White balance (WB) algorithms in many commercial cameras assume single and uniform illumination leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene. Prior research on multi-illuminant WB typically predicts illumination at the pixel level without fully grasping the scene's actual lighting conditions including the number and color of light sources. This often results in unnatural outcomes lacking in overall consistency. To handle this problem we present a deep white balancing model that leverages the slot attention where each slot is in charge of representing individual illuminants. This design enables the model to generate chromaticities and weight maps for individual illuminants which are then fused to compose the final illumination map. Furthermore we propose the centroid-matching loss which regulates the activation of each slot based on the color range thereby enhancing the model to separate illumination more effectively. Our method achieves the state-of-the-art performance on both single- and multi-illuminant WB benchmarks and also offers additional information such as the number of illuminants in the scene and their chromaticity. This capability allows for illumination editing an application not feasible with prior methods.
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SPECAT: SPatial-spEctral Cumulative-Attention Transformer for High-Resolution Hyperspectral Image Reconstruction
PDF ↗Compressive spectral image reconstruction is a critical method for acquiring images with high spatial and spectral resolution. Current advanced methods which involve designing deeper networks or adding more self-attention modules are limited by the scope of attention modules and the irrelevance of attentions across different dimensions. This leads to difficulties in capturing non-local mutation features in the spatial-spectral domain and results in a significant parameter increase but only limited performance improvement. To address these issues we propose SPECAT a SPatial-spEctral Cumulative-Attention Transformer designed for high-resolution hyperspectral image reconstruction. SPECAT utilizes Cumulative-Attention Blocks (CABs) within an efficient hierarchical framework to extract features from non-local spatial-spectral details. Furthermore it employs a projection-object Dual-domain Loss Function (DLF) to integrate the optical path constraint a physical aspect often overlooked in current methodologies. Ultimately SPECAT not only significantly enhances the reconstruction quality of spectral details but also breaks through the bottleneck of mutual restriction between the cost and accuracy in existing algorithms. Our experimental results demonstrate the superiority of SPECAT achieving 40.3 dB in hyperspectral reconstruction benchmarks outperforming the state-of-the-art (SOTA) algorithms by 1.2 dB while using only 5% of the network parameters and 10% of the computational cost. The code is available at https://github.com/THU-luvision/SPECAT.
Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e. samples from open-set anomaly classes) while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies current OSAD methods can often largely reduce false positive errors. However these methods are trained in a closed-set setting and treat the anomaly examples as from a homogeneous distribution rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. This paper proposes to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end we introduce a novel approach namely Anomaly Heterogeneity Learning (AHL) that simulates a diverse set of heterogeneous anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model in surrogate open-set environments. Further AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies and 2) effectively generalize to unseen anomalies in new domains. Code is available at https://github.com/mala-lab/AHL.
Local Interpretable Model-agnostic Explanations (LIME) - a widely used post-ad-hoc model agnostic explainable AI (XAI) technique. It works by training a simple transparent (surrogate) model using random samples drawn around the neighborhood of the instance (image) to be explained (IE). Explanations are then extracted for a black-box model and a given IE using the surrogate model. However the explanations of LIME suffer from inconsistency across different runs for the same model and the same IE. We identify two main types of inconsistencies: variance in the sign and importance ranks of the segments (superpixels). These factors hinder LIME from obtaining consistent explanations. We analyze these inconsistencies and propose a new method Stabilized LIME for Consistent Explanations (SLICE). The proposed method handles the stabilization problem in two aspects: using a novel feature selection technique to eliminate spurious superpixels and an adaptive perturbation technique to generate perturbed images in the neighborhood of IE. Our results demonstrate that the explanations from SLICE exhibit significantly better consistency and fidelity than LIME (and its variant BayLime).
This paper addresses the challenge of object-centric layout generation under spatial constraints seen in multiple domains including floorplan design process. The design process typically involves specifying a set of spatial constraints that include object attributes like size and inter-object relations such as relative positioning. Existing works which typically represent objects as single nodes lack the granularity to accurately model complex interactions between objects. For instance often only certain parts of an object like a room's right wall interact with adjacent objects. To address this gap we introduce a factor graph based approach with four latent variable nodes for each room and a factor node for each constraint. The factor nodes represent dependencies among the variables to which they are connected effectively capturing constraints that are potentially of a higher order. We then develop message-passing on the bipartite graph forming a factor graph neural network that is trained to produce a floorplan that aligns with the desired requirements. Our approach is simple and generates layouts faithful to the user requirements demonstrated by a large improvement in IOU scores over existing methods. Additionally our approach being inferential and accurate is well-suited to the practical human-in-the-loop design process where specifications evolve iteratively offering a practical and powerful tool for AI-guided design.
Cooperative perception offers several benefits for enhancing the capabilities of autonomous vehicles and improving road safety. Using roadside sensors in addition to onboard sensors increases reliability and extends the sensor range. External sensors offer higher situational awareness for automated vehicles and prevent occlusions. We propose CoopDet3D a cooperative multi-modal fusion model and TUMTraf-V2X a perception dataset for the cooperative 3D object detection and tracking task. Our dataset contains 2000 labeled point clouds and 5000 labeled images from five roadside and four onboard sensors. It includes 30k 3D boxes with track IDs and precise GPS and IMU data. We labeled nine categories and covered occlusion scenarios with challenging driving maneuvers like traffic violations near-miss events overtaking and U-turns. Through multiple experiments we show that our CoopDet3D camera-LiDAR fusion model achieves an increase of +14.36 3D mAP compared to a vehicle camera-LiDAR fusion model. Finally we make our dataset model labeling tool and devkit publicly available on our website: https://tum-traffic-dataset.github.io/tumtraf-v2x.
We address the problem of online action segmentation for egocentric procedural task videos. While previous studies have mostly focused on offline action segmentation where entire videos are available for both training and inference the transition to online action segmentation is crucial for practical applications like AR/VR task assistants. Notably applying an offline-trained model directly to online inference results in a significant performance drop due to the inconsistency between training and inference. We propose an online action segmentation framework by first modifying existing architectures to make them causal. Second we develop a novel action progress prediction module to dynamically estimate the progress of ongoing actions and using them to refine the predictions of causal action segmentation. Third we propose to learn task graphs from training videos and leverage them to obtain smooth and procedure-consistent segmentations. With the combination of progress and task graph with casual action segmentation our framework effectively addresses prediction uncertainty and oversegmentation in online action segmentation and achieves significant improvement on three egocentric datasets.
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 propose SceneTex a novel method for effectively generating high-quality and style-consistent textures for indoor scenes using depth-to-image diffusion priors. Unlike previous methods that either iteratively warp 2D views onto a mesh surface or distillate diffusion latent features without accurate geometric and style cues SceneTex formulates the texture synthesis task as an optimization problem in the RGB space where style and geometry consistency are properly reflected. At its core SceneTex proposes a multiresolution texture field to implicitly encode the mesh appearance. We optimize the target texture via a score-distillation-based objective function in respective RGB renderings. To further secure the style consistency across views we introduce a cross-attention decoder to predict the RGB values by cross-attending to the pre-sampled reference locations in each instance. SceneTex enables various and accurate texture synthesis for 3D-FRONT scenes demonstrating significant improvements in visual quality and prompt fidelity over the prior texture generation methods.
The problem of calibrating deep neural networks (DNNs) for multi-label learning is considered. It is well-known that DNNs trained by cross-entropy for single-label or one-hot classification are poorly calibrated. Many calibration techniques have been proposed to address the problem. However little attention has been paid to the calibration of multi-label DNNs. In this literature the focus has been on improving labeling accuracy in the face of severe dataset unbalance. This is addressed by the introduction of asymmetric losses which have became very popular. However these losses do not induce well calibrated classifiers. In this work we first provide a theoretical explanation for this poor calibration performance by showing that these loses losses lack the strictly proper property a necessary condition for accurate probability estimation. To overcome this problem we propose a new Strictly Proper Asymmetric (SPA) loss. This is complemented by a Label Pair Regularizer (LPR) that increases the number of calibration constraints introduced per training example. The effectiveness of both contributions is validated by extensive experiments on various multi-label datasets. The resulting training method is shown to significantly decrease the calibration error while maintaining state-of-the-art accuracy.
Stationary Representations: Optimally Approximating Compatibility and Implications for Improved Model Replacements
PDF ↗Learning compatible representations enables the interchangeable use of semantic features as models are updated over time. This is particularly relevant in search and retrieval systems where it is crucial to avoid reprocessing of the gallery images with the updated model. While recent research has shown promising empirical evidence there is still a lack of comprehensive theoretical understanding about learning compatible representations. In this paper we demonstrate that the stationary representations learned by the d-Simplex fixed classifier optimally approximate compatibility representation according to the two inequality constraints of its formal definition. This not only establishes a solid foundation for future works in this line of research but also presents implications that can be exploited in practical learning scenarios. An exemplary application is the now-standard practice of downloading and fine-tuning new pre-trained models. Specifically we show the strengths and critical issues of stationary representations in the case in which a model undergoing sequential fine-tuning is asynchronously replaced by downloading a better-performing model pre-trained elsewhere. Such a representation enables seamless delivery of retrieval service (i.e. no reprocessing of gallery images) and offers improved performance without operational disruptions during model replacement. Code available at: https://github.com/miccunifi/iamcl2r.
Customizing robotic behaviors to be aligned with diverse human preferences is an underexplored challenge in the field of embodied AI. In this paper we present Promptable Behaviors a novel framework that facilitates efficient personalization of robotic agents to diverse human preferences in complex environments. We use multi-objective reinforcement learning to train a single policy adaptable to a broad spectrum of preferences. We introduce three distinct methods to infer human preferences by leveraging different types of interactions: (1) human demonstrations (2) preference feedback on trajectory comparisons and (3) language instructions. We evaluate the proposed method in personalized object-goal navigation and flee navigation tasks in ProcTHOR and RoboTHOR demonstrating the ability to prompt agent behaviors to satisfy human preferences in various scenarios.
HiLo: Detailed and Robust 3D Clothed Human Reconstruction with High-and Low-Frequency Information of Parametric Models
PDF ↗Reconstructing 3D clothed human involves creating a detailed geometry of individuals in clothing with applications ranging from virtual try-on movies to games. To enable practical and widespread applications recent advances propose to generate a clothed human from an RGB image. However they struggle to reconstruct detailed and robust avatars simultaneously. We empirically find that the high-frequency (HF) and low-frequency (LF) information from a parametric model has the potential to enhance geometry details and improve robustness to noise respectively. Based on this we propose HiLo namely clothed human reconstruction with high- and low-frequency information which contains two components. 1) To recover detailed geometry using HF information we propose a progressive HF Signed Distance Function to enhance the detailed 3D geometry of a clothed human. We analyze that our progressive learning manner alleviates large gradients that hinder model convergence. 2) To achieve robust reconstruction against inaccurate estimation of the parametric model by using LF information we propose a spatial interaction implicit function. This function effectively exploits the complementary spatial information from a low-resolution voxel grid of the parametric model. Experimental results demonstrate that HiLo outperforms the state-of-the-art methods by 10.43% and 9.54% in terms of Chamfer distance on the Thuman2.0 and CAPE datasets respectively. Additionally HiLo demonstrates robustness to noise from the parametric model challenging poses and various clothing styles.
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation
PDF ↗Diffusion-based models have gained significant popularity for text-to-image generation due to their exceptional image-generation capabilities. A risk with these models is the potential generation of inappropriate content such as biased or harmful images. However the underlying reasons for generating such undesired content from the perspective of the diffusion model's internal representation remain unclear. Previous work interprets vectors in an interpretable latent space of diffusion models as semantic concepts. However existing approaches cannot discover directions for arbitrary concepts such as those related to inappropriate concepts. In this work we propose a novel self-supervised approach to find interpretable latent directions for a given concept. With the discovered vectors we further propose a simple approach to mitigate inappropriate generation. Extensive experiments have been conducted to verify the effectiveness of our mitigation approach namely for fair generation safe generation and responsible text-enhancing generation. Project page: https://interpretdiffusion.github.io.
Channel pruning approaches for convolutional neural networks (ConvNets) deactivate the channels statically or dynamically and require special implementation. In addition channel squeezing in representative ConvNets is carried out via 1 x 1 convolutions which dominates a large portion of computations and network parameters. Given these challenges we propose an effective multi-purpose module for dynamic channel sampling namely Pick-or-Mix (PiX) which does not require special implementation. PiX divides a set of channels into subsets and then picks from them where the picking decision is dynamically made per each pixel based on the input activations. We plug PiX into prominent ConvNet architectures and verify its multi-purpose utilities. After replacing 1 x 1 channel squeezing layers in ResNet with PiX the network becomes 25% faster without losing accuracy. We show that PiX allows ConvNets to learn better data representation than widely adopted approaches to enhance networks' representation power (e.g. SE CBAM AFF SKNet and DWP). We also show that PiX achieves state-of-the-art performance on network downscaling and dynamic channel pruning applications.
This work aims to improve the efficiency of text-to-image diffusion models. While diffusion models use computationally expensive UNet-based denoising operations in every generation step we identify that not all operations are equally relevant for the final output quality. In particular we observe that UNet layers operating on high-res feature maps are relatively sensitive to small perturbations. In contrast low-res feature maps influence the semantic layout of the final image and can often be perturbed with no noticeable change in the output. Based on this observation we propose Clockwork Diffusion a method that periodically reuses computation from preceding denoising steps to approximate low-res feature maps at one or more subsequent steps. For multiple base- lines and for both text-to-image generation and image editing we demonstrate that Clockwork leads to comparable or improved perceptual scores with drastically reduced computational complexity. As an example for Stable Diffusion v1.5 with 8 DPM++ steps we save 32% of FLOPs with negligible FID and CLIP change. We re- lease code at https://github.com/Qualcomm-AI-research/clockwork-diffusion
In the field of deep point cloud understanding KPConv is a unique architecture that uses kernel points to locate convolutional weights in space instead of relying on Multi-Layer Perceptron (MLP) encodings. While it initially achieved success it has since been surpassed by recent MLP networks that employ updated designs and training strategies. Building upon the kernel point principle we present two novel designs: KPConvD (depthwise KPConv) a lighter design that enables the use of deeper architectures and KPConvX an innovative design that scales the depthwise convolutional weights of KPConvD with kernel attention values. Using KPConvX with a modern architecture and training strategy we are able to outperform current state-of-the-art approaches on the ScanObjectNN Scannetv2 and S3DIS datasets. We validate our design choices through ablation studies and release our code and models.
This paper views the DETR's non-duplicate detection ability as a competition result among object queries. Around each object there are usually multiple queries within which only a single one can win the chance to become the final detection. Such a competition is hard: while some competing queries initially have very close prediction scores their leading query has to dramatically enlarge its score superiority after several decoder layers. To help the leading query stands out this paper proposes EASE-DETR which eases the competition by introducing bias that favours the leading one. EASE-DETR is very simple: in every intermediate decoder layer we identify the "leading / trailing" relationship between any two queries and encode this binary relationship into the following decoder layer to amplify the superiority of the leading one. More concretely the leading query is to be protected from mutual query suppression in the self-attention layer and encouraged to absorb more object features in the cross-attention layer therefore accelerating to win. Experimental results show that EASE-DETR brings consistent and remarkable improvement to various DETRs.
Spike camera is a neuromorphic vision sensor that can capture highly dynamic scenes by generating a continuous stream of binary spikes to represent the arrival of photons at very high temporal resolution. Equipped with Bayer color filter array (CFA) color spike camera (CSC) has been invented to capture color information. Although spike camera has already demonstrated great potential for high-speed imaging its spatial resolution is limited compared with conventional digital cameras. This paper proposes a Color Spike Camera Super-Resolution (CSCSR) network to super-resolve higher-resolution color images from spike camera streams with Bayer CFA. To be specific we first propose a representation for Bayer-pattern spike streams exploring local temporal information with global perception to represent the binary data. Then we exploit the CFA layout and sub-pixel level motion to collect temporal pixels for the spatial super-resolution of each color channel. In particular a residual-based module for feature refinement is developed to reduce the impact of motion estimation errors. Considering color correlation we jointly utilize the multi-stage temporal-pixel features of color channels to reconstruct the high-resolution color image. Experimental results demonstrate that the proposed scheme can reconstruct satisfactory color images with both high temporal and spatial resolution from low-resolution Bayer-pattern spike streams. The source codes are available at https://github.com/csycdong/CSCSR.
Single-source domain generalization (SDG) for object detection is a challenging yet essential task as the distribution bias of the unseen domain degrades the algorithm performance significantly. However existing methods attempt to extract domain-invariant features neglecting that the biased data leads the network to learn biased features that are non-causal and poorly generalizable. To this end we propose an Unbiased Faster R-CNN (UFR) for generalizable feature learning. Specifically we formulate SDG in object detection from a causal perspective and construct a Structural Causal Model (SCM) to analyze the data bias and feature bias in the task which are caused by scene confounders and object attribute confounders. Based on the SCM we design a Global-Local Transformation module for data augmentation which effectively simulates domain diversity and mitigates the data bias. Additionally we introduce a Causal Attention Learning module that incorporates a designed attention invariance loss to learn image-level features that are robust to scene confounders. Moreover we develop a Causal Prototype Learning module with an explicit instance constraint and an implicit prototype constraint which further alleviates the negative impact of object attribute confounders. Experimental results on five scenes demonstrate the prominent generalization ability of our method with an improvement of 3.9% mAP on the Night-Clear scene.