CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
In recent decades the vision community has witnessed remarkable progress in visual recognition partially owing to advancements in dataset benchmarks. Notably the established COCO benchmark has propelled the development of modern detection and segmentation systems. However the COCO segmentation benchmark has seen comparatively slow improvement over the last decade. Originally equipped with coarse polygon annotations for thing instances it gradually incorporated coarse superpixel annotations for stuff regions which were subsequently heuristically amalgamated to yield panoptic segmentation annotations. These annotations executed by different groups of raters have resulted not only in coarse segmentation masks but also in inconsistencies between segmentation types. In this study we undertake a comprehensive reevaluation of the COCO segmentation annotations. By enhancing the annotation quality and expanding the dataset to encompass 383K images with more than 5.18M panoptic masks we introduce COCONut the COCO Next Universal segmenTation dataset. COCONut harmonizes segmentation annotations across semantic instance and panoptic segmentation with meticulously crafted high-quality masks and establishes a robust benchmark for all segmentation tasks. To our knowledge COCONut stands as the inaugural large-scale universal segmentation dataset verified by human raters. We anticipate that the release of COCONut will significantly contribute to the community's ability to assess the progress of novel neural networks.
A novel algorithm called semantic line combination detector (SLCD) to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First we generate various line combinations from reliable lines. Second we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. Moreover it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection symmetry axis detection and composition-based image retrieval. Our codes are available at https://github.com/Jinwon-Ko/SLCD.
Single-domain generalization aims to learn a model from single source domain data attaining generalized performance on other unseen target domains. Existing works primarily focus on improving the generalization ability of static networks. However static networks are unable to dynamically adapt to the diverse variations in different image scenes leading to limited generalization capability. Different scenes exhibit varying levels of complexity and the complexity of images further varies significantly in cross-domain scenarios. In this paper we propose a dynamic object-centric perception network based on prompt learning aiming to adapt to the variations in image complexity. Specifically we propose an object-centric gating module based on prompt learning to focus attention on the object-centric features guided by the various scene prompts. Then with the object-centric gating masks the dynamic selective module dynamically selects highly correlated feature regions in both spatial and channel dimensions enabling the model to adaptively perceive object-centric relevant features thereby enhancing the generalization capability. Extensive experiments were conducted on single-domain generalization tasks in image classification and object detection. The experimental results demonstrate that our approach outperforms state-of-the-art methods which validates the effectiveness and versatility of our proposed method.
In the context of pose-invariant object recognition and retrieval we demonstrate that it is possible to achieve significant improvements in performance if both the category-based and the object-identity-based embeddings are learned simultaneously during training. In hindsight that sounds intuitive because learning about the categories is more fundamental than learning about the individual objects that correspond to those categories. However to the best of what we know no prior work in pose invariant learning has demonstrated this effect. This paper presents an attention-based dual-encoder architecture with specially designed loss functions that optimize the inter- and intra-class distances simultaneously in two different embedding spaces one for the category embeddings and the other for the object level embeddings. The loss functions we have proposed are pose-invariant ranking losses that are designed to minimize the intra-class distances and maximize the inter-class distances in the dual representation spaces. We demonstrate the power of our approach with three challenging multi-view datasets ModelNet-40 ObjectPI and FG3D. With our dual approach for single view object recognition we outperform the previous best by 20.0% on ModelNet40 2.0% on ObjectPI and 46.5% on FG3D.
With our dual approach for single view object recognition we outperform the previous best by 20.0% on ModelNet40 2.0% on ObjectPI and 46.5% on FG3D. On the other hand for single-view object retrieval we outperform the previous best by 33.7% on ModelNet40 18.8% on ObjectPI and 56.9% on FG3D.
Video Transformers have become the prevalent solution for various video downstream tasks with superior expressive power and flexibility. However these video transformers suffer from heavy computational costs induced by the massive number of tokens across the entire video frames which has been the major barrier to training the model. Further the patches irrelevant to the main contents e.g. backgrounds degrade the generalization performance of models. To tackle these issues we propose training free token merging for lightweight video Transformer (vid-TLDR) that aims to enhance the efficiency of video Transformers by merging the background tokens without additional training. For vid-TLDR we introduce a novel approach to capture the salient regions in videos only with the attention map. Further we introduce the saliency-aware token merging strategy by dropping the background tokens and sharpening the object scores. Our experiments show that vid-TLDR significantly mitigates the computational complexity of video Transformers while achieving competitive performance compared to the base model without vid-TLDR. Code is available at https://github.com/mlvlab/vid-TLDR.
We present DRESS a large vision language model (LVLM) that innovatively exploits Natural Language feedback (NLF) from Large Language Models to enhance its alignment and interactions by addressing two key limitations in the state-of-the-art LVLMs. First prior LVLMs generally rely only on the instruction finetuning stage to enhance alignment with human preferences. Without incorporating extra feedback they are still prone to generate unhelpful hallucinated or harmful responses. Second while the visual instruction tuning data is generally structured in a multi-turn dialogue format the connections and dependencies among consecutive conversational turns are weak. This reduces the capacity for effective multi-turn interactions. To tackle these we propose a novel categorization of the NLF into two key types: critique and refinement. The critique NLF identifies the strengths and weaknesses of the responses and is used to align the LVLMs with human preferences. The refinement NLF offers concrete suggestions for improvement and is adopted to improve the interaction ability of the LVLMs-- which focuses on LVLMs' ability to refine responses by incorporating feedback in multi-turn interactions. To address the non-differentiable nature of NLF we generalize conditional reinforcement learning for training.
The refinement NLF offers concrete suggestions for improvement and is adopted to improve the interaction ability of the LVLMs-- which focuses on LVLMs' ability to refine responses by incorporating feedback in multi-turn interactions. To address the non-differentiable nature of NLF we generalize conditional reinforcement learning for training. Our experimental results demonstrate that DRESS can generate more helpful (9.76%) honest (11.52%) and harmless (21.03%) responses and more effectively learn from feedback during multi-turn interactions compared to SOTA LVLMs.
In this work we introduce two types of makeup prior models to extend existing 3D face prior models: PCA-based and StyleGAN2-based priors. The PCA-based prior model is a linear model that is easy to construct and is computationally efficient. However it retains only low-frequency information. Conversely the StyleGAN2-based model can represent high-frequency information with relatively higher computational cost than the PCA-based model. Although there is a trade-off between the two models both are applicable to 3D facial makeup estimation and related applications. By leveraging makeup prior models and designing a makeup consistency module we effectively address the challenges that previous methods faced in robustly estimating makeup particularly in the context of handling self-occluded faces. In experiments we demonstrate that our approach reduces computational costs by several orders of magnitude achieving speeds up to 180 times faster. In addition by improving the accuracy of the estimated makeup we confirm that our methods are highly advantageous for various 3D facial makeup applications such as 3D makeup face reconstruction user-friendly makeup editing makeup transfer and interpolation.
DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-attention which is proven effective for improving performance but also introduces a heavy computational burden and high dependence on stable query selection. This paper demonstrates that suboptimal two-stage selection strategies result in scale bias and redundancy due to the mismatch between selected queries and objects in two-stage initialization. To address these issues we propose hierarchical salience filtering refinement which performs transformer encoding only on filtered discriminative queries for a better trade-off between computational efficiency and precision. The filtering process overcomes scale bias through a novel scale-independent salience supervision. To compensate for the semantic misalignment among queries we introduce elaborate query refinement modules for stable two-stage initialization. Based on above improvements the proposed Salience DETR achieves significant improvements of +4.0% AP +0.2% AP +4.4% AP on three challenging task-specific detection datasets as well as 49.2% AP on COCO 2017 with less FLOPs. The code is available at https://github.com/xiuqhou/Salience-DETR.
The rapid advancement of large language models (LLMs) has accelerated the emergence of in-context learning (ICL) as a cutting-edge approach in the natural language processing domain. Recently ICL has been employed in visual understanding tasks such as semantic segmentation and image captioning yielding promising results. However existing visual ICL framework can not enable producing content across multiple modalities which limits their potential usage scenarios. To address this issue we present a new ICL framework for visual understanding with multi-modal output enabled. First we quantize and embed both text and visual prompt into a unified representational space structured as interleaved in-context sequences. Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them facilitating in-context learning. Thanks to this design the model is capable of handling in-context vision understanding tasks with multimodal output in a unified pipeline. Experimental results demonstrate that our model achieves competitive performance compared with specialized models and previous ICL baselines. Overall our research takes a further step toward unified multimodal in-context learning.
In this paper we propose an efficient data-driven solution to self-localization within a floorplan. Floorplan data is readily available long-term persistent and inherently robust to changes in the visual appearance. Our method does not require retraining per map and location or demand a large database of images of the area of interest. We propose a novel probabilistic model consisting of an observation and a novel temporal filtering module. Operating internally with an efficient ray-based representation the observation module consists of a single and a multiview module to predict horizontal depth from images and fuses their results to benefit from advantages offered by either methodology. Our method operates on conventional consumer hardware and overcomes a common limitation of competing methods that often demand upright images. Our full system meets real-time requirements while outperforming the state-of-the-art by a significant margin.
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes. However recovering a high-quality NeRF typically requires tens to hundreds of input images resulting in a time-consuming capture process. We present ReconFusion to reconstruct real-world scenes using only a few photos. Our approach leverages a diffusion prior for novel view synthesis trained on synthetic and multiview datasets which regularizes a NeRF-based 3D reconstruction pipeline at novel camera poses beyond those captured by the set of input images. Our method synthesizes realistic geometry and texture in underconstrained regions while preserving the appearance of observed regions. We perform an extensive evaluation across various real-world datasets including forward-facing and 360-degree scenes demonstrating significant performance improvements over previous few-view NeRF reconstruction approaches. Please see our project page at reconfusion.github.io.
We are living in a world surrounded by diverse and "smart" devices with rich modalities of sensing ability. Conveniently capturing the interactions between us humans and these objects remains far-reaching. In this paper we present I'm-HOI a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minimal amount of RGB camera and object-mounted Inertial Measurement Unit (IMU). It combines general motion inference and category-aware refinement. For the former we introduce a holistic human-object tracking method to fuse the IMU signals and the RGB stream and progressively recover the human motions and subsequently the companion object motions. For the latter we tailor a category-aware motion diffusion model which is conditioned on both the raw IMU observations and the results from the previous stage under over-parameterization representation. It significantly refines the initial results and generates vivid body hand and object motions. Moreover we contribute a large dataset with ground truth human and object motions dense RGB inputs and rich object-mounted IMU measurements. Extensive experiments demonstrate the effectiveness of I'm-HOI under a hybrid capture setting. Our dataset and code will be released to the community.
Multi-Instance Learning (MIL) has shown impressive performance for histopathology whole slide image (WSI) analysis using bags or pseudo-bags. It involves instance sampling feature representation and decision-making. However existing MIL-based technologies at least suffer from one or more of the following problems: 1) requiring high storage and intensive pre-processing for numerous instances (sampling); 2) potential over-fitting with limited knowledge to predict bag labels (feature representation); 3) pseudo-bag counts and prior biases affect model robustness and generalizability (decision-making). Inspired by clinical diagnostics using the past sampling instances can facilitate the final WSI analysis but it is barely explored in prior technologies. To break free these limitations we integrate the dynamic instance sampling and reinforcement learning into a unified framework to improve the instance selection and feature aggregation forming a novel Dynamic Policy Instance Selection (DPIS) scheme for better and more credible decision-making. Specifically the measurement of feature distance and reward function are employed to boost continuous instance sampling. To alleviate the over-fitting we explore the latent global relations among instances for more robust and discriminative feature representation while establishing reward and punishment mechanisms to correct biases in pseudo-bags using contrastive learning.
Specifically the measurement of feature distance and reward function are employed to boost continuous instance sampling. To alleviate the over-fitting we explore the latent global relations among instances for more robust and discriminative feature representation while establishing reward and punishment mechanisms to correct biases in pseudo-bags using contrastive learning. These strategies form the final Dynamic Policy-Driven Adaptive Multi-Instance Learning (PAMIL) method for WSI tasks. Extensive experiments reveal that our PAMIL method outperforms the state-of-the-art by 3.8% on CAMELYON16 and 4.4% on TCGA lung cancer datasets.
The exponential growth of large language models (LLMs) has opened up numerous possibilities for multi-modal AGI systems. However the progress in vision and vision-language foundation models which are also critical elements of multi-modal AGI has not kept pace with LLMs. In this work we design a large-scale vision-language foundation model (InternVL) which scales up the vision foundation model to 6 billion parameters and progressively aligns it with the LLM using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks including visual perception tasks such as image-level or pixel-level recognition vision-language tasks such as zero-shot image/video classification zero-shot image/video-text retrieval and link with LLMs to create multi-modal dialogue systems. It has powerful visual capabilities and can be a good alternative to the ViT-22B. We hope that our research could contribute to the development of multi-modal large models.
We present Multi-View Attentive Contextualization (MvACon) a simple yet effective method for improving 2D-to-3D feature lifting in query-based multi-view 3D (MV3D) object detection. Despite remarkable progress witnessed in the field of query-based MV3D object detection prior art often suffers from either the lack of exploiting high-resolution 2D features in dense attention-based lifting due to high computational costs or from insufficiently dense grounding of 3D queries to multi-scale 2D features in sparse attention-based lifting. Our proposed MvACon hits the two birds with one stone using a representationally dense yet computationally sparse attentive feature contextualization scheme that is agnostic to specific 2D-to-3D feature lifting approaches. In experiments the proposed MvACon is thoroughly tested on the nuScenes benchmark using both the BEVFormer and its recent 3D deformable attention (DFA3D) variant as well as the PETR showing consistent detection performance improvement especially in enhancing performance in location orientation and velocity prediction. It is also tested on the Waymo-mini benchmark using BEVFormer with similar improvement.
It is also tested on the Waymo-mini benchmark using BEVFormer with similar improvement. We qualitatively and quantitatively show that global cluster-based contexts effectively encode dense scene-level contexts for MV3D object detection. The promising results of our proposed MvACon reinforces the adage in computer vision "(contextualized) feature matters".
We propose a novel echocardiographical video segmentation model by adapting SAM to medical videos to address some long-standing challenges in ultrasound video segmentation including (1) massive speckle noise and artifacts (2) extremely ambiguous boundaries and (3) large variations of targeting objects across frames. The core technique of our model is a temporal-aware and noise-resilient prompting scheme. Specifically we employ a space-time memory that contains both spatial and temporal information to prompt the segmentation of current frame and thus we call the proposed model as MemSAM. In prompting the memory carrying temporal cues sequentially prompt the video segmentation frame by frame. Meanwhile as the memory prompt propagates high-level features it avoids the issue of misidentification caused by mask propagation and improves representation consistency. To address the challenge of speckle noise we further propose a memory reinforcement mechanism which leverages predicted masks to improve the quality of the memory before storing it. We extensively evaluate our method on two public datasets and demonstrate state-of-the-art performance compared to existing models. Particularly our model achieves comparable performance with fully supervised approaches with limited annotations. Codes are available at https://github.com/dengxl0520/MemSAM.
Although neural radiance fields (NeRFs) have achieved triumphs in image novel view synthesis (NVS) LiDAR NVS remains largely unexplored. Previous LiDAR NVS methods employ a simple shift from image NVS methods while ignoring the dynamic nature and the large-scale reconstruction problem of LiDAR point clouds. In light of this we propose LiDAR4D a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis. In consideration of the sparsity and large-scale characteristics we design a 4D hybrid representation combined with multi-planar and grid features to achieve effective reconstruction in a coarse-to-fine manner. Furthermore we introduce geometric constraints derived from point clouds to improve temporal consistency. For the realistic synthesis of LiDAR point clouds we incorporate the global optimization of ray-drop probability to preserve cross-region patterns. Extensive experiments on KITTI-360 and NuScenes datasets demonstrate the superiority of our method in accomplishing geometry-aware and time-consistent dynamic reconstruction. Codes are available at https://github.com/ispc-lab/LiDAR4D.
Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap. We introduce DMP a pipeline utilizing pre-trained T2I models as a prior for dense prediction tasks. To address the misalignment between deterministic prediction tasks and stochastic T2I models we reformulate the diffusion process through a sequence of interpolations establishing a deterministic mapping between input RGB images and output prediction distributions. To preserve generalizability we use low-rank adaptation to fine-tune pre-trained models. Extensive experiments across five tasks including 3D property estimation semantic segmentation and intrinsic image decomposition showcase the efficacy of the proposed method. Despite limited-domain training data the approach yields faithful estimations for arbitrary images surpassing existing state-of-the-art algorithms.