CVPR 2024 Papers

Self-supervised learning (SSL) has been successful in building patch embeddings of small histology images (e.g. 224 x 224 pixels) but scaling these models to learn slide embeddings from the entirety of giga-pixel whole-slide images (WSIs) remains challenging. Here we leverage complementary information from gene expression profiles to guide slide representation learning using multi-modal pre-training. Expression profiles constitute highly detailed molecular descriptions of a tissue that we hypothesize offer a strong task-agnostic training signal for learning slide embeddings. Our slide and expression (S+E) pretraining strategy called TANGLE employs modality-specific encoders the outputs of which are aligned via contrastive learning. TANGLE was pre-trained on samples from three different organs: liver (n=6597 S+E pairs) breast (n=1020) and lung (n=1012) from two different species (Homo sapiens and Rattus norvegicus). Across three independent test datasets consisting of 1265 breast WSIs 1946 lung WSIs and 4584 liver WSIs TANGLE shows significantly better few-shot performance compared to supervised and SSL baselines. When assessed using prototype-based classification and slide retrieval TANGLE also shows a substantial performance improvement over all baselines. Code available at https://github.com/mahmoodlab/TANGLE.

We address the problem of regressing 3D human pose and shape from a single image with a focus on 3D accuracy. The current best methods leverage large datasets of 3D pseudo-ground-truth (p-GT) and 2D keypoints leading to robust performance. With such methods however we observe a paradoxical decline in 3D pose accuracy with increasing 2D accuracy. This is caused by biases in the p-GT and the use of an approximate camera projection model. We quantify the error induced by current camera models and show that fitting 2D keypoints and p-GT accurately causes incorrect 3D poses. Our analysis defines the invalid distances within which minimizing 2D and p-GT losses is detrimental. We use this to formulate a new loss "Threshold-Adaptive Loss Scaling" (TALS) that penalizes gross 2D and p-GT errors but not smaller ones. With such a loss there are many 3D poses that could equally explain the 2D evidence. To reduce this ambiguity we need a prior over valid human poses but such priors can introduce unwanted bias. To address this we exploit a tokenized representation of human pose and reformulate the problem as token prediction. This restricts the estimated poses to the space of valid poses effectively improving robustness to occlusion. Extensive experiments on the EMDB and 3DPW datasets show that our reformulated loss and tokenization allows us to train on in-the-wild data while improving 3D accuracy over the state-of-the-art. Our models and code are available for research at https://tokenhmr.is.tue.mpg.de.

Considerable efforts have been devoted to Oriented Object Detection (OOD). However one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) representation remains unresolved which is an inherent bottleneck for extant OOD methods. This paper endeavors to completely solve this issue in a theoretically guaranteed manner and puts an end to the ad-hoc efforts in this direction. Prior studies typically can only address one of the two cases of discontinuity: rotation and aspect ratio and often inadvertently introduce decoding discontinuity e.g. Decoding Incompleteness (DI) and Decoding Ambiguity (DA) as discussed in literature. Specifically we propose a novel representation method called Continuous OBB (COBB) which can be readily integrated into existing detectors e.g. Faster-RCNN as a plugin. It can theoretically ensure continuity in bounding box regression which to our best knowledge has not been achieved in literature for rectangle-based object representation. For fairness and transparency of experiments we have developed a modularized benchmark based on the open-source deep learning framework Jittor's detection toolbox JDet for OOD evaluation. On the popular DOTA dataset by integrating Faster-RCNN as the same baseline model our new method outperforms the peer method Gliding Vertex by 1.13% mAP50 (relative improvement 1.54%) and 2.46% mAP75 (relative improvement 5.91%) without any tricks.

Current self-supervised methods can primarily be categorized into contrastive learning and masked image modeling. Extensive studies have demonstrated that combining these two approaches can achieve state-of-the-art performance. However these methods essentially reinforce the global consistency of contrastive learning without taking into account the conflicts between these two approaches which hinders their generalizability to arbitrary scenarios. In this paper we theoretically prove that MAE serves as a patch-level contrastive learning where each patch within an image is considered as a distinct category. This presents a significant conflict with global-level contrastive learning which treats all patches in an image as an identical category. To address this conflict this work abandons the non-generalizable global-level constraints and proposes explicit patch-level contrastive learning as a solution. Specifically this work employs the encoder of MAE to generate dual-branch features which then perform patch-level learning through a decoder. In contrast to global-level data augmentation in contrastive learning our approach leverages patch-level feature augmentation to mitigate interference from global-level learning. Consequently our approach can learn heterogeneous representations from a single image while avoiding the conflicts encountered by previous methods. Massive experiments affirm the potential of our method for learning from arbitrary scenarios.

Self-supervised pre-training has been proved to be effective in learning transferable representations that benefit various visual tasks. This paper asks this question: can self-supervised pre-training learn general facial representations for various facial analysis tasks? Recent efforts toward this goal are limited to treating each face image as a whole i.e. learning consistent facial representations at the image-level which overlooks the consistency of local facial representations (i.e. facial regions like eyes nose etc). In this work we make a first attempt to propose a novel self-supervised facial representation learning framework to learn consistent global and local facial representations Facial Region Awareness (FRA). Specifically we explicitly enforce the consistency of facial regions by matching the local facial representations across views which are extracted with learned heatmaps highlighting the facial regions. Inspired by the mask prediction in supervised semantic segmentation we obtain the heatmaps via cosine similarity between the per-pixel projection of feature maps and facial mask embeddings computed from learnable positional embeddings which leverage the attention mechanism to globally look up the facial image for facial regions. To learn such heatmaps we formulate the learning of facial mask embeddings as a deep clustering problem by assigning the pixel features from the feature maps to them. The transfer learning results on facial classification and regression tasks show that our FRA outperforms previous pre-trained models and more importantly using ResNet as the unified backbone for various tasks our FRA achieves comparable or even better performance compared with SOTA methods in facial analysis tasks.

Contrastive learning has emerged as a promising paradigm for 3D open-world understanding i.e. aligning point cloud representation to image and text embedding space individually. In this paper we introduce MixCon3D a simple yet effective method aiming to sculpt holistic 3D representation in contrastive language-image-3D pre-training. In contrast to point cloud only we develop the 3D object-level representation from complementary perspectives e.g. multi-view rendered images with the point cloud. Then MixCon3D performs language-3D contrastive learning comprehensively depicting real-world 3D objects and bolstering text alignment. Additionally we pioneer the first thorough investigation of various training recipes for the 3D contrastive learning paradigm building a solid baseline with improved performance. Extensive experiments conducted on three representative benchmarks reveal that our method significantly improves over the baseline surpassing the previous state-of-the-art performance on the challenging 1156-category Objaverse-LVIS dataset by 5.7%. The versatility of MixCon3D is showcased in applications such as text-to-3D retrieval and point cloud captioning further evidencing its efficacy in diverse scenarios. The code is available at https://github.com/UCSC-VLAA/MixCon3D.

We introduce SODA a self-supervised diffusion model designed for representation learning. The model incorporates an image encoder which distills a source view into a compact representation that in turn guides the generation of related novel views. We show that by imposing a tight bottleneck between the encoder and a denoising decoder and leveraging novel view synthesis as a self-supervised objective we can turn diffusion models into strong representation learners capable of capturing visual semantics in an unsupervised manner. To the best of our knowledge SODA is the first diffusion model to succeed at ImageNet linear-probe classification and at the same time it accomplishes reconstruction editing and synthesis tasks across a wide range of datasets. Further investigation reveals the disentangled nature of its emergent latent space that serves as an effective interface to control and manipulate the produced images. All in all we aim to shed light on the exciting and promising potential of diffusion models not only for image generation but also for learning rich and robust representations. See our website at soda-diffusion.github.io.

This paper addresses complex challenges in histopathological image analysis through three key contributions. Firstly it introduces a fast patch selection method FPS for whole-slide image (WSI) analysis significantly reducing computational cost while maintaining accuracy. Secondly it presents PathDino a lightweight histopathology feature extractor with a minimal configuration of five Transformer blocks and only ? 9 million parameters markedly fewer than alternatives. Thirdly it introduces a rotation-agnostic representation learning paradigm using self-supervised learning effectively mitigating overfitting. We also show that our compact model outperforms existing state-of-the-art histopathology-specific vision transformers on 12 diverse datasets including both internal datasets spanning four sites (breast liver skin and colorectal) and seven public datasets (PANDA CAMELYON16 BRACS DigestPath Kather PanNuke and WSSS4LUAD). Notably even with a training dataset of ? 6 million histopathology patches from The Cancer Genome Atlas (TCGA) our approach demonstrates an average 8.5% improvement in patch-level majority vote performance. These contributions provide a robust framework for enhancing image analysis in digital pathology rigorously validated through extensive evaluation.

Federated learning achieves effective performance in modeling decentralized data. In practice client data are not well-labeled which makes it potential for federated unsupervised learning (FUSL) with non-IID data. However the performance of existing FUSL methods suffers from insufficient representations i.e. (1) representation collapse entanglement among local and global models and (2) inconsistent representation spaces among local models. The former indicates that representation collapse in local model will subsequently impact the global model and other local models. The latter means that clients model data representation with inconsistent parameters due to the deficiency of supervision signals. In this work we propose FedU2 which enhances generating uniform and unified representation in FUSL with non-IID data. Specifically FedU2 consists of flexible uniform regularizer (FUR) and efficient unified aggregator (EUA). FUR in each client avoids representation collapse via dispersing samples uniformly and EUA in server promotes unified representation by constraining consistent client model updating. To extensively validate the performance of FedU2 we conduct both cross-device and cross-silo evaluation experiments on two benchmark datasets i.e. CIFAR10 and CIFAR100.

Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth analysis of existing approaches in this domain highlighting a commonly overlooked aspect: the redundancy between view-consistent and view-specific representations. To this end we propose an innovative framework for multi-view representation learning which incorporates a technique we term 'distilled disentangling'. Our method introduces the concept of masked cross-view prediction enabling the extraction of compact high-quality view-consistent representations from various sources without incurring extra computational overhead. Additionally we develop a distilled disentangling module that efficiently filters out consistency-related information from multi-view representations resulting in purer view-specific representations. This approach significantly reduces redundancy between view-consistent and view-specific representations enhancing the overall efficiency of the learning process. Our empirical evaluations reveal that higher mask ratios substantially improve the quality of view-consistent representations. Moreover we find that reducing the dimensionality of view-consistent representations relative to that of view-specific representations further refines the quality of the combined representations.

While recent model-free Reinforcement Learning (RL) methods have demonstrated human-level effectiveness in gaming environments their success in everyday tasks like visual navigation has been limited particularly under significant appearance variations. This limitation arises from (i) poor sample efficiency and (ii) over-fitting to training scenarios. To address these challenges we present a world model that learns invariant features using (i) contrastive unsupervised learning and (ii) an intervention-invariant regularizer. Learning an explicit representation of the world dynamics i.e. a world model improves sample efficiency while contrastive learning implicitly enforces learning of invariant features which improves generalization. However the naive integration of contrastive loss to world models is not good enough as world-model-based RL methods independently optimize representation learning and agent policy. To overcome this issue we propose an intervention-invariant regularizer in the form of an auxiliary task such as depth prediction image denoising image segmentation etc. that explicitly enforces invariance to style interventions. Our method outperforms current state-of-the-art model-based and model-free RL methods and significantly improves on out-of-distribution point navigation tasks evaluated on the iGibson benchmark. With only visual observations we further demonstrate that our approach outperforms recent language-guided foundation models for point navigation which is essential for deployment on robots with limited computation capabilities. Finally we demonstrate that our proposed model excels at the sim-to-real transfer of its perception module on the Gibson benchmark.

Although polygon meshes have been a standard representation in geometry processing their irregular and combinatorial nature hinders their suitability for learning-based applications. In this work we introduce a novel learnable mesh representation through a set of local 3D sample Points and their associated Normals and Quadric error metrics (QEM) w.r.t. the underlying shape which we denote PoNQ. A global mesh is directly derived from PoNQ by efficiently leveraging the knowledge of the local quadric errors. Besides marking the first use of QEM within a neural shape representation our contribution guarantees both topological and geometrical properties by ensuring that a PoNQ mesh does not self-intersect and is always the boundary of a volume. Notably our representation does not rely on a regular grid is supervised directly by the target surface alone and also handles open surfaces with boundaries and/or sharp features. We demonstrate the efficacy of PoNQ through a learning-based mesh prediction from SDF grids and show that our method surpasses recent state-of-the-art techniques in terms of both surface and edge-based metrics.

The primary focus of Neural Representation for Videos (NeRV) is to effectively model its spatiotemporal consistency. However current NeRV systems often face a significant issue of spatial inconsistency leading to decreased perceptual quality. To address this issue we introduce the Pyramidal Neural Representation for Videos (PNeRV) which is built on a multi-scale information connection and comprises a lightweight rescaling operator Kronecker Fully-connected layer (KFc) and a Benign Selective Memory (BSM) mechanism. The KFc inspired by the tensor decomposition of the vanilla Fully-connected layer facilitates low-cost rescaling and global correlation modeling. BSM merges high-level features with granular ones adaptively. Furthermore we provide an analysis based on the Universal Approximation Theory of the NeRV system and validate the effectiveness of the proposed PNeRV. We conducted comprehensive experiments to demonstrate that PNeRV surpasses the performance of contemporary NeRV models achieving the best results in video regression on UVG and DAVIS under various metrics (PSNR SSIM LPIPS and FVD). Compared to vanilla NeRV PNeRV achieves a +4.49 dB gain in PSNR and a 231% increase in FVD on UVG along with a +3.28 dB PSNR and 634% FVD increase on DAVIS.

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.

We investigate a fundamental aspect of machine vision: the measurement of features by revisiting clustering one of the most classic approaches in machine learning and data analysis. Existing visual feature extractors including ConvNets ViTs and MLPs represent an image as rectangular regions. Though prevalent such a grid-style paradigm is built upon engineering practice and lacks explicit modeling of data distribution. In this work we propose feature extraction with clustering (FEC) a conceptually elegant yet surprisingly ad-hoc interpretable neural clustering framework which views feature extraction as a process of selecting representatives from data and thus automatically captures the underlying data distribution. Given an image FEC alternates between grouping pixels into individual clusters to abstract representatives and updating the deep features of pixels with current representatives. Such an iterative working mechanism is implemented in the form of several neural layers and the final representatives can be used for downstream tasks. The cluster assignments across layers which can be viewed and inspected by humans make the forward process of FEC fully transparent and empower it with promising ad-hoc interpretability. Extensive experiments on various visual recognition models and tasks verify the effectiveness generality and interpretability of FEC. We expect this work will provoke a rethink of the current de facto grid-style paradigm.

The emergence of Neural Radiance Fields (NeRF) has greatly impacted 3D scene modeling and novel-view synthesis. As a kind of visual media for 3D scene representation compression with high rate-distortion performance is an eternal target. Motivated by advances in neural compression and neural field representation we propose NeRFCodec an end-to-end NeRF compression framework that integrates non-linear transform quantization and entropy coding for memory-efficient scene representation. Since training a non-linear transform directly on a large scale of NeRF feature planes is impractical we discover that pre-trained neural 2D image codec can be utilized for compressing the features when adding content-specific parameters. Specifically we reuse neural 2D image codec but modify its encoder and decoder heads while keeping the other parts of the pre-trained decoder frozen. This allows us to train the full pipeline via supervision of rendering loss and entropy loss yielding the rate-distortion balance by updating the content-specific parameters. At test time the bitstreams containing latent code feature decoder head and other side information are transmitted for communication. Experimental results demonstrate our method outperforms existing NeRF compression methods enabling high-quality novel view synthesis with a memory budget of 0.5 MB.

Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However the slide representations resulting from this approach are highly tailored to specific clinical tasks which limits their expressivity and generalization particularly in scenarios with limited data. Instead we hypothesize that morphological redundancy in tissue can be leveraged to build a task-agnostic slide representation in an unsupervised fashion. To this end we introduce PANTHER a prototype-based approach rooted in the Gaussian mixture model that summarizes the set of WSI patches into a much smaller set of morphological prototypes. Specifically each patch is assumed to have been generated from a mixture distribution where each mixture component represents a morphological exemplar. Utilizing the estimated mixture parameters we then construct a compact slide representation that can be readily used for a wide range of downstream tasks. By performing an extensive evaluation of PANTHER on subtyping and survival tasks using 13 datasets we show that 1) PANTHER outperforms or is on par with supervised MIL baselines and 2) the analysis of morphological prototypes brings new qualitative and quantitative insights into model interpretability. The code is available at https://github.com/mahmoodlab/Panther.

Crack segmentation datasets make great efforts to obtain the ground truth crack or non-crack labels as clearly as possible. However it can be observed that ambiguities are still inevitable when considering the marginal non-crack region due to low contrast and heterogeneous texture. To solve this problem we propose a novel clustering-inspired representation learning framework which contains a two-phase strategy for automatic crack segmentation. In the first phase a pre-process is proposed to localize the marginal non-crack region. Then we propose an ambiguity-aware segmentation loss (Aseg Loss) that enables crack segmentation models to capture ambiguities in the above regions via learning segmentation variance which allows us to further localize ambiguous regions. In the second phase to learn the discriminative features of the above regions we propose a clustering-inspired loss (CI Loss) that alters the supervision learning of these regions into an unsupervised clustering manner. We demonstrate that the proposed method could surpass the existing crack segmentation models on various datasets and our constructed CrackSeg5k dataset.