CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Identifying the chemical structure from a graphical representation or image of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet existing methods for chemical structure recognition do not typically generalize well and show diminished effectiveness when confronted with domains where data is sparse or costly to generate such as hand-drawn molecule images. To address this limitation we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches our method provides atom-level localization and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency accuracy and atom-level entity prediction.
3D visual language multi-modal modeling plays an important role in actual human-computer interaction. However the inaccessibility of large-scale 3D-language pairs restricts their applicability in real-world scenarios. In this paper we aim to handle a real-time multi-task for 6-DoF pose tracking of unknown objects leveraging 3D-language pre-training scheme from a series of 3D point cloud video streams while simultaneously performing 3D shape reconstruction in current observation. To this end we present a generic Language-to-4D modeling paradigm termed L4D-Track that tackles zero-shot 6-DoF \underline Track ing and shape reconstruction by learning pairwise implicit 3D representation and multi-level multi-modal alignment. Our method constitutes two core parts. 1) Pairwise Implicit 3D Space Representation that establishes spatial-temporal to language coherence descriptions across continuous 3D point cloud video. 2) Language-to-4D Association and Contrastive Alignment enables multi-modality semantic connections between 3D point cloud video and language. Our method trained exclusively on public NOCS-REAL275 dataset achieves promising results on both two publicly benchmarks. This not only shows powerful generalization performance but also proves its remarkable capability in zero-shot inference.
The pre-training architectures of large language models encompass various types including autoencoding models autoregressive models and encoder-decoder models. We posit that any modality can potentially benefit from a large language model as long as it undergoes vector quantization to become discrete tokens. Inspired by the General Language Model we propose a General Point Model (GPM) that seamlessly integrates autoencoding and autoregressive tasks in a point cloud transformer. This model is versatile allowing fine-tuning for downstream point cloud representation tasks as well as unconditional and conditional generation tasks. GPM enhances masked prediction in autoencoding through various forms of mask padding tasks leading to improved performance in point cloud understanding. Additionally GPM demonstrates highly competitive results in unconditional point cloud generation tasks even exhibiting the potential for conditional generation tasks by modifying the input's conditional information. Compared to models like Point-BERT MaskPoint and PointMAE our GPM achieves superior performance in point cloud understanding tasks. Furthermore the integration of autoregressive and autoencoding within the same transformer underscores its versatility across different downstream tasks.
Implicit neural representations (INRs) were recently proposed as a new video compression paradigm with existing approaches performing on par with HEVC. However such methods only perform well in limited settings e.g. specific model sizes fixed aspect ratios and low-motion videos. We address this issue by proposing T-NeRV a hybrid video INR that combines frame-specific embeddings with GOP-specific features providing a lever for content-specific fine-tuning. We employ entropy-constrained training to jointly optimize our model for rate and distortion and demonstrate that T-NeRV can thereby automatically adjust this lever during training effectively fine-tuning itself to the target content. We evaluate T-NeRV on the UVG dataset where it achieves state-of-the-art results on the video representation task outperforming previous works by up to 3dB PSNR on challenging high-motion sequences. Further our method improves on the compression performance of previous methods and is the first video INR to outperform HEVC on all UVG sequences.
Camera-based person re-identification (ReID) systems have been widely applied in the field of public security. However cameras often lack the perception of 3D morphological information of human and are susceptible to various limitations such as inadequate illumination complex background and personal privacy. In this paper we propose a LiDAR-based ReID framework ReID3D that utilizes pre-training strategy to retrieve features of 3D body shape and introduces Graph-based Complementary Enhancement Encoder for extracting comprehensive features. Due to the lack of LiDAR datasets we build LReID the first LiDAR-based person ReID dataset which is collected in several outdoor scenes with variations in natural conditions. Additionally we introduce LReID-sync a simulated pedestrian dataset designed for pre-training encoders with tasks of point cloud completion and shape parameter learning. Extensive experiments on LReID show that ReID3D achieves exceptional performance with a rank-1 accuracy of 94.0 highlighting the significant potential of LiDAR in addressing person ReID tasks. To the best of our knowledge we are the first to propose a solution for LiDAR-based ReID. The code and dataset are available at https://github.com/GWxuan/ReID3D.
As an important pillar of underwater intelligence Marine Animal Segmentation (MAS) involves segmenting animals within marine environments. Previous methods don't excel in extracting long-range contextual features and overlook the connectivity between discrete pixels. Recently Segment Anything Model (SAM) offers a universal framework for general segmentation tasks. Unfortunately trained with natural images SAM does not obtain the prior knowledge from marine images. In addition the single-position prompt of SAM is very insufficient for prior guidance. To address these issues we propose a novel feature learning framework named Dual-SAM for high-performance MAS. To this end we first introduce a dual structure with SAM's paradigm to enhance feature learning of marine images. Then we propose a Multi-level Coupled Prompt (MCP) strategy to instruct comprehensive underwater prior information and enhance the multi-level features of SAM's encoder with adapters. Subsequently we design a Dilated Fusion Attention Module (DFAM) to progressively integrate multi-level features from SAM's encoder. Finally instead of directly predicting the masks of marine animals we propose a Criss-Cross Connectivity Prediction (C3P) paradigm to capture the inter-connectivity between discrete pixels.
Subsequently we design a Dilated Fusion Attention Module (DFAM) to progressively integrate multi-level features from SAM's encoder. Finally instead of directly predicting the masks of marine animals we propose a Criss-Cross Connectivity Prediction (C3P) paradigm to capture the inter-connectivity between discrete pixels. With dual decoders it generates pseudo-labels and achieves mutual supervision for complementary feature representations resulting in considerable improvements over previous techniques. Extensive experiments verify that our proposed method achieves state-of-the-art performances on five widely-used MAS datasets. The code is available at https://github.com/Drchip61/Dual SAM.
Video and audio content creation serves as the core technique for the movie industry and professional users. Recently existing diffusion-based methods tackle video and audio generation separately which hinders the technique transfer from academia to industry. In this work we aim at filling the gap with a carefully designed optimization-based framework for cross-visual-audio and joint-visual-audio generation. We observe the powerful generation ability of off-the-shelf video or audio generation models. Thus instead of training the giant models from scratch we propose to bridge the existing strong models with a shared latent representation space. Specifically we propose a multimodality latent aligner with the pre-trained ImageBind model. Our latent aligner shares a similar core as the classifier guidance that guides the diffusion denoising process during inference time. Through carefully designed optimization strategy and loss functions we show the superior performance of our method on joint video-audio generation visual-steered audio generation and audio-steered visual generation tasks. The project website can be found at \href https://yzxing87.github.io/Seeing-and-Hearing/  https://yzxing87.github.io/Seeing-and-Hearing/ .
When adopting a deep learning model for embodied agents it is required that the model structure be optimized for specific tasks and operational conditions. Such optimization can be static such as model compression or dynamic such as adaptive inference. Yet these techniques have not been fully investigated for embodied control systems subject to time constraints which necessitate sequential decision-making for multiple tasks each with distinct inference latency limitations. In this paper we present MoDeC a time constraint-aware embodied control framework using the modular model adaptation. We formulate model adaptation to varying operational conditions on resource and time restrictions as dynamic routing on a modular network incorporating these conditions as part of multi-task objectives. Our evaluation across several vision-based embodied environments demonstrates the robustness of MoDeC showing that it outperforms other model adaptation methods in both performance and adherence to time constraints in robotic manipulation and autonomous driving applications.
We develop a theory for the representation of opaque solids as volumes. Starting from a stochastic representation of opaque solids as random indicator functions we prove the conditions under which such solids can be modeled using exponential volumetric transport. We also derive expressions for the volumetric attenuation coefficient as a functional of the probability distributions of the underlying indicator functions. We generalize our theory to account for isotropic and anisotropic scattering at different parts of the solid and for representations of opaque solids as stochastic implicit surfaces. We derive our volumetric representation from first principles which ensures that it satisfies physical constraints such as reciprocity and reversibility. We use our theory to explain compare and correct previous volumetric representations as well as propose meaningful extensions that lead to improved performance in 3D reconstruction tasks.
The pretraining-finetuning paradigm has gained popularity in various computer vision tasks. In this paradigm the emergence of active finetuning arises due to the abundance of large-scale data and costly annotation requirements. Active finetuning involves selecting a subset of data from an unlabeled pool for annotation facilitating subsequent finetuning. However the use of a limited number of training samples can lead to a biased distribution potentially resulting in model overfitting. In this paper we propose a new method called ActiveDC for the active finetuning tasks. Firstly we select samples for annotation by optimizing the distribution similarity between the subset to be selected and the entire unlabeled pool in continuous space. Secondly we calibrate the distribution of the selected samples by exploiting implicit category information in the unlabeled pool. The feature visualization provides an intuitive sense of the effectiveness of our approach to distribution calibration. We conducted extensive experiments on three image classification datasets with different sampling ratios. The results indicate that ActiveDC consistently outperforms the baseline performance in all image classification tasks. The improvement is particularly significant when the sampling ratio is low with performance gains of up to 10%. Our code will be released.
Machine learning holds tremendous promise for transforming the fundamental practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing stream of research data collection it would be appealing to autonomously explore patterns and insights from observational data for discovering novel classes of phenotypes and concepts. However in the biomedical domain there are several challenges inherently presented in the cumulated data which hamper the progress of novel class discovery. The non-i.i.d. data distribution accompanied by the severe imbalance among different groups of classes essentially leads to ambiguous and biased semantic representations. In this work we present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. First we propose to parameterize the approximated posterior of instance embedding as a marginal von Mises-Fisher distribution to account for the interference of distributional latent bias. Then we incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space which in turn minimizes the uncontrollable risk for unknown class learning and structuring. Furthermore a spectral graph-theoretic method is devised to estimate the number of potential novel classes. It inherits two intriguing merits compared to existent approaches namely high computational efficiency and flexibility for taxonomy-adaptive estimation.
Furthermore a spectral graph-theoretic method is devised to estimate the number of potential novel classes. It inherits two intriguing merits compared to existent approaches namely high computational efficiency and flexibility for taxonomy-adaptive estimation. Extensive experiments across various biomedical scenarios substantiate the effectiveness and general applicability of our method.
In this era the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets. However in the realm of 3D vision while remarkable progress has been made with models trained on large-scale synthetic and real-captured object data like Objaverse and MVImgNet a similar level of progress has not been observed in the domain of human-centric tasks partially due to the lack of a large-scale human dataset. Existing datasets of high-fidelity 3D human capture continue to be mid-sized due to the significant challenges in acquiring large-scale high-quality 3D human data. To bridge this gap we present MVHumanNet a dataset that comprises multi-view human action sequences of 4500 human identities. The primary focus of our work is on collecting human data that features a large number of diverse identities and everyday clothing using a multi-view human capture system which facilitates easily scalable data collection. Our dataset contains 9000 daily outfits 60000 motion sequences and 645 million frames with extensive annotations including human masks camera parameters 2D and 3D keypoints SMPL/SMPLX parameters and corresponding textual descriptions.
Our dataset contains 9000 daily outfits 60000 motion sequences and 645 million frames with extensive annotations including human masks camera parameters 2D and 3D keypoints SMPL/SMPLX parameters and corresponding textual descriptions. To explore the potential of MVHumanNet in various 2D and 3D visual tasks we conducted pilot studies on view-consistent action recognition human NeRF reconstruction text-driven view-unconstrained human image generation as well as 2D view-unconstrained human image and 3D avatar generation. Extensive experiments demonstrate the performance improvements and effective applications enabled by the scale provided by MVHumanNet. As the current largest-scale 3D human dataset we hope that the release of MVHumanNet data with annotations will foster further innovations in the domain of 3D human-centric tasks at scale.
Federated learning often suffers from slow and unstable convergence due to the heterogeneous characteristics of participating client datasets. Such a tendency is aggravated when the client participation ratio is low since the information collected from the clients has large variations. To address this challenge we propose a simple but effective federated learning framework which improves the consistency across clients and facilitates the convergence of the server model. This is achieved by making the server broadcast a global model with a lookahead gradient. This strategy enables the proposed approach to convey the projected global update information to participants effectively without additional client memory and extra communication costs. We also regularize local updates by aligning each client with the overshot global model to reduce bias and improve the stability of our algorithm. We provide the theoretical convergence rate of our algorithm and demonstrate remarkable performance gains in terms of accuracy and communication efficiency compared to the state-of-the-art methods especially with low client participation rates. The source code is available at our project page.
Skeleton-based action recognition has attracted lots of research attention. Recently to build an accurate skeleton-based action recognizer a variety of works have been proposed. Among them some works use large model architectures as backbones of their recognizers to boost the skeleton data representation capability while some other works pre-train their recognizers on external data to enrich the knowledge. In this work we observe that large language models which have been extensively used in various natural language processing tasks generally hold both large model architectures and rich implicit knowledge. Motivated by this we propose a novel LLM-AR framework in which we investigate treating the Large Language Model as an Action Recognizer. In our framework we propose a linguistic projection process to project each input action signal (i.e. each skeleton sequence) into its "sentence format" (i.e. an "action sentence"). Moreover we also incorporate our framework with several designs to further facilitate this linguistic projection process. Extensive experiments demonstrate the efficacy of our proposed framework.
Generative models have been very popular in the recent years for their image generation capabilities. GAN-based models are highly regarded for their disentangled latent space which is a key feature contributing to their success in controlled image editing. On the other hand diffusion models have emerged as powerful tools for generating high-quality images. However the latent space of diffusion models is not as thoroughly explored or understood. Existing methods that aim to explore the latent space of diffusion models usually relies on text prompts to pinpoint specific semantics. However this approach may be restrictive in areas such as art fashion or specialized fields like medicine where suitable text prompts might not be available or easy to conceive thus limiting the scope of existing work. In this paper we propose an unsupervised method to discover latent semantics in text-to-image diffusion models without relying on text prompts. Our method takes a small set of unlabeled images from specific domains such as faces or cats and a pre-trained diffusion model and discovers diverse semantics in unsupervised fashion using a contrastive learning objective. Moreover the learned directions can be applied simultaneously either within the same domain (such as various types of facial edits) or across different domains (such as applying cat and face edits within the same image) without interfering with each other.
Moreover the learned directions can be applied simultaneously either within the same domain (such as various types of facial edits) or across different domains (such as applying cat and face edits within the same image) without interfering with each other. Our extensive experiments show that our method achieves highly disentangled edits outperforming existing approaches in both diffusion-based and GAN-based latent space editing methods.
Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However existing approaches still struggle with the view-dependent appearance of glossy surfaces especially under complex lighting of indoor environments. Unlike existing methods which typically assume distant lighting like an environment map we propose a learnable Gaussian directional encoding to better model the view-dependent effects under near-field lighting conditions. Importantly our new directional encoding captures the spatially-varying nature of near-field lighting and emulates the behavior of prefiltered environment maps. As a result it enables the efficient evaluation of preconvolved specular color at any 3D location with varying roughness coefficients. We further introduce a data-driven geometry prior that helps alleviate the shape radiance ambiguity in reflection modeling. We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields which helps decompose appearance into more physically meaningful components.
In subject-driven text-to-image synthesis the synthesis process tends to be heavily influenced by the reference images provided by users often overlooking crucial attributes detailed in the text prompt. In this work we propose Subject-Agnostic Guidance (SAG) a simple yet effective solution to remedy the problem. We show that through constructing a subject-agnostic condition and applying our proposed dual classifier-free guidance one could obtain outputs consistent with both the given subject and input text prompts. We validate the efficacy of our approach through both optimization-based and encoder-based methods. Additionally we demonstrate its applicability in second-order customization methods where an encoder-based model is fine-tuned with DreamBooth. Our approach is conceptually simple and requires only minimal code modifications but leads to substantial quality improvements as evidenced by our evaluations and user studies.
Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' preferences. In contrast to LLMs human preference learning has not been widely explored in text-to-image diffusion models; the best existing approach is to fine-tune a pretrained model using carefully curated high quality images and captions to improve visual appeal and text alignment. We propose Diffusion-DPO a method to align diffusion models to human preferences by directly optimizing on human comparison data. Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO) a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective. We re-formulate DPO to account for a diffusion model notion of likelihood utilizing the evidence lower bound to derive a differentiable objective. Using the Pick-a-pic dataset of 851K crowdsourced pairwise preferences we fine-tune the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1.0 model with Diffusion-DPO.
Using the Pick-a-pic dataset of 851K crowdsourced pairwise preferences we fine-tune the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1.0 model with Diffusion-DPO. Our fine-tuned base model significantly outperforms both base SDXL-1.0 and the larger SDXL-1.0 model consisting of an additional refinement model in human evaluation improving visual appeal and prompt alignment. We also develop a variant that uses AI feedback and has comparable performance to training on human preferences opening the door for scaling of diffusion model alignment methods.