Materials Discovery

Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vision models the Visual Concept Connectome (VCC) which discovers human interpretable concepts and their interlayer connections in a fully unsupervised manner. Our approach simultaneously reveals fine-grained concepts at a layer connection weightings across all layers and is amendable to global analysis of network structure (e.g. branching pattern of hierarchical concept assemblies). Previous work yielded ways to extract interpretable concepts from single layers and examine their impact on classification but did not afford multilayer concept analysis across an entire network architecture. Quantitative and qualitative empirical results show the effectiveness of VCCs in the domain of image classification. Also we leverage VCCs for the application of failure mode debugging to reveal where mistakes arise in deep networks.

This paper studies the problem of concept-based interpretability of transformer representations for videos. Concretely we seek to explain the decision-making process of video transformers based on high-level spatiotemporal concepts that are automatically discovered. Prior research on concept-based interpretability has concentrated solely on image-level tasks. Comparatively video models deal with the added temporal dimension increasing complexity and posing challenges in identifying dynamic concepts over time. In this work we systematically address these challenges by introducing the first Video Transformer Concept Discovery (VTCD) algorithm. To this end we propose an efficient approach for unsupervised identification of units of video transformer representations - concepts and ranking their importance to the output of a model. The resulting concepts are highly interpretable revealing spatio-temporal reasoning mechanisms and object-centric representations in unstructured video models. Performing this analysis jointly over a diverse set of supervised and self-supervised representations we discover that some of these mechanism are universal in video transformers. Finally we show that VTCD can be used for fine-grained action recognition and video object segmentation.

Generalized Category Discovery (GCD) aims to identify a mix of known and novel categories within unlabeled data sets providing a more realistic setting for image recognition. Essentially GCD needs to remember existing patterns thoroughly to recognize novel categories. Recent state-of-the-art method SimGCD transfers the knowledge from known-class data to the learning of novel classes through debiased learning. However some patterns are catastrophically forgot during adaptation and thus lead to poor performance in novel categories classification. To address this issue we propose a novel learning approach LegoGCD which is seamlessly integrated into previous methods to enhance the discrimination of novel classes while maintaining performance on previously encountered known classes. Specifically we design two types of techniques termed as \underline L ocal \underline E ntropy Re\underline g ularization (LER) and Dual-views Kullback-Leibler divergence c\underline o nstraint (DKL). The LER optimizes the distribution of potential known class samples in unlabeled data thus ensuring the preservation of knowledge related to known categories while learning novel classes. Meanwhile DKL introduces Kullback-Leibler divergence to encourage the model to produce a similar prediction distribution of two view samples from the same image. In this way it successfully avoids mismatched prediction and generates more reliable potential known class samples simultaneously. Extensive experiments validate that the proposed LegoGCD effectively addresses the known category forgetting issue across all datasets e.g. delivering a 7.74% and 2.51% accuracy boost on known and novel classes in CUB respectively. Our code is available at: https://github.com/Cliffia123/LegoGCD.

Despite exciting progress in automatic 3D reconstruction from images excessive and irregular triangular faces in the resulting meshes still constitute a significant challenge when it comes to adoption in practical artist workflows. Therefore we propose a method to extract regular quad-dominant meshes from posed images. More specifically we generate a high-quality 3D model through decomposition into an easily editable quad-dominant mesh with pixel-level details such as displacement materials and lighting. To enable end-to-end learning of shape and quad topology we QUADify a neural implicit representation using our novel differentiable re-meshing objective. Distinct from previous work our method exploits artifact-free Catmull-Clark subdivision combined with vertex displacement to extract pixel-level details linked to the base geometry. Finally we apply differentiable rendering techniques for material and lighting decomposition to optimize for image reconstruction. Our experiments show the benefits of end-to-end re-meshing and that our method yields state-of-the-art geometric accuracy while providing lightweight meshes with displacements and textures that are directly compatible with professional renderers and game engines.

Material appearance is a key component of photorealism with a pronounced impact on human perception. Although there are many prior works targeting at measuring opaque materials using light-weight setups (e.g. consumer-level cameras) little attention is paid on acquiring the optical properties of translucent materials which are also quite common in nature. In this paper we present a practical method for acquiring scattering properties of translucent materials based solely on ordinary images captured with unknown lighting and camera parameters. The key to our method is an inter-pixel translucency prior which states that image pixels of a given homogeneous translucent material typically form curves (dubbed translucent curves) in the RGB space of which the shapes are determined by the parameters of the material. We leverage this prior in a specially-designed convolutional neural network comprising multiple encoders a translucency-aware feature fusion module and a cascaded decoder. We demonstrate through both visual comparisons and quantitative evaluations that high accuracy can be achieved on a wide range of real-world translucent materials.

Ultra-fine-grained visual categorization (Ultra-FGVC) aims at distinguishing highly similar sub-categories within fine-grained objects such as different soybean cultivars. Compared to traditional fine-grained visual categorization Ultra-FGVC encounters more hurdles due to the small inter-class and large intra-class variation. Given these challenges relying on human annotation for Ultra-FGVC is impractical. To this end our work introduces a novel task termed Ultra-Fine-Grained Novel Class Discovery (UFG-NCD) which leverages partially annotated data to identify new categories of unlabeled images for Ultra-FGVC. To tackle this problem we devise a Region-Aligned Proxy Learning (RAPL) framework which comprises a Channel-wise Region Alignment (CRA) module and a Semi-Supervised Proxy Learning (SemiPL) strategy. The CRA module is designed to extract and utilize discriminative features from local regions facilitating knowledge transfer from labeled to unlabeled classes. Furthermore SemiPL strengthens representation learning and knowledge transfer with proxy-guided supervised learning and proxy-guided contrastive learning. Such techniques leverage class distribution information in the embedding space improving the mining of subtle differences between labeled and unlabeled ultra-fine-grained classes. Extensive experiments demonstrate that RAPL significantly outperforms baselines across various datasets indicating its effectiveness in handling the challenges of UFG-NCD. Code is available at https://github.com/SSDUT-Caiyq/UFG-NCD.

We introduce MatSynth a dataset of 4000+ CC0 ultra-high resolution PBR materials. Materials are crucial components of virtual relightable assets defining the interaction of light at the surface of geometries. Given their importance significant research effort was dedicated to their representation creation and acquisition. However in the past 6 years most research in material acquisition or generation relied either on the same unique dataset or on company-owned huge library of procedural materials. With this dataset we propose a significantly larger more diverse and higher resolution set of materials than previously publicly available. We carefully discuss the data collection process and demonstrate the benefits of this dataset for material acquisition and generation applications. The complete data further contains metadata with each material's origin license category tags creation method and when available descriptions and physical size as well as 3M+ renderings of the augmented materials in 1K under various environment lightings. The MatSynth dataset is released through the project page at: https://www.gvecchio.com/matsynth.

Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes given labeled data of known classes. To meet the recent decentralization trend in the community we introduce a practical yet challenging task Federated GCD (Fed-GCD) where the training data are distributed in local clients and cannot be shared among clients. Fed-GCD aims to train a generic GCD model by client collaboration under the privacy-protected constraint. The Fed-GCD leads to two challenges: 1) representation degradation caused by training each client model with fewer data than centralized GCD learning and 2) highly heterogeneous label spaces across different clients. To this end we propose a novel Associated Gaussian Contrastive Learning (AGCL) framework based on learnable GMMs which consists of a Client Semantics Association (CSA) and a global-local GMM Contrastive Learning (GCL). On the server CSA aggregates the heterogeneous categories of local-client GMMs to generate a global GMM containing more comprehensive category knowledge. On each client GCL builds class-level contrastive learning with both local and global GMMs. The local GCL learns robust representation with limited local data. The global GCL encourages the model to produce more discriminative representation with the comprehensive category relationships that may not exist in local data. We build a benchmark based on six visual datasets to facilitate the study of Fed-GCD. Extensive experiments show that our AGCL outperforms multiple baselines on all datasets.

Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult even when the object's 3D shape is known beforehand. This problem is especially challenging for diffuse objects: it is ill-conditioned because diffuse materials severely blur incoming light and it is ill-posed because diffuse materials under high-frequency lighting can be indistinguishable from shiny materials under low-frequency lighting. We show that it is possible to recover precise materials and illumination---even from diffuse objects---by exploiting unintended shadows like the ones cast onto an object by the photographer who moves around it. These shadows are a nuisance in most previous inverse rendering pipelines but here we exploit them as signals that improve conditioning and help resolve material-lighting ambiguities. We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatially-varying materials the surrounding illumination environment and the shapes of the unseen light occluders who inadvertently cast shadows upon it.

We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown. To address this generalized image clustering problem we revisit the mean-shift algorithm i.e. a classic powerful technique for mode seeking and incorporate it into a contrastive learning framework. The proposed method dubbed Contrastive Mean-Shift (CMS) learning trains an embedding network to produce representations with better clustering properties by an iterative process of mean shift and contrastive update. Experiments demonstrate that our method both in settings with and without the total number of clusters being known achieves state-of-the-art performance on six public GCD benchmarks without bells and whistles.

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task which endeavors to cluster unlabeled samples from both novel and old classes leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes and that novel categories are fully unlabeled GCD inherently faces intractable problems including imbalanced classification performance and inconsistent confidence between old and new classes especially in the low-labeling regime. Hence some annotations of new classes are deemed necessary. However labeling new classes is extremely costly. To address this issue we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem we devise an adaptive sampling strategy which jointly considers novelty informativeness and diversity to adaptively select novel samples with proper uncertainty. However owing to the varied orderings of label indices caused by the clustering of novel classes the queried labels are not directly applicable to subsequent training. To overcome this issue we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github.com/mashijie1028/ActiveGCD