mtybilly/Instruct2DS huggingface dataset:
Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?
Authors: Zhengyue Zhao, Jinhao Duan, Kaidi Xu, Chenan Wang, Rui Zhang, Zidong Du, Qi Guo, Xing Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Stable Diffusion has established itself as a foundation model in generative AI artistic applications receiving widespread research and application. Some recent fine-tuning methods have made it feasible for individuals to implant personalized concepts onto the basic Stable Diffusion model with minimal computational costs on small datasets. However these innovations have also given rise to issues like facial privacy forgery and artistic copyright infringement. In recent studies researchers have explored the addition of imperceptible adversarial perturbations to images to prevent potential unauthorized exploitation and infringements when personal data is used for fine-tuning Stable Diffusion. Although these studies have demonstrated the ability to protect images it is essential to consider that these methods may not be entirely applicable in real-world scenarios. In this paper we systematically evaluate the use of perturbations to protect images within a practical threat model. The results suggest that these approaches may not be sufficient to safeguard image privacy and copyright effectively. Furthermore we introduce a purification method capable of removing protected perturbations while preserving the original image structure to the greatest extent possible. Experiments reveal that Stable Diffusion can effectively learn from purified images over all protective methods.
Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework
Authors: Ziyao Huang, Fan Tang, Yong Zhang, Xiaodong Cun, Juan Cao, Jintao Li, Tong-Yee Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Despite the remarkable process of talking-head-based avatar-creating solutions directly generating anchor-style videos with full-body motions remains challenging. In this study we propose Make-Your-Anchor a novel system necessitating only a one-minute video clip of an individual for training subsequently enabling the automatic generation of anchor-style videos with precise torso and hand movements. Specifically we finetune a proposed structure-guided diffusion model on input video to render 3D mesh conditions into human appearances. We adopt a two-stage training strategy for the diffusion model effectively binding movements with specific appearances. To produce arbitrary long temporal video we extend the 2D U-Net in the frame-wise diffusion model to a 3D style without additional training cost and a simple yet effective batch-overlapped temporal denoising module is proposed to bypass the constraints on video length during inference. Finally a novel identity-specific face enhancement module is introduced to improve the visual quality of facial regions in the output videos. Comparative experiments demonstrate the effectiveness and superiority of the system in terms of visual quality temporal coherence and identity preservation outperforming SOTA diffusion/non-diffusion methods. Project page: https://github.com/ICTMCG/Make-Your-Anchor.
MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World
Authors: Yining Hong, Zishuo Zheng, Peihao Chen, Yian Wang, Junyan Li, Chuang Gan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human beings possess the capability to multiply a melange of multisensory cues while actively exploring and interacting with the 3D world. Current multi-modal large language models however passively absorb sensory data as inputs lacking the capacity to actively interact with the objects in the 3D environment and dynamically collect their multisensory information. To usher in the study of this area we propose MultiPLY a multisensory embodied large language model that could incorporate multisensory interactive data including visual audio tactile and thermal information into large language models thereby establishing the correlation among words actions and percepts. To this end we first collect Multisensory Universe a large-scale multisensory interaction dataset comprising 500k data by deploying an LLM-powered embodied agent to engage with the 3D environment. To perform instruction tuning with pre-trained LLM on such generated data we first encode the 3D scene as abstracted object-centric representations and then introduce action tokens denoting that the embodied agent takes certain actions within the environment as well as state tokens that represent the multisensory state observations of the agent at each time step. In the inference time MultiPLY could generate action tokens instructing the agent to take the action in the environment and obtain the next multisensory state observation. The observation is then appended back to the LLM via state tokens to generate subsequent text or action tokens. We demonstrate that MultiPLY outperforms baselines by a large margin through a diverse set of embodied tasks involving object retrieval tool use multisensory captioning and task decomposition.
Learning to Visually Localize Sound Sources from Mixtures without Prior Source Knowledge
Authors: Dongjin Kim, Sung Jin Um, Sangmin Lee, Jung Uk Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The goal of the multi-sound source localization task is to localize sound sources from the mixture individually. While recent multi-sound source localization methods have shown improved performance they face challenges due to their reliance on prior information about the number of objects to be separated. In this paper to overcome this limitation we present a novel multi-sound source localization method that can perform localization without prior knowledge of the number of sound sources. To achieve this goal we propose an iterative object identification (IOI) module which can recognize sound-making objects in an iterative manner. After finding the regions of sound-making objects we devise object similarity-aware clustering (OSC) loss to guide the IOI module to effectively combine regions of the same object but also distinguish between different objects and backgrounds. It enables our method to perform accurate localization of sound-making objects without any prior knowledge. Extensive experimental results on the MUSIC and VGGSound benchmarks show the significant performance improvements of the proposed method over the existing methods for both single and multi-source. Our code is available at: https://github.com/VisualAIKHU/NoPrior_MultiSSL
Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis
Authors: Zicheng Zhang, Ruobing Zheng, Bonan Li, Congying Han, Tianqi Li, Meng Wang, Tiande Guo, Jingdong Chen, Ziwen Liu, Ming Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent works in implicit representations such as Neural Radiance Fields (NeRF) have advanced the generation of realistic and animatable head avatars from video sequences. These implicit methods are still confronted by visual artifacts and jitters since the lack of explicit geometric constraints poses a fundamental challenge in accurately modeling complex facial deformations. In this paper we introduce Dynamic Tetrahedra (DynTet) a novel hybrid representation that encodes explicit dynamic meshes by neural networks to ensure geometric consistency across various motions and viewpoints. DynTet is parameterized by the coordinate-based networks which learn signed distance deformation and material texture anchoring the training data into a predefined tetrahedra grid. Leveraging Marching Tetrahedra DynTet efficiently decodes textured meshes with a consistent topology enabling fast rendering through a differentiable rasterizer and supervision via a pixel loss. To enhance training efficiency we incorporate classical 3D Morphable Models to facilitate geometry learning and define a canonical space for simplifying texture learning. These advantages are readily achievable owing to the effective geometric representation employed in DynTet. Compared with prior works DynTet demonstrates significant improvements in fidelity lip synchronization and real-time performance according to various metrics. Beyond producing stable and visually appealing synthesis videos our method also outputs the dynamic meshes which is promising to enable many emerging applications. Code is available at https://github.com/zhangzc21/DynTet.
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline
Authors: Anas Al-lahham, Muhammad Zaigham Zaheer, Nurbek Tastan, Karthik Nandakumar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: nsupervised (US) video anomaly detection (VAD) in surveillance applications is gaining more popularity lately due to its practical real-world applications. Due to the extremely challenging nature of this task where learning is carried out without any annotations privacy-critical collaborative learning of US-VAD systems has not been studied yet. As surveillance videos are privacy sensitive and the availability of large-scale video data may enable better US-VAD systems collaborative learning can be highly rewarding in this setting. In this paper we propose a new baseline for anomaly detection capable of localizing anomalous events in complex surveillance scenarios in a fully unsupervised fashion without any labels on a privacy-retaining participant-based distributed training configuration. Additionally we propose three new evaluation protocols to extensively evaluate anomaly detection approaches on various scenarios of collaborations and data availability. Moreover based on these protocols we modify existing VAD datasets to extensively evaluate our approach as well as existing US SOTA methods on two large-scale datasets including UCF-Crime and XD-Violence. All proposed evaluation protocols dataset splits and codes are available here: \href https://github.com/AnasEmad11/CLAP  https://github.com/AnasEmad11/CLAP .
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting
Authors: Mingyue Guo, Li Yuan, Zhaoyi Yan, Binghui Chen, Yaowei Wang, Qixiang Ye
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Crowd counting has achieved significant progress by training regressors to predict instance positions. In heavily crowded scenarios however regressors are challenged by uncontrollable annotation variance which causes density map bias and context information inaccuracy. In this study we propose mutual prompt learning (mPrompt) which leverages a regressor and a segmenter as guidance for each other solving bias and inaccuracy caused by annotation variance while distinguishing foreground from background. In specific mPrompt leverages point annotations to tune the segmenter and predict pseudo head masks in a way of point prompt learning. It then uses the predicted segmentation masks which serve as spatial constraint to rectify biased point annotations as context prompt learning. mPrompt defines a way of mutual information maximization from prompt learning mitigating the impact of annotation variance while improving model accuracy. Experiments show that mPrompt significantly reduces the Mean Average Error (MAE) demonstrating the potential to be general framework for down-stream vision tasks. Code is available at https://github.com/csguomy/mPrompt.
Instantaneous Perception of Moving Objects in 3D
Authors: Di Liu, Bingbing Zhuang, Dimitris N. Metaxas, Manmohan Chandraker
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The perception of 3D motion of surrounding traffic participants is crucial for driving safety. While existing works primarily focus on general large motions we contend that the instantaneous detection and quantification of subtle motions is equally important as they indicate the nuances in driving behavior that may be safety critical such as behaviors near a stop sign of parking positions. We delve into this under-explored task examining its unique challenges and developing our solution accompanied by a carefully designed benchmark. Specifically due to the lack of correspondences between consecutive frames of sparse Lidar point clouds static objects might appear to be moving - the so-called swimming effect. This intertwines with the true object motion thereby posing ambiguity in accurate estimation especially for subtle motion. To address this we propose to leverage local occupancy completion of object point clouds to densify the shape cue and mitigate the impact of swimming artifacts. The occupancy completion is learned in an end-to-end fashion together with the detection of moving objects and the estimation of their motion instantaneously as soon as objects start to move. Extensive experiments demonstrate superior performance compared to standard 3D motion estimation approaches particularly highlighting our method's specialized treatment of subtle motion.
CORE-MPI: Consistency Object Removal with Embedding MultiPlane Image
Authors: Donggeun Yoon, Donghyeon Cho
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Novel view synthesis is attractive for social media but it often contains unwanted details such as personal information that needs to be edited out for a better experience. Multiplane image (MPI) is desirable for social media because of its generality but it is complex and computationally expensive making object removal challenging. To address these challenges we propose CORE-MPI which employs embedding images to improve the consistency and accessibility of MPI object removal. CORE-MPI allows for real-time transmission and interaction with embedding images on social media facilitating object removal with a single mask. However recovering the geometric information hidden in the embedding images is a significant challenge. Therefore we propose a dual-network approach where one network focuses on color restoration and the other on inpainting the embedding image including geometric information. For the training of CORE-MPI we introduce a pseudo-reference loss aimed at proficient color recovery even in complex scenes or with large masks. Furthermore we present a disparity consistency loss to preserve the geometric consistency of the inpainted region. We demonstrate the effectiveness of CORE-MPI on RealEstate10K and UCSD datasets.
3D Geometry-Aware Deformable Gaussian Splatting for Dynamic View Synthesis
Authors: Zhicheng Lu, Xiang Guo, Le Hui, Tianrui Chen, Min Yang, Xiao Tang, Feng Zhu, Yuchao Dai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we propose a 3D geometry-aware deformable Gaussian Splatting method for dynamic view synthesis. Existing neural radiance fields (NeRF) based solutions learn the deformation in an implicit manner which cannot incorporate 3D scene geometry. Therefore the learned deformation is not necessarily geometrically coherent which results in unsatisfactory dynamic view synthesis and 3D dynamic reconstruction. Recently 3D Gaussian Splatting provides a new representation of the 3D scene building upon which the 3D geometry could be exploited in learning the complex 3D deformation. Specifically the scenes are represented as a collection of 3D Gaussian where each 3D Gaussian is optimized to move and rotate over time to model the deformation. To enforce the 3D scene geometry constraint during deformation we explicitly extract 3D geometry features and integrate them in learning the 3D deformation. In this way our solution achieves 3D geometry-aware deformation modeling which enables improved dynamic view synthesis and 3D dynamic reconstruction. Extensive experimental results on both synthetic and real datasets prove the superiority of our solution which achieves new state-of-the-art performance. The project is available at \href https://npucvr.github.io/GaGS/  https://npucvr.github.io/GaGS/ .
Person-in-WiFi 3D: End-to-End Multi-Person 3D Pose Estimation with Wi-Fi
Authors: Kangwei Yan, Fei Wang, Bo Qian, Han Ding, Jinsong Han, Xing Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Wi-Fi signals in contrast to cameras offer privacy protection and occlusion resilience for some practical scenarios such as smart homes elderly care and virtual reality. Recent years have seen remarkable progress in the estimation of single-person 2D pose single-person 3D pose and multi-person 2D pose. This paper takes a step forward by introducing Person-in-WiFi 3D a pioneering Wi-Fi system that accomplishes multi-person 3D pose estimation. Person-in-WiFi 3D has two main updates. Firstly it has a greater number of Wi-Fi devices to enhance the capability for capturing spatial reflections from multiple individuals. Secondly it leverages the Transformer for end-to-end estimation. Compared to its predecessor Person-in-WiFi 3D is storage-efficient and fast. We deployed a proof-of-concept system in 4mx3.5m areas and collected a dataset of over 97K frames with seven volunteers. Person-in-WiFi 3D attains 3D joint localization errors of 91.7mm (1-person) 108.1mm (2-person) and 125.3mm (3-person) comparable to cameras and millimeter-wave radars.
Backpropagation-free Network for 3D Test-time Adaptation
Authors: Yanshuo Wang, Ali Cheraghian, Zeeshan Hayder, Jie Hong, Sameera Ramasinghe, Shafin Rahman, David Ahmedt-Aristizabal, Xuesong Li, Lars Petersson, Mehrtash Harandi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Real-world systems often encounter new data over time which leads to experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally heavy and memory-intensive backpropagation-based approaches to handle this. Here we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data. Our model uses a two-stream architecture to maintain knowledge about the source domain as well as complementary target-domain-specific information. The backpropagation-free property of our model helps address the well-known forgetting problem and mitigates the error accumulation issue. The proposed method also eliminates the need for the usually noisy process of pseudo-labeling and reliance on costly self-supervised training. Moreover our method leverages subspace learning effectively reducing the distribution variance between the two domains. Furthermore the source-domain-specific and the target-domain-specific streams are aligned using a novel entropy-based adaptive fusion strategy. Extensive experiments on popular benchmarks demonstrate the effectiveness of our method. The code will be available at https://github.com/abie-e/BFTT3D.
Resource-Efficient Transformer Pruning for Finetuning of Large Models
Authors: Fatih Ilhan, Gong Su, Selim Furkan Tekin, Tiansheng Huang, Sihao Hu, Ling Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: With the recent advances in vision transformers and large language models (LLMs) finetuning costly large models on downstream learning tasks poses significant challenges under limited computational resources. This paper presents a REsource and ComputAtion-efficient Pruning framework (RECAP) for the finetuning of transformer-based large models. RECAP by design bridges the gap between efficiency and performance through an iterative process cycling between pruning finetuning and updating stages to explore different chunks of the given large-scale model. At each iteration we first prune the model with Taylor-approximation-based importance estimation and then only update a subset of the pruned model weights based on the Fisher-information criterion. In this way RECAP achieves two synergistic and yet conflicting goals: reducing the GPU memory footprint while maintaining model performance unlike most existing pruning methods that require the model to be finetuned beforehand for better preservation of model performance. We perform extensive experiments with a wide range of large transformer-based architectures on various computer vision and natural language understanding tasks. Compared to recent pruning techniques we demonstrate that RECAP offers significant improvements in GPU memory efficiency capable of reducing the footprint by up to 65%.
ParamISP: Learned Forward and Inverse ISPs using Camera Parameters
Authors: Woohyeok Kim, Geonu Kim, Junyong Lee, Seungyong Lee, Seung-Hwan Baek, Sunghyun Cho
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera ISPs has been recently demonstrated enabling physically-meaningful RAW-level image processing on input sRGB images. However existing learning-based ISP methods fail to handle the large variations in the ISP processes with respect to camera parameters such as ISO and exposure time and have limitations when used for various applications. In this paper we propose ParamISP a learning-based method for forward and inverse conversion between sRGB and RAW images that adopts a novel neural-network module to utilize camera parameters which is dubbed as ParamNet. Given the camera parameters provided in the EXIF data ParamNet converts them into a feature vector to control the ISP networks. Extensive experiments demonstrate that ParamISP achieve superior RAW and sRGB reconstruction results compared to previous methods and it can be effectively used for a variety of applications such as deblurring dataset synthesis raw deblurring HDR reconstruction and camera-to-camera transfer.
Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models
Authors: Jingyao Xu, Yuetong Lu, Yandong Li, Siyang Lu, Dongdong Wang, Xiang Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion models (DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high-quality and realistic data samples. However their widespread use has also brought forth new challenges in model security which motivates the creation of more effective adversarial attackers on DMs to understand its vulnerability. We propose CAAT a simple but generic and efficient approach that does not require costly training to effectively fool latent diffusion models (LDMs). The approach is based on the observation that cross-attention layers exhibits higher sensitivity to gradient change allowing for leveraging subtle perturbations on published images to significantly corrupt the generated images. We show that a subtle perturbation on an image can significantly impact the cross-attention layers thus changing the mapping between text and image during the fine-tuning of customized diffusion models. Extensive experiments demonstrate that CAAT is compatible with diverse diffusion models and outperforms baseline attack methods in a more effective (more noise) and efficient (twice as fast as Anti-DreamBooth and Mist) manner.
Fairy: Fast Parallelized Instruction-Guided Video-to-Video Synthesis
Authors: Bichen Wu, Ching-Yao Chuang, Xiaoyan Wang, Yichen Jia, Kapil Krishnakumar, Tong Xiao, Feng Liang, Licheng Yu, Peter Vajda
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we introduce Fairy a minimalist yet robust adaptation of image-editing diffusion models enhancing them for video editing applications. Our approach centers on the concept of anchor-based cross-frame attention a mechanism that implicitly propagates diffusion features across frames ensuring superior temporal coherence and high-fidelity synthesis. Fairy not only addresses limitations of previous models including memory and processing speed. It also improves temporal consistency through a unique data augmentation strategy. This strategy renders the model equivariant to affine transformations in both source and target images. Remarkably efficient Fairy generates 120-frame 512x384 videos (4-second duration at 30 FPS) in just 14 seconds outpacing prior works by at least 44x. A comprehensive user study involving 1000 generated samples confirms that our approach delivers superior quality decisively outperforming established methods.
SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
Authors: Yuzhou Huang, Liangbin Xie, Xintao Wang, Ziyang Yuan, Xiaodong Cun, Yixiao Ge, Jiantao Zhou, Chao Dong, Rui Huang, Ruimao Zhang, Ying Shan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Current instruction-based image editing methods such as InstructPix2Pix often fail to produce satisfactory results in complex scenarios due to their dependence on the simple CLIP text encoder in diffusion models. To rectify this this paper introduces SmartEdit a novel approach of instruction-based image editing that leverages Multimodal Large Language Models (MLLMs) to enhance its understanding and reasoning capabilities. However direct integration of these elements still faces challenges in situations requiring complex reasoning. To mitigate this we propose a Bidirectional Interaction Module (BIM) that enables comprehensive bidirectional information interactions between the input image and the MLLM output. During training we initially incorporate perception data to boost the perception and understanding capabilities of diffusion models. Subsequently we demonstrate that a small amount of complex instruction editing data can effectively stimulate SmartEdit's editing capabilities for more complex instructions. We further construct a new evaluation dataset Reason-Edit specifically tailored for complex instruction-based image editing. Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods paving the way for the practical application of complex instruction-based image editing.
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder
Authors: Dihan Zheng, Yihang Zou, Xiaowen Zhang, Chenglong Bao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The data bottleneck has emerged as a fundamental challenge in learning based image restoration methods. Researchers have attempted to generate synthesized training data using paired or unpaired samples to address this challenge. This study proposes SeNM-VAE a semi-supervised noise modeling method that leverages both paired and unpaired datasets to generate realistic degraded data. Our approach is based on modeling the conditional distribution of degraded and clean images with a specially designed graphical model. Under the variational inference framework we develop an objective function for handling both paired and unpaired data. We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks. Our approach excels in the quality of synthetic degraded images compared to other unpaired and paired noise modeling methods. Furthermore our approach demonstrates remarkable performance in downstream image restoration tasks even with limited paired data. With more paired data our method achieves the best performance on the SIDD dataset.
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping
Authors: Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti, Luigi Di Stefano
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advancements have shown the potential of leveraging both point clouds and images to localize anomalies. Nevertheless their applicability in industrial manufacturing is often constrained by significant drawbacks such as the use of memory banks which leads to a substantial increase in terms of memory footprint and inference times. We propose a novel light and fast framework that learns to map features from one modality to the other on nominal samples and detect anomalies by pinpointing inconsistencies between observed and mapped features. Extensive experiments show that our approach achieves state-of-the-art detection and segmentation performance in both the standard and few-shot settings on the MVTec 3D-AD dataset while achieving faster inference and occupying less memory than previous multimodal AD methods. Furthermore we propose a layer pruning technique to improve memory and time efficiency with a marginal sacrifice in performance.
FFF: Fixing Flawed Foundations in Contrastive Pre-Training Results in Very Strong Vision-Language Models
Authors: Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training in this paper we show that the full potential of improving the training process by addressing such issues is yet to be realized. Specifically we firstly study and analyze two issues affecting training: incorrect assignment of negative pairs and low caption quality and diversity. Then we devise effective solutions for addressing both problems which essentially require training with multiple true positive pairs. Finally we propose training with sigmoid loss to address such a requirement. We show very large gains over the current state-of-the-art for both image recognition (  +6% on average over 11 datasets) and image retrieval (  +19% on Flickr30k and   +15% on MSCOCO).
Anchor-based Robust Finetuning of Vision-Language Models
Authors: Jinwei Han, Zhiwen Lin, Zhongyisun Sun, Yingguo Gao, Ke Yan, Shouhong Ding, Yuan Gao, Gui-Song Xia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization i.e. i) domain shift such as natural to sketch images and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably the diminished OOD generalization after finetuning stems from the excessively simplified finetuning target which only provides the class information such as "a photo of a [CLASS]". This is distinct from the process in that CLIP was pretrained where there is abundant text supervision with rich semantic information. Therefore we propose to compensate for the finetune process using auxiliary supervision with rich semantic information which acts as anchors to preserve the OOD generalization. Specifically two types of anchors are elaborated in our methods including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner ii) image-text-pair anchor which is retrieved from the dataset similar to pretraining data of CLIP according to the downstream task associating with the original CLIP text with rich semantics. Those anchors are utilized as auxiliary semantic information to maintain the original feature space of CLIP thereby preserving the OOD generalization capabilities. Comprehensive experiments demonstrate that our method achieves in-distribution performance akin to conventional finetuning while attaining new state-of-the-art results on domain shift and zero-shot learning benchmarks.
Low-power Continuous Remote Behavioral Localization with Event Cameras
Authors: Friedhelm Hamann, Suman Ghosh, Ignacio Juarez Martinez, Tom Hart, Alex Kacelnik, Guillermo Gallego
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Researchers in natural science need reliable methods for quantifying animal behavior. Recently numerous computer vision methods emerged to automate the process. However observing wild species at remote locations remains a challenging task due to difficult lighting conditions and constraints on power supply and data storage. Event cameras offer unique advantages for battery-dependent remote monitoring due to their low power consumption and high dynamic range capabilities. We use this novel sensor to quantify a behavior in Chinstrap penguins called ecstatic display. We formulate the problem as a temporal action detection task determining the start and end times of the behavior. For this purpose we recorded a colony of breeding penguins in Antarctica for several weeks and labeled event data on 16 nests. The developed method consists of a generator of candidate time intervals (proposals) and a classifier of the actions within them. The experiments show that the event cameras' natural response to motion is effective for continuous behavior monitoring and detection reaching a mean average precision (mAP) of 58% (which increases to 63% in good weather conditions). The results also demonstrate the robustness against various lighting conditions contained in the challenging dataset. The low-power capabilities of the event camera allow it to record significantly longer than with a conventional camera. This work pioneers the use of event cameras for remote wildlife observation opening new interdisciplinary opportunities. https:// tub-rip.github.io/ eventpenguins/
SportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos
Authors: Tao Wu, Runyu He, Gangshan Wu, Limin Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Video-based visual relation detection tasks such as video scene graph generation play important roles in fine-grained video understanding. However current video visual relation detection datasets have two main limitations that hinder the progress of research in this area. First they do not explore complex human-human interactions in multi-person scenarios. Second the relation types of existing datasets have relatively low-level semantics and can be often recognized by appearance or simple prior information without the need for detailed spatio-temporal context reasoning. Nevertheless comprehending high-level interactions between humans is crucial for understanding complex multi-person videos such as sports and surveillance videos. To address this issue we propose a new video visual relation detection task: video human-human interaction detection and build a dataset named SportsHHI for it. SportsHHI contains 34 high-level interaction classes from basketball and volleyball sports. 118075 human bounding boxes and 50649 interaction instances are annotated on 11398 keyframes. To benchmark this we propose a two-stage baseline method and conduct extensive experiments to reveal the key factors for a successful human-human interaction detector. We hope that SportsHHI can stimulate research on human interaction understanding in videos and promote the development of spatio-temporal context modeling techniques in video visual relation detection.
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRF
Authors: Jie Long Lee, Chen Li, Gim Hee Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present DiSR-NeRF a diffusion-guided framework for view-consistent super-resolution (SR) NeRF. Unlike prior works we circumvent the requirement for high-resolution (HR) reference images by leveraging existing powerful 2D super-resolution models. Nonetheless independent SR 2D images are often inconsistent across different views. We thus propose Iterative 3D Synchronization (I3DS) to mitigate the inconsistency problem via the inherent multi-view consistency property of NeRF. Specifically our I3DS alternates between upscaling low-resolution (LR) rendered images with diffusion models and updating the underlying 3D representation with standard NeRF training. We further introduce Renoised Score Distillation (RSD) a novel score-distillation objective for 2D image resolution. Our RSD combines features from ancestral sampling and Score Distillation Sampling (SDS) to generate sharp images that are also LR-consistent. Qualitative and quantitative results on both synthetic and real-world datasets demonstrate that our DiSR-NeRF can achieve better results on NeRF super-resolution compared with existing works. Code and video results available at the project website.
Dispersed Structured Light for Hyperspectral 3D Imaging
Authors: Suhyun Shin, Seokjun Choi, Felix Heide, Seung-Hwan Baek
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Hyperspectral 3D imaging aims to acquire both depth and spectral information of a scene. However existing methods are either prohibitively expensive and bulky or compromise on spectral and depth accuracy. In this paper we present Dispersed Structured Light (DSL) a cost-effective and compact method for accurate hyperspectral 3D imaging. DSL modifies a traditional projector-camera system by placing a sub-millimeter thick diffraction grating film front of the projector. This configuration enables dispersing structured light based on light wavelength. To utilize the dispersed structured light we devise a model for dispersive projection image formation and a per-pixel hyperspectral 3D reconstruction method. We validate DSL by instantiating a compact experimental prototype. DSL achieves spectral accuracy of 18.8nm full-width half-maximum (FWHM) and depth error of 1mm outperforming prior work on practical hyperspectral 3D imaging. DSL promises accurate and practical hyperspectral 3D imaging for diverse application domains including computer vision and graphics cultural heritage geology and biology.
CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion Models
Authors: Yasiru Ranasinghe, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Crowd counting is a fundamental problem in crowd analysis which is typically accomplished by estimating a crowd density map and summing over the density values. However this approach suffers from background noise accumulation and loss of density due to the use of broad Gaussian kernels to create the ground truth density maps. This issue can be overcome by narrowing the Gaussian kernel. However existing approaches perform poorly when trained with ground truth density maps with broad kernels. To deal with this limitation we propose using conditional diffusion models to predict density maps as diffusion models show high fidelity to training data during generation. With that we present CrowdDiff that generates the crowd density map as a reverse diffusion process. Furthermore as the intermediate time steps of the diffusion process are noisy we incorporate a regression branch for direct crowd estimation only during training to improve the feature learning. In addition owing to the stochastic nature of the diffusion model we introduce producing multiple density maps to improve the counting performance contrary to the existing crowd counting pipelines. We conduct extensive experiments on publicly available datasets to validate the effectiveness of our method. CrowdDiff outperforms existing \sota crowd counting methods on several public crowd analysis benchmarks with significant improvements. CrowdDiff project is available at: https://dylran.github.io/crowddiff.github.io.
It's All About Your Sketch: Democratising Sketch Control in Diffusion Models
Authors: Subhadeep Koley, Ayan Kumar Bhunia, Deeptanshu Sekhri, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper unravels the potential of sketches for diffusion models addressing the deceptive promise of direct sketch control in generative AI. We importantly democratise the process enabling amateur sketches to generate precise images living up to the commitment of "what you sketch is what you get". A pilot study underscores the necessity revealing that deformities in existing models stem from spatial-conditioning. To rectify this we propose an abstraction-aware framework utilising a sketch adapter adaptive time-step sampling and discriminative guidance from a pre-trained fine-grained sketch-based image retrieval model working synergistically to reinforce fine-grained sketch-photo association. Our approach operates seamlessly during inference without the need for textual prompts; a simple rough sketch akin to what you and I can create suffices! We welcome everyone to examine results presented in the paper and its supplementary. Contributions include democratising sketch control introducing an abstraction-aware framework and leveraging discriminative guidance validated through extensive experiments.
GLID: Pre-training a Generalist Encoder-Decoder Vision Model
Authors: Jihao Liu, Jinliang Zheng, Yu Liu, Hongsheng Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks. While self-supervised pre-training approaches e.g. Masked Autoencoder have shown success in transfer learning task-specific sub-architectures are still required to be appended for different downstream tasks which cannot enjoy the benefits of large-scale pre-training. GLID overcomes this challenge by allowing the pre-trained generalist encoder-decoder to be fine-tuned on various vision tasks with minimal task-specific architecture modifications. In the GLID training scheme pre-training pretext task and other downstream tasks are modeled as "query-to-answer" problems including the pre-training pretext task and other downstream tasks. We pre-train a task-agnostic encoder-decoder with query-mask pairs. During fine-tuning GLID maintains the pre-trained encoder-decoder and queries only replacing the topmost linear transformation layer with task-specific linear heads. This minimizes the pretrain-finetune architecture inconsistency and enables the pre-trained model to better adapt to downstream tasks. GLID achieves competitive performance on various vision tasks including object detection image segmentation pose estimation and depth estimation outperforming or matching specialist models such as Mask2Former DETR ViTPose and BinsFormer.
Diffusion-FOF: Single-View Clothed Human Reconstruction via Diffusion-Based Fourier Occupancy Field
Authors: Yuanzhen Li, Fei Luo, Chunxia Xiao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Reconstructing a clothed human from a single-view image has several challenging issues including flexibly representing various body shapes and poses estimating complete 3D geometry and consistent texture and achieving more fine-grained details. To address them we propose a new diffusion-based Fourier occupancy field method to improve the human representing ability and the geometry generating ability. First we estimate the back-view image from the given reference image by incorporating a style consistency constraint. Then we extract multi-scale features of the two images as conditional and design a diffusion model to generate the Fourier occupancy field in the wavelet domain. We refine the initial estimated Fourier occupancy field with image features as conditions to improve the geometric accuracy. Finally the reference and estimated back-view images are mapped onto the human model creating a textured clothed human model. Substantial experiments are conducted and the experimental results show that our method outperforms the state-of-the-art methods in geometry and texture reconstruction performance.
When StyleGAN Meets Stable Diffusion: a W+ Adapter for Personalized Image Generation
Authors: Xiaoming Li, Xinyu Hou, Chen Change Loy
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image diffusion models have remarkably excelled in producing diverse high-quality and photo-realistic images. This advancement has spurred a growing interest in incorporating specific identities into generated content. Most current methods employ an inversion approach to embed a target visual concept into the text embedding space using a single reference image. However the newly synthesized faces either closely resemble the reference image in terms of facial attributes such as expression or exhibit a reduced capacity for identity preservation. Text descriptions intended to guide the facial attributes of the synthesized face may fall short owing to the intricate entanglement of identity information with identity-irrelevant facial attributes derived from the reference image. To address these issues we present the novel use of the extended StyleGAN embedding space \mathcal W _+ to achieve enhanced identity preservation and disentanglement for diffusion models. By aligning this semantically meaningful human face latent space with text-to-image diffusion models we succeed in maintaining high fidelity in identity preservation coupled with the capacity for semantic editing. Additionally we propose new training objectives to balance the influences of both prompt and identity conditions ensuring that the identity-irrelevant background remains \lxm negligibly affected  during facial attribute modifications. Extensive experiments reveal that our method adeptly generates personalized text-to-image outputs that are not only compatible with prompt descriptions but also amenable to common StyleGAN editing directions in diverse settings. Our code and model are available at https://github.com/csxmli2016/w-plus-adapter.