Humans constantly interact with their surrounding environments. Current human-centric generative models mainly focus on synthesizing humans plausibly interacting with static scenes and objects while the dynamic human action-reaction synthesis for ubiquitous causal human-human interactions is less explored. Human-human interactions can be regarded as asymmetric with actors and reactors in atomic interaction periods. In this paper we comprehensively analyze the asymmetric dynamic synchronous and detailed nature of human-human interactions and propose the first multi-setting human action-reaction synthesis benchmark to generate human reactions conditioned on given human actions. To begin with we propose to annotate the actor-reactor order of the interaction sequences for the NTU120 InterHuman and Chi3D datasets. Based on them a diffusion-based generative model with a Transformer decoder architecture called ReGenNet together with an explicit distance-based interaction loss is proposed to predict human reactions in an online manner where the future states of actors are unavailable to reactors. Quantitative and qualitative results show that our method can generate instant and plausible human reactions compared to the baselines and can generalize to unseen actor motions and viewpoint changes.
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Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy. A promising strategy to overcome this challenge is wavefront modulation which induces measurement diversity during image acquisition. Despite its importance designing optimal wavefront modulations to image through scattering remains under-explored. This paper introduces a novel learning-based framework to address the gap. Our approach jointly optimizes wavefront modulations and a computationally lightweight feedforward "proxy" reconstruction network. This network is trained to recover scenes obscured by scattering using measurements that are modified by these modulations. The learned modulations produced by our framework generalize effectively to unseen scattering scenarios and exhibit remarkable versatility. During deployment the learned modulations can be decoupled from the proxy network to augment other more computationally expensive restoration algorithms. Through extensive experiments we demonstrate our approach significantly advances the state of the art in imaging through scattering media. Our project webpage is at https://wavemo-2024.github.io/.
Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities i.e. scale. Predicting a 3D scene from text description(s) is similarly ill-posed i.e. spatial arrangements of objects described. We investigate the question of whether two inherently ambiguous modalities can be used in conjunction to produce metric-scaled reconstructions. To test this we focus on monocular depth estimation the problem of predicting a dense depth map from a single image but with an additional text caption describing the scene. To this end we begin by encoding the text caption as a mean and standard deviation; using a variational framework we learn the distribution of the plausible metric reconstructions of 3D scenes corresponding to the text captions as a prior. To "select" a specific reconstruction or depth map we encode the given image through a conditional sampler that samples from the latent space of the variational text encoder which is then decoded to the output depth map. Our approach is trained alternatingly between the text and image branches: in one optimization step we predict the mean and standard deviation from the text description and sample from a standard Gaussian and in the other we sample using a (image) conditional sampler. Once trained we directly predict depth from the encoded text using the conditional sampler. We demonstrate our approach on indoor (NYUv2) and outdoor (KITTI) scenarios where we show that language can consistently improve performance in both. Code: https://github.com/Adonis-galaxy/WorDepth.
Trajectory prediction plays an important role in various applications including autonomous driving robotics and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets typically employing a standardized input duration. However a notable issue arises when these models are evaluated with varying observation lengths leading to a significant performance drop a phenomenon we term the Observation Length Shift. To address this issue we introduce a general and effective framework the FlexiLength Network (FLN) to enhance the robustness of existing trajectory prediction techniques against varying observation periods. Specifically FLN integrates trajectory data with diverse observation lengths incorporates FlexiLength Calibration (FLC) to acquire temporal invariant representations and employs FlexiLength Adaptation (FLA) to further refine these representations for more accurate future trajectory predictions. Comprehensive experiments on multiple datasets i.e. ETH/UCY nuScenes and Argoverse 1 demonstrate the effectiveness and flexibility of our proposed FLN framework.
Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modeling
PDF ↗Normalizing flows have proven their efficacy for density estimation in Euclidean space but their application to rotational representations crucial in various domains such as robotics or human pose modeling remains underexplored. Probabilistic models of the human pose can benefit from approaches that rigorously consider the rotational nature of human joints. For this purpose we introduce HuProSO3 a normalizing flow model that operates on a high-dimensional product space of SO(3) manifolds modeling the joint distribution for human joints with three degrees of freedom. HuProSO3's advantage over state-of-the-art approaches is demonstrated through its superior modeling accuracy in three different applications and its capability to evaluate the exact likelihood. This work not only addresses the technical challenge of learning densities on SO(3) manifolds but it also has broader implications for domains where the probabilistic regression of correlated 3D rotations is of importance. Code will be available at https://github.com/odunkel/HuProSO.
Recent advancements in multimodal pre-training have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes their 2D counterparts and language descriptions. However the methods used by existing frameworks to curate such multimodal data in particular language descriptions for 3D shapes are not scalable and the collected language descriptions are not diverse. To address this we introduce ULIP-2 a simple yet effective tri-modal pretraining framework that leverages large multimodal models to automatically generate holistic language descriptions for 3D shapes. It only needs 3D data as input eliminating the need for any manual 3D annotations and is therefore scalable to large datasets. ULIP-2 is also equipped with scaled-up backbones for better multi-modal representation learning. We conduct experiments on two large-scale 3D datasets Objaverse and ShapeNet and augment them with tri-modal datasets of 3D point clouds images and language for training ULIP-2. Experiments show that ULIP-2 demonstrates substantial benefits in three downstream tasks: zero-shot 3D classification standard 3D classification with fine-tuning and 3D captioning (3D-to-language generation). It achieves a new SOTA of 50.6% (top- 1) on Objaverse-LVIS and 84.7% (top-1) on ModelNet40 in zero-shot classification. In the ScanObjectNN benchmark for standard fine-tuning ULIP-2 reaches an overall accuracy of 91.5% with a compact model of only 1.4 million parameters. ULIP-2 sheds light on a new paradigm for scalable multimodal 3D representation learning without human annotations and shows significant improvements over existing baselines. The code and datasets are released at https://github.com/salesforce/ULIP.
Multi-view photometric stereo (MVPS) recovers a high-fidelity 3D shape of a scene by benefiting from both multi-view stereo and photometric stereo. While photometric stereo boosts detailed shape reconstruction it necessitates recording images under various light conditions for each viewpoint. In particular calibrating the light directions for each view significantly increases the cost of acquiring images. To make MVPS more accessible we introduce a practical and easy-to-implement setup multi-view constrained photometric stereo (MVCPS) where the light directions are unknown but constrained to move together with the camera. Unlike conventional multi-view uncalibrated photometric stereo our constrained setting reduces the ambiguities of surface normal estimates from per-view linear ambiguities to a single and global linear one thereby simplifying the disambiguation process. The proposed method integrates the ambiguous surface normal into neural surface reconstruction (NeuS) to simultaneously resolve the global ambiguity and estimate the detailed 3D shape. Experiments demonstrate that our method estimates accurate shapes under sparse viewpoints using only a few multi-view constrained light sources.
While real-world anime super-resolution (SR) has gained increasing attention in the SR community existing methods still adopt techniques from the photorealistic domain. In this paper we analyze the anime production workflow and rethink how to use characteristics of it for the sake of the real-world anime SR. First we argue that video networks and datasets are not necessary for anime SR due to the repetition use of hand-drawing frames. Instead we propose an anime image collection pipeline by choosing the least compressed and the most informative frames from the video sources. Based on this pipeline we introduce the Anime Production-oriented Image (API) dataset. In addition we identify two anime-specific challenges of distorted and faint hand-drawn lines and unwanted color artifacts. We address the first issue by introducing a prediction-oriented compression module in the image degradation model and a pseudo-ground truth preparation with enhanced hand-drawn lines. In addition we introduce the balanced twin perceptual loss combining both anime and photorealistic high-level features to mitigate unwanted color artifacts and increase visual clarity. We evaluate our method through extensive experiments on the public benchmark showing our method outperforms state-of-the-art anime dataset-trained approaches.
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions
PDF ↗The many variations of Implicit Neural Representations (INRs) where a neural network is trained as a continuous representation of a signal have tremendous practical utility for downstream tasks including novel view synthesis video compression and image super-resolution. Unfortunately the inner workings of these networks are seriously understudied. Our work eXplaining the Implicit Neural Canvas (XINC) is a unified framework for explaining properties of INRs by examining the strength of each neuron's contribution to each output pixel. We call the aggregate of these contribution maps the Implicit Neural Canvas and we use this concept to demonstrate that the INRs we study learn to "see" the frames they represent in surprising ways. For example INRs tend to have highly distributed representations. While lacking high-level object semantics they have a significant bias for color and edges and are almost entirely space-agnostic. We arrive at our conclusions by examining how objects are represented across time in video INRs using clustering to visualize similar neurons across layers and architectures and show that this is dominated by motion. These insights demonstrate the general usefulness of our analysis framework.
Spatio-temporal video grounding (or STVG) task aims at locating a spatio-temporal tube for a specific instance given a text query. Despite advancements current methods easily suffer the distractors or heavy object appearance variations in videos due to insufficient object information from the text leading to degradation. Addressing this we propose a novel framework context-guided STVG (CG-STVG) which mines discriminative instance context for object in videos and applies it as a supplementary guidance for target localization. The key of CG-STVG lies in two specially designed modules including instance context generation (ICG) which focuses on discovering visual context information (in both appearance and motion) of the instance and instance context refinement (ICR) which aims to improve the instance context from ICG by eliminating irrelevant or even harmful information from the context. During grounding ICG together with ICR are deployed at each decoding stage of a Transformer architecture for instance context learning. Particularly instance context learned from one decoding stage is fed to the next stage and leveraged as a guidance containing rich and discriminative object feature to enhance the target-awareness in decoding feature which conversely benefits generating better new instance context for improving localization finally. Compared to existing methods CG-STVG enjoys object information in text query and guidance from mined instance visual context for more accurate target localization. In our experiments on three benchmarks including HCSTVG-v1/-v2 and VidSTG CG-STVG sets new state-of-the-arts in m_tIoU and m_vIoU on all of them showing efficacy. Code is released at https://github.com/HengLan/CGSTVG.
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners
PDF ↗Few-shot learning (FSL) facilitates a variety of computer vision tasks yet remains vulnerable to adversarial attacks. Existing adversarially robust FSL methods rely on either visual similarity learning or class concept learning. Our analysis reveals that these two learning paradigms are complementary exhibiting distinct robustness due to their unique decision boundary types (concepts clustering by the visual similarity label vs. classification by the class labels). To bridge this gap we propose a novel framework unifying adversarially robust similarity learning and class concept learning. Specifically we distill parameters from both network branches into a "unified embedding model" during robust optimization and redistribute them to individual network branches periodically. To capture generalizable robustness across diverse branches we initialize adversaries in each episode with cross-branch class-wise "global adversarial perturbations" instead of less informative random initialization. We also propose a branch robustness harmonization to modulate the optimization of similarity and class concept learners via their relative adversarial robustness. Extensive experiments demonstrate the state-of-the-art performance of our method in diverse few-shot scenarios.
The boundless possibility of neural networks which can be used to solve a problem - each with different performance - leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removing the need for experts. Neural Architecture Search (NAS) offers a solution to this by automatically identifying the best architecture. However to date NAS work has focused on a small set of datasets which we argue are not representative of real-world problems. We introduce eight new datasets created for a series of NAS Challenges: AddNIST Language MultNIST CIFARTile Gutenberg Isabella GeoClassing and Chesseract. These datasets and challenges are developed to direct attention to issues in NAS development and to encourage authors to consider how their models will perform on datasets unknown to them at development time. We present experimentation using standard Deep Learning methods as well as the best results from challenge participants
We tackle semi-supervised object detection based on motion cues. Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to pseudo-label instances of moving objects and use these as supervisory signals to train 3D object detectors in Lidar data without manual supervision. We re-think this approach and suggest that both object detection as well as motion-inspired pseudo-labeling can be tackled in a data-driven manner. We leverage recent advances in scene flow estimation to obtain point trajectories from which we extract long-term class-agnostic motion patterns. Revisiting correlation clustering in the context of message passing networks we learn to group those motion patterns to cluster points to object instances. By estimating the full extent of the objects we obtain per-scan 3D bounding boxes that we use to supervise a Lidar object detection network. Our method not only outperforms prior heuristic-based approaches (57.5 AP +14 improvement over prior work) more importantly we show we can pseudo-label and train object detectors across datasets.
In this work we present Omni-SMoLA a multimodal architecture that mixes many multi-modal experts efficiently and achieves both high specialist and generalist performance. In contrast to previous models for which we see performance degradation on average when training the models on a wide range of tasks we show that the SMoLA low-rank experts are able to model different skills and task and overall improve the performance of a generalist model. This finding indicates that simple LMM fine-tuning is suboptimal for handling a wide range of tasks and that pairing the act of fine-tuning with specifically-designed architecture changes leads to better performing models.
Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene and dense methods estimate all such correspondences. The aim is to learn a robust model i.e. a model able to match under challenging real-world changes. In this work we propose such a model leveraging frozen pretrained features from the foundation model DINOv2. Although these features are significantly more robust than local features trained from scratch they are inherently coarse. We therefore combine them with specialized ConvNet fine features creating a precisely localizable feature pyramid. To further improve robustness we propose a tailored transformer match decoder that predicts anchor probabilities which enables it to express multimodality. Finally we propose an improved loss formulation through regression-by-classification with subsequent robust regression. We conduct a comprehensive set of experiments that show that our method RoMa achieves significant gains setting a new state-of-the-art. In particular we achieve a 36% improvement on the extremely challenging WxBS benchmark. Code is provided at github.com/Parskatt/RoMa.
We study text-based image editing (TBIE) of a single image by counterfactual inference because it is an elegant formulation to precisely address the requirement: the edited image should retain the fidelity of the original one. Through the lens of the formulation we find that the crux of TBIE is that existing techniques hardly achieve a good trade-off between editability and fidelity mainly due to the overfitting of the single-image fine-tuning. To this end we propose a Doubly Abductive Counterfactual inference framework (DAC). We first parameterize an exogenous variable as a UNet LoRA whose abduction can encode all the image details. Second we abduct another exogenous variable parameterized by a text encoder LoRA which recovers the lost editability caused by the overfitted first abduction. Thanks to the second abduction which exclusively encodes the visual transition from post-edit to pre-edit its inversion---subtracting the LoRA---effectively reverts pre-edit back to post-edit thereby accomplishing the edit. Through extensive experiments our DAC achieves a good trade-off between editability and fidelity. Thus we can support a wide spectrum of user editing intents including addition removal manipulation replacement style transfer and facial change which are extensively validated in both qualitative and quantitative evaluations. Codes are in https://github.com/xuesong39/DAC.
Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences of location tokens. This paradigm lacks pixel-level representations that are important for fine-grained visual understanding and diagnosis. In this work we introduce GROUNDHOG an MLLM developed by grounding Large Language Models to holistic segmentation. GROUNDHOG incorporates a masked feature extractor and converts extracted features into visual entity tokens for the MLLM backbone which then connects groundable phrases to unified grounding masks by retrieving and merging the entity masks. To train GROUNDHOG we carefully curated M3G2 a grounded visual instruction tuning dataset with Multi-Modal Multi-Grained Grounding by harvesting a collection of segmentation-grounded datasets with rich annotations. Our experimental results show that GROUNDHOG achieves superior performance on various language grounding tasks without task-specific fine-tuning and significantly reduces object hallucination. GROUNDHOG also demonstrates better grounding towards complex forms of visual input and provides easy-to-understand diagnosis in failure cases.
Low-shot image classification is a fundamental task in computer vision and the emergence of large-scale vision-language models such as CLIP has greatly advanced the forefront of research in this field. However most existing CLIP-based methods lack the flexibility to effectively incorporate other pre-trained models that encompass knowledge distinct from CLIP. To bridge the gap this work proposes a simple and effective probabilistic model ensemble framework based on Gaussian processes which have previously demonstrated remarkable efficacy in processing small data. We achieve the integration of prior knowledge by specifying the mean function with CLIP and the kernel function with an ensemble of deep kernels built upon various pre-trained models. By regressing the classification label directly our framework enables analytical inference straightforward uncertainty quantification and principled hyper-parameter tuning. Through extensive experiments on standard benchmarks we demonstrate that our method consistently outperforms competitive ensemble baselines regarding predictive performance. Additionally we assess the robustness of our method and the quality of the yielded uncertainty estimates on out-of-distribution datasets. We also illustrate that our method despite relying on label regression still enjoys superior model calibration compared to most deterministic baselines.
Large foundation models known for their strong zero-shot generalization have excelled in visual and language applications. However applying them to medical image segmentation a domain with diverse imaging types and target labels remains an open challenge. Current approaches such as adapting interactive segmentation models like Segment Anything Model (SAM) require user prompts for each sample during inference. Alternatively transfer learning methods like few/one-shot models demand labeled samples leading to high costs. This paper introduces a new paradigm toward the universal medical image segmentation termed 'One-Prompt Segmentation.' One-Prompt Segmentation combines the strengths of one-shot and interactive methods. In the inference stage with just one prompted sample it can adeptly handle the unseen task in a single forward pass. We train One-Prompt Model on 64 open-source medical datasets accompanied by the collection of over 3000 clinician-labeled prompts. Tested on 14 previously unseen datasets the One-Prompt Model showcases superior zero-shot segmentation capabilities outperforming a wide range of related methods. The code and data is released as https://github.com/KidsWithTokens/one-prompt.
It is a well-known fact that the performance of deep learning models deteriorates when they encounter a distribution shift at test time. Test-time adaptation (TTA) algorithms have been proposed to adapt the model online while inferring test data. However existing research predominantly focuses on classification tasks through the optimization of batch normalization layers or classification heads but this approach limits its applicability to various model architectures like Transformers and makes it challenging to apply to other tasks such as object detection. In this paper we propose a novel online adaption approach for object detection in continually changing test domains considering which part of the model to update how to update it and when to perform the update. By introducing architecture-agnostic and lightweight adaptor modules and only updating these while leaving the pre-trained backbone unchanged we can rapidly adapt to new test domains in an efficient way and prevent catastrophic forgetting. Furthermore we present a practical and straightforward class-wise feature aligning method for object detection to resolve domain shifts. Additionally we enhance efficiency by determining when the model is sufficiently adapted or when additional adaptation is needed due to changes in the test distribution. Our approach surpasses baselines on widely used benchmarks achieving improvements of up to 4.9%p and 7.9%p in mAP for COCO ? COCO-corrupted and SHIFT respectively while maintaining about 20 FPS or higher. The implementation code is available at https://github.com/natureyoo/ContinualTTA_ObjectDetection.