David Crandall

Juhyung Ha, Jong Sung Park, David Crandall et al.

Medical image translation is the process of converting from one imaging modality to another, in order to reduce the need for multiple image acquisitions from the same patient. This can enhance the efficiency of treatment by reducing the time, equipment, and labor needed. In this paper, we introduce a multi-resolution guided Generative Adversarial Network (GAN)-based framework for 3D medical image translation. Our framework uses a 3D multi-resolution Dense-Attention UNet (3D-mDAUNet) as the generator and a 3D multi-resolution UNet as the discriminator, optimized with a unique combination of loss functions including voxel-wise GAN loss and 2.5D perception loss. Our approach yields promising results in volumetric image quality assessment (IQA) across a variety of imaging modalities, body regions, and age groups, demonstrating its robustness. Furthermore, we propose a synthetic-to-real applicability assessment as an additional evaluation to assess the effectiveness of synthetic data in downstream applications such as segmentation. This comprehensive evaluation shows that our method produces synthetic medical images not only of high-quality but also potentially useful in clinical applications. Our code is available at github.com/juhha/3D-mADUNet.

Vibhas Vats, Md. Alimoor Reza, David Crandall et al.

Traditional multi-view stereo (MVS) methods primarily depend on photometric and geometric consistency constraints. In contrast, modern learning-based algorithms often rely on the plane sweep algorithm to infer 3D geometry, applying explicit geometric consistency (GC) checks only as a post-processing step, with no impact on the learning process itself. In this work, we introduce GC MVSNet plus plus, a novel approach that actively enforces geometric consistency of reference view depth maps across multiple source views (multi view) and at various scales (multi scale) during the learning phase (see Fig. 1). This integrated GC check significantly accelerates the learning process by directly penalizing geometrically inconsistent pixels, effectively halving the number of training iterations compared to other MVS methods. Furthermore, we introduce a densely connected cost regularization network with two distinct block designs simple and feature dense optimized to harness dense feature connections for enhanced regularization. Extensive experiments demonstrate that our approach achieves a new state of the art on the DTU and BlendedMVS datasets and secures second place on the Tanks and Temples benchmark. To our knowledge, GC MVSNet plus plus is the first method to enforce multi-view, multi-scale supervised geometric consistency during learning. Our code is available.

Chi-Hsi Kung, Frangil Ramirez, Juhyung Ha et al.

Understanding a procedural activity requires modeling both how action steps transform the scene, and how evolving scene transformations can influence the sequence of action steps, even those that are accidental or erroneous. Existing work has studied procedure-aware video representations by modeling the temporal order of actions, but has not explicitly learned the state changes (scene transformations). In this work, we study procedure-aware video representation learning by incorporating state-change descriptions generated by Large Language Models (LLMs) as supervision signals for video encoders. Moreover, we generate state-change counterfactuals that simulate hypothesized failure outcomes, allowing models to learn by imagining unseen "What if" scenarios. This counterfactual reasoning facilitates the model's ability to understand the cause and effect of each step in an activity. We conduct extensive experiments on procedure-aware tasks, including temporal action segmentation, error detection, action phase classification, frame retrieval, multi-instance retrieval, and action recognition. Our results demonstrate the effectiveness of the proposed state-change descriptions and their counterfactuals, and achieve significant improvements on multiple tasks.

Haoran Liu, Anthony Tai, David J. Crandall et al.

Neural tangent kernels (NTKs) have been proposed to study the behavior of trained neural networks from the perspective of Gaussian processes. An important result in this body of work is the theorem of equivalence between a trained neural network and kernel regression with the corresponding NTK. This theorem allows for an interpretation of neural networks as special cases of kernel regression. However, does this theorem of equivalence hold in practice? In this paper, we revisit the derivation of the NTK rigorously and conduct numerical experiments to evaluate this equivalence theorem. We observe that adding a layer to a neural network and the corresponding updated NTK do not yield matching changes in the predictor error. Furthermore, we observe that kernel regression with a Gaussian process kernel in the literature that does not account for neural network training produces prediction errors very close to that of kernel regression with NTKs. These observations suggest the equivalence theorem does not hold well in practice and puts into question whether neural tangent kernels adequately address the training process of neural networks.

Frangil Ramirez, Elizabeth Clerkin, David J. Crandall et al.

Young children readily recognize and generalize visual objects labeled by common nouns, suggesting that these basic level object categories may be given. Yet if they are, how they arise remains unclear. We propose that the answer lies in the statistics of infant daily life visual experiences. Whereas large and diverse datasets typically support robust learning and generalization in human and machine learning, infants achieve this generalization from limited experiences. We suggest that the resolution of this apparent contradiction lies in the visual diversity of daily life, repeated experiences with single object instances. Analyzing egocentric images from 14 infants (aged 7 to 11 months) we show that their everyday visual input exhibits a lumpy similarity structure, with clusters of highly similar images interspersed with rarer, more variable ones, across eight early-learned categories. Computational experiments show that mimicking this structure in machines improves generalization from small datasets in machine learning. The natural lumpiness of infant experience may thus support early category learning and generalization and, more broadly, offer principles for efficient learning across a variety of problems and kinds of learners.

Yu Wang, Juhyung Ha, David J. Crandall

Active speaker detection (ASD) in egocentric videos presents unique challenges due to unstable viewpoints, motion blur, and off-screen speech sources - conditions under which traditional visual-centric methods degrade significantly. We introduce PAIR-Net (Pretrained Audio-Visual Integration with Regularization Network), an effective model that integrates a partially frozen Whisper audio encoder with a fine-tuned AV-HuBERT visual backbone to robustly fuse cross-modal cues. To counteract modality imbalance, we introduce an inter-modal alignment loss that synchronizes audio and visual representations, enabling more consistent convergence across modalities. Without relying on multi-speaker context or ideal frontal views, PAIR-Net achieves state-of-the-art performance on the Ego4D ASD benchmark with 76.6% mAP, surpassing LoCoNet and STHG by 8.2% and 12.9% mAP, respectively. Our results highlight the value of pretrained audio priors and alignment-based fusion for robust ASD under real-world egocentric conditions.

Chi-Hsi Kung, Frangil Ramirez, Juhyung Ha et al.

Understanding a procedural activity requires modeling both how action steps transform the scene, and how evolving scene transformations can influence the sequence of action steps, even those that are accidental or erroneous. Yet, existing work on procedure-aware video representations fails to explicitly learned the state changes (scene transformations). In this work, we study procedure-aware video representation learning by incorporating state-change descriptions generated by LLMs as supervision signals for video encoders. Moreover, we generate state-change counterfactuals that simulate hypothesized failure outcomes, allowing models to learn by imagining the unseen ``What if'' scenarios. This counterfactual reasoning facilitates the model's ability to understand the cause and effect of each step in an activity. To verify the procedure awareness of our model, we conduct extensive experiments on procedure-aware tasks, including temporal action segmentation, error detection, and more. Our results demonstrate the effectiveness of the proposed state-change descriptions and their counterfactuals, and achieve significant improvements on multiple tasks.

Yuchen Wang, Mingze Xu, John Paden et al.

Understanding the structure of Earth's polar ice sheets is important for modeling how global warming will impact polar ice and, in turn, the Earth's climate. Ground-penetrating radar is able to collect observations of the internal structure of snow and ice, but the process of manually labeling these observations is slow and laborious. Recent work has developed automatic techniques for finding the boundaries between the ice and the bedrock, but finding internal layers - the subtle boundaries that indicate where one year's ice accumulation ended and the next began - is much more challenging because the number of layers varies and the boundaries often merge and split. In this paper, we propose a novel deep neural network for solving a general class of tiered segmentation problems. We then apply it to detecting internal layers in polar ice, evaluating on a large-scale dataset of polar ice radar data with human-labeled annotations as ground truth.

Mingze Xu, Chenyou Fan, John D Paden et al.

Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical. It is less clear how well these techniques may apply on structured prediction problems where fine-grained output with high precision is required, such as in scientific imaging domains. Here we consider the problem of segmenting echogram radar data collected from the polar ice sheets, which is challenging because segmentation boundaries are often very weak and there is a high degree of noise. We propose a multi-task spatiotemporal neural network that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice surface boundaries from sequences of tomographic radar images. We show that our model outperforms the state-of-the-art on this problem by (1) avoiding the need for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being about 6 times faster.

Mingze Xu, David J Crandall, Geoffrey C Fox et al.

Ground-penetrating radar on planes and satellites now makes it practical to collect 3D observations of the subsurface structure of the polar ice sheets, providing crucial data for understanding and tracking global climate change. But converting these noisy readings into useful observations is generally done by hand, which is impractical at a continental scale. In this paper, we propose a computer vision-based technique for extracting 3D ice-bottom surfaces by viewing the task as an inference problem on a probabilistic graphical model. We first generate a seed surface subject to a set of constraints, and then incorporate additional sources of evidence to refine it via discrete energy minimization. We evaluate the performance of the tracking algorithm on 7 topographic sequences (each with over 3000 radar images) collected from the Canadian Arctic Archipelago with respect to human-labeled ground truth.