Existing 3D scene understanding methods are heavily focused on 3D semantic and instance segmentation. However identifying objects and their parts only constitutes an intermediate step towards a more fine-grained goal which is effectively interacting with the functional interactive elements (e.g. handles knobs buttons) in the scene to accomplish diverse tasks. To this end we introduce SceneFun3D a large-scale dataset with more than 14.8k highly accurate interaction annotations for 710 high-resolution real-world 3D indoor scenes. We accompany the annotations with motion parameter information describing how to interact with these elements and a diverse set of natural language descriptions of tasks that involve manipulating them in the scene context. To showcase the value of our dataset we introduce three novel tasks namely functionality segmentation task-driven affordance grounding and 3D motion estimation and adapt existing state-of-the-art methods to tackle them. Our experiments show that solving these tasks in real 3D scenes remains challenging despite recent progress in closed-set and open-set 3D scene understanding methods.
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Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
PDF ↗Chain-of-Thought (CoT) guides large language models (LLMs) to reason step-by-step and can motivate their logical reasoning ability. While effective for logical tasks CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements. In this paper we explore the Leap-of-Thought (LoT) abilities within LLMs -- a non-sequential creative paradigm involving strong associations and knowledge leaps. To this end we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image text or both and thus is suitable for LoT study. Then to investigate LLMs' LoT ability in the Oogiri game we first build a multimodal and multilingual Oogiri-GO dataset which contains over 130000 samples from the Oogiri game and observe the insufficient LoT ability or failures of most existing LLMs on the Oogiri game. Accordingly we introduce a creative Leap-of-Thought (CLoT) paradigm to improve LLM's LoT ability. CLoT first formulates the Oogiri-GO dataset into LoT-oriented instruction tuning data to train pretrained LLM for achieving certain LoT humor generation and discrimination abilities. Then CLoT designs an explorative self-refinement that encourages the LLM to generate more creative LoT data via exploring parallels between seemingly unrelated concepts and selects high-quality data to train itself for self-refinement. CLoT not only excels in humor generation in the Oogiri game as shown in Fig. 1 but also boosts creative abilities in various tasks like "cloud guessing game" and "divergent association task". These findings advance our understanding and offer a pathway to improve LLMs' creative capacities for innovative applications across domains. The dataset code and models have been released online: https://zhongshsh.github.io/CLoT.
Training deep neural networks has become a common approach for addressing image restoration problems. An alternative for training a "task-specific" network for each observation model is to use pretrained deep denoisers for imposing only the signal's prior within iterative algorithms without additional training. Recently a sampling-based variant of this approach has become popular with the rise of diffusion/score-based generative models. Using denoisers for general purpose restoration requires guiding the iterations to ensure agreement of the signal with the observations. In low-noise settings guidance that is based on back-projection (BP) has been shown to be a promising strategy (used recently also under the names "pseudoinverse" or "range/-space" guidance). However the presence of noise in the observations hinders the gains from this approach. In this paper we propose a novel guidance technique based on preconditioning that allows traversing from BP-based guidance to least squares based guidance along the restoration scheme. The proposed approach is robust to noise while still having much simpler implementation than alternative methods (e.g. it does not require SVD or a large number of iterations). We use it within both an optimization scheme and a sampling-based scheme and demonstrate its advantages over existing methods for image deblurring and super-resolution.
One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion
PDF ↗Recent advancements in open-world 3D object generation have been remarkable with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However most existing models fall short in simultaneously providing rapid generation speeds and high fidelity to input images - two features essential for practical applications. In this paper we present One-2-3-45++ an innovative method that transforms a single image into a detailed 3D textured mesh in approximately one minute. Our approach aims to fully harness the extensive knowledge embedded in 2D diffusion models and priors from valuable yet limited 3D data. This is achieved by initially finetuning a 2D diffusion model for consistent multi-view image generation followed by elevating these images to 3D with the aid of multi-view-conditioned 3D native diffusion models. Extensive experimental evaluations demonstrate that our method can produce high-quality diverse 3D assets that closely mirror the original input image.
As a new embodied vision task Instance ImageGoal Navigation (IIN) aims to navigate to a specified object depicted by a goal image in an unexplored environment. The main challenge of this task lies in identifying the target object from different viewpoints while rejecting similar distractors. Existing ImageGoal Navigation methods usually adopt the simple Exploration-Exploitation framework and ignore the identification of specific instance during navigation. In this work we propose to imitate the human behaviour of "getting closer to confirm" when distinguishing objects from a distance. Specifically we design a new modular navigation framework named Instance-aware Exploration-Verification-Exploitation (IEVE) for instancelevel image goal navigation. Our method allows for active switching among the exploration verification and exploitation actions thereby facilitating the agent in making reasonable decisions under different situations. On the challenging HabitatMatterport 3D semantic (HM3DSEM) dataset our method surpasses previous state-of-theart work with a classical segmentation model (0.684 vs. 0.561 success) or a robust model (0.702 vs. 0.561 success). Our code will be made publicly available at https://github.com/XiaohanLei/IEVE.
Inliers estimation constitutes a pivotal step in partially overlapping point cloud registration. Existing methods broadly obey coordinate-based scheme where inlier confidence is scored through simply capturing coordinate differences in the context. However this scheme results in massive inlier misinterpretation readily consequently affecting the registration performance. In this paper we explore to extend a new definition called inlier confidence calibration (ICC) to alleviate the above issues. Firstly we provide finely initial correspondences for ICC in order to generate high quality reference point cloud copy corresponding to the source point cloud. In particular we develop a soft assignment matrix optimization theorem that offers faster speed and greater precision compared to Sinkhorn. Benefiting from the high quality reference copy we argue the neighborhood patch formed by inlier and its neighborhood should have consistency between source point cloud and its reference copy. Based on this insight we construct transformation-invariant geometric constraints and capture geometric structure consistency to calibrate inlier confidence for estimated correspondences between source point cloud and its reference copy. Finally transformation is further calculated by the weighted SVD algorithm with the calibrated inlier confidence. Our model is trained in an unsupervised manner and extensive experiments on synthetic and real-world datasets illustrate the effectiveness of the proposed method.
We introduce MeshGPT a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes in contrast to dense triangle meshes extracted by iso-surfacing methods from neural fields. Inspired by recent advances in powerful large language models we adopt a sequence-based approach to autoregressively generate triangle meshes as sequences of triangles. We first learn a vocabulary of latent quantized embeddings using graph convolutions which inform these embeddings of the local mesh geometry and topology. These embeddings are sequenced and decoded into triangles by a decoder ensuring that they can effectively reconstruct the mesh. A transformer is then trained on this learned vocabulary to predict the index of the next embedding given previous embeddings. Once trained our model can be autoregressively sampled to generate new triangle meshes directly generating compact meshes with sharp edges more closely imitating the efficient triangulation patterns of human-crafted meshes. MeshGPT demonstrates a notable improvement over state of the art mesh generation methods with a 9% increase in shape coverage and a 30-point enhancement in FID scores across various categories.
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
PDF ↗Heterogeneous Federated Learning (HtFL) enables collaborative learning on multiple clients with different model architectures while preserving privacy. Despite recent research progress knowledge sharing in HtFL is still difficult due to data and model heterogeneity. To tackle this issue we leverage the knowledge stored in pre-trained generators and propose a new upload-efficient knowledge transfer scheme called Federated Knowledge-Transfer Loop (FedKTL). Our FedKTL can produce client-task-related prototypical image-vector pairs via the generator's inference on the server. With these pairs each client can transfer pre-existing knowledge from the generator to its local model through an additional supervised local task. We conduct extensive experiments on four datasets under two types of data heterogeneity with 14 kinds of models including CNNs and ViTs. Results show that our upload-efficient FedKTL surpasses seven state-of-the-art methods by up to 7.31% in accuracy. Moreover our knowledge transfer scheme is applicable in scenarios with only one edge client. Code: https://github.com/TsingZ0/FedKTL
We present GLEE in this work an object-level foundation model for locating and identifying objects in images and videos. Through a unified framework GLEEaccomplishes detection segmentation tracking grounding and identification of arbitrary objects in the open world scenario for various object perception tasks. Adopting a cohesive learning strategy GLEE acquires knowledge from diverse data sources with varying supervision levels to formulate general object representations excelling in zero-shot transfer to new data and tasks. Specifically we employ an image encoder text encoder and visual prompter to handle multi-modal inputs enabling to simultaneously solve various object-centric downstream tasks while maintaining state-of-the-art performance. Demonstrated through extensive training on over five million images from diverse benchmarks GLEE exhibits remarkable versatility and improved generalization performance efficiently tackling downstream tasks without the need for task-specific adaptation. By integrating large volumes of automatically labeled data we further enhance its zero-shot generalization capabilities. Additionally GLEE is capable of being integrated into Large Language Models serving as a foundational model to provide universal object-level information for multi-modal tasks. We hope that the versatility and universality of our method will mark a significant step in the development of efficient visual foundation models for AGI systems. The models and code are released at https://github.com/FoundationVision/GLEE.
3D object detection and pose estimation from a single-view image is challenging due to the high uncertainty caused by the absence of 3D perception. As a solution recent monocular 3D detection methods leverage additional modalities such as stereo image pairs and LiDAR point clouds to enhance image features at the expense of additional annotation costs. We propose using diffusion models to learn effective representations for monocular 3D detection without additional modalities or training data. We present MonoDiff a novel framework that employs the reverse diffusion process to estimate 3D bounding box and orientation. But considering the variability in bounding box sizes along different dimensions it is ineffective to sample noise from a standard Gaussian distribution. Hence we adopt a Gaussian mixture model to sample noise during the forward diffusion process and initialize the reverse diffusion process. Furthermore since the diffusion model generates the 3D parameters for a given object image we leverage 2D detection information to provide additional supervision by maintaining the correspondence between 3D/2D projection. Finally depending on the signal-to-noise ratio we incorporate a dynamic weighting scheme to account for the level of uncertainty in the supervision by projection at different timesteps. MonoDiff outperforms current state-of-the-art monocular 3D detection methods on the KITTI and Waymo benchmarks without additional depth priors. MonoDiff project is available at: https://dylran.github.io/monodiff.github.io.
Characters are an important aspect of any storyline and identifying and including them in descriptions is necessary for story understanding. While previous work has largely ignored identity and generated captions with someone (anonymized names) recent work formulates id-aware captioning as a fill-in-the-blanks (FITB) task where given a caption with blanks the goal is to predict person id labels. However to predict captions with ids a two-stage approach is required: first predict captions with someone then fill in identities. In this work we present a new single stage approach that can seamlessly switch between id-aware caption generation or FITB when given a caption with blanks. Our model Movie-Identity Captioner (MICap) uses a shared auto-regressive decoder that benefits from training with FITB and full-caption generation objectives while the encoder can benefit from or disregard captions with blanks as input. Another challenge with id-aware captioning is the lack of a metric to capture subtle differences between person ids. To this end we introduce iSPICE a caption evaluation metric that focuses on identity tuples created through intermediate scene graphs. We evaluate MICap on Large-Scale Movie Description Challenge (LSMDC) where we show a 4.2% improvement in FITB accuracy and a 1-2% bump in classic captioning metrics.
Surgical decisions are informed by aligning rapid portable 2D intraoperative images (e.g. X-rays) to a high-fidelity 3D preoperative reference scan (e.g. CT). However 2D/3D registration can often fail in practice: conventional optimization methods are prohibitively slow and susceptible to local minima while neural networks trained on small datasets fail on new patients or require impractical landmark supervision. We present DiffPose a self-supervised approach that leverages patient-specific simulation and differentiable physics-based rendering to achieve accurate 2D/3D registration without relying on manually labeled data. Preoperatively a CNN is trained to regress the pose of a randomly oriented synthetic X-ray rendered from the preoperative CT. The CNN then initializes rapid intraoperative test-time optimization that uses the differentiable X-ray renderer to refine the solution. Our work further proposes several geometrically principled methods for sampling camera poses from SE(3) for sparse differentiable rendering and for driving registration in the tangent space se(3) with geodesic and multiscale locality-sensitive losses. DiffPose achieves sub-millimeter accuracy across surgical datasets at intraoperative speeds improving upon existing unsupervised methods by an order of magnitude and even outperforming supervised baselines. Our implementation is at https://github.com/eigenvivek/DiffPose.
In this paper we address the weakly-supervised Audio-Visual Video Parsing (AVVP) problem which aims at labeling events in a video as audible visible or both and temporally localizing and classifying them into known categories. This is challenging since we only have access to video-level (weak) event labels when training but need to predict event labels at the segment (frame) level at test time. Recent methods employ multiple-instance learning (MIL) techniques that tend to focus solely on the most discriminative segments resulting in frequent misclassifications. Our idea is to first construct several prototype features for each event class by clustering key segments identified for the event in the training data. We then assign pseudo labels to all training segments based on their feature similarities with these prototypes and re-train the model under weak and strong supervision. We facilitate this by structuring the feature space with contrastive learning using pseudo labels. Experiments show that we outperform existing methods for weakly-supervised AVVP. We also show that learning with weak and iteratively re-estimated pseudo labels can be interpreted as an expectation-maximization (EM) algorithm providing further insight for our training procedure.
While recent Vision-Language (VL) models excel at open-vocabulary tasks it is unclear how to use them with specific or uncommon concepts. Personalized Text-to-Image Retrieval (TIR) or Generation (TIG) are recently introduced tasks that represent this challenge where the VL model has to learn a concept from few images and respectively discriminate or generate images of the target concept in arbitrary contexts. We identify the ability to learn new meanings and their compositionality with known ones as two key properties of a personalized system. We show that the available benchmarks offer a limited validation of personalized textual concept learning from images with respect to the above properties and introduce ConCon-Chi as a benchmark for both personalized TIR and TIG designed to fill this gap. We modelled the new-meaning concepts by crafting chimeric objects and formulating a large varied set of contexts where we photographed each object. To promote the compositionality assessment of the learned concepts with known contexts we combined different contexts with the same concept and vice-versa. We carry out a thorough evaluation of state-of-the-art methods on the resulting dataset. Our study suggests that future work on personalized TIR and TIG methods should focus on the above key properties and we propose principles and a dataset for their performance assessment. Dataset: https://doi.org/10.48557/QJ1166 and code: https://github.com/hsp-iit/concon-chi_benchmark.
In this work we present RepKPU an efficient network for point cloud upsampling. We propose to promote upsampling performance by exploiting better shape representation and point generation strategy. Inspired by KPConv we propose a novel representation called RepKPoints to effectively characterize the local geometry whose advantages over prior representations are as follows: (1) density-sensitive; (2) large receptive fields; (3) position-adaptive which makes RepKPoints a generalized form of previous representations. Moreover we propose a novel paradigm namely Kernel-to-Displacement generation for point generation where point cloud upsampling is reformulated as the deformation of kernel points. Specifically we propose KP-Queries which is a set of kernel points with predefined positions and learned features to serve as the initial state of upsampling. Using cross-attention mechanisms we achieve interactions between RepKPoints and KP-Queries and subsequently KP-Queries are converted to displacement features followed by a MLP to predict the new positions of KP-Queries which serve as the generated points. Extensive experimental results demonstrate that RepKPU outperforms state-of-the-art methods on several widely-used benchmark datasets with high efficiency.
Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics treatment evaluation and clinical research. The complex kidney system comprises various components across multiple levels including regions (cortex medulla) functional units (glomeruli tubules) and cells (podocytes mesangial cells in glomerulus). Prior studies have predominantly overlooked the intricate spatial interrelations among objects from clinical knowledge. In this research we introduce a novel universal proposition learning approach called panoramic renal pathology segmentation (PrPSeg) designed to segment comprehensively panoramic structures within kidney by integrating extensive knowledge of kidney anatomy. In this paper we propose (1) the design of a comprehensive universal proposition matrix for renal pathology facilitating the incorporation of classification and spatial relationships into the segmentation process; (2) a token-based dynamic head single network architecture with the improvement of the partial label image segmentation and capability for future data enlargement; and (3) an anatomy loss function quantifying the inter-object relationships across the kidney.
Text-to-video (T2V) synthesis has gained increasing attention in the community in which the recently emerged diffusion models (DMs) have promisingly shown stronger performance than the past approaches. While existing state-of-the-art DMs are competent to achieve high-resolution video generation they may largely suffer from key limitations (e.g. action occurrence disorders crude video motions) with respect to the intricate temporal dynamics modeling one of the crux of video synthesis. In this work we investigate strengthening the awareness of video dynamics for DMs for high-quality T2V generation. Inspired by human intuition we design an innovative dynamic scene manager (dubbed as Dysen) module which includes (step-1) extracting from input text the key actions with proper time-order arrangement (step-2) transforming the action schedules into the dynamic scene graph (DSG) representations and (step-3) enriching the scenes in the DSG with sufficient and reasonable details. Taking advantage of the existing powerful LLMs (e.g. ChatGPT) via in-context learning Dysen realizes (nearly) human-level temporal dynamics understanding. Finally the resulting video DSG with rich action scene details is encoded as fine-grained spatio-temporal features integrated into the backbone T2V DM for video generating. Experiments on popular T2V datasets suggest that our Dysen-VDM consistently outperforms prior arts with significant margins especially in scenarios with complex actions.
We introduce a new attention mechanism dubbed structural self-attention (StructSA) that leverages rich correlation patterns naturally emerging in key-query interactions of attention. StructSA generates attention maps by recognizing space-time structures of key-query correlations via convolution and uses them to dynamically aggregate local contexts of value features. This effectively leverages rich structural patterns in images and videos such as scene layouts object motion and inter-object relations.Using StructSA as a main building block we develop the structural vision transformer (StructViT) and evaluate its effectiveness on both image and video classification tasks achieving state-of-the-art results on ImageNet-1K Kinetics-400 Something-Something V1 & V2 Diving-48 and FineGym.
Unveiling the Power of Audio-Visual Early Fusion Transformers with Dense Interactions through Masked Modeling
PDF ↗Humans possess a remarkable ability to integrate auditory and visual information enabling a deeper understanding of the surrounding environment. This early fusion of audio and visual cues demonstrated through cognitive psychology and neuroscience research offers promising potential for developing multimodal perception models. However training early fusion architectures poses significant challenges as the increased model expressivity requires robust learning frameworks to harness their enhanced capabilities. In this paper we address this challenge by leveraging the masked reconstruction framework previously successful in unimodal settings to train audio-visual encoders with early fusion. Additionally we propose an attention-based fusion module that captures interactions between local audio and visual representations enhancing the model's ability to capture fine-grained interactions. While effective this procedure can become computationally intractable as the number of local representations increases. Thus to address the computational complexity we propose an alternative procedure that factorizes the local representations before representing audio-visual interactions. Extensive evaluations on a variety of datasets demonstrate the superiority of our approach in audio-event classification visual sound localization sound separation and audio-visual segmentation. These contributions enable the efficient training of deeply integrated audio-visual models and significantly advance the usefulness of early fusion architectures.
Point clouds captured by different sensors such as RGB-D cameras and LiDAR possess non-negligible domain gaps. Most existing methods design different network architectures and train separately on point clouds from various sensors. Typically point-based methods achieve outstanding performances on even-distributed dense point clouds from RGB-D cameras while voxel-based methods are more efficient for large-range sparse LiDAR point clouds. In this paper we propose geometry-to-voxel auxiliary learning to enable voxel representations to access point-level geometric information which supports better generalisation of the voxel-based backbone with additional interpretations of multi-sensor point clouds. Specifically we construct hierarchical geometry pools generated by a voxel-guided dynamic point network which efficiently provide auxiliary fine-grained geometric information adapted to different stages of voxel features. We conduct experiments on joint multi-sensor datasets to demonstrate the effectiveness of GeoAuxNet. Enjoying elaborate geometric information our method outperforms other models collectively trained on multi-sensor datasets and achieve competitive results with the-state-of-art experts on each single dataset.