mtybilly/Instruct2DS huggingface dataset:
MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors
Authors: He Zhang, Shenghao Ren, Haolei Yuan, Jianhui Zhao, Fan Li, Shuangpeng Sun, Zhenghao Liang, Tao Yu, Qiu Shen, Xun Cao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Foot contact is an important cue for human motion capture understanding and generation. Existing datasets tend to annotate dense foot contact using visual matching with thresholding or incorporating pressure signals. However these approaches either suffer from low accuracy or are only designed for small-range and slow motion. There is still a lack of a vision-pressure multimodal dataset with large-range and fast human motion as well as accurate and dense foot-contact annotation. To fill this gap we propose a Multimodal MoCap Dataset with Vision and Pressure sensors named MMVP. MMVP provides accurate and dense plantar pressure signals synchronized with RGBD observations which is especially useful for both plausible shape estimation robust pose fitting without foot drifting and accurate global translation tracking. To validate the dataset we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework VP-MoCap for human motion capture. Experiments demonstrate that our RGBD-P SMPL Fitting results significantly outperform pure visual motion capture. Moreover VP-MoCap outperforms SOTA methods in foot-contact and global translation estimation accuracy. We believe the configuration of the dataset and the baseline frameworks will stimulate the research in this direction and also provide a good reference for MoCap applications in various domains. Project page: https://metaverse-ai-lab-thu.github.io/MMVP-Dataset/.
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
Authors: Alon Zolfi, Guy Amit, Amit Baras, Satoru Koda, Ikuya Morikawa, Yuval Elovici, Asaf Shabtai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Out-of-distribution (OOD) detection has attracted a large amount of attention from the machine learning research community in recent years due to its importance in deployed systems. Most of the previous studies focused on the detection of OOD samples in the multi-class classification task. However OOD detection in the multi-label classification task a more common real-world use case remains an underexplored domain. In this research we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task. Object detection models have an inherent ability to distinguish between objects of interest (in-distribution data) and irrelevant objects (OOD data) in images that contain multiple objects belonging to different class categories. These abilities allow us to convert a regular object detection model into an image classifier with inherent OOD detection capabilities with just minor changes. We compare our approach to state-of-the-art OOD detection methods and demonstrate YolOOD's ability to outperform these methods on a comprehensive suite of in-distribution and OOD benchmark datasets.
SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System
Authors: Yunfei Fan, Tianyu Zhao, Guidong Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Accuracy and computational efficiency are the most important metrics to Visual Inertial Navigation System (VINS). The existing VINS algorithms with either high accuracy or low computational complexity are difficult to provide the high precision localization in resource-constrained devices. To this end we propose a novel filter-based VINS framework named SchurVINS (SV) which could guarantee both high accuracy by building a complete residual model and low computational complexity with Schur complement. Technically we first formulate the full residual model where Gradient Hessian and observation covariance are explicitly modeled. Then Schur complement is employed to decompose the full model into ego-motion residual model and landmark residual model. Finally Extended Kalman Filter (EKF) update is implemented in these two models with high efficiency. Experiments on EuRoC and TUM-VI datasets show that our method notably outperforms state-of-the-art (SOTA) methods in both accuracy and computational complexity. The experimental code of SchurVINS is available at https://github.com/bytedance/SchurVINS.
Collaborating Foundation Models for Domain Generalized Semantic Segmentation
Authors: Yasser Benigmim, Subhankar Roy, Slim Essid, Vicky Kalogeiton, Stéphane Lathuilière
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail CLOUDS is a framework that integrates Foundation Models of various kinds: (i) CLIP backbone for its robust feature representation (ii) Diffusion Model to diversify the content thereby covering various modes of the possible target distribution and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions notably outperforming prior methods by 5.6% and 6.7% on averaged mIoU respectively. Our code is available at https://github.com/yasserben/CLOUDS
Towards Variable and Coordinated Holistic Co-Speech Motion Generation
Authors: Yifei Liu, Qiong Cao, Yandong Wen, Huaiguang Jiang, Changxing Ding
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper addresses the problem of generating lifelike holistic co-speech motions for 3D avatars focusing on two key aspects: variability and coordination. Variability allows the avatar to exhibit a wide range of motions even with similar speech content while coordination ensures a harmonious alignment among facial expressions hand gestures and body poses. We aim to achieve both with ProbTalk a unified probabilistic framework designed to jointly model facial hand and body movements in speech. ProbTalk builds on the variational autoencoder (VAE) architecture and incorporates three core designs. First we introduce product quantization (PQ) to the VAE which enriches the representation of complex holistic motion. Second we devise a novel non-autoregressive model that embeds 2D positional encoding into the product-quantized representation thereby preserving essential structure information of the PQ codes. Last we employ a secondary stage to refine the preliminary prediction further sharpening the high-frequency details. Coupling these three designs enables ProbTalk to generate natural and diverse holistic co-speech motions outperforming several state-of-the-art methods in qualitative and quantitative evaluations particularly in terms of realism. Our code and model will be released for research purposes at https://feifeifeiliu.github.io/probtalk/.
JoAPR: Cleaning the Lens of Prompt Learning for Vision-Language Models
Authors: Yuncheng Guo, Xiaodong Gu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Leveraging few-shot datasets in prompt learning for Vision-Language Models eliminates the need for manual prompt engineering while highlighting the necessity of accurate annotations for the labels. However high-level or complex label noise challenges prompt learning for Vision-Language Models. Aiming at this issue we propose a new framework for improving its robustness. Specifically we introduce the Joint Adaptive Partitioning for Label Refurbishment (JoAPR) a structured framework encompassing two key steps. 1) Data Partitioning where we differentiate between clean and noisy data using joint adaptive thresholds. 2) Label Refurbishment where we correct the labels based on the partition outcomes before retraining the network. Our comprehensive experiments confirm that JoAPR substantially enhances the robustness of prompt learning for Vision-Language Models against label noise offering a promising direction for future research.
AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
Authors: Haonan Wang, Qixiang Zhang, Yi Li, Xiaomeng Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods train the labeled data with ground truths and unlabeled data with pseudo labels. However the two training flows are separate which allows labeled data to dominate the training process resulting in low-quality pseudo labels and consequently sub-optimal results. To alleviate this issue we present AllSpark which reborns the labeled features from unlabeled ones with the channel-wise cross-attention mechanism. We further introduce a Semantic Memory along with a Channel Semantic Grouping strategy to ensure that unlabeled features adequately represent labeled features. The AllSpark shed new light on the architecture level designs of SSSS rather than framework level which avoids increasingly complicated training pipeline designs. It can also be regarded as a flexible bottleneck module that can be seamlessly integrated into a general transformer-based segmentation model. The proposed AllSpark outperforms existing methods across all evaluation protocols on Pascal Cityscapes and COCO benchmarks without bells-and-whistles. Code and model weights are available at: https://github.com/xmed-lab/AllSpark.
Open-Vocabulary 3D Semantic Segmentation with Foundation Models
Authors: Li Jiang, Shaoshuai Shi, Bernt Schiele
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In dynamic 3D environments the ability to recognize a diverse range of objects without the constraints of predefined categories is indispensable for real-world applications. In response to this need we introduce OV3D an innovative framework designed for open-vocabulary 3D semantic segmentation. OV3D leverages the broad open-world knowledge embedded in vision and language foundation models to establish a fine-grained correspondence between 3D points and textual entity descriptions. These entity descriptions are enriched with contextual information enabling a more open and comprehensive understanding. By seamlessly aligning 3D point features with entity text features OV3D empowers open-vocabulary recognition in the 3D domain achieving state-of-the-art open-vocabulary semantic segmentation performance across multiple datasets including ScanNet Matterport3D and nuScenes.
SIGNeRF: Scene Integrated Generation for Neural Radiance Fields
Authors: Jan-Niklas Dihlmann, Andreas Engelhardt, Hendrik Lensch
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Advances in image diffusion models have recently led to notable improvements in the generation of high-quality images. In combination with Neural Radiance Fields (NeRFs) they enabled new opportunities in 3D generation. However most generative 3D approaches are object-centric and applying them to editing existing photorealistic scenes is not trivial. We propose SIGNeRF a novel approach for fast and controllable NeRF scene editing and scene-integrated object generation. A new generative update strategy ensures 3D consistency across the edited images without requiring iterative optimization. We find that depth-conditioned diffusion models inherently possess the capability to generate 3D consistent views by requesting a grid of images instead of single views. Based on these insights we introduce a multi-view reference sheet of modified images. Our method updates an image collection consistently based on the reference sheet and refines the original NeRF with the newly generated image set in one go. By exploiting the depth conditioning mechanism of the image diffusion model we gain fine control over the spatial location of the edit and enforce shape guidance by a selected region or an external mesh.
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
Authors: Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: While existing large vision-language multimodal models focus on whole image understanding there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge we introduce a novel multimodal model capable of decoding arbitrary (free-form) visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow'". Our simple design directly overlays visual markers onto the RGB image eliminating the need for complex region encodings yet achieves state-of-the-art performance on region-understanding tasks like Visual7W PointQA and Visual Commonsense Reasoning benchmark. Furthermore we present ViP-Bench a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions enabling future research in this domain. Code data and model are publicly available.
OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation
Authors: Ganlong Zhao, Guanbin Li, Weikai Chen, Yizhou Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advances in Iterative Vision-and-Language Navigation(IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task it poses more challenges on how to utilize the highly unstructured navigation memory with extremely sparse supervision. Towards this end we propose OVER-NAV which aims to go over and beyond the current arts of IVLN techniques. In particular we propose to incorporate LLMs and open-vocabulary detectors to distill key information and establish correspondence between multi-modal signals. Such a mechanism introduces reliable cross-modal supervision and enables on-the-fly generalization to unseen scenes without the need of extra annotation and re-training. To fully exploit the interpreted navigation data we further introduce a structured representation coded Omnigraph to effectively integrate multi-modal information along the tour. Accompanied with a novel omnigraph fusion mechanism OVER-NAV is able to extract the most relevant knowledge from omnigraph for a more accurate navigating action. In addition OVER-NAV seamlessly supports both discrete and continuous environments under a unified framework. We demonstrate the superiority of OVER-NAV in extensive experiments.
1-Lipschitz Layers Compared: Memory Speed and Certifiable Robustness
Authors: Bernd Prach, Fabio Brau, Giorgio Buttazzo, Christoph H. Lampert
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical systems. Recently the scientific community has focused on enhancing certifiable robustness guarantees by crafting \ols neural networks that leverage Lipschitz bounded dense and convolutional layers. Different methods have been proposed in the literature to achieve this goal however comparing the performance of such methods is not straightforward since different metrics can be relevant (e.g. training time memory usage accuracy certifiable robustness) for different applications. Therefore this work provides a thorough comparison between different methods covering theoretical aspects such as computational complexity and memory requirements as well as empirical measurements of time per epoch required memory accuracy and certifiable robust accuracy. The paper also provides some guidelines and recommendations to support the user in selecting the methods that work best depending on the available resources. We provide code at github.com/berndprach/1LipschitzLayersCompared
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
Authors: Yue Niu, Ramy E. Ali, Saurav Prakash, Salman Avestimehr
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Data privacy is of great concern in cloud machine-learning service platforms when sensitive data are exposed to service providers. While private computing environments (e.g. secure enclaves) and cryptographic approaches (e.g. homomorphic encryption) provide strong privacy protection their computing performance still falls short compared to cloud GPUs. To achieve privacy protection with high computing performance we propose Delta a new private training and inference framework with comparable model performance as non-private centralized training. Delta features two asymmetric data flows: the main information-sensitive flow and the residual flow. The main part flows into a small model while the residuals are offloaded to a large model. Specifically Delta embeds the information-sensitive representations into a low-dimensional space while pushing the information-insensitive part into high-dimension residuals. To ensure privacy protection the low-dimensional information-sensitive part is secured and fed to a small model in a private environment. On the other hand the residual part is sent to fast cloud GPUs and processed by a large model. To further enhance privacy and reduce the communication cost Delta applies a random binary quantization technique along with a DP-based technique to the residuals before sharing them with the public platform. We theoretically show that Delta guarantees differential privacy in the public environment and greatly reduces the complexity in the private environment. We conduct empirical analyses on CIFAR-10 CIFAR-100 and ImageNet datasets and ResNet-18 and ResNet-34 showing that Delta achieves strong privacy protection fast training and inference without significantly compromising the model utility.
Generating Illustrated Instructions
Authors: Sachit Menon, Ishan Misra, Rohit Girdhar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce a new task of generating "Illustrated Instructions" i.e. visual instructions customized to a user's needs. We identify desiderata unique to this task and formalize it through a suite of automatic and human evaluation metrics designed to measure the validity consistency and efficacy of the generations. We combine the power of large language models (LLMs) together with strong text-to-image generation diffusion models to propose a simple approach called StackedDiffusion which generates such illustrated instructions given text as input. The resulting model strongly outperforms baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases users even prefer it to human-generated articles. Most notably it enables various new and exciting applications far beyond what static articles on the web can provide such as personalized instructions complete with intermediate steps and pictures in response to a user's individual situation.
Robust Image Denoising through Adversarial Frequency Mixup
Authors: Donghun Ryou, Inju Ha, Hyewon Yoo, Dongwan Kim, Bohyung Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Image denoising approaches based on deep neural networks often struggle with overfitting to specific noise distributions present in training data. This challenge persists in existing real-world denoising networks which are trained using a limited spectrum of real noise distributions and thus show poor robustness to out-of-distribution real noise types. To alleviate this issue we develop a novel training framework called Adversarial Frequency Mixup (AFM). AFM leverages mixup in the frequency domain to generate noisy images with distinctive and challenging noise characteristics all the while preserving the properties of authentic real-world noise. Subsequently incorporating these noisy images into the training pipeline enhances the denoising network's robustness to variations in noise distributions. Extensive experiments and analyses conducted on a wide range of real noise benchmarks demonstrate that denoising networks trained with our proposed framework exhibit significant improvements in robustness to unseen noise distributions. The code is available at https://github.com/dhryougit/AFM.
HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions
Authors: Hao Xu, Haipeng Li, Yinqiao Wang, Shuaicheng Liu, Chi-Wing Fu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Reconstructing 3D hand mesh robustly from a single image is very challenging due to the lack of diversity in existing real-world datasets. While data synthesis helps relieve the issue the syn-to-real gap still hinders its usage. In this work we present HandBooster a new approach to uplift the data diversity and boost the 3D hand-mesh reconstruction performance by training a conditional generative space on hand-object interactions and purposely sampling the space to synthesize effective data samples. First we construct versatile content-aware conditions to guide a diffusion model to produce realistic images with diverse hand appearances poses views and backgrounds; favorably accurate 3D annotations are obtained for free. Then we design a novel condition creator based on our similarity-aware distribution sampling strategies to deliberately find novel and realistic interaction poses that are distinctive from the training set. Equipped with our method several baselines can be significantly improved beyond the SOTA on the HO3D and DexYCB benchmarks. Our code will be released on https://github.com/hxwork/HandBooster_Pytorch.
A-Teacher: Asymmetric Network for 3D Semi-Supervised Object Detection
Authors: Hanshi Wang, Zhipeng Zhang, Jin Gao, Weiming Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This work proposes the first online asymmetric semi-supervised framework namely A-Teacher for LiDAR-based 3D object detection. Our motivation stems from the observation that 1) existing symmetric teacher-student methods for semi-supervised 3D object detection have characterized simplicity but impede the distillation performance between teacher and student because of the demand for an identical model structure and input data format. 2) The offline asymmetric methods with a complex teacher model constructed differently can generate more precise pseudo-labels but is challenging to jointly optimize the teacher and student model. Consequently in this paper we devise a different path from the conventional paradigm which can harness the capacity of a strong teacher while preserving the advantages of online teacher model updates. The essence is the proposed attention-based refinement model that can be seamlessly integrated into a vanilla teacher. The refinement model works in the divide-and-conquer manner that respectively handles three challenging scenarios including 1) objects detected in the current timestamp but with suboptimal box quality 2) objects are missed in the current timestamp but are detected in past or future frames 3) objects are neglected in all frames. It is worth noting that even while tackling these complex cases our model retains the efficiency of the online teacher-student semi-supervised framework. Experimental results on Waymo show that our method outperforms previous state-of-the-art HSSDA for 4.7 on mAP (L1) while consuming fewer training resources.
GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo
Authors: Jiang Wu, Rui Li, Haofei Xu, Wenxun Zhao, Yu Zhu, Jinqiu Sun, Yanning Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However directly aggregating adjacent costs can lead to suboptimal results due to local geometric inconsistency. Related methods either seek selective aggregation or improve aggregated depth in the 2D space both are unable to handle geometric inconsistency in the cost volume effectively. In this paper we propose GoMVS to aggregate geometrically consistent costs yielding better utilization of adjacent geometries. More specifically we correspond and propagate adjacent costs to the reference pixel by leveraging the local geometric smoothness in conjunction with surface normals. We achieve this by the geometric consistent propagation (GCP) module. It computes the correspondence from the adjacent depth hypothesis space to the reference depth space using surface normals then uses the correspondence to propagate adjacent costs to the reference geometry followed by a convolution for aggregation. Our method achieves new state-of-the-art performance on DTU Tanks & Temple and ETH3D datasets. Notably our method ranks 1st on the Tanks & Temple Advanced benchmark. Code is available at https://github.com/Wuuu3511/GoMVS.
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods
Authors: Mengyu Dai, Amir Hossein Raffiee, Aashish Jain, Joshua Correa
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Retrieval tasks play central roles in real-world machine learning systems such as search engines recommender systems and retrieval-augmented generation (RAG). Achieving decent performance in these tasks often requires fine-tuning various pre-trained models on specific datasets and selecting the best candidate a process that can be both time and resource-consuming. To tackle the problem we introduce a novel and efficient method called RetMMD that leverages Maximum Mean Discrepancy (MMD) and kernel methods to assess the transferability of pretrained models in retrieval tasks. RetMMD is calculated on pretrained model and target dataset without any fine-tuning involved. Specifically given some query we quantify the distribution discrepancy between relevant and irrelevant document embeddings by estimating the similarities within their mappings in the fine-tuned embedding space through kernel method. This discrepancy is averaged over multiple queries taking into account the distribution characteristics of the target dataset. Experiments suggest that the proposed metric calculated on pre-trained models closely aligns with retrieval performance post-fine-tuning. The observation holds across a variety of datasets including image text and multi-modal domains indicating the potential of using MMD and kernel methods for transfer learning evaluation in retrieval scenarios. In addition we also design a way of evaluating dataset transferability for retrieval tasks with experimental results demonstrating the effectiveness of the proposed approach.
AnyScene: Customized Image Synthesis with Composited Foreground
Authors: Ruidong Chen, Lanjun Wang, Weizhi Nie, Yongdong Zhang, An-An Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advancements in text-to-image technology have significantly advanced the field of image customization. Among various applications the task of customizing diverse scenes for user-specified composited elements holds great application value but has not been extensively explored. Addressing this gap we propose AnyScene a specialized framework designed to create varied scenes from composited foreground using textual prompts. AnyScene addresses the primary challenges inherent in existing methods particularly scene disharmony due to a lack of foreground semantic understanding and distortion of foreground elements. Specifically we develop a foreground injection module that guides a pre-trained diffusion model to generate cohesive scenes in visual harmony with the provided foreground. To enhance robust generation we implement a layout control strategy that prevents distortions of foreground elements. Furthermore an efficient image blending mechanism seamlessly reintegrates foreground details into the generated scenes producing outputs with overall visual harmony and precise foreground details. In addition we propose a new benchmark and a series of quantitative metrics to evaluate this proposed image customization task. Extensive experimental results demonstrate the effectiveness of AnyScene which confirms its potential in various applications.
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts
Authors: Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Super-resolution (SR) is an ill-posed inverse problem where the size of the set of feasible solutions that are consistent with a given low-resolution image is very large. Many algorithms have been proposed to find a "good" solution among the feasible solutions that strike a balance between fidelity and perceptual quality. Unfortunately all known methods generate artifacts and hallucinations while trying to reconstruct high-frequency (HF) image details. A fundamental question is: Can a model learn to distinguish genuine image details from artifacts? Although some recent works focused on the differentiation of details and artifacts this is a very challenging problem and a satisfactory solution is yet to be found. This paper shows that the characterization of genuine HF details versus artifacts can be better learned by training GAN-based SR models using wavelet-domain loss functions compared to RGB-domain or Fourier-space losses. Although wavelet-domain losses have been used in the literature before they have not been used in the context of the SR task. More specifically we train the discriminator only on the HF wavelet sub-bands instead of on RGB images and the generator is trained by a fidelity loss over wavelet subbands to make it sensitive to the scale and orientation of structures. Extensive experimental results demonstrate that our model achieves better perception-distortion trade-off according to multiple objective measures and visual evaluations.
Visual Objectification in Films: Towards a New AI Task for Video Interpretation
Authors: Julie Tores, Lucile Sassatelli, Hui-Yin Wu, Clement Bergman, Léa Andolfi, Victor Ecrement, Frédéric Precioso, Thierry Devars, Magali Guaresi, Virginie Julliard, Sarah Lecossais
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In film gender studies the concept of "male gaze" refers to the way the characters are portrayed on-screen as objects of desire rather than subjects. In this article we introduce a novel video-interpretation task to detect character objectification in films. The purpose is to reveal and quantify the usage of complex temporal patterns operated in cinema to produce the cognitive perception of objectification. We introduce the ObyGaze12 dataset made of 1914 movie clips densely annotated by experts for objectification concepts identified in film studies and psychology. We evaluate recent vision models show the feasibility of the task and where the challenges remain with concept bottleneck models. Our new dataset and code are made available to the community.
OMG-Seg: Is One Model Good Enough For All Segmentation?
Authors: Xiangtai Li, Haobo Yuan, Wei Li, Henghui Ding, Size Wu, Wenwei Zhang, Yining Li, Kai Chen, Chen Change Loy
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this work we address various segmentation tasks each traditionally tackled by distinct or partially unified models. We propose OMG-Seg One Model that is Good enough to efficiently and effectively handle all the segmentation tasks including image semantic instance and panoptic segmentation as well as their video counterparts open vocabulary settings prompt-driven interactive segmentation like SAM and video object segmentation. To our knowledge this is the first model to handle all these tasks in one model and achieve satisfactory performance. We show that OMG-Seg a transformer-based encoder-decoder architecture with task-specific queries and outputs can support over ten distinct segmentation tasks and yet significantly reduce computational and parameter overhead across various tasks and datasets. We rigorously evaluate the inter-task influences and correlations during co-training. Code and models are available at https://github.com/lxtGH/OMG-Seg.
BiTT: Bi-directional Texture Reconstruction of Interacting Two Hands from a Single Image
Authors: Minje Kim, Tae-Kyun Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Creating personalized hand avatars is important to offer a realistic experience to users on AR / VR platforms. While most prior studies focused on reconstructing 3D hand shapes some recent work has tackled the reconstruction of hand textures on top of shapes. However these methods are often limited to capturing pixels on the visible side of a hand requiring diverse views of the hand in a video or multiple images as input. In this paper we propose a novel method BiTT(Bi-directional Texture reconstruction of Two hands) which is the first end-to-end train- able method for relightable pose-free texture reconstruction of two interacting hands taking only a single RGB image by three novel components: 1) bi-directional (left ? right) texture reconstruction using the texture symmetry of left / right hands 2) utilizing a texture parametric model for hand texture recovery and 3) the overall coarse-to-fine stage pipeline for reconstructing personalized texture of two interacting hands. BiTT first estimates the scene light condition and albedo image from an input image then reconstructs the texture of both hands through the texture parametric model and bi-directional texture reconstructor. In experiments using InterHand2.6M and RGB2Hands datasets our method significantly outperforms state-of-the-art hand texture reconstruction methods quantitatively and qualitatively. The code is available at https://github.com/ yunminjin2/BiTT.
DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection
Authors: Lewei Yao, Renjie Pi, Jianhua Han, Xiaodan Liang, Hang Xu, Wei Zhang, Zhenguo Li, Dan Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing open-vocabulary object detectors typically require a predefined set of categories from users significantly confining their application scenarios. In this paper we introduce DetCLIPv3 a high-performing detector that excels not only at both open-vocabulary object detection but also generating hierarchical labels for detected objects. DetCLIPv3 is characterized by three core designs: 1. Versatile model architecture: we derive a robust open-set detection framework which is further empowered with generation ability via the integration of a caption head. 2. High information density data: we develop an auto-annotation pipeline leveraging visual large language model to refine captions for large-scale image-text pairs providing rich multi-granular object labels to enhance the training. 3. Efficient training strategy: we employ a pre-training stage with low-resolution inputs that enables the object captioner to efficiently learn a broad spectrum of visual concepts from extensive image-text paired data. This is followed by a fine-tuning stage that leverages a small number of high-resolution samples to further enhance detection performance. With these effective designs DetCLIPv3 demonstrates superior open-vocabulary detection performance e.g. our Swin-T backbone model achieves a notable 47.0 zero-shot fixed AP on the LVIS minival benchmark outperforming GLIPv2 GroundingDINO and DetCLIPv2 by 18.0/19.6/6.6 AP respectively. DetCLIPv3 also achieves a state-of-the-art 19.7 AP in dense captioning task on VG dataset showcasing its strong generative capability.
UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement
Authors: Yaofeng Xie, Lingwei Kong, Kai Chen, Ziqiang Zheng, Xiao Yu, Zhibin Yu, Bing Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Learning-based underwater image enhancement (UIE) methods have made great progress. However the lack of large-scale and high-quality paired training samples has become the main bottleneck hindering the development of UIE. The inter-frame information in underwater videos can accelerate or optimize the UIE process. Thus we constructed the first large-scale high-resolution underwater video enhancement benchmark (UVEB) to promote the development of underwater vision.It contains 1308 pairs of video sequences and more than 453000 high-resolution with 38% Ultra-High-Definition (UHD) 4K frame pairs. UVEB comes from multiple countries containing various scenes and video degradation types to adapt to diverse and complex underwater environments. We also propose the first supervised underwater video enhancement method UVE-Net. UVE-Net converts the current frame information into convolutional kernels and passes them to adjacent frames for efficient inter-frame information exchange. By fully utilizing the redundant degraded information of underwater videos UVE-Net completes video enhancement better. Experiments show the effective network design and good performance of UVE-Net.
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs
Authors: Kanchana Ranasinghe, Satya Narayan Shukla, Omid Poursaeed, Michael S. Ryoo, Tsung-Yu Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Integration of Large Language Models (LLMs) into visual domain tasks resulting in visual-LLMs (V-LLMs) has enabled exceptional performance in vision-language tasks particularly for visual question answering (VQA). However existing V-LLMs (e.g. BLIP-2 LLaVA) demonstrate weak spatial reasoning and localization awareness. Despite generating highly descriptive and elaborate textual answers these models fail at simple tasks like distinguishing a left vs right location. In this work we explore how image-space coordinate based instruction fine-tuning objectives could inject spatial awareness into V-LLMs. We discover optimal coordinate representations data-efficient instruction fine-tuning objectives and pseudo-data generation strategies that lead to improved spatial awareness in V-LLMs. Additionally our resulting model improves VQA across image and video domains reduces undesired hallucination and generates better contextual object descriptions. Experiments across 5 vision-language tasks involving 14 different datasets establish the clear performance improvements achieved by our proposed framework.
Monocular Identity-Conditioned Facial Reflectance Reconstruction
Authors: Xingyu Ren, Jiankang Deng, Yuhao Cheng, Jia Guo, Chao Ma, Yichao Yan, Wenhan Zhu, Xiaokang Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent 3D face reconstruction methods have made remarkable advancements yet there remain huge challenges in monocular high-quality facial reflectance reconstruction. Existing methods rely on a large amount of light-stage captured data to learn facial reflectance models. However the lack of subject diversity poses challenges in achieving good generalization and widespread applicability. In this paper we learn the reflectance prior in image space rather than UV space and present a framework named ID2Reflectance. Our framework can directly estimate the reflectance maps of a single image while using limited reflectance data for training. Our key insight is that reflectance data shares facial structures with RGB faces which enables obtaining expressive facial prior from inexpensive RGB data thus reducing the dependency on reflectance data. We first learn a high-quality prior for facial reflectance. Specifically we pretrain multi-domain facial feature codebooks and design a codebook fusion method to align the reflectance and RGB domains. Then we propose an identity-conditioned swapping module that injects facial identity from the target image into the pre-trained auto-encoder to modify the identity of the source reflectance image. Finally we stitch multi-view swapped reflectance images to obtain renderable assets. Extensive experiments demonstrate that our method exhibits excellent generalization capability and achieves state-of-the-art facial reflectance reconstruction results for in-the-wild faces.
C3: High-Performance and Low-Complexity Neural Compression from a Single Image or Video
Authors: Hyunjik Kim, Matthias Bauer, Lucas Theis, Jonathan Richard Schwarz, Emilien Dupont
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Most neural compression models are trained on large datasets of images or videos in order to generalize to unseen data. Such generalization typically requires large and expressive architectures with a high decoding complexity. Here we introduce C3 a neural compression method with strong rate-distortion (RD) performance that instead overfits a small model to each image or video separately. The resulting decoding complexity of C3 can be an order of magnitude lower than neural baselines with similar RD performance. C3 builds on COOL-CHIC [Ladune et al 2023] and makes several simple and effective improvements for images. We further develop new methodology to apply C3 to videos. On the CLIC2020 image benchmark we match the RD performance of VTM the reference implementation of the H.266 codec with less than3k MACs/pixel for decoding. On the UVG video benchmark we match the RD performance of the Video Compression Transformer [Mentzer er al 2022] a well-established neural video codec with less than 5k MACs/pixel for decoding.
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
Authors: Nicolae-C?t?lin Ristea, Florinel-Alin Croitoru, Radu Tudor Ionescu, Marius Popescu, Fahad Shahbaz Khan, Mubarak Shah
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level. The novelty of the proposed model is threefold. First we introduce an approach to weight tokens based on motion gradients thus shifting the focus from the static background scene to the foreground objects. Second we integrate a teacher decoder and a student decoder into our architecture leveraging the discrepancy between the outputs given by the two decoders to improve anomaly detection. Third we generate synthetic abnormal events to augment the training videos and task the masked AE model to jointly reconstruct the original frames (without anomalies) and the corresponding pixel-level anomaly maps. Our design leads to an efficient and effective model as demonstrated by the extensive experiments carried out on four benchmarks: Avenue ShanghaiTech UBnormal and UCSD Ped2. The empirical results show that our model achieves an excellent trade-off between speed and accuracy obtaining competitive AUC scores while processing 1655 FPS. Hence our model is between 8 and 70 times faster than competing methods. We also conduct an ablation study to justify our design. Our code is freely available at: https://github.com/ristea/aed-mae.
Revisiting Non-Autoregressive Transformers for Efficient Image Synthesis
Authors: Zanlin Ni, Yulin Wang, Renping Zhou, Jiayi Guo, Jinyi Hu, Zhiyuan Liu, Shiji Song, Yuan Yao, Gao Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The field of image synthesis is currently flourishing due to the advancements in diffusion models. While diffusion models have been successful their computational intensity has prompted the pursuit of more efficient alternatives. As a representative work non-autoregressive Transformers (NATs) have been recognized for their rapid generation. However a major drawback of these models is their inferior performance compared to diffusion models. In this paper we aim to re-evaluate the full potential of NATs by revisiting the design of their training and inference strategies. Specifically we identify the complexities in properly configuring these strategies and indicate the possible sub-optimality in existing heuristic-driven designs. Recognizing this we propose to go beyond existing methods by directly solving the optimal strategies in an automatic framework. The resulting method named AutoNAT advances the performance boundaries of NATs notably and is able to perform comparably with the latest diffusion models with a significantly reduced inference cost. The effectiveness of AutoNAT is comprehensively validated on four benchmark datasets i.e. ImageNet-256 & 512 MS-COCO and CC3M. Code and pre-trained models will be available at https://github.com/LeapLabTHU/ImprovedNAT.