mtybilly/Instruct2DS huggingface dataset:
VCoder: Versatile Vision Encoders for Multimodal Large Language Models
Authors: Jianwei Yang, Humphrey Shi Jitesh Jain
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Humans possess the remarkable skill of Visual Perception the ability to see and understand the seen helping them make sense of the visual world and in turn reason. Multimodal Large Language Models (MLLM) have recently achieved impressive performance on vision-language tasks ranging from visual question-answering and image captioning to visual reasoning and image generation. However when prompted to identify or count (perceive) the entities in a given image existing MLLM systems fail. Working towards developing an accurate MLLM system for perception and reasoning we propose using Versatile vision enCoders (VCoder) as perception eyes for Multimodal LLMs. We feed the VCoder with perception modalities such as segmentation or depth maps improving the MLLM's perception abilities. Secondly we leverage the images from COCO and outputs from off-the-shelf vision perception models to create our COCO Segmentation Text (COST) dataset for training and evaluating MLLMs on the object perception task. Thirdly we introduce metrics to assess the object perception abilities in MLLMs on our COST dataset. Lastly we provide extensive experimental evidence proving the VCoder's improved object-level perception skills over existing Multimodal LLMs including GPT-4V. We open-source our dataset code and models to promote research.
Event-based Visible and Infrared Fusion via Multi-task Collaboration
Authors: Lin Zhu, Lizhi Wang, Wei Zhang, Ruiqin Xiong, Yonghong Tian Mengyue Geng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Visible and Infrared image Fusion (VIF) offers a comprehensive scene description by combining thermal infrared images with the rich textures from visible cameras. However conventional VIF systems may capture over/under exposure or blurry images in extreme lighting and high dynamic motion scenarios leading to degraded fusion results. To address these problems we propose a novel Event-based Visible and Infrared Fusion (EVIF) system that employs a visible event camera as an alternative to traditional frame-based cameras for the VIF task. With extremely low latency and high dynamic range event cameras can effectively address blurriness and are robust against diverse luminous ranges. To produce high-quality fused images we develop a multi-task collaborative framework that simultaneously performs event-based visible texture reconstruction event-guided infrared image deblurring and visible-infrared fusion. Rather than independently learning these tasks our framework capitalizes on their synergy leveraging cross-task event enhancement for efficient deblurring and bi-level min-max mutual information optimization to achieve higher fusion quality. Experiments on both synthetic and real data show that EVIF achieves remarkable performance in dealing with extreme lighting conditions and high-dynamic scenes ensuring high-quality fused images across a broad range of practical scenarios.
Open-World Semantic Segmentation Including Class Similarity
Authors: Federico Magistri, Lucas Nunes, Jens Behley, Cyrill Stachniss Matteo Sodano
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Interpreting camera data is key for autonomously acting systems such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand their surroundings and need the ability to deal with novel situations. This paper tackles open-world semantic segmentation i.e. the variant of interpreting image data in which objects occur that have not been seen during training. We propose a novel approach that performs accurate closed-world semantic segmentation and at the same time can identify new categories without requiring any additional training data. Our approach additionally provides a similarity measure for every newly discovered class in an image to a known category which can be useful information in downstream tasks such as planning or mapping. Through extensive experiments we show that our model achieves state-of-the-art results on classes known from training data as well as for anomaly segmentation and can distinguish between different unknown classes.
RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding
Authors: Runyu Ding, Weipeng Deng, Zhe Wang, Xiaojuan Qi Jihan Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a lightweight and scalable Regional Point-Language Contrastive learning framework namely RegionPLC for open-world 3D scene understanding aiming to identify and recognize open-set objects and categories. Specifically based on our empirical studies we introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models yielding high-quality dense region-level language descriptions without human 3D annotations. Subsequently we devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning from dense regional language supervision. We carry out extensive experiments on ScanNet ScanNet200 and nuScenes datasets and our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2% and 9.1% for semantic and instance segmentation respectively while maintaining greater scalability and lower resource demands. Furthermore our method has the flexibility to be effortlessly integrated with language models to enable open-ended grounded 3D reasoning without extra task-specific training. Code will be released.
Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning
Authors: Wugen Zhou, Yingdian Cao, Hongbin Zha Youqi Pan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Visual-inertial odometry (VIO) has demonstrated remarkable success due to its low-cost and complementary sensors. However existing VIO methods lack the generalization ability to adjust to different environments and sensor attributes. In this paper we propose Adaptive VIO a new monocular visual-inertial odometry that combines online continual learning with traditional nonlinear optimization. Adaptive VIO comprises two networks to predict visual correspondence and IMU bias. Unlike end-to-end approaches that use networks to fuse the features from two modalities (camera and IMU) and predict poses directly we combine neural networks with visual-inertial bundle adjustment in our VIO system. The optimized estimates will be fed back to the visual and IMU bias networks refining the networks in a self-supervised manner. Such a learning-optimization-combined framework and feedback mechanism enable the system to perform online continual learning. Experiments demonstrate that our Adaptive VIO manifests adaptive capability on EuRoC and TUM-VI datasets. The overall performance exceeds the currently known learning-based VIO methods and is comparable to the state-of-the-art optimization-based methods.
Towards Memorization-Free Diffusion Models
Authors: Daochang Liu, Chang Xu Chen Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Pretrained diffusion models and their outputs are widely accessible due to their exceptional capacity for synthesizing high-quality images and their open-source nature. The users however may face litigation risks owing to the models' tendency to memorize and regurgitate training data during inference. To address this we introduce Anti-Memorization Guidance (AMG) a novel framework employing three targeted guidance strategies for the main causes of memorization: image and caption duplication and highly specific user prompts. Consequently AMG ensures memorization-free outputs while maintaining high image quality and text alignment leveraging the synergy of its guidance methods each indispensable in its own right. AMG also features an innovative automatic detection system for potential memorization during each step of inference process allows selective application of guidance strategies minimally interfering with the original sampling process to preserve output utility. We applied AMG to pretrained Denoising Diffusion Probabilistic Models (DDPM) and Stable Diffusion across various generation tasks. The results demonstrate that AMG is the first approach to successfully eradicates all instances of memorization with no or marginal impacts on image quality and text-alignment as evidenced by FID and CLIP scores.
Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching
Authors: Zeyuan Yin, Muxin Zhou, Xindong Zhang, Zhiqiang Shen Shitong Shao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The lightweight "local-match-global" matching introduced by SRe2L successfully creates a distilled dataset with comprehensive information on the full 224x224 ImageNet-1k. However this one-sided approach is limited to a particular backbone layer and statistics which limits the improvement of the generalization of a distilled dataset. We suggest that sufficient and various "local-match-global" matching are more precise and effective than a single one and has the ability to create a distilled dataset with richer information and better generalization. We call this perspective "generalized matching" and propose Generalized Various Backbone and Statistical Matching (G-VBSM) in this work which aims to create a synthetic dataset with densities ensuring consistency with the complete dataset across various backbones layers and statistics. As experimentally demonstrated G-VBSM is the first algorithm to obtain strong performance across both small-scale and large-scale datasets. Specifically G-VBSM achieves a performance of 38.7% on CIFAR-100 with 128-width ConvNet 47.6% on Tiny-ImageNet with ResNet18 and 31.4% on the full 224x224 ImageNet-1k with ResNet18 under images per class (IPC) 10 50 and 10 respectively. These results surpass all SOTA methods by margins of 3.9% 6.5% and 10.1% respectively.
Three Pillars Improving Vision Foundation Model Distillation for Lidar
Authors: Spyros Gidaris, Alexandre Boulch, Oriane Siméoni, Corentin Sautier, Patrick Pérez, Andrei Bursuc, Renaud Marlet Gilles Puy
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Self-supervised image backbones can be used to address complex 2D tasks (e.g. semantic segmentation object discovery) very efficiently and with little or no downstream supervision. Ideally 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results obtained thanks to distillation methods that keep improving. Yet we still notice a large performance gap when measuring by linear probing the quality of distilled vs fully supervised features. In this work instead of focusing only on the distillation method we study the effect of three pillars for distillation: the 3D backbone the pretrained 2D backbone and the pretraining 2D+3D dataset. In particular thanks to our scalable distillation method named ScaLR we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features and to improve the robustness of the pretrained backbones to domain gaps and perturbations.
On Train-Test Class Overlap and Detection for Image Retrieval
Authors: Jooyoung Yoon, Taebaek Hwang, Shunghyun Choi, Yeong Hyeon Gu, Yannis Avrithis Chull Hwan Song
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: How important is it for training and evaluation sets to not have class overlap in image retrieval? We revisit Google Landmarks v2 clean the most popular training set by identifying and removing class overlap with Revisited Oxford and Paris the most popular training set. By comparing the original and the new RGLDv2-clean on a benchmark of reproduced state-of-the-art methods our findings are striking. Not only is there a dramatic drop in performance but it is inconsistent across methods changing the ranking. What does it take to focus on objects or interest and ignore background clutter when indexing? Do we need to analyze the evaluation set? Do we need to train an object detector and the representation separately? Do we need location supervision? We introduce Single-stage Detect-to-Retrieve (CiDeR) an end-to-end single-stage pipeline to detect objects of interest and extract a global image representation. We outperform previous state-of-the-art on both existing training sets and the new RGLDv2-clean.
AttriHuman-3D: Editable 3D Human Avatar Generation with Attribute Decomposition and Indexing
Authors: Tianyi Chen, Xiaosheng He, Zhongang Cai, Lei Yang, Si Wu, Guosheng Lin Fan Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Editable 3D-aware generation which supports user-interacted editing has witnessed rapid development recently. However existing editable 3D GANs either fail to achieve high-accuracy local editing or suffer from huge computational costs. We propose AttriHuman-3D an editable 3D human generation model which address the aforementioned problems with attribute decomposition and indexing. The core idea of the proposed model is to generate all attributes (e.g. human body hair clothes and so on) in an overall attribute space with six feature planes which are then decomposed and manipulated with different attribute indexes. To precisely extract features of different attributes from the generated feature planes we propose a novel attribute indexing method as well as an orthogonal projection regularization to enhance the disentanglement. We also introduce a hyper-latent training strategy and an attribute-specific sampling strategy to avoid style entanglement and misleading punishment from the discriminator. Our method allows users to interactively edit selected attributes in the generated 3D human avatars while keeping others fixed. Both qualitative and quantitative experiments demonstrate that our model provides a strong disentanglement between different attributes allows fine-grained image editing and generates high-quality 3D human avatars.
IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame Interpolation
Authors: Kui Jiang, Zhihang Zhong, Zheng Wang, Yinqiang Zheng Mengshun Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Advanced video frame interpolation (VFI) algorithms approximate intermediate motions between two input frames to synthesize intermediate frame. However they struggle to handle complex scenarios with curvilinear motions since they overlook the latent acceleration information between the input frames. Moreover the supervision of predicted motions is tricky because ground-truth motions are not available. To this end we propose a novel framework for implicit quadratic video frame interpolation (IQ-VFI) which explores latent acceleration information and accurate intermediate motions via knowledge distillation. Specifically the proposed IQ-VFI consists of an implicit acceleration estimation network (IANet) and a VFI backbone the former fully leverages spatio-temporal information to explore latent acceleration priors between two input frames which is then used to progressively modulate linear motions from the latter into quadratic motions in coarse-to-fine manner. Furthermore to encourage both components to distill more acceleration and motion cues oriented towards VFI we propose a knowledge distillation strategy in which implicit acceleration distillation loss and implicit motion distillation loss are employed to adaptively guide latent acceleration priors and intermediate motions learning respectively. Extensive experiments show that our proposed IQ-VFI can achieve state-of-the-art performances on various benchmark datasets.
KeyPoint Relative Position Encoding for Face Recognition
Authors: Yiyang Su, Feng Liu, Anil Jain, Xiaoming Liu Minchul Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we address the challenge of making ViT models more robust to unseen affine transformations. Such robustness becomes useful in various recognition tasks such as face recognition when image alignment failures occur. We propose a novel method called KP-RPE which leverages key points (e.g.facial landmarks) to make ViT more resilient to scale translation and pose variations. We begin with the observation that Relative Position Encoding (RPE) is a good way to bring affine transform generalization to ViTs. RPE however can only inject the model with prior knowledge that nearby pixels are more important than far pixels. Keypoint RPE (KP-RPE) is an extension of this principle where the significance of pixels is not solely dictated by their proximity but also by their relative positions to specific keypoints within the image. By anchoring the significance of pixels around keypoints the model can more effectively retain spatial relationships even when those relationships are disrupted by affine transformations. We show the merit of KP-RPE in face and gait recognition. The experimental results demonstrate the effectiveness in improving face recognition performance from low-quality images particularly where alignment is prone to failure. Code and pre-trained models are available.
Hyper-MD: Mesh Denoising with Customized Parameters Aware of Noise Intensity and Geometric Characteristics
Authors: Hongliang Wei, Xiaopeng Fan, Debin Zhao Xingtao Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Mesh denoising (MD) is a critical task in geometry processing as meshes from scanning or AIGC techniques are susceptible to noise contamination. The challenge of MD lies in the diverse nature of mesh facets in terms of geometric characteristics and noise distributions. Despite recent advancements in deep learning-based MD methods existing MD networks typically neglect the consideration of geometric characteristics and noise distributions. In this paper we propose Hyper-MD a hyper-network-based approach that addresses this limitation by dynamically customizing denoising parameters for each facet based on its noise intensity and geometric characteristics. Specifically Hyper-MD is composed of a hyper-network and an MD network. For each noisy facet the hyper-network takes two angles as input to customize parameters for the MD network. These two angles are specially defined to reveal the noise intensity and geometric characteristics of the current facet respectively. The MD network receives a facet patch as input and outputs the denoised normal using the customized parameters. Experimental results on synthetic and real-scanned meshes demonstrate that Hyper-MD outperforms state-of-the-art mesh denoising methods.
Learning Object State Changes in Videos: An Open-World Perspective
Authors: Kumar Ashutosh, Kristen Grauman Zihui Xue
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Object State Changes (OSCs) are pivotal for video understanding. While humans can effortlessly generalize OSC understanding from familiar to unknown objects current approaches are confined to a closed vocabulary. Addressing this gap we introduce a novel open-world formulation for the video OSC problem. The goal is to temporally localize the three stages of an OSC---the object's initial state its transitioning state and its end state---whether or not the object has been observed during training. Towards this end we develop VidOSC a holistic learning approach that: (1) leverages text and vision-language models for supervisory signals to obviate manually labeling OSC training data and (2) abstracts fine-grained shared state representations from objects to enhance generalization. Furthermore we present HowToChange the first open-world benchmark for video OSC localization which offers an order of magnitude increase in the label space and annotation volume compared to the best existing benchmark. Experimental results demonstrate the efficacy of our approach in both traditional closed-world and open-world scenarios.
Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion
Authors: Yujia Chen, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, Wen-Sheng Chu Litu Rout
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Sampling from the posterior distribution in latent diffusion models for inverse problems is computationally challenging. Existing methods often rely on Tweedie's first-order moments that tend to induce biased results. Second-order approximations are computationally prohibitive making standard reverse diffusion processes intractable for posterior sampling. This paper presents Second-order Tweedie sampler from Surrogate Loss (STSL) a novel sampler offering efficiency comparable to first-order Tweedie while enabling tractable reverse processes using second-order approximation. Theoretical results reveal that our approach utilizing for the trace of the Hessian with only O(1) compute establishes a lower bound through a surrogate loss and enables a tractable reverse process. We show STSL outperforms SoTA solvers PSLD and P2L by reducing neural function evaluations by 4X and 8X respectively while enhancing sampling quality on FFHQ ImageNet and COCO benchmarks. Moreover STSL extends to text guided image editing and mitigates residual distortions in corrupted images. To our best knowledge this is the first work to offer an efficient second order approximation for solving inverse problems using latent diffusion and editing real world images with corruptions.
Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis
Authors: Xintao Wang, Yixiao Ge, Ying Shan, Mike Zheng Shou Yuchao Gu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Vector-Quantized (VQ-based) generative models usually consist of two basic components i.e. VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers. In this paper we find that improving the reconstruction fidelity of VQ tokenizers does not necessarily improve the generation. Instead learning to compress semantic features within VQ tokenizers significantly improves generative transformers' ability to capture textures and structures. We thus highlight two competing objectives of VQ tokenizers for image synthesis: semantic compression and details preservation. Different from previous work that prioritizes better details preservation we propose Semantic-Quantized GAN (SeQ-GAN) with two learning phases to balance the two objectives. In the first phase we propose a semantic-enhanced perceptual loss for better semantic compression. In the second phase we fix the encoder and codebook but finetune the decoder to achieve better details preservation. Our proposed SeQ-GAN significantly improves VQ-based generative models for both unconditional and conditional image generation. Specifically SeQ-GAN achieves a Frechet Inception Distance (FID) of 6.25 and Inception Score (IS) of 140.9 on 256x256 ImageNet generation a remarkable improvement over VIT-VQGAN which obtains 11.2 FID and 97.2 IS.
ShapeWalk: Compositional Shape Editing Through Language-Guided Chains
Authors: Mohamed Elhoseiny Habib Slim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Editing 3D shapes through natural language instructions is a challenging task that requires the comprehension of both language semantics and fine-grained geometric details. To bridge this gap we introduce ShapeWalk a carefully designed synthetic dataset designed to advance the field of language-guided shape editing. The dataset consists of 158K unique shapes connected through 26K edit chains with an average length of 14 chained shapes. Each consecutive pair of shapes is associated with precise language instructions describing the applied edits. We synthesize edit chains by reconstructing and interpolating shapes sampled from a realistic CAD-designed 3D dataset in the parameter space of the GeoCode shape program. We leverage rule-based methods and language models to generate accurate and realistic natural language prompts corresponding to each edit. To illustrate the practicality of our contribution we train neural editor modules in the latent space of shape autoencoders and demonstrate the ability of our dataset to enable a variety of language-guided shape edits. Finally we introduce multi-step editing metrics to benchmark the capacity of our models to perform recursive shape edits. We hope that our work will enable further study of compositional language-guided shape editing and finds application in 3D CAD design and interactive modeling.
MESA: Matching Everything by Segmenting Anything
Authors: Xu Zhao Yesheng Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Feature matching is a crucial task in the field of computer vision which involves finding correspondences between images. Previous studies achieve remarkable performance using learning-based feature comparison. However the pervasive presence of matching redundancy between images gives rise to unnecessary and error-prone computations in these methods imposing limitations on their accuracy. To address this issue we propose MESA a novel approach to establish precise area (or region) matches for efficient matching redundancy reduction. MESA first leverages the advanced image understanding capability of SAM a state-of-the-art foundation model for image segmentation to obtain image areas with implicit semantic. Then a multi-relational graph is proposed to model the spatial structure of these areas and construct their scale hierarchy. Based on graphical models derived from the graph the area matching is reformulated as an energy minimization task and effectively resolved. Extensive experiments demonstrate that MESA yields substantial precision improvement for multiple point matchers in indoor and outdoor downstream tasks e.g. +13.61% for DKM in indoor pose estimation.
Learning Degradation-Independent Representations for Camera ISP Pipelines
Authors: Fangzhou Luo, Xiaolin Wu Yanhui Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Image signal processing (ISP) pipeline plays a fundamental role in digital cameras which converts raw Bayer sensor data to RGB images. However ISP-generated images usually suffer from imperfections due to the compounded degradations that stem from sensor noises demosaicing noises compression artifacts and possibly adverse effects of erroneous ISP hyperparameter settings such as ISO and gamma values. In a general sense these ISP imperfections can be considered as degradations. The highly complex mechanisms of ISP degradations some of which are even unknown pose great challenges to the generalization capability of deep neural networks (DNN) for image restoration and to their adaptability to downstream tasks. To tackle the issues we propose a novel DNN approach to learn degradation-independent representations (DiR) through the refinement of a self-supervised learned baseline representation. The proposed DiR learning technique has remarkable domain generalization capability and consequently it outperforms state-of-the-art methods across various downstream tasks including blind image restoration object detection and instance segmentation as verified in our experiments.
SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation
Authors: Peter Schaldenbrand, Beverley-Claire Okogwu, Wenxuan Peng, Youngsik Yun, Andrew Hundt, Jihie Kim, Jean Oh Zhixuan Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Accurate representation in media is known to improve the well-being of the people who consume it. Generative image models trained on large web-crawled datasets such as LAION are known to produce images with harmful stereotypes and misrepresentations of cultures. We improve inclusive representation in generated images by (1) engaging with communities to collect a culturally representative dataset that we call the Cross-Cultural Understanding Benchmark (CCUB) and (2) proposing a novel Self-Contrastive Fine-Tuning (SCoFT pronounced /soft/) method that leverages the model's known biases to self-improve. SCoFT is designed to prevent overfitting on small datasets encode only high-level information from the data and shift the generated distribution away from misrepresentations encoded in a pretrained model. Our user study conducted on 51 participants from 5 different countries based on their self-selected national cultural affiliation shows that fine-tuning on CCUB consistently generates images with higher cultural relevance and fewer stereotypes when compared to the Stable Diffusion baseline which is further improved with our SCoFT technique.
Continuous Pose for Monocular Cameras in Neural Implicit Representation
Authors: Danda Pani Paudel, Ajad Chhatkuli, Luc Van Gool Qi Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we showcase the effectiveness of optimizing monocular camera poses as a continuous function of time. The camera poses are represented using an implicit neural function which maps the given time to the corresponding camera pose. The mapped camera poses are then used for the downstream tasks where joint camera pose optimization is also required. While doing so the network parameters - that implicitly represent camera poses - are optimized. We exploit the proposed method in four diverse experimental settings namely (1) NeRF from noisy poses; (2) NeRF from asynchronous Events; (3) Visual Simultaneous Localization and Mapping (vSLAM); and (4) vSLAM with IMUs. In all four settings the proposed method performs significantly better than the compared baselines and the state-of-the-art methods. Additionally using the assumption of continuous motion changes in pose may actually live in a manifold that has lower than 6 degrees of freedom (DOF) is also realized. We call this low DOF motion representation as the intrinsic motion and use the approach in vSLAM settings show ing impressive camera tracking performance.
OmniGlue: Generalizable Feature Matching with Foundation Model Guidance
Authors: Arjun Karpur, Bingyi Cao, Qixing Huang, André Araujo Hanwen Jiang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The image matching field has been witnessing a continuous emergence of novel learnable feature matching techniques with ever-improving performance on conventional benchmarks. However our investigation shows that despite these gains their potential for real-world applications is restricted by their limited generalization capabilities to novel image domains. In this paper we introduce OmniGlue the first learnable image matcher that is designed with generalization as a core principle. OmniGlue leverages broad knowledge from a vision foundation model to guide the feature matching process boosting generalization to domains not seen at training time. Additionally we propose a novel keypoint position-guided attention mechanism which disentangles spatial and appearance information leading to enhanced matching descriptors. We perform comprehensive experiments on a suite of 6 datasets with varied image domains including scene-level object-centric and aerial images. OmniGlue's novel components lead to relative gains on unseen domains of 20.9% with respect to a directly comparable reference model while also outperforming the recent LightGlue method by 9.5% relatively. Code and model can be found at https://hwjiang1510.github.io/OmniGlue.
D^4: Dataset Distillation via Disentangled Diffusion Model
Authors: Junjie Hou, Weizhi Gao, Yingjie Tian, Bowen Tang Duo Su
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures.With empirical observations we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this we introduce Dataset Distillation via Disentangled Diffusion Model (D^4M) an efficient framework for dataset distillation. Compared to architecture-dependent methods D^4M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments D^4M demonstrates superior performance and robust generalization surpassing the SOTA methods across most aspects.
OmniSDF: Scene Reconstruction using Omnidirectional Signed Distance Functions and Adaptive Binoctrees
Authors: Andreas Meuleman, Hyeonjoong Jang, James Tompkin, Min H. Kim Hakyeong Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present a method to reconstruct indoor and outdoor static scene geometry and appearance from an omnidirectional video moving in a small circular sweep. This setting is challenging because of the small baseline and large depth ranges making it difficult to find ray crossings. To better constrain the optimization we estimate geometry as a signed distance field within a spherical binoctree data structure and use a complementary efficient tree traversal strategy based on a breadth-first search for sampling. Unlike regular grids or trees the shape of this structure well-matches the camera setting creating a better memory-quality trade-off. From an initial depth estimate the binoctree is adaptively subdivided throughout the optimization; previous methods use a fixed depth that leaves the scene undersampled. In comparison with three neural optimization methods and two non-neural methods ours shows decreased geometry error on average especially in a detailed scene while significantly reducing the required number of voxels to represent such details.
Generating Content for HDR Deghosting from Frequency View
Authors: Qingsen Yan, Yuankai Qi, Yanning Zhang Tao Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recovering ghost-free High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images becomes challenging when the LDR images exhibit saturation and significant motion. Recent Diffusion Models (DMs) have been introduced in HDR imaging field demonstrating promising performance particularly in achieving visually perceptible results compared to previous DNN-based methods. However DMs require extensive iterations with large models to estimate entire images resulting in inefficiency that hinders their practical application. To address this challenge we propose the Low-Frequency aware Diffusion (LF-Diff) model for ghost-free HDR imaging. The key idea of LF-Diff is implementing the DMs in a highly compacted latent space and integrating it into a regression-based model to enhance the details of reconstructed images. Specifically as low-frequency information is closely related to human visual perception we propose to utilize DMs to create compact low-frequency priors for the reconstruction process. In addition to take full advantage of the above low-frequency priors the Dynamic HDR Reconstruction Network (DHRNet) is carried out in a regression-based manner to obtain final HDR images. Extensive experiments conducted on synthetic and real-world benchmark datasets demonstrate that our LF-Diff performs favorably against several state-of-the-art methods and is 10x faster than previous DM-based methods.
Iterated Learning Improves Compositionality in Large Vision-Language Models
Authors: Jieyu Zhang, Aniruddha Kembhavi, Ranjay Krishna Chenhao Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet despite the performance gains contributed by large vision and language pretraining recent investigations find that most--if not all--our state-of-the-art vision-language models struggle at compositionality. They are unable to distinguish between images of "a girl in white facing a man in black" and "a girl in black facing a man in white". Moreover prior work suggests that compositionality doesn't arise with scale: larger model sizes or training data don't help. This paper develops a new iterated training algorithm that incentivizes compositionality. We draw on decades of cognitive science research that identifies cultural transmission--the need to teach a new generation--as a necessary inductive prior that incentivizes humans to develop compositional languages. Specifically we reframe vision-language contrastive learning as the Lewis Signaling Game between a vision agent and a language agent and operationalize cultural transmission by iteratively resetting one of the agent's weights during training. After every iteration this training paradigm induces representations that become "easier to learn" a property of compositional languages: e.g. our model trained on CC3M and CC12M improves standard CLIP by 4.7% 4.0% respectfully in the SugarCrepe benchmark.
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
Authors: Shiao Wang, Chuanming Tang, Lin Zhu, Bo Jiang, Yonghong Tian, Jin Tang Xiao Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Tracking with bio-inspired event cameras has garnered increasing interest in recent years. Existing works either utilize aligned RGB and event data for accurate tracking or directly learn an event-based tracker. The former incurs higher inference costs while the latter may be susceptible to the impact of noisy events or sparse spatial resolution. In this paper we propose a novel hierarchical knowledge distillation framework that can fully utilize multi-modal / multi-view information during training to facilitate knowledge transfer enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals. Specifically a teacher Transformer-based multi-modal tracking framework is first trained by feeding the RGB frame and event stream simultaneously. Then we design a new hierarchical knowledge distillation strategy which includes pairwise similarity feature representation and response maps-based knowledge distillation to guide the learning of the student Transformer network. In particular since existing event-based tracking datasets are all low-resolution (346 * 260) we propose the first large-scale high-resolution (1280 * 720) dataset named EventVOT. It contains 1141 videos and covers a wide range of categories such as pedestrians vehicles UAVs ping pong etc. Extensive experiments on both low-resolution (FE240hz VisEvent COESOT) and our newly proposed high-resolution EventVOT dataset fully validated the effectiveness of our proposed method. The dataset evaluation toolkit and source code will be released.
LiDAR-Net: A Real-scanned 3D Point Cloud Dataset for Indoor Scenes
Authors: Yuanqi Li, Dayong Ren, Xiaohong Zhang, Jiawei Li, Liang Pu, Changfeng Ma, Xiaoyu Zhan, Jie Guo, Mingqiang Wei, Yan Zhang, Piaopiao Yu, Shuangyu Yang, Donghao Ji, Huisheng Ye, Hao Sun, Yansong Liu, Yinuo Chen, Jiaqi Zhu, Hongyu Liu Yanwen Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset containing nearly 3.6 billion precisely point-level annotated points covering an expansive area of 30000m^2. It encompasses three prevalent daily environments including learning scenes working scenes and living scenes. LiDAR-Net is characterized by its non-uniform point distribution e.g. scanning holes and scanning lines. Additionally it meticulously records and annotates scanning anomalies including reflection noise and ghost. These anomalies stem from specular reflections on glass or metal as well as distortions due to moving persons. LiDAR-Net's realistic representation of non-uniform distribution and anomalies significantly enhances the training of deep learning models leading to improved generalization in practical applications. We thoroughly evaluate the performance of state-of-the-art algorithms on LiDAR-Net and provide a detailed analysis of the results. Crucially our research identifies several fundamental challenges in understanding indoor point clouds contributing essential insights to future explorations in this field. Our dataset can be found online: http://lidar-net.njumeta.com
Dual DETRs for Multi-Label Temporal Action Detection
Authors: Guozhen Zhang, Jing Tan, Gangshan Wu, Limin Wang Yuhan Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Temporal Action Detection (TAD) aims to identify the action boundaries and the corresponding category within untrimmed videos. Inspired by the success of DETR in object detection several methods have adapted the query-based framework to the TAD task. However these approaches primarily followed DETR to predict actions at the instance level (i.e. identify each action by its center point) leading to sub-optimal boundary localization. To address this issue we propose a new Dual-level query-based TAD framework namely DualDETR to detect actions from both instance-level and boundary-level. Decoding at different levels requires semantics of different granularity therefore we introduce a two-branch decoding structure. This structure builds distinctive decoding processes for different levels facilitating explicit capture of temporal cues and semantics at each level. On top of the two-branch design we present a joint query initialization strategy to align queries from both levels. Specifically we leverage encoder proposals to match queries from each level in a one-to-one manner. Then the matched queries are initialized using position and content prior from the matched action proposal. The aligned dual-level queries can refine the matched proposal with complementary cues during subsequent decoding. We evaluate DualDETR on three challenging multi-label TAD benchmarks. The experimental results demonstrate the superior performance of DualDETR to the existing state-of-the-art methods achieving a substantial improvement under det-mAP and delivering impressive results under seg-mAP.
Rich Human Feedback for Text-to-Image Generation
Authors: Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, Junjie Ke, Krishnamurthy Dj Dvijotham, Katherine M. Collins, Yiwen Luo, Yang Li, Kai J Kohlhoff, Deepak Ramachandran, Vidhya Navalpakkam Youwei Liang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. However many generated images still suffer from issues such as artifacts/implausibility misalignment with text descriptions and low aesthetic quality. Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) for large language models prior works collected human-provided scores as feedback on generated images and trained a reward model to improve the T2I generation. In this paper we enrich the feedback signal by (i) marking image regions that are implausible or misaligned with the text and (ii) annotating which words in the text prompt are misrepresented or missing on the image. We collect such rich human feedback on 18K generated images (RichHF-18K) and train a multimodal transformer to predict the rich feedback automatically. We show that the predicted rich human feedback can be leveraged to improve image generation for example by selecting high-quality training data to finetune and improve the generative models or by creating masks with predicted heatmaps to inpaint the problematic regions. Notably the improvements generalize to models (Muse) beyond those used to generate the images on which human feedback data were collected (Stable Diffusion variants). The RichHF-18K data set will be released in our GitHub repository: https://github.com/google-research/google-research/tree/master/richhf_18k.
360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model
Authors: Weiqi Li, Chong Mou, Xinhua Cheng, Jian Zhang Qian Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Panorama video recently attracts more interest in both study and application courtesy of its immersive experience. Due to the expensive cost of capturing 360-degree panoramic videos generating desirable panorama videos by prompts is urgently required. Lately the emerging text-to-video (T2V) diffusion methods demonstrate notable effectiveness in standard video generation. However due to the significant gap in content and motion patterns between panoramic and standard videos these methods encounter challenges in yielding satisfactory 360-degree panoramic videos. In this paper we propose a pipeline named 360-Degree Video Diffusion model (360DVD) for generating 360-degree panoramic videos based on the given prompts and motion conditions. Specifically we introduce a lightweight 360-Adapter accompanied by 360 Enhancement Techniques to transform pre-trained T2V models for panorama video generation. We further propose a new panorama dataset named WEB360 consisting of panoramic video-text pairs for training 360DVD addressing the absence of captioned panoramic video datasets. Extensive experiments demonstrate the superiority and effectiveness of 360DVD for panorama video generation.