mtybilly/Instruct2DS huggingface dataset:
Optimizing Diffusion Noise Can Serve As Universal Motion Priors
Authors: Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwajanakorn, Siyu Tang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose Diffusion Noise Optimization (DNO) a new method that effectively leverages existing motion diffusion models as motion priors for a wide range of motion-related tasks. Instead of training a task-specific diffusion model for each new task DNO operates by optimizing the diffusion latent noise of an existing pre-trained text-to-motion model. Given the corresponding latent noise of a human motion it propagates the gradient from the target criteria defined on the motion space through the whole denoising process to update the diffusion latent noise. As a result DNO supports any use cases where criteria can be defined as a function of motion. In particular we show that for motion editing and control DNO outperforms existing methods in both achieving the objective and preserving the motion content. DNO accommodates a diverse range of editing modes including changing trajectory pose joint locations or avoiding newly added obstacles. In addition DNO is effective in motion denoising and completion producing smooth and realistic motion from noisy and partial inputs. DNO achieves these results at inference time without the need for model retraining offering great versatility for any defined reward or loss function on the motion representation.
MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection
Authors: Boyang Peng, Sanqing Qu, Yong Wu, Tianpei Zou, Lianghua He, Alois Knoll, Guang Chen, Changjun Jiang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep learning has achieved remarkable progress in various applications heightening the importance of safeguarding the intellectual property (IP) of well-trained models. It entails not only authorizing usage but also ensuring the deployment of models in authorized data domains i.e. making models exclusive to certain target domains. Previous methods necessitate concurrent access to source training data and target unauthorized data when performing IP protection making them risky and inefficient for decentralized private data. In this paper we target a practical setting where only a well-trained source model is available and investigate how we can realize IP protection. To achieve this we propose a novel MAsk Pruning (MAP) framework. MAP stems from an intuitive hypothesis i.e. there are target-related parameters in a well-trained model locating and pruning them is the key to IP protection. Technically MAP freezes the source model and learns a target-specific binary mask to prevent unauthorized data usage while minimizing performance degradation on authorized data. Moreover we introduce a new metric aimed at achieving a better balance between source and target performance degradation. To verify the effectiveness and versatility we have evaluated MAP in a variety of scenarios including vanilla source-available practical source-free and challenging data-free. Extensive experiments indicate that MAP yields new state-of-the-art performance.
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
Authors: Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M. Saquib Sarfraz, Mohsen Ali
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this work we tackle the problem of domain generalization for object detection specifically focusing on the scenario where only a single source domain is available. We propose an effective approach that involves two key steps: diversifying the source domain and aligning detections based on class prediction confidence and localization. Firstly we demonstrate that by carefully selecting a set of augmentations a base detector can outperform existing methods for single domain generalization by a good margin. This highlights the importance of domain diversification in improving the performance of object detectors. Secondly we introduce a method to align detections from multiple views considering both classification and localization outputs. This alignment procedure leads to better generalized and well-calibrated object detector models which are crucial for accurate decision-making in safety-critical applications. Our approach is detector-agnostic and can be seamlessly applied to both single-stage and two-stage detectors. To validate the effectiveness of our proposed methods we conduct extensive experiments and ablations on challenging domain-shift scenarios. The results consistently demonstrate the superiority of our approach compared to existing methods.
OVFoodSeg: Elevating Open-Vocabulary Food Image Segmentation via Image-Informed Textual Representation
Authors: Xiongwei Wu, Sicheng Yu, Ee-Peng Lim, Chong-Wah Ngo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In the realm of food computing segmenting ingredients from images poses substantial challenges due to the large intra-class variance among the same ingredients the emergence of new ingredients and the high annotation costs associated with large food segmentation datasets. Existing approaches primarily utilize a closed-vocabulary and static text embeddings setting. These methods often fall short in effectively handling the ingredients particularly new and diverse ones. In response to these limitations we introduce OVFoodSeg a framework that adopts an open-vocabulary setting and enhances text embeddings with visual context. By integrating vision-language models (VLMs) our approach enriches text embedding with image-specific information through two innovative modules e.g. an image-to-text learner FoodLearner and an Image-Informed Text Encoder. The training process of OVFoodSeg is divided into two stages: the pre-training of FoodLearner and the subsequent learning phase for segmentation. The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task. By addressing the deficiencies of previous models OVFoodSeg demonstrates a significant improvement achieving an 4.9% increase in mean Intersection over Union (mIoU) on the FoodSeg103 dataset setting a new milestone for food image segmentation.
XFeat: Accelerated Features for Lightweight Image Matching
Authors: Guilherme Potje, Felipe Cadar, André Araujo, Renato Martins, Erickson R. Nascimento
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method dubbed XFeat (Accelerated Features) revisits fundamental design choices in convolutional neural networks for detecting extracting and matching local features. Our new model satisfies a critical need for fast and robust algorithms suitable to resource-limited devices. In particular accurate image matching requires sufficiently large image resolutions -- for this reason we keep the resolution as large as possible while limiting the number of channels in the network. Besides our model is designed to offer the choice of matching at the sparse or semi-dense levels each of which may be more suitable for different downstream applications such as visual navigation and augmented reality. Our model is the first to offer semi-dense matching efficiently leveraging a novel match refinement module that relies on coarse local descriptors. XFeat is versatile and hardware-independent surpassing current deep learning-based local features in speed (up to 5x faster) with comparable or better accuracy proven in pose estimation and visual localization. We showcase it running in real-time on an inexpensive laptop CPU without specialized hardware optimizations. Code and weights are available at verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24.
Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach
Authors: Mir Rayat Imtiaz Hossain, Mennatullah Siam, Leonid Sigal, James J. Little
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The emergence of attention-based transformer models has led to their extensive use in various tasks due to their superior generalization and transfer properties. Recent research has demonstrated that such models when prompted appropriately are excellent for few-shot inference. However such techniques are under-explored for dense prediction tasks like semantic segmentation. In this work we examine the effectiveness of prompting a transformer-decoder with learned visual prompts for the generalized few-shot segmentation (GFSS) task. Our goal is to achieve strong performance not only on novel categories with limited examples but also to retain performance on base categories. We propose an approach to learn visual prompts with limited examples. These learned visual prompts are used to prompt a multiscale transformer decoder to facilitate accurate dense predictions. Additionally we introduce a unidirectional causal attention mechanism between the novel prompts learned with limited examples and the base prompts learned with abundant data. This mechanism enriches the novel prompts without deteriorating the base class performance. Overall this form of prompting helps us achieve state-of-the-art performance for GFSS on two different benchmark datasets: COCO-20^i and Pascal-5^i without the need for test-time optimization (or transduction). Furthermore test-time optimization leveraging unlabelled test data can be used to improve the prompts which we refer to as transductive prompt tuning.
ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
Authors: Yifan Bai, Zeyang Zhao, Yihong Gong, Xing Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present ARTrackV2 which integrates two pivotal aspects of tracking: determining where to look (localization) and how to describe (appearance analysis) the target object across video frames. Building on the foundation of its predecessor ARTrackV2 extends the concept by introducing a unified generative framework to "read out" object's trajectory and "retell" its appearance in an autoregressive manner. This approach fosters a time-continuous methodology that models the joint evolution of motion and visual features guided by previous estimates. Furthermore ARTrackV2 stands out for its efficiency and simplicity obviating the less efficient intra-frame autoregression and hand-tuned parameters for appearance updates. Despite its simplicity ARTrackV2 achieves state-of-the-art performance on prevailing benchmark datasets while demonstrating a remarkable efficiency improvement. In particular ARTrackV2 achieves an AO score of 79. 5% on GOT-10k and an AUC of 86. 1% on TrackingNet while being 3.6 xfaster than ARTrack.
A Vision Check-up for Language Models
Authors: Pratyusha Sharma, Tamar Rott Shaham, Manel Baradad, Stephanie Fu, Adrian Rodriguez-Munoz, Shivam Duggal, Phillip Isola, Antonio Torralba
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: What does learning to model relationships between strings teach Large Language Models (LLMs) about the visual world? We systematically evaluate LLMs' abilities to generate and recognize an assortment of visual concepts of increasing complexity and then demonstrate how a preliminary visual representation learning system can be trained using models of text. As language models lack the ability to consume or output visual information as pixels we use code to represent images in our study. Although LLM-generated images do not look like natural images results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world. Furthermore experiments on self-supervised visual representation learning utilizing images generated with text models highlight the potential to train vision models capable of making semantic assessments of natural images using just LLMs.
Memory-based Adapters for Online 3D Scene Perception
Authors: Xiuwei Xu, Chong Xia, Ziwei Wang, Linqing Zhao, Yueqi Duan, Jie Zhou, Jiwen Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we propose a new framework for online 3D scene perception. Conventional 3D scene perception methods are offline i.e. take an already reconstructed 3D scene geometry as input which is not applicable in robotic applications where the input data is streaming RGB-D videos rather than a complete 3D scene reconstructed from pre- collected RGB-D videos. To deal with online 3D scene per- ception tasks where data collection and perception should be performed simultaneously the model should be able to process 3D scenes frame by frame and make use of the temporal information. To this end we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability. Specifically we propose a queued memory mechanism to cache the supporting point cloud and image features. Then we devise aggregation modules which directly perform on the memory and pass temporal information to current frame. We further propose 3D-to-2D adapter to enhance image features with strong global context. Our adapters can be easily inserted into mainstream offline architectures of different tasks and significantly boost their performance on online tasks. Extensive experiments on ScanNet and SceneNN datasets demonstrate our approach achieves leading performance on three 3D scene perception tasks compared with state-of-the-art online methods by simply finetuning existing offline models without any model and task-specific designs.
SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining
Authors: Chull Hwan Song, Taebaek Hwang, Jooyoung Yoon, Shunghyun Choi, Yeong Hyeon Gu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However in fashion domain datasets often exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text leading to cases where some textual details are not visible in individual images. This mismatch particularly when non-co-occurring elements are masked undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem we propose Synchronized attentional Masking (SyncMask) which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model ensuring a precise alignment between the two modalities. Additionally we enhance grouped batch sampling with semi-hard negatives effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach outperforming existing methods in three downstream tasks.
A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech Recognition
Authors: Yusheng Dai, Hang Chen, Jun Du, Ruoyu Wang, Shihao Chen, Haotian Wang, Chin-Hui Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames performing even worse than single-modality models. While applying the common dropout techniques to the video modality enhances robustness to missing frames it simultaneously results in a performance loss when dealing with complete data input. In this study we delve into this contrasting phenomenon through the lens of modality bias and uncover that an excessive modality bias towards the audio modality induced by dropout constitutes the fundamental cause. Next we present the Modality Bias Hypothesis (MBH) to systematically describe the relationship between the modality bias and the robustness against missing modality in multimodal systems. Building on these findings we propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality maintaining performance and robustness simultaneously. Finally to address an entirely missing modality we adopt adapters to dynamically switch decision strategies. The effectiveness of our proposed approach is evaluated through comprehensive experiments on the MISP2021 and MISP2022 datasets. Our code is available at https://github.com/dalision/ModalBiasAVSR.
A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling
Authors: Wentao Qu, Yuantian Shao, Lingwu Meng, Xiaoshui Huang, Liang Xiao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Point cloud upsampling (PCU) enriches the representation of raw point clouds significantly improving the performance in downstream tasks such as classification and reconstruction. Most of the existing point cloud upsampling methods focus on sparse point cloud feature extraction and upsampling module design. In a different way we dive deeper into directly modelling the gradient of data distribution from dense point clouds. In this paper we proposed a conditional denoising diffusion probabilistic model (DDPM) for point cloud upsampling called PUDM. Specifically PUDM treats the sparse point cloud as a condition and iteratively learns the transformation relationship between the dense point cloud and the noise. Simultaneously PUDM aligns with a dual mapping paradigm to further improve the discernment of point features. In this context PUDM enables learning complex geometry details in the ground truth through the dominant features while avoiding an additional upsampling module design. Furthermore to generate high-quality arbitrary-scale point clouds during inference PUDM exploits the prior knowledge of the scale between sparse point clouds and dense point clouds during training by parameterizing a rate factor. Moreover PUDM exhibits strong noise robustness in experimental results. In the quantitative and qualitative evaluations on PU1K and PUGAN PUDM significantly outperformed existing methods in terms of Chamfer Distance (CD) and Hausdorff Distance (HD) achieving state of the art (SOTA) performance.
VideoRF: Rendering Dynamic Radiance Fields as 2D Feature Video Streams
Authors: Liao Wang, Kaixin Yao, Chengcheng Guo, Zhirui Zhang, Qiang Hu, Jingyi Yu, Lan Xu, Minye Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However rendering dynamic long-duration radiance fields on ubiquitous devices remains challenging due to data storage and computational constraints. In this paper we introduce VideoRF the first approach to enable real-time streaming and rendering of dynamic human-centric radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy we show that the feature image stream can be efficiently compressed by 2D video codecs which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand based on the feature image stream we propose a novel rendering pipeline for VideoRF which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes offering a seamless and immersive free-viewpoint experience across a range of devices from desktops to mobile phones. Our project page is available at https://aoliao12138.github.io/VideoRF/.
DPHMs: Diffusion Parametric Head Models for Depth-based Tracking
Authors: Jiapeng Tang, Angela Dai, Yinyu Nie, Lev Markhasin, Justus Thies, Matthias Nießner
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce Diffusion Parametric Head Models (DPHMs) a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences. While recent volumetric head models such as NPHMs can now excel in representing high-fidelity head geometries tracking and reconstructing heads from real-world single-view depth sequences remains very challenging as the fitting to partial and noisy observations is underconstrained. To tackle these challenges we propose a latent diffusion-based prior to regularize volumetric head reconstruction and tracking. This prior-based regularizer effectively constrains the identity and expression codes to lie on the underlying latent manifold which represents plausible head shapes. To evaluate the effectiveness of the diffusion-based prior we collect a dataset of monocular Kinect sequences consisting of various complex facial expression motions and rapid transitions. We compare our method to state-of-the-art tracking methods and demonstrate improved head identity reconstruction as well as robust expression tracking.
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception
Authors: Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou, Yingjie Cai, Lanqing Hong, Zhenguo Li, Lihui Jiang, Dit-Yan Yeung, Qiang Xu, Kai Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Current perceptive models heavily depend on resource-intensive datasets prompting the need for innovative solutions. Leveraging recent advances in diffusion models synthetic data by constructing image inputs from various annotations proves beneficial for downstream tasks. While prior methods have separately addressed generative and perceptive models DetDiffusion for the first time harmonizes both tackling the challenges in generating effective data for perceptive models. To enhance image generation with perceptive models we introduce perception-aware loss (P.A. loss) through segmentation improving both quality and controllability. To boost the performance of specific perceptive models our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation. Experimental results from the object detection task highlight DetDiffusion's superior performance establishing a new state-of-the-art in layout-guided generation. Furthermore image syntheses from DetDiffusion can effectively augment training data significantly enhancing downstream detection performance.
GAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection
Authors: Xiaotian Li, Baojie Fan, Jiandong Tian, Huijie Fan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent years have witnessed the remarkable progress of 3D multi-modality object detection methods based on the Bird's-Eye-View (BEV) perspective. However most of them overlook the complementary interaction and guidance between LiDAR and camera. In this work we propose a novel multi-modality 3D objection detection method named GAFusion with LiDAR-guided global interaction and adaptive fusion. Specifically we introduce sparse depth guidance (SDG) and LiDAR occupancy guidance (LOG) to generate 3D features with sufficient depth information. In the following LiDAR-guided adaptive fusion transformer (LGAFT) is developed to adaptively enhance the interaction of different modal BEV features from a global perspective. Meanwhile additional downsampling with sparse height compression and multi-scale dual-path transformer (MSDPT) are designed to enlarge the receptive fields of different modal features. Finally a temporal fusion module is introduced to aggregate features from previous frames. GAFusion achieves state-of-the-art 3D object detection results with 73.6% mAP and 74.9% NDS on the nuScenes test set.
Perception-Oriented Video Frame Interpolation via Asymmetric Blending
Authors: Guangyang Wu, Xin Tao, Changlin Li, Wenyi Wang, Xiaohong Liu, Qingqing Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Previous methods for Video Frame Interpolation (VFI) have encountered challenges notably the manifestation of blur and ghosting effects. These issues can be traced back to two pivotal factors: unavoidable motion errors and misalignment in supervision. In practice motion estimates often prove to be error-prone resulting in misaligned features. Furthermore the reconstruction loss tends to bring blurry results particularly in misaligned regions. To mitigate these challenges we propose a new paradigm called PerVFI (Perception-oriented Video Frame Interpolation). Our approach incorporates an Asymmetric Synergistic Blending module (ASB) that utilizes features from both sides to synergistically blend intermediate features. One reference frame emphasizes primary content while the other contributes complementary information. To impose a stringent constraint on the blending process we introduce a self-learned sparse quasi-binary mask which effectively mitigates ghosting and blur artifacts in the output. Additionally we employ a normalizing flow-based generator and utilize the negative log-likelihood loss to learn the conditional distribution of the output which further facilitates the generation of clear and fine details. Experimental results validate the superiority of PerVFI demonstrating significant improvements in perceptual quality compared to existing methods. Codes are available at https://github.com/mulns/PerVFI
Countering Personalized Text-to-Image Generation with Influence Watermarks
Authors: Hanwen Liu, Zhicheng Sun, Yadong Mu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: State-of-the-art personalized text-to-image generation systems are usually trained on a few reference images to learn novel visual representations. However this is likely to incur infringement of copyright for the reference image owners when these images are personal and publicly available. Recent progress has been made in protecting these images from unauthorized use by adding protective noises. Yet current protection methods work under the assumption that these protected images are not changed which is in contradiction to the fact that most public platforms intend to modify user-uploaded content e.g. image compression. This paper introduces a robust watermarking method namely InMark to protect images from unauthorized learning. Inspired by influence functions the proposed method forges protective watermarks on more important pixels for these reference images from both heuristic and statistical perspectives. In this way the personal semantics of these images are under protection even if these images are modified to some extent. Extensive experiments demonstrate that the proposed InMark outperforms previous state-of-the-art methods in both protective performance and robustness.
DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling
Authors: Miguel Fainstein, Viviana Siless, Emmanuel Iarussi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In recent years there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. However UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients generally resulting in fragmented and discontinuous surfaces. In this paper we propose to learn a hyperbolic scaling of the unsigned distance field which defines a new Eikonal problem with distinct boundary conditions. This allows our formulation to integrate seamlessly with state-of-the-art continuously differentiable implicit neural representation networks largely applied in the literature to represent signed distance fields. Our approach not only addresses the challenge of open surface representation but also demonstrates significant improvement in reconstruction quality and training performance. Moreover the unlocked field's differentiability allows the accurate computation of essential topological properties such as normal directions and curvatures pervasive in downstream tasks such as rendering. Through extensive experiments we validate our approach across various data sets and against competitive baselines. The results demonstrate enhanced accuracy and up to an order of magnitude increase in speed compared to previous methods.
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
Authors: Xiaofan Li, Zhizhong Zhang, Xin Tan, Chengwei Chen, Yanyun Qu, Yuan Xie, Lizhuang Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The vision-language model has brought great improvement to few-shot industrial anomaly detection which usually needs to design of hundreds of prompts through prompt engineering. For automated scenarios we first use conventional prompt learning with many-class paradigm as the baseline to automatically learn prompts but found that it can not work well in one-class anomaly detection. To address the above problem this paper proposes a one-class prompt learning method for few-shot anomaly detection termed PromptAD. First we propose semantic concatenation which can transpose normal prompts into anomaly prompts by concatenating normal prompts with anomaly suffixes thus constructing a large number of negative samples used to guide prompt learning in one-class setting. Furthermore to mitigate the training challenge caused by the absence of anomaly images we introduce the concept of explicit anomaly margin which is used to explicitly control the margin between normal prompt features and anomaly prompt features through a hyper-parameter. For image-level/pixel-level anomaly detection PromptAD achieves first place in 11/12 few-shot settings on MVTec and VisA.
Improving Graph Contrastive Learning via Adaptive Positive Sampling
Authors: Jiaming Zhuo, Feiyang Qin, Can Cui, Kun Fu, Bingxin Niu, Mengzhu Wang, Yuanfang Guo, Chuan Wang, Zhen Wang, Xiaochun Cao, Liang Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Graph Contrastive Learning (GCL) a Self-Supervised Learning (SSL) architecture tailored for graphs has shown notable potential for mitigating label scarcity. Its core idea is to amplify feature similarities between the positive sample pairs and reduce them between the negative sample pairs. Unfortunately most existing GCLs consistently present suboptimal performances on both homophilic and heterophilic graphs. This is primarily attributed to two limitations of positive sampling that is incomplete local sampling and blind sampling. To address these limitations this paper introduces a novel GCL framework with an adaptive positive sampling module named grapH contrastivE Adaptive posiTive Samples (HEATS). Motivated by the observation that the affinity matrix corresponding to optimal positive sample sets has a block-diagonal structure with equal weights within each block a self-expressive learning objective incorporating the block and idempotent constraint is presented. This learning objective and the contrastive learning objective are iteratively optimized to improve the adaptability and robustness of HEATS. Extensive experiments on graphs and images validate the effectiveness and generality of HEATS.
UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing
Authors: Xiaoyang Wang, Hongping Gan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep unfolding networks (DUNs) renowned for their interpretability and superior performance have invigorated the realm of compressive sensing (CS). Nonetheless existing DUNs frequently suffer from issues related to insufficient feature extraction and feature attrition during the iterative steps. In this paper we propose Unrolling Fixed-point Continuous Network (UFC-Net) a novel deep CS framework motivated by the traditional fixed-point continuous optimization algorithm. Specifically we introduce Convolution-guided Attention Module (CAM) to serve as a critical constituent within the reconstruction phase encompassing tailored components such as Multi-head Attention Residual Block (MARB) Auxiliary Iterative Reconstruction Block (AIRB) etc. MARB effectively integrates multi-head attention mechanisms with convolution to reinforce feature extraction transcending the confinement of localized attributes and facilitating the apprehension of long-range correlations. Meanwhile AIRB introduces auxiliary variables significantly bolstering the preservation of features within each iterative stage. Extensive experiments demonstrate that our proposed UFC-Net achieves remarkable performance both on image CS and CS-magnetic resonance imaging (CS-MRI) in contrast to state-of-the-art methods.
ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation
Authors: Suraj Patni, Aradhye Agarwal, Chetan Arora
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In the absence of parallax cues a learning-based single image depth estimation (SIDE) model relies heavily on shading and contextual cues in the image. While this simplicity is attractive it is necessary to train such models on large and varied datasets which are difficult to capture. It has been shown that using embeddings from pre-trained foundational models such as CLIP improves zero shot transfer in several applications. Taking inspiration from this in our paper we explore the use of global image priors generated from a pre-trained ViT model to provide more detailed contextual information. We argue that the embedding vector from a ViT model pre-trained on a large dataset captures greater relevant information for SIDE than the usual route of generating pseudo image captions followed by CLIP based text embeddings. Based on this idea we propose a new SIDE model using a diffusion backbone which is conditioned on ViT embeddings. Our proposed design establishes a new state-of-the-art (SOTA) for SIDE on NYUv2 dataset achieving Abs Rel error of 0.059(14% improvement) compared to 0.069 by the current SOTA (VPD). And on KITTI dataset achieving Sq Rel error of 0.139 (2% improvement) compared to 0.142 by the current SOTA (GEDepth). For zero-shot transfer with a model trained on NYUv2 we report mean relative improvement of (20% 23% 81% 25%) over NeWCRFs on (Sun-RGBD iBims1 DIODE HyperSim) datasets compared to (16% 18% 45% 9%) by ZoeDepth. The project page is available at https://ecodepth-iitd.github.io
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
Authors: Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, Xuanmao Li, Xingpeng Sun, Rohan Ashok, Aniruddha Mukherjee, Hao Kang, Xiangrui Kong, Gang Hua, Tianyi Zhang, Bedrich Benes, Aniket Bera
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We have witnessed significant progress in deep learning-based 3D vision ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However existing scene-level datasets for deep learning-based 3D vision limited to either synthetic environments or a narrow selection of real-world scenes are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap we present DL3DV-10K a large-scale scene dataset featuring 51.2 million frames from 10510 videos captured from 65 types of point-of-interest (POI) locations covering both bounded and unbounded scenes with different levels of reflection transparency and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K which revealed valuable insights for future research in NVS. In addition we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset benchmark results and models will be publicly accessible.
2S-UDF: A Novel Two-stage UDF Learning Method for Robust Non-watertight Model Reconstruction from Multi-view Images
Authors: Junkai Deng, Fei Hou, Xuhui Chen, Wencheng Wang, Ying He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently building on the foundation of neural radiance field various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from multi-view images. Yet a central challenge in UDF-based volume rendering is formulating a proper way to convert unsigned distance values into volume density ensuring that the resulting weight function remains unbiased and sensitive to occlusions. Falling short on these requirements often results in incorrect topology or large reconstruction errors in resulting models. This paper addresses this challenge by presenting a novel two-stage algorithm 2S-UDF for learning a high-quality UDF from multi-view images. Initially the method applies an easily trainable density function that while slightly biased and transparent aids in coarse reconstruction. The subsequent stage then refines the geometry and appearance of the object to achieve a high-quality reconstruction by directly adjusting the weight function used in volume rendering to ensure that it is unbiased and occlusion-aware. Decoupling density and weight in two stages makes our training stable and robust distinguishing our technique from existing UDF learning approaches. Evaluations on the DeepFashion3D DTU and BlendedMVS datasets validate the robustness and effectiveness of our proposed approach. In both quantitative metrics and visual quality the results indicate our superior performance over other UDF learning techniques in reconstructing 3D non-watertight models from multi-view images. Our code is available at https://bitbucket.org/jkdeng/2sudf/.
DETRs Beat YOLOs on Real-time Object Detection
Authors: Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, Jie Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The YOLO series has become the most popular framework for real-time object detection due to its reasonable trade-off between speed and accuracy. However we observe that the speed and accuracy of YOLOs are negatively affected by the NMS. Recently end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS. Nevertheless the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS. In this paper we propose the Real-Time DEtection TRansformer (RT-DETR) the first real-time end-to-end object detector to our best knowledge that addresses the above dilemma. We build RT-DETR in two steps drawing on the advanced DETR: first we focus on maintaining accuracy while improving speed followed by maintaining speed while improving accuracy. Specifically we design an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed. Then we propose the uncertainty-minimal query selection to provide high-quality initial queries to the decoder thereby improving accuracy. In addition RT-DETR supports flexible speed tuning by adjusting the number of decoder layers to adapt to various scenarios without retraining. Our RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU outperforming previously advanced YOLOs in both speed and accuracy. We also develop scaled RT-DETRs that outperform the lighter YOLO detectors (S and M models). Furthermore RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS. After pre-training with Objects365 RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP. The project page: https://zhao-yian.github.io/RTDETR.
UniVS: Unified and Universal Video Segmentation with Prompts as Queries
Authors: Minghan Li, Shuai Li, Xindong Zhang, Lei Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Despite the recent advances in unified image segmentation (IS) developing a unified video segmentation (VS) model remains a challenge. This is mainly because generic category-specified VS tasks need to detect all objects and track them across consecutive frames while prompt-guided VS tasks require re-identifying the target with visual/text prompts throughout the entire video making it hard to handle the different tasks with the same architecture. We make an attempt to address these issues and present a novel unified VS architecture namely UniVS by using prompts as queries. UniVS averages the prompt features of the target from previous frames as its initial query to explicitly decode masks and introduces a target-wise prompt cross-attention layer in the mask decoder to integrate prompt features in the memory pool. By taking the predicted masks of entities from previous frames as their visual prompts UniVS converts different VS tasks into prompt-guided target segmentation eliminating the heuristic inter-frame matching process. Our framework not only unifies the different VS tasks but also naturally achieves universal training and testing ensuring robust performance across different scenarios. UniVS shows a commendable balance between performance and universality on 10 challenging VS benchmarks covering video instance semantic panoptic object and referring segmentation tasks. Code can be found at https://github.com/MinghanLi/UniVS.
Bilateral Adaptation for Human-Object Interaction Detection with Occlusion-Robustness
Authors: Guangzhi Wang, Yangyang Guo, Ziwei Xu, Mohan Kankanhalli
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human-Object Interaction (HOI) Detection constitutes an important aspect of human-centric scene understanding which requires precise object detection and interaction recognition. Despite increasing advancement in detection recognizing subtle and intricate interactions remains challenging. Recent methods have endeavored to leverage the rich semantic representation from pre-trained CLIP yet fail to efficiently capture finer-grained spatial features that are highly informative for interaction discrimination. In this work instead of solely using representations from CLIP we fill the gap by proposing a spatial adapter that efficiently utilizes the multi-scale spatial information in the pre-trained detector. This leads to a bilateral adaptation that mutually produces complementary features. To further improve interaction recognition under occlusion which is common in crowded scenarios we propose an Occluded Part Extrapolation module that guides the model to recover the spatial details from manually occluded feature maps. Moreover we design a Conditional Contextual Mining module that further mines informative contextual clues from the spatial features via a tailored cross-attention mechanism. Extensive experiments on V-COCO and HICO-DET benchmarks demonstrate that our method significantly outperforms prior art on both standard and zero-shot settings resulting in new state-of-the-art performance. Additional ablation studies further validate the effectiveness of each component in our method.
An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing
Authors: Feiran Hu, Chenlin Zhang, Jiangliang Guo, Xiu-Shen Wei, Lin Zhao, Anqi Xu, Lingyan Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Unsupervised fine-grained image hashing aims to learn compact binary hash codes in unsupervised settings addressing challenges posed by large-scale datasets and dependence on supervision. In this paper we first identify a granularity gap between generic and fine-grained datasets for unsupervised hashing methods highlighting the inadequacy of conventional self-supervised learning for fine-grained visual objects. To bridge this gap we propose the Asymmetric Augmented Self-Supervised Learning (A^2-SSL) method comprising three modules. The asymmetric augmented SSL module employs suitable augmentation strategies for positive/negative views preventing fine-grained category confusion inherent in conventional SSL. Part-oriented dense contrastive learning utilizes the Fisher Vector framework to capture and model fine-grained object parts enhancing unsupervised representations through part-level dense contrastive learning. Self-consistent hash code learning introduces a reconstruction task aligned with the self-consistency principle guiding the model to emphasize comprehensive features particularly fine-grained patterns. Experimental results on five benchmark datasets demonstrate the superiority of A^2-SSL over existing methods affirming its efficacy in unsupervised fine-grained image hashing.