Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. The effectiveness of audio-visual learning critically depends on achieving accurate cross-modal alignment between sound and visual objects. Successful audio-visual learning requires two essential components: 1) a challenging dataset with high-quality pixel-level multi-class annotated images associated with audio files and 2) a model that can establish strong links between audio information and its corresponding visual object. However these requirements are only partially addressed by current methods with training sets containing biased audio-visual data and models that generalise poorly beyond this biased training set. In this work we propose a new cost-effective strategy to build challenging and relatively unbiased high-quality audio-visual segmentation benchmarks. We also propose a new informative sample mining method for audio-visual supervised contrastive learning to leverage discriminative contrastive samples to enforce cross-modal understanding. We show empirical results that demonstrate the effectiveness of our benchmark. Furthermore experiments conducted on existing AVS datasets and on our new benchmark show that our method achieves state-of-the-art (SOTA) segmentation accuracy.
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Recent advancements in machine learning have spotlighted the potential of hyperbolic spaces as they effectively learn hierarchical feature representations. While there has been progress in leveraging hyperbolic spaces in single-modality contexts its exploration in multimodal settings remains under explored. Some recent efforts have sought to transpose Euclidean multimodal learning techniques to hyperbolic spaces by adopting geodesic distance based contrastive losses. However we show both theoretically and empirically that such spatial proximity based contrastive loss significantly disrupts hierarchies in the latent space. To remedy this we advocate that the cross-modal representations should accept the inherent modality gap between text and images and introduce a novel approach to measure cross-modal similarity that does not enforce spatial proximity. Our approach show remarkable capabilities in preserving unimodal hierarchies while aligning the two modalities. Our experiments on a series of downstream tasks demonstrate that better latent structure emerges with our objective function while being superior in text-to-image and image-to-text retrieval tasks.
Recent breakthroughs in vision-language models (VLMs) start a new page in the vision community. The VLMs provide stronger and more generalizable feature embeddings compared to those from ImageNet-pretrained models thanks to the training on the large-scale Internet image-text pairs. However despite the amazing achievement from the VLMs vanilla Vision Transformers (ViTs) remain the default choice for the image encoder. Although pure transformer proves its effectiveness in the text encoding area it remains questionable whether it is also the case for image encoding especially considering that various types of networks are proposed on the ImageNet benchmark which unfortunately are rarely studied in VLMs. Due to small data/model scale the original conclusions of model design on ImageNet can be limited and biased. In this paper we aim at building an evaluation protocol of vision models in the vision-language era under the contrastive language-image pretraining (CLIP) framework. We provide a comprehensive way to benchmark different vision models covering their zero-shot performance and scalability in both model and training data sizes. To this end we introduce ViTamin a new vision models tailored for VLMs. ViTamin-L significantly outperforms ViT-L by 2.0% ImageNet zero-shot accuracy when using the same publicly available DataComp-1B dataset and the same OpenCLIP training scheme. ViTamin-L presents promising results on 60 diverse benchmarks including classification retrieval open-vocabulary detection and segmentation and large multi-modal models. When further scaling up the model size our ViTamin-XL with only 436M parameters attains 82.9% ImageNet zero-shot accuracy surpassing 82.0% achieved by EVA-E that has ten times more parameters (4.4B).
We introduce Mind Artist (MindArt) a novel and efficient neural decoding architecture to snap artistic photographs from our mind in a controllable manner. Recently progress has been made in image reconstruction with non-invasive brain recordings but it's still difficult to generate realistic images with high semantic fidelity due to the scarcity of data annotations. Unlike previous methods this work casts the neural decoding into optimal transport (OT) and representation decoupling problems. Specifically under discrete OT theory we design a graph matching-guided neural representation learning framework to seek the underlying correspondences between conceptual semantics and neural signals which yields a natural and meaningful self-supervisory task. Moreover the proposed MindArt structured with multiple stand-alone modal branches enables the seamless incorporation of semantic representation into any visual style information thus leaving it to have multi-modal reconstruction and training-free semantic editing capabilities. By doing so the reconstructed images of MindArt have phenomenal realism both in terms of semantics and appearance. We compare our MindArt with leading alternatives and achieve SOTA performance in different decoding tasks. Importantly our approach can directly generate a series of stylized "mind snapshots" w/o extra optimizations which may open up more potential applications. Code is available at https://github.com/JxuanC/MindArt.
A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a semantically meaningful way such that the resulting new NeRF retains the target geometry but has an appearance that is an analogy to the source NeRF. To this end we generalize classic image analogies from 2D images to NeRFs. We leverage correspondence transfer along semantic affinity that is driven by semantic features from large pre-trained 2D image models to achieve multi-view consistent appearance transfer. Our method allows exploring the mix-and-match product space of 3D geometry and appearance. We show that our method outperforms traditional stylization-based methods and that a large majority of users prefer our method over several typical baselines. Project page: https://mfischer-ucl.github.io/nerf_analogies
Visual grounding refers to the process of associating natural language expressions with corresponding regions within an image. Existing benchmarks for visual grounding primarily operate within small-scale scenes with a few objects. Nevertheless recent advances in imaging technology have enabled the acquisition of gigapixel-level images providing high-resolution details in large-scale scenes containing numerous objects. To bridge this gap between imaging and computer vision benchmarks and make grounding more practically valuable we introduce a novel dataset named GigaGrounding designed to challenge visual grounding models in gigapixel-level large-scale scenes. We extensively analyze and compare the dataset with existing benchmarks demonstrating that GigaGrounding presents unique challenges such as large-scale scene understanding gigapixel-level resolution significant variations in object scales and the "multi-hop expressions". Furthermore we introduced a simple yet effective grounding approach which employs a "glance-to-zoom-in" paradigm and exhibits enhanced capabilities for addressing the GigaGrounding task. The dataset is available at www.gigavision.ai.
Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation empowering non-experts to generate stunning images with unique styles. While promising animating these personalized images with realistic motions poses significant challenges in preserving distinct styles high-fidelity details and achieving motion controllability by text. In this paper we present PIA a Personalized Image Animator that excels in aligning with condition images achieving motion controllability by text and the compatibility with various personalized T2I models without specific tuning. To achieve these goals PIA builds upon a base T2I model with well-trained temporal alignment layers allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module which takes as inputs the condition frame and inter-frame affinity. This module leverages the affinity hint to transfer appearance information from the condition frame to individual frames in the latent space. This design mitigates the challenges of appearance-related frame alignment within PIA and allows for a stronger focus on aligning with motion-related guidance. To address the lack of a benchmark for this field we introduce AnimateBench a comprehensive benchmark comprising diverse personalized T2I models curated images and motion-related prompts. We show extensive evaluations and applications on AnimateBench to verify the superiority of PIA.
Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their training task their intermediate representations are useful for other visual tasks such as detection and segmentation. Given that such models can classify delineate and localize objects in 2D we ask whether they also represent their 3D structure? In this work we analyze the 3D awareness of visual foundation models. We posit that 3D awareness implies that representations (1) encode the 3D structure of the scene and (2) consistently represent the surface across views. We conduct a series of experiments using task-specific probes and zero-shot inference procedures on frozen features. Our experiments reveal several limitations of the current models. Our code and analysis can be found at https://github.com/mbanani/probe3d.
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts
PDF ↗Federated learning facilitates the collaborative learning of a global model across multiple distributed medical institutions without centralizing data. Nevertheless the expensive cost of annotation on local clients remains an obstacle to effectively utilizing local data. To mitigate this issue federated active learning methods suggest leveraging local and global model predictions to select a relatively small amount of informative local data for annotation. However existing methods mainly focus on all local data sampled from the same domain making them unreliable in realistic medical scenarios with domain shifts among different clients. In this paper we make the first attempt to assess the informativeness of local data derived from diverse domains and propose a novel methodology termed Federated Evidential Active Learning (FEAL) to calibrate the data evaluation under domain shift. Specifically we introduce a Dirichlet prior distribution in both local and global models to treat the prediction as a distribution over the probability simplex and capture both aleatoric and epistemic uncertainties by using the Dirichlet-based evidential model. Then we employ the epistemic uncertainty to calibrate the aleatoric uncertainty. Afterward we design a diversity relaxation strategy to reduce data redundancy and maintain data diversity. Extensive experiments and analysis on five real multi-center medical image datasets demonstrate the superiority of FEAL over the state-of-the-art active learning methods in federated scenarios with domain shifts. The code will be available at https://github.com/JiayiChen815/FEAL.
The field of 3D detailed human mesh reconstruction has made significant progress in recent years. However current methods still face challenges when used in industrial applications due to unstable results low-quality meshes and a lack of UV unwrapping and skinning weights. In this paper we present SHERT a novel pipeline that can reconstruct semantic human meshes with textures and high-precision details. SHERT applies semantic- and normal-based sampling between the detailed surface (e.g. mesh and SDF) and the corresponding SMPL-X model to obtain a partially sampled semantic mesh and then generates the complete semantic mesh by our specifically designed self-supervised completion and refinement networks. Using the complete semantic mesh as a basis we employ a texture diffusion model to create human textures that are driven by both images and texts. Our reconstructed meshes have stable UV unwrapping high-quality triangle meshes and consistent semantic information. The given SMPL-X model provides semantic information and shape priors allowing SHERT to perform well even with incorrect and incomplete inputs. The semantic information also makes it easy to substitute and animate different body parts such as the face body and hands. Quantitative and qualitative experiments demonstrate that SHERT is capable of producing high-fidelity and robust semantic meshes that outperform state-of-the-art methods.
Estimating disparities in challenging areas is difficult and limits the performance of stereo matching models. In this paper we exploit local structure information (LSI) to enhance stereo matching. Specifically our LSI comprises a series of key elements including the slant plane (parameterised by disparity gradients) disparity offset details and neighbouring relations. This LSI empowers our method to effectively handle intricate structures including object boundaries and curved surfaces. We bootstrap the LSI from monocular depth and subsequently iteratively refine it to better capture the underlying scene geometry constraints. Building upon the LSI we introduce the Local Structure-Guided Propagation (LSGP) which enhances the disparity initialization optimization and refinement processes. By combining LSGP with a Gated Recurrent Unit (GRU) we present our novel stereo matching method referred to as Local Structure-guided stereo matching (LoS). Remarkably LoS achieves top-ranking results on four widely recognized public benchmark datasets (ETH3D Middlebury KITTI 15 & 12) demonstrating the superior capabilities of our proposed model.
Omnidirectional cameras are extensively used in various applications to provide a wide field of vision. However they face a challenge in synthesizing novel views due to the inevitable presence of dynamic objects including the photographer in their wide field of view. In this paper we introduce a new approach called Omnidirectional Local Radiance Fields (OmniLocalRF) that can render static-only scene views removing and inpainting dynamic objects simultaneously. Our approach combines the principles of local radiance fields with the bidirectional optimization of omnidirectional rays. Our input is an omnidirectional video and we evaluate the mutual observations of the entire angle between the previous and current frames. To reduce ghosting artifacts of dynamic objects and inpaint occlusions we devise a multi-resolution motion mask prediction module. Unlike existing methods that primarily separate dynamic components through the temporal domain our method uses multi-resolution neural feature planes for precise segmentation which is more suitable for long 360-degree videos. Our experiments validate that OmniLocalRF outperforms existing methods in both qualitative and quantitative metrics especially in scenarios with complex real-world scenes. In particular our approach eliminates the need for manual interaction such as drawing motion masks by hand and additional pose estimation making it a highly effective and efficient solution.
Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding methodologies by introducing an Open-Vocabulary Spatio-Temporal Video Grounding task. Unlike prevalent closed-set approaches that struggle with open-vocabulary scenarios due to limited training data and predefined vocabularies our model leverages pre-trained representations from foundational spatial grounding models. This empowers it to effectively bridge the semantic gap between natural language and diverse visual content achieving strong performance in closed-set and open-vocabulary settings. Our contributions include a novel spatio-temporal video grounding model surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios. Notably the proposed model outperforms state-of-the-art methods in closed-set settings on VidSTG (Declarative and Interrogative) and HC-STVG (V1 and V2) datasets. Furthermore in open-vocabulary evaluations on HC-STVG V1 and YouCook-Interactions our model surpasses the recent best-performing models by 4.88 m_vIoU and 1.83 accuracy demonstrating its efficacy in handling diverse linguistic and visual concepts for improved video understanding. Our codes will be publicly released.
In this paper we present a novel indoor 3D reconstruction method with occluded surface completion given a sequence of depth readings. Prior state-of-the-art (SOTA) methods only focus on the reconstruction of the visible areas in a scene neglecting the invisible areas due to the occlusions e.g. the contact surface between furniture occluded wall and floor. Our method tackles the task of completing the occluded scene surfaces resulting in a complete 3D scene mesh. The core idea of our method is learning 3D geometry prior from various complete scenes to infer the occluded geometry of an unseen scene from solely depth measurements. We design a coarse-fine hierarchical octree representation coupled with a dual-decoder architecture i.e. Geo-decoder and 3D Inpainter which jointly reconstructs the complete 3D scene geometry. The Geo-decoder with detailed representation at fine levels is optimized online for each scene to reconstruct visible surfaces. The 3D Inpainter with abstract representation at coarse levels is trained offline using various scenes to complete occluded surfaces. As a result while the Geo-decoder is specialized for an individual scene the 3D Inpainter can be generally applied across different scenes. We evaluate the proposed method on the 3D Completed Room Scene (3D-CRS) and iTHOR datasets significantly outperforming the SOTA methods by a gain of 16.8% and 24.2% in terms of the completeness of 3D reconstruction. 3D-CRS dataset including a complete 3D mesh of each scene is provided at project webpage.
Modeling and visualizing relationships between tasks or datasets is an important step towards solving various meta-tasks such as dataset discovery multi-tasking and transfer learning. However many relationships such as containment and transferability are naturally asymmetric and current approaches for representation and visualization (e.g. t-SNE) do not readily support this. We propose Task2Box an approach to represent tasks using box embeddings---axis-aligned hyperrectangles in low dimensional spaces---that can capture asymmetric relationships between them through volumetric overlaps. We show that Task2Box accurately predicts unseen hierarchical relationships between nodes in ImageNet and iNaturalist datasets as well as transferability between tasks in the Taskonomy benchmark. We also show that box embeddings estimated from task representations (e.g. CLIP Task2Vec or attribute based) can be used to predict relationships between unseen tasks more accurately than classifiers trained on the same representations as well as handcrafted asymmetric distances (e.g. KL divergence). This suggests that low-dimensional box embeddings can effectively capture these task relationships and have the added advantage of being interpretable. We use the approach to visualize relationships among publicly available image classification datasets on popular dataset hosting platform called Hugging Face.
Non-Exemplar Class Incremental Learning (NECIL) involves learning a classification model on a sequence of data without access to exemplars from previously encountered old classes. Such a stringent constraint always leads to catastrophic forgetting of the learned knowledge. Currently existing methods either employ knowledge distillation techniques or preserved class prototypes to sustain prior knowledge. However two critical issues still persist. On the one hand as the model is continually updated the preserved prototypes of old classes will inevitably derive from the suitable location in the feature space of the new model. On the other hand due to the lack of exemplars the features of new classes will take the place of similar old classes which breaks the classification boundary. To address these challenges we propose a Feature Calibration and Separation (FCS) method for NECIL. Our approach comprises a Feature Calibration Network (FCN) that adapts prototypes of old classes to the new model via optimal transport learning approximating the drift of prototypes caused by model evolution. Additionally we also propose a Prototype-Involved Contrastive Loss (PIC) that enhances feature separation among different classes. Specifically to mitigate the boundary distortion arising from the interplay of classes from different learning stages prototypes are involved in pushing the feature of new classes away from the old classes. Extensive experiments on three datasets with different settings have demonstrated the superiority of our FCS method against the state-of-the-art class incremental learning approaches. Code is available at https://github.com/zhoujiahuan1991/CVPR2024-FCS.
Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However the high costs and methodological limitations of ST necessitate a more robust predictive model. In response this paper introduces TRIPLEX a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features capturing cellular morphology at individual spots the local context around these spots and the global tissue organization. By integrating these features through an effective fusion strategy TRIPLEX achieves accurate gene expression prediction. Our comprehensive benchmark study conducted on three public ST datasets and supplemented with Visium data from 10X Genomics demonstrates that TRIPLEX outperforms current state-of-the-art models in Mean Squared Error (MSE) Mean Absolute Error (MAE) and Pearson Correlation Coefficient (PCC). The model's predictions align closely with ground truth gene expression profiles and tumor annotations underscoring TRIPLEX's potential in advancing cancer diagnosis and treatment.
Vision Transformer (ViT) has emerged as a prominent architecture for various computer vision tasks. In ViT we divide the input image into patch tokens and process them through a stack of self-attention blocks. However unlike Convolutional Neural Network (CNN) ViT's simple architecture has no informative inductive bias (e.g. locality etc.). Due to this ViT requires a large amount of data for pre-training. Various data-efficient approaches (DeiT) have been proposed to train ViT on balanced datasets effectively. However limited literature discusses the use of ViT for datasets with long-tailed imbalances. In this work we introduce DeiT-LT to tackle the problem of training ViTs from scratch on long-tailed datasets. In DeiT-LT we introduce an efficient and effective way of distillation from CNN via distillation \texttt DIST token by using out-of-distribution images and re-weighting the distillation loss to enhance focus on tail classes. This leads to the learning of local CNN-like features in early ViT blocks improving generalization for tail classes. Further to mitigate overfitting we propose distilling from a flat CNN teacher which leads to learning low-rank generalizable features for DIST tokens across all ViT blocks. With the proposed DeiT-LT scheme the distillation DIST token becomes an expert on the tail classes and the classifier CLS token becomes an expert on the head classes. The experts help to effectively learn features corresponding to both the majority and minority classes using a distinct set of tokens within the same ViT architecture. We show the effectiveness of DeiT-LT for training ViT from scratch on datasets ranging from small-scale CIFAR-10 LT to large-scale iNaturalist-2018. Project Page: https://rangwani-harsh.github.io/DeiT-LT.
3D object generation has undergone significant advancements yielding high-quality results. However fall short in achieving precise user control often yielding results that do not align with user expectations thus limiting their applicability. User-envisioning 3D object generation faces significant challenges in realizing its concepts using current generative models due to limited interaction capabilities. Existing methods mainly offer two approaches: (i) interpreting textual instructions with constrained controllability or (ii) reconstructing 3D objects from 2D images. Both of them limit customization to the confines of the 2D reference and potentially introduce undesirable artifacts during the 3D lifting process restricting the scope for direct and versatile 3D modifications. In this work we introduce Interactive3D an innovative framework for interactive 3D generation that grants users precise control over the generative process through extensive 3D interaction capabilities. Interactive3D is constructed in two cascading stages utilizing distinct 3D representations. The first stage employs Gaussian Splatting for direct user interaction allowing modifications and guidance of the generative direction at any intermediate step through (i) Adding and Removing components (ii) Deformable and Rigid Dragging (iii) Geometric Transformations and (iv) Semantic Editing. Subsequently the Gaussian splats are transformed into InstantNGP. We introduce a novel (v) Interactive Hash Refinement module to further add details and extract the geometry in the second stage. Our experiments demonstrate that proposed Interactive3D markedly improves the controllability and quality of 3D generation. Our project webpage is available at https://interactive-3d.github.io/.
Humans naturally interact with both others and the surrounding multiple objects engaging in various social activities. However recent advances in modeling human-object interactions mostly focus on perceiving isolated individuals and objects due to fundamental data scarcity. In this paper we introduce HOI-M^3 a novel large-scale dataset for modeling the interactions of Multiple huMans and Multiple objects. Notably it provides accurate 3D tracking for both humans and objects from dense RGB and object-mounted IMU inputs covering 199 sequences and 181M frames of diverse humans and objects under rich activities. With the unique HOI-M^3 dataset we introduce two novel data-driven tasks with companion strong baselines: monocular capture and unstructured generation of multiple human-object interactions. Extensive experiments demonstrate that our dataset is challenging and worthy of further research about multiple human-object interactions and behavior analysis. Our HOI-M^3 dataset corresponding codes and pre-trained models will be disseminated to the community for future research.