The rapid advancement of large language models (LLMs) has accelerated the emergence of in-context learning (ICL) as a cutting-edge approach in the natural language processing domain. Recently ICL has been employed in visual understanding tasks such as semantic segmentation and image captioning yielding promising results. However existing visual ICL framework can not enable producing content across multiple modalities which limits their potential usage scenarios. To address this issue we present a new ICL framework for visual understanding with multi-modal output enabled. First we quantize and embed both text and visual prompt into a unified representational space structured as interleaved in-context sequences. Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them facilitating in-context learning. Thanks to this design the model is capable of handling in-context vision understanding tasks with multimodal output in a unified pipeline. Experimental results demonstrate that our model achieves competitive performance compared with specialized models and previous ICL baselines. Overall our research takes a further step toward unified multimodal in-context learning.
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Modeling Multimodal Social Interactions: New Challenges and Baselines with Densely Aligned Representations
PDF ↗Understanding social interactions involving both verbal and non-verbal cues is essential for effectively interpreting social situations. However most prior works on multimodal social cues focus predominantly on single-person behaviors or rely on holistic visual representations that are not aligned to utterances in multi-party environments. Consequently they are limited in modeling the intricate dynamics of multi-party interactions. In this paper we introduce three new challenging tasks to model the fine-grained dynamics between multiple people: speaking target identification pronoun coreference resolution and mentioned player prediction. We contribute extensive data annotations to curate these new challenges in social deduction game settings. Furthermore we propose a novel multimodal baseline that leverages densely aligned language-visual representations by synchronizing visual features with their corresponding utterances. This facilitates concurrently capturing verbal and non-verbal cues pertinent to social reasoning. Experiments demonstrate the effectiveness of the proposed approach with densely aligned multimodal representations in modeling fine-grained social interactions. Project website: https://sangmin-git.github.io/projects/MMSI.
Long-term and accurate forecasting is the long-standing pursuit of the human motion prediction task. Existing methods typically suffer from dramatic degradation in prediction accuracy with the increasing prediction horizon. It comes down to two reasons:1? Insufficient numerical stability.Unforeseen high noise and complex feature relationships in the data. 2? Inadequate modeling stability. Unreasonable step sizes and undesirable parameter updates in the prediction.In this paper we design a novel and symplectic integral-inspired framework named symplectic integral neural network (SINN) which engages symplectic trajectories to optimize the pose representation and employs a stable symplectic operator to alternately model the dynamic context. Specifically we design a Symplectic Representation Encoder that performs on enhanced human pose representation to obtain trajectories on the symplectic manifold ensuring numerical stability based on Hamiltonian mechanics and symplectic spatial splitting algorithm. We further present the Symplectic Temporal Aggregation module in the light of the symplectic temporal splitting algorithm which splits the long-term prediction into multiple accurate short-term predictions generated by a symplectic operator to secure modeling stability. Moreover our approach is model-agnostic and can be efficiently integrated with different physical dynamics models.The experimental results demonstrate that our method achieves the new state-of-the-art outperforming existing methods by large margins:20.1%on Human3.6M16.7%on CUM Mocap and 10.2% on 3DPW.
Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g. faces on the back view) and inaccurate shapes (e.g. animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure multi-view consistency while struggling to balance 2D generation quality with 3D consistency. In this paper we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. Moreover to ensure accurate appearances of different views we further modulate the output of the 2D diffusion model to the correct patterns of the template views without altering the generated object's style. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model. Extensive experiments show our method can largely improve the multi-view consistency while retaining fidelity and diversity.
Current vision-language pre-training (VLP) methodologies predominantly depend on paired image-text datasets a resource that is challenging to acquire in radiology due to privacy considerations and labelling complexities. Data augmentation provides a practical solution to overcome the issue of data scarcity however most augmentation methods exhibit a limited focus prioritising either image or text augmentation exclusively. Acknowledging this limitation our objective is to devise a framework capable of concurrently augmenting medical image and text data. We design a Pairwise Augmentation (PairAug) approach that contains an Inter-patient Augmentation (InterAug) branch and an Intra-patient Augmentation (IntraAug) branch. Specifically the InterAug branch of our approach generates radiology images using synthesised yet plausible reports derived from a Large Language Model (LLM). The generated pairs can be considered a collection of new patient cases since they are artificially created and may not exist in the original dataset. In contrast the IntraAug branch uses newly generated reports to manipulate images. This process allows us to create new paired data for each individual with diverse medical conditions. Our extensive experiments on various downstream tasks covering medical image classification zero-shot and fine-tuning analysis demonstrate that our PairAug concurrently expanding both image and text data substantially outperforms image-/text-only expansion baselines and advanced medical VLP baselines. Our code is released at https://github.com/YtongXie/PairAug.
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
PDF ↗Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid development. However the recent development of IAD approach has encountered certain difficulties due to dataset limitations. On the one hand most of the state-of-the-art methods have achieved saturation (over 99% in AUROC) on mainstream datasets such as MVTec and the differences of methods cannot be well distinguished leading to a significant gap between public datasets and actual application scenarios. On the other hand the research on various new practical anomaly detection settings is limited by the scale of the dataset posing a risk of overfitting in evaluation results. Therefore we propose a large-scale Real-world and multi-view Industrial Anomaly Detection dataset named Real-IAD which contains 150K high-resolution images of 30 different objects an order of magnitude larger than existing datasets. It has a larger range of defect area and ratio proportions making it more challenging than previous datasets. To make the dataset closer to real application scenarios we adopted a multi-view shooting method and proposed sample-level evaluation metrics. In addition beyond the general unsupervised anomaly detection setting we propose a new setting for Fully Unsupervised Industrial Anomaly Detection (FUIAD) based on the observation that the yield rate in industrial production is usually greater than 60% which has more practical application value. Finally we report the results of popular IAD methods on the Real-IAD dataset providing a highly challenging benchmark to promote the development of the IAD field.
Advances in NERFs have allowed for 3D scene reconstructions and novel view synthesis. Yet efficiently editing these representations while retaining photorealism is an emerging challenge. Recent methods face three primary limitations: they're slow for interactive use lack precision at object boundaries and struggle to ensure multi-view consistency. We introduce IReNe to address these limitations enabling swift near real-time color editing in NeRF. Leveraging a pre-trained NeRF model and a single training image with user-applied color edits IReNe swiftly adjusts network parameters in seconds. This adjustment allows the model to generate new scene views accurately representing the color changes from the training image while also controlling object boundaries and view-specific effects. Object boundary control is achieved by integrating a trainable segmentation module into the model. The process gains efficiency by retraining only the weights of the last network layer. We observed that neurons in this layer can be classified into those responsible for view-dependent appearance and those contributing to diffuse appearance. We introduce an automated classification approach to identify these neuron types and exclusively fine-tune the weights of the diffuse neurons. This further accelerates training and ensures consistent color edits across different views. A thorough validation on a new dataset with edited object colors shows significant quantitative and qualitative advancements over competitors accelerating speeds by 5x and 500x.
Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models
PDF ↗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.
FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders
PDF ↗In recent years automated Gallbladder Cancer (GBC) detection has gained the attention of researchers. Current state-of-the-art (SOTA) methodologies relying on ultrasound sonography (US) images exhibit limited generalization emphasizing the need for transformative approaches. We observe that individual US frames may lack sufficient information to capture disease manifestation. This study advocates for a paradigm shift towards video-based GBC detection leveraging the inherent advantages of spatiotemporal representations. Employing the Masked Autoencoder (MAE) for representation learning we address shortcomings in conventional image-based methods. We propose a novel design called FocusMAE to systematically bias the selection of masking tokens from high-information regions fostering a more refined representation of malignancy. Additionally we contribute the most extensive US video dataset for GBC detection. We also note that this is the first study on US video-based GBC detection. We validate the proposed methods on the curated dataset and report a new SOTA accuracy of 96.4% for the GBC detection problem against an accuracy of 84% by current Image-based SOTA - GBCNet and RadFormer and 94.7% by Video-based SOTA - AdaMAE. We further demonstrate the generality of the proposed FocusMAE on a public CT-based Covid detection dataset reporting an improvement in accuracy by 3.3% over current baselines. Project page with source code trained models and data is available at: https://gbc-iitd.github.io/focusmae.
We introduce PhysGaussian a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis. Employing a customized Material Point Method (MPM) our approach enriches 3D Gaussian kernels with physically meaningful kinematic deformation and mechanical stress attributes all evolved in line with continuum mechanics principles. A defining characteristic of our method is the seamless integration between physical simulation and visual rendering: both components utilize the same 3D Gaussian kernels as their discrete representations. This negates the necessity for triangle/tetrahedron meshing marching cubes cage meshes or any other geometry embedding highlighting the principle of "what you see is what you simulate (WS^2)". Our method demonstrates exceptional versatility across a wide variety of materials--including elastic entities plastic metals non-Newtonian fluids and granular materials--showcasing its strong capabilities in creating diverse visual content with novel viewpoints and movements.
Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However a notable limitation of diffusion models in comparison to GANs is their difficulty in smoothly interpolating between two image samples due to their highly unstructured latent space. Such a smooth interpolation is intriguing as it naturally serves as a solution for the image morphing task with many applications. In this work we address this limitation via DiffMorpher an approach that enables smooth and natural image interpolation by harnessing the prior knowledge of a pre-trained diffusion model. Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition where correspondence automatically emerges without the need for annotation. In addition we propose an attention interpolation and injection technique an adaptive normalization adjustment method and a new sampling schedule to further enhance the smoothness between consecutive images. Extensive experiments demonstrate that DiffMorpher achieves starkly better image morphing effects than previous methods across a variety of object categories bridging a critical functional gap that distinguished diffusion models from GANs.
PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera Settings via Invariant Risk Minimization
PDF ↗Camera-parameter-free multi-view pose estimation is an emerging technique for 3D human pose estimation (HPE). They can infer the camera settings implicitly or explicitly to mitigate the depth uncertainty impact showcasing significant potential in real applications. However due to the limited camera setting diversity in the available datasets the inferred camera parameters are always simply hardcoded into the model during training and not adaptable to the input in inference making the learned models cannot generalize well under unseen camera settings. A natural solution is to artificially synthesize some samples i.e. 2D-3D pose pairs under massive new camera settings. Unfortunately to prevent over-fitting the existing camera setting the number of synthesized samples for each new camera setting should be comparable with that for the existing one which multiplies the scale of training and even makes it computationally prohibitive. In this paper we propose a novel HPE approach under the invariant risk minimization (IRM) paradigm. Precisely we first synthesize 2D poses from myriad camera settings. We then train our model under the IRM paradigm which targets at learning a common optimal model across all camera settings and thus enforces the model to automatically learn the camera parameters based on the input data. This allows the model to accurately infer 3D poses on unseen data by training on only a handful of samples from each synthesized setting and thus avoid the unbearable training cost increment. Another appealing feature of our method is that benefited from the capability of IRM in identifying the invariant features its performance on the seen camera settings is enhanced as well. Comprehensive experiments verify the superiority of our approach.
With the immense growth of dataset sizes and computing resources in recent years so-called foundation models have become popular in NLP and vision tasks. In this work we propose to explore foundation models for the task of keypoint detection on 3D shapes. A unique characteristic of keypoint detection is that it requires semantic and geometric awareness while demanding high localization accuracy. To address this problem we propose first to back-project features from large pre-trained 2D vision models onto 3D shapes and employ them for this task. We show that we obtain robust 3D features that contain rich semantic information and analyze multiple candidate features stemming from different 2D foundation models. Second we employ a keypoint candidate optimization module which aims to match the average observed distribution of keypoints on the shape and is guided by the back-projected features. The resulting approach achieves a new state of the art for few-shot keypoint detection on the KeyPointNet dataset almost doubling the performance of the previous best methods.
Text-guided diffusion models have revolutionized image and video generation and have also been successfully used for optimization-based 3D object synthesis. Here we instead focus on the underexplored text-to-4D setting and synthesize dynamic animated 3D objects using score distillation methods with an additional temporal dimension. Compared to previous work we pursue a novel compositional generation-based approach and combine text-to-image text-to-video and 3D-aware multiview diffusion models to provide feedback during 4D object optimization thereby simultaneously enforcing temporal consistency high-quality visual appearance and realistic geometry. Our method called Align Your Gaussians (AYG) leverages dynamic 3D Gaussian Splatting with deformation fields as 4D representation. Crucial to AYG is a novel method to regularize the distribution of the moving 3D Gaussians and thereby stabilize the optimization and induce motion. We also propose a motion amplification mechanism as well as a new autoregressive synthesis scheme to generate and combine multiple 4D sequences for longer generation. These techniques allow us to synthesize vivid dynamic scenes outperform previous work qualitatively and quantitatively and achieve state-of-the-art text-to-4D performance. Due to the Gaussian 4D representation different 4D animations can be seamlessly combined as we demonstrate. AYG opens up promising avenues for animation simulation and digital content creation as well as synthetic data generation.
Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps leading to better perceptual performance. Our approach improves upon the current state-of-the-art in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model trained on Pascal VOC achieves SOTA results on Watercolor2K. Our cross-domain segmentation method trained on Cityscapes achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: vision.caltech.edu/TADP/. Code: github.com/damaggu/TADP
This paper aims to achieve universal segmentation of arbitrary semantic level. Despite significant progress in recent years specialist segmentation approaches are limited to specific tasks and data distribution. Retraining a new model for adaptation to new scenarios or settings takes expensive computation and time cost which raises the demand for versatile and universal segmentation model that can cater to various granularity. Although some attempts have been made for unifying different segmentation tasks or generalization to various scenarios limitations in the definition of paradigms and input-output spaces make it difficult for them to achieve accurate understanding of content at arbitrary granularity. To this end we present UniLSeg a universal segmentation model that can perform segmentation at any semantic level with the guidance of language instructions. For training UniLSeg we reorganize a group of tasks from original diverse distributions into a unified data format where images with texts describing segmentation targets as input and corresponding masks are output. Combined with a automatic annotation engine for utilizing numerous unlabeled data UniLSeg achieves excellent performance on various tasks and settings surpassing both specialist and unified segmentation models.
Understanding the world in first-person view is fundamental in Augmented Reality (AR). This immersive perspective brings dramatic visual changes and unique challenges compared to third-person views. Synthetic data has empowered third-person-view vision models but its application to embodied egocentric perception tasks remains largely unexplored. A critical challenge lies in simulating natural human movements and behaviors that effectively steer the embodied cameras to capture a faithful egocentric representation of the 3D world. To address this challenge we introduce EgoGen a new synthetic data generator that can produce accurate and rich ground-truth training data for egocentric perception tasks. At the heart of EgoGen is a novel human motion synthesis model that directly leverages egocentric visual inputs of a virtual human to sense the 3D environment. Combined with collision-avoiding motion primitives and a two-stage reinforcement learning approach our motion synthesis model offers a closed-loop solution where the embodied perception and movement of the virtual human are seamlessly coupled. Compared to previous works our model eliminates the need for a pre-defined global path and is directly applicable to dynamic environments. Combined with our easy-to-use and scalable data generation pipeline we demonstrate EgoGen's efficacy in three tasks: mapping and localization for head-mounted cameras egocentric camera tracking and human mesh recovery from egocentric views. EgoGen will be fully open-sourced offering a practical solution for creating realistic egocentric training data and aiming to serve as a useful tool for egocentric computer vision research.
We introduce a new family of minimal problems for reconstruction from multiple views. Our primary focus is a novel approach to autocalibration a long-standing problem in computer vision. Traditional approaches to this problem such as those based on Kruppa's equations or the modulus constraint rely explicitly on the knowledge of multiple fundamental matrices or a projective reconstruction. In contrast we consider a novel formulation involving constraints on image points the unknown depths of 3D points and a partially specified calibration matrix K. For 2 and 3 views we present a comprehensive taxonomy of minimal autocalibration problems obtained by relaxing some of these constraints. These problems are organized into classes according to the number of views and any assumed prior knowledge of K. Within each class we determine problems with the fewest---or a relatively small number of---solutions. From this zoo of problems we devise three practical solvers. Experiments with synthetic and real data and interfacing our solvers with COLMAP demonstrate that we achieve superior accuracy compared to state-of-the-art calibration methods. The code is available at https://github.com/andreadalcin/MinimalPerspectiveAutocalibration.
Visual perception evolves over time. This is particularly the case of oracle bone scripts where visual glyphs seem intuitive to people from distant past prove difficult to be understood in contemporary eyes. While semantic correspondence of an oracle can be found via a dictionary lookup this proves to be not enough for public viewers to connect the dots i.e. why does this oracle mean that? Common solution relies on a laborious curation process to collect visual guide for each oracle (Fig.1) which hinges on the case-by-case effort and taste of curators. This paper delves into one natural follow-up question: can AI take over?Begin with a comprehensive human study we show participants could indeed make better sense of an oracle glyph subjected to a proper visual guide and its efficacy can be approximated via a novel metric termed TransOV (Transferable Oracle Visuals). We then define a new conditional visual generation task based on an oracle glyph and its semantic meaning and importantly approach it by circumventing any form of model training in the presence of fatal lack of oracle data. At its heart is to leverage foundation model like GPT-4V to reason about the visual cues hidden inside an oracle and take advantage of an existing text-to-image model for final visual guide generation. Extensive empirical evidence shows our AI-enabled visual guides achieve significantly comparable TransOV performance compared with those collected under manual efforts. Finally we demonstrate the versatility of our system under a more complex setting where it is required to work alongside an AI image denoiser to cope with raw oracle scan image inputs (cf. processed clean oracle glyphs). Code is available at https://github.com/RQ-Lab/OBS-Visual.
Although neural networks excel in video action recognition tasks their "black-box" nature makes it challenging to understand the rationale behind their decisions. Recent approaches used inherently interpretable models to analyze video actions in a manner akin to human reasoning. However it has been observed that these interpretable models tend to underperform when compared to their black-box counterparts. In this work we present a new framework called Language-guided Interpretable Action Recognition framework (LaIAR). This framework leverages knowledge from language models to enhance both the recognition capabilities and the interpretability of video models. In essence we reframe the challenge of understanding video model decisions as a task of aligning video and language models. Using the logical reasoning captured by the language model we steer the training of the video model. This integrated approach not only improves the video model's adaptability to different domains but also boosts its overall performance. Extensive experiments on Charades and CAD-120 datasets demonstrate the superior performance and interpretability of our proposed method. The code of LaIAR is available at https://github.com/NingWang2049/LaIAR.