE Solvers of Diffusion Models into Smaller Steps
Authors: Hao Tang, Fisher Yu Sanghwan Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Abstract Diffusion models have recently gained prominence as a novel category of generative models. Despite their success these models face a notable drawback in terms of slow sampling speeds requiring a high number of function evaluations (NFE) in the order of hundreds or thousands. In response both learning-free and learning-based sampling strategies have been explored to expedite the sampling process. Learning-free sampling employs various ordinary differential equation (ODE) solvers based on the formulation of diffusion ODEs. However it encounters challenges in faithfully tracking the true sampling trajectory particularly for small NFE. Conversely learning-based sampling methods such as knowledge distillation demand extensive additional training limiting their practical applicability. To overcome these limitations we introduce Distilled-ODE solvers (D-ODE solvers) a straightforward distillation approach grounded in ODE solver formulations. Our method seamlessly integrates the strengths of both learning-free and learning-based sampling. D-ODE solvers are constructed by introducing a single parameter adjustment to existing ODE solvers. Furthermore we optimize D-ODE solvers with smaller steps using knowledge distillation from ODE solvers with larger steps across a batch of samples. Comprehensive experiments demonstrate the superior performance of D-ODE solvers compared to existing ODE solvers including DDIM PNDM DPM-Solver DEIS and EDM particularly in scenarios with fewer NFE. Notably our method incurs negligible computational overhead compared to previous distillation techniques facilitating straightforward and rapid integration with existing samplers. Qualitative analysis reveals that D-ODE solvers not only enhance image quality but also faithfully follow the target ODE trajectory.
Navigating Beyond Dropout: An Intriguing Solution towards Generalizable Image Super Resolution
Authors: Jiyuan Chen, Yinqiang Zheng, Tieyong Zeng Hongjun Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. While most existing work assumes a simple and fixed degradation model (e.g. bicubic downsampling) the research of Blind SR seeks to improve model generalization ability with unknown degradation. Recently Kong et al. pioneer the investigation of a more suitable training strategy for Blind SR using Dropout. Although such method indeed brings substantial generalization improvements via mitigating overfitting we argue that Dropout simultaneously introduces undesirable side-effect that compromises model's capacity to faithfully reconstruct fine details. We show both the theoretical and experimental analyses in our paper and furthermore we present another easy yet effective training strategy that enhances the generalization ability of the model by simply modulating its first and second-order features statistics. Experimental results have shown that our method could serve as a model-agnostic regularization and outperforms Dropout on seven benchmark datasets including both synthetic and real-world scenarios.
Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes
Authors: Subhadeep Koley, Ayan Das, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song Hmrishav Bandyopadhyay
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we democratise 3D content creation enabling precise generation of 3D shapes from abstract sketches while overcoming limitations tied to drawing skills. We introduce a novel part-level modelling and alignment framework that facilitates abstraction modelling and cross-modal correspondence. Leveraging the same part-level decoder our approach seamlessly extends to sketch modelling by establishing correspondence between CLIPasso edgemaps and projected 3D part regions eliminating the need for a dataset pairing human sketches and 3D shapes. Additionally our method introduces a seamless in-position editing process as a byproduct of cross-modal part-aligned modelling. Operating in a low-dimensional implicit space our approach significantly reduces computational demands and processing time.
LightIt: Illumination Modeling and Control for Diffusion Models
Authors: Julien Philip, Kalyan Sunkavalli, Matthias Nießner, Yannick Hold-Geoffroy Peter Kocsis
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce LightIt a method for explicit illumination control for image generation. Recent generative methods lack lighting control which is crucial to numerous artistic aspects of image generation such as setting the overall mood or cinematic appearance. To overcome these limitations we propose to condition the generation on shading and normal maps. We model the lighting with single bounce shading which includes cast shadows. We first train a shading estimation module to generate a dataset of real-world images and shading pairs. Then we train a control network using the estimated shading and normals as input. Our method demonstrates high-quality image generation and lighting control in numerous scenes. Additionally we use our generated dataset to train an identity-preserving relighting model conditioned on an image and a target shading. Our method is the first that enables the generation of images with controllable consistent lighting and performs on par with specialized relighting state-of-the-art methods.
Single View Refractive Index Tomography with Neural Fields
Authors: Aviad Levis, Liam Connor, Pratul P. Srinivasan, Katherine L. Bouman Brandon Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Refractive Index Tomography is the inverse problem of reconstructing the continuously-varying 3D refractive index in a scene using 2D projected image measurements. Although a purely refractive field is not directly visible it bends light rays as they travel through space thus providing a signal for reconstruction. The effects of such fields appear in many scientific computer vision settings ranging from refraction due to transparent cells in microscopy to the lensing of distant galaxies caused by dark matter in astrophysics. Reconstructing these fields is particularly difficult due to the complex nonlinear effects of the refractive field on observed images. Furthermore while standard 3D reconstruction and tomography settings typically have access to observations of the scene from many viewpoints many refractive index tomography problem settings only have access to images observed from a single viewpoint. We introduce a method that leverages prior knowledge of light sources scattered throughout the refractive medium to help disambiguate the single-view refractive index tomography problem. We differentiably trace curved rays through a neural field representation of the refractive field and optimize its parameters to best reproduce the observed image. We demonstrate the efficacy of our approach by reconstructing simulated refractive fields analyze the effects of light source distribution on the recovered field and test our method on a simulated dark matter mapping problem where we successfully recover the 3D refractive field caused by a realistic dark matter distribution.