CVPR 2024 Papers

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in various multimodal tasks. However their potential in the medical domain remains largely unexplored. A significant challenge arises from the scarcity of diverse medical images spanning various modalities and anatomical regions which is essential in real-world medical applications. To solve this problem in this paper we introduce OmniMedVQA a novel comprehensive medical Visual Question Answering (VQA) benchmark. This benchmark is collected from 73 different medical datasets including 12 different modalities and covering more than 20 distinct anatomical regions. Importantly all images in this benchmark are sourced from authentic medical scenarios ensuring alignment with the requirements of the medical field and suitability for evaluating LVLMs. Through our extensive experiments we have found that existing LVLMs struggle to address these medical VQA problems effectively. Moreover what surprises us is that medical-specialized LVLMs even exhibit inferior performance to those general-domain models calling for a more versatile and robust LVLM in the biomedical field. The evaluation results not only reveal the current limitations of LVLM in understanding real medical images but also highlight our dataset's significance. Our code with dataset are available at https://github.com/OpenGVLab/Multi-Modality-Arena.

Hallucination posed as a pervasive challenge of multi-modal large language models (MLLMs) has significantly impeded their real-world usage that demands precise judgment. Existing methods mitigate this issue with either training with specific designed data or inferencing with external knowledge from other sources incurring inevitable additional costs. In this paper we present OPERA a novel MLLM decoding method grounded in an Over-trust Penalty and a Retrospection-Allocation strategy serving as a nearly free lunch to alleviate the hallucination issue without additional data knowledge or training. Our approach begins with an interesting observation that most hallucinations are closely tied to the knowledge aggregation patterns manifested in the self-attention matrix i.e. MLLMs tend to generate new tokens by focusing on a few summary tokens but not all the previous tokens. Such partial over-trust inclination results in the neglecting of image tokens and describes the image content with hallucination. Based on the observation OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue along with a rollback strategy that retrospects the presence of summary tokens in the previously generated tokens and re-allocate the token selection if necessary. With extensive experiments OPERA shows significant hallucination-mitigating performance on different MLLMs and metrics proving its effectiveness and generality. Our code is available at: https://github.com/shikiw/OPERA.

State-of-the-art models on contemporary 3D segmentation benchmarks like ScanNet consume and label dataset-provided 3D point clouds obtained through post processing of sensed multiview RGB-D images. They are typically trained in-domain forego large-scale 2D pre-training and outperform alternatives that featurize the posed RGB-D multiview images instead. The gap in performance between methods that consume posed images versus post-processed 3D point clouds has fueled the belief that 2D and 3D perception require distinct model architectures. In this paper we challenge this view and propose ODIN (Omni-Dimensional INstance segmentation) a model that can segment and label both 2D RGB images and 3D point clouds using a transformer architecture that alternates between 2D within-view and 3D cross-view information fusion. Our model differentiates 2D and 3D feature operations through the positional encodings of the tokens involved which capture pixel coordinates for 2D patch tokens and 3D coordinates for 3D feature tokens. ODIN achieves state-of-the-art performance on ScanNet200 Matterport3D and AI2THOR 3D instance segmentation benchmarks and competitive performance on ScanNet S3DIS and COCO. It outperforms all previous works by a wide margin when the sensed 3D point cloud is used in place of the point cloud sampled from 3D mesh. When used as the 3D perception engine in an instructable embodied agent architecture it sets a new state-of-the-art on the TEACh action-from-dialogue benchmark. Our code and checkpoints can be found at the project website: https://odin-seg.github.io.

The scarcity of ground-truth labels poses one major challenge in developing optical flow estimation models that are both generalizable and robust. While current methods rely on data augmentation they have yet to fully exploit the rich information available in labeled video sequences. We propose OCAI a method that supports robust frame interpolation by generating intermediate video frames alongside optical flows in between. Utilizing a forward warping approach OCAI employs occlusion awareness to resolve ambiguities in pixel values and fills in missing values by leveraging the forward-backward consistency of optical flows. Additionally we introduce a teacher-student style semi-supervised learning method on top of the interpolated frames. Using a pair of unlabeled frames and the teacher model's predicted optical flow we generate interpolated frames and flows to train a student model. The teacher's weights are maintained using Exponential Moving Averaging of the student. Our evaluations demonstrate perceptually superior interpolation quality and enhanced optical flow accuracy on established benchmarks such as Sintel and KITTI.

We present OAKINK2 a dataset of bimanual object manipulation tasks for complex daily activities. In pursuit of constructing the complex tasks into a structured representation OAKINK2 introduces three level of abstraction to organize the manipulation tasks: Affordance Primitive Task and Complex Task. OAKINK2 features on an object-centric perspective for decoding the complex tasks treating them as a sequence of object affordance fulfillment. The first level Affordance outlines the functionalities that objects in the scene can afford the second level Primitive Task describes the minimal interaction units that humans interact with the object to achieve its affordance and the third level Complex Task illustrates how Primitive Tasks are composed and interdependent. OAKINK2 dataset provides multi-view image streams and precise pose annotations for the human body hands and various interacting objects. This extensive collection supports applications such as interaction reconstruction and motion synthesis. Based on the 3-level abstraction of OAKINK2 we explore a task-oriented framework for Complex Task Completion (CTC). CTC aims to generate a sequence of bimanual manipulation to achieve task objectives. Within the CTC framework we employ Large Language Models (LLMs) to decompose the complex task objectives into sequences of Primitive Tasks and have developed a Motion Fulfillment Model that generates bimanual hand motion for each Primitive Task. OAKINK2 datasets and models are available at https://oakink.net/v2.

The consistency training (CT)-based semi-supervised learning (SSL) bites state-of-the-art performance on SSL-based image classification. However the existing CT-based SSL methods do not highlight the non-Euclidean characteristics and class-wise varieties of embedding spaces in an SSL model thus they cannot fully utilize the effectiveness of CT. Thus we propose a metric tensor-based consistency regularization exploiting the class-variant geometrical structure of embeddings on the high-dimensional feature space. The proposed method not only minimizes the prediction discrepancy between different views of a given image but also estimates the intrinsic geometric curvature of embedding spaces by employing the global and local metric tensors. The global metric tensor is used to globally estimate the class-invariant embeddings from the whole data distribution while the local metric tensor is exploited to estimate the class-variant embeddings of each cluster. The two metric tensors are optimized by the consistency regularization based on the weak and strong augmentation strategy. The proposed method provides the highest classification accuracy on average compared to the existing state-of-the-art SSL methods on conventional datasets.

Two-view homography estimation is a classic and fundamental problem in computer vision. While conceptually simple the problem quickly becomes challenging when multiple planes are visible in the image pair. Even with correct matches each individual plane (homography) might have a very low number of inliers when comparing to the set of all correspondences. In practice this requires a large number of RANSAC iterations to generate a good model hypothesis. The current state-of-the-art methods therefore seek to reduce the sample size from four point correspondences originally by including additional information such as keypoint orientation/angles or local affine information. In this work we continue in this direction and propose a novel one-point solver that leverages different approximate constraints derived from the same auxiliary information. In experiments we obtain state-of-the-art results with execution time speed-ups on large benchmark datasets and show that it is more beneficial for the solver to be sample efficient compared to generating more accurate homographies.

Inherent ambiguity in layout annotations poses significant challenges to developing accurate 360deg room layout estimation models. To address this issue we propose a novel Bi-Layout model capable of predicting two distinct layout types. One stops at ambiguous regions while the other extends to encompass all visible areas. Our model employs two global context embeddings where each embedding is designed to capture specific contextual information for each layout type. With our novel feature guidance module the image feature retrieves relevant context from these embeddings generating layout-aware features for precise bi-layout predictions. A unique property of our Bi-Layout model is its ability to inherently detect ambiguous regions by comparing the two predictions. To circumvent the need for manual correction of ambiguous annotations during testing we also introduce a new metric for disambiguating ground truth layouts. Our method demonstrates superior performance on benchmark datasets notably outperforming leading approaches. Specifically on the MatterportLayout dataset it improves 3DIoU from 81.70% to 82.57% across the full test set and notably from 54.80% to 59.97% in subsets with significant ambiguity.

Among the numerous efforts towards digitally recovering the physical world Neural Radiance Fields (NeRFs) have proved effective in most cases. However underwater scene introduces unique challenges due to the absorbing water medium the local change in lighting and the dynamic contents in the scene. We aim at developing a neural underwater scene representation for these challenges modeling the complex process of attenuation unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.

The complex dynamicity of open-world objects presents non-negligible challenges for multi-object tracking (MOT) often manifested as severe deformations fast motion and occlusions. Most methods that solely depend on coarse-grained object cues such as boxes and the overall appearance of the object are susceptible to degradation due to distorted internal relationships of dynamic objects. To address this problem this work proposes NetTrack an efficient generic and affordable tracking framework to introduce fine-grained learning that is robust to dynamicity. Specifically NetTrack constructs a dynamicity-aware association with a fine-grained Net leveraging point-level visual cues. Correspondingly a fine-grained sampler and matching method have been incorporated. Furthermore NetTrack learns object-text correspondence for fine-grained localization. To evaluate MOT in extremely dynamic open-world scenarios a bird flock tracking (BFT) dataset is constructed which exhibits high dynamicity with diverse species and open-world scenarios. Comprehensive evaluation on BFT validates the effectiveness of fine-grained learning on object dynamicity and thorough transfer experiments on challenging open-world benchmarks i.e. TAO TAO-OW AnimalTrack and GMOT-40 validate the strong generalization ability of NetTrack even without finetuning.

We present NeRSP a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images. Reflective surface reconstruction is extremely challenging as specular reflections are view-dependent and thus violate the multiview consistency for multiview stereo. On the other hand sparse image inputs as a practical capture setting commonly cause incomplete or distorted results due to the lack of correspondence matching. This paper jointly handles the challenges from sparse inputs and reflective surfaces by leveraging polarized images. We derive photometric and geometric cues from the polarimetric image formation model and multiview azimuth consistency which jointly optimize the surface geometry modeled via implicit neural representation. Based on the experiments on our synthetic and real datasets we achieve the state-of-the-art surface reconstruction results with only 6 views as input.

We present a method for automatically modifying a NeRF representation based on a single observation of a non-rigid transformed version of the original scene. Our method defines the transformation as a 3D flowspecifically as a weighted linear blending of rigid transformations of 3D anchor points that are defined on the surface of the scene. In order to identify anchor points we introduce a novel correspondence algorithm that first matches RGB-based pairs then leverages multi-view information and 3D reprojection to robustly filter false positives in two steps. We also introduce a new dataset for exploring the problem of modifying a NeRF scene through a single observation. Our dataset contains 113 scenes leveraging 47 3D assets.We show that our proposed method outperforms NeRF editing methods as well as diffusion-based methods and we also explore different methods for filtering correspondences.

Neural Rendering representations have significantly contributed to the field of 3D computer vision. Given their potential considerable efforts have been invested to improve their performance. Nonetheless the essential question of selecting training views is yet to be thoroughly investigated. This key aspect plays a vital role in achieving high-quality results and aligns with the well-known tenet of deep learning: "garbage in garbage out". In this paper we first illustrate the importance of view selection by demonstrating how a simple rotation of the test views within the most pervasive NeRF dataset can lead to consequential shifts in the performance rankings of state-of-the-art techniques. To address this challenge we introduce a unified framework for view selection methods and devise a thorough benchmark to assess its impact. Significant improvements can be achieved without leveraging error or uncertainty estimation but focusing on uniform view coverage of the reconstructed object resulting in a training-free approach. Using this technique we show that high-quality renderings can be achieved faster by using fewer views. We conduct extensive experiments on both synthetic datasets and realistic data to demonstrate the effectiveness of our proposed method compared with random conventional error-based and uncertainty-guided view selection.

Recent studies have noted an intriguing phenomenon termed Neural Collapse that is when the neural networks establish the right correlation between feature spaces and the training targets their last-layer features together with the classifier weights will collapse into a stable and symmetric structure. In this paper we extend the investigation of Neural Collapse to the biased datasets with imbalanced attributes. We observe that models will easily fall into the pitfall of shortcut learning and form a biased non-collapsed feature space at the early period of training which is hard to reverse and limits the generalization capability. To tackle the root cause of biased classification we follow the recent inspiration of prime training and propose an avoid-shortcut learning framework without additional training complexity. With well-designed shortcut primes based on Neural Collapse structure the models are encouraged to skip the pursuit of simple shortcuts and naturally capture the intrinsic correlations. Experimental results demonstrate that our method induces a better convergence property during training and achieves state-of-the-art generalization performance on both synthetic and real-world biased datasets.

In this paper we investigate a new problem called narrative action evaluation (NAE). NAE aims to generate professional commentary that evaluates the execution of an action. Unlike traditional tasks such as score-based action quality assessment and video captioning involving superficial sentences NAE focuses on creating detailed narratives in natural language. These narratives provide intricate descriptions of actions along with objective evaluations. NAE is a more challenging task because it requires both narrative flexibility and evaluation rigor. One existing possible solution is to use multi-task learning where narrative language and evaluative information are predicted separately. However this approach results in reduced performance for individual tasks because of variations between tasks and differences in modality between language information and evaluation information. To address this we propose a prompt-guided multimodal interaction framework. This framework utilizes a pair of transformers to facilitate the interaction between different modalities of information. It also uses prompts to transform the score regression task into a video-text matching task thus enabling task interactivity. To support further research in this field we re-annotate the MTL-AQA and FineGym datasets with high-quality and comprehensive action narration. Additionally we establish benchmarks for NAE. Extensive experiment results prove that our method outperforms separate learning methods and naive multi-task learning methods. Data and code will be released at https://github.com/shiyi-zh0408/NAE_CVPR2024.

We propose NViST a transformer-based model for efficient and generalizable novel-view synthesis from a single image for real-world scenes. In contrast to many methods that are trained on synthetic data object-centred scenarios or in a category-specific manner NViST is trained on MVImgNet a large-scale dataset of casually-captured real-world videos of hundreds of object categories with diverse backgrounds. NViST transforms image inputs directly into a radiance field conditioned on camera parameters via adaptive layer normalisation. In practice NViST exploits fine-tuned masked autoencoder (MAE) features and translates them to 3D output tokens via cross-attention while addressing occlusions with self-attention. To move away from object-centred datasets and enable full scene synthesis NViST adopts a 6-DOF camera pose model and only requires relative pose dropping the need for canonicalization of the training data which removes a substantial barrier to it being used on casually captured datasets. We show results on unseen objects and categories from MVImgNet and even generalization to casual phone captures. We conduct qualitative and quantitative evaluations on MVImgNet and ShapeNet to show that our model represents a step forward towards enabling true in-the-wild generalizable novel-view synthesis from a single image. Project webpage: https://wbjang.github.io/nvist_webpage.

Neural 3D reconstruction from multi-view images has recently attracted increasing attention from the community. Existing methods normally learn a neural field for the whole scene while it is still under-explored how to reconstruct a target object indicated by users. Considering the Segment Anything Model (SAM) has shown effectiveness in segmenting any 2D images in this paper we propose NTO3D a novel high-quality Neural Target Object 3D (NTO3D) reconstruction method which leverages the benefits of both neural field and SAM. We first propose a novel strategy to lift the multi-view 2D segmentation masks of SAM into a unified 3D occupancy field. The 3D occupancy field is then projected into 2D space and generates the new prompts for SAM. This process is iterative until convergence to separate the target object from the scene. After this we then lift the 2D features of the SAM encoder into a 3D feature field in order to improve the reconstruction quality of the target object. NTO3D lifts the 2D masks and features of SAM into the 3D neural field for high-quality neural target object 3D reconstruction. We conduct detailed experiments on several benchmark datasets to demonstrate the advantages of our method. The code will be available at: https://github.com/ucwxb/NTO3D.

This paper studies the problem of structured 3D recon- struction using wireframes that consist of line segments and junctions focusing on the computation of structured boundary geometries of scenes. Instead of leveraging matching-based solutions from 2D wireframes (or line segments) for 3D wireframe reconstruction as done in prior arts we present NEAT a rendering-distilling formulation using neural fields to represent 3D line segments with 2D observations and bipartite matching for perceiving and dis- tilling of a sparse set of 3D global junctions. The proposed NEAT enjoys the joint optimization of the neural fields and the global junctions from scratch using view-dependent 2D observations without precomputed cross-view feature matching. Comprehensive experiments on the DTU and BlendedMVS datasets demonstrate our NEAT's superiority over state-of-the-art alternatives for 3D wireframe recon- struction. Moreover the distilled 3D global junctions by NEAT are a better initialization than SfM points for the recently-emerged 3D Gaussian Splatting for high-fidelity novel view synthesis using about 20 times fewer initial 3D points. Project page: https://xuenan.net/neat

State-of-the-art neural implicit surface representations have achieved impressive results in indoor scene reconstruction by incorporating monocular geometric priors as additional supervision. However we have observed that multi-view inconsistency between such priors poses a challenge for high-quality reconstructions. In response we present NC-SDF a neural signed distance field (SDF) 3D reconstruction framework with view-dependent normal compensation (NC). Specifically we integrate view-dependent biases in monocular normal priors into the neural implicit representation of the scene. By adaptively learning and correcting the biases our NC-SDF effectively mitigates the adverse impact of inconsistent supervision enhancing both the global consistency and local details in the reconstructions. To further refine the details we introduce an informative pixel sampling strategy to pay more attention to intricate geometry with higher information content. Additionally we design a hybrid geometry modeling approach to improve the neural implicit representation. Experiments on synthetic and real-world datasets demonstrate that NC-SDF outperforms existing approaches in terms of reconstruction quality.