CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Music is a universal language that can communicate emotions and feelings. It forms an essential part of the whole spectrum of creative media ranging from movies to social media posts. Machine learning models that can synthesize music are predominantly conditioned on textual descriptions of it. Inspired by how musicians compose music not just from a movie script but also through visualizations we propose MeLFusion a model that can effectively use cues from a textual description and the corresponding image to synthesize music. MeLFusion is a text-to-music diffusion model with a novel "visual synapse" which effectively infuses the semantics from the visual modality into the generated music. To facilitate research in this area we introduce a new dataset MeLBench and propose a new evaluation metric IMSM. Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music measured both objectively and subjectively with a relative gain of up to 67.98% on the FAD score. We hope that our work will gather attention to this pragmatic yet relatively under-explored research area.
Trajectory prediction is a challenging problem that requires considering interactions among multiple actors and the surrounding environment. While data-driven approaches have been used to address this complex problem they suffer from unreliable predictions under distribution shifts during test time. Accordingly several online learning methods have been proposed using regression loss from the ground truth of observed data leveraging the auto-labeling nature of trajectory prediction task. We mainly tackle the following two issues. First previous works underfit and overfit as they only optimize the last layer of motion decoder. To this end we employ the masked autoencoder (MAE) for representation learning to encourage complex interaction modeling in shifted test distribution for updating deeper layers. Second utilizing the sequential nature of driving data we propose an actor-specific token memory that enables the test-time learning of actor-wise motion characteristics. Our proposed method has been validated across various challenging cross-dataset distribution shift scenarios including nuScenes Lyft Waymo and Interaction. Our method surpasses the performance of existing state-of-the-art online learning methods in terms of both prediction accuracy and computational efficiency. The code is available at https://github.com/daeheepark/T4P.
Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community which aims to retrieve the target person based on a textual query. Although numerous TIReID methods have been proposed and achieved promising performance they implicitly assume the training image-text pairs are correctly aligned which is not always the case in real-world scenarios. In practice the image-text pairs inevitably exist under-correlated or even false-correlated a.k.a noisy correspondence (NC) due to the low quality of the images and annotation errors. To address this problem we propose a novel Robust Dual Embedding method (RDE) that can learn robust visual-semantic associations even with NC. Specifically RDE consists of two main components: 1) A Confident Consensus Division (CCD) module that leverages the dual-grained decisions of dual embedding modules to obtain a consensus set of clean training data which enables the model to learn correct and reliable visual-semantic associations. 2) A Triplet Alignment Loss (TAL) relaxes the conventional Triplet Ranking loss with the hardest negative samples to a log-exponential upper bound over all negative ones thus preventing the model collapse under NC and can also focus on hard-negative samples for promising performance.
2) A Triplet Alignment Loss (TAL) relaxes the conventional Triplet Ranking loss with the hardest negative samples to a log-exponential upper bound over all negative ones thus preventing the model collapse under NC and can also focus on hard-negative samples for promising performance. We conduct extensive experiments on three public benchmarks namely CUHK-PEDES ICFG-PEDES and RSTPReID to evaluate the performance and robustness of our RDE. Our method achieves state-of-the-art results both with and without synthetic noisy correspondences on all three datasets. Code is available at https://github.com/QinYang79/RDE.
In this paper we present a novel paradigm to enhance the ability of object detector e.g. expanding categories or improving detection performance by training on syn- thetic dataset generated from diffusion models. Specifically we integrate an instance-level grounding head into a pre- trained generative diffusion model to augment it with the ability of localising instances in the generated images. The grounding head is trained to align the text embedding of category names with the regional visual feature of the diffusion model using supervision from an off-the-shelf object detector and a novel self-training scheme on (novel) categories not covered by the detector. We conduct thorough experiments to show that this enhanced version of diffusion model termed as InstaGen can serve as a data synthesizer to enhance object detectors by training on its generated samples demonstrating superior performance over existing state-of-the-art methods in open-vocabulary (+4.5 AP) and data-sparse (+1.2 ? 5.2 AP) scenarios.
We introduce the Panoptic 3D Reconstruction task a unified and holistic scene understanding task for a monocular video. And we present PanoRecon - a novel framework to address this new task which realizes an online geometry reconstruction alone with dense semantic and instance labeling. Specifically PanoRecon incrementally performs panoptic 3D reconstruction for each video fragment consisting of multiple consecutive key frames from a volumetric feature representation using feed-forward neural networks. We adopt a depth-guided back-projection strategy to sparse and purify the volumetric feature representation. We further introduce a voxel clustering module to get object instances in each local fragment and then design a tracking and fusion algorithm for the integration of instances from different fragments to ensure temporal coherence. Such design enables our PanoRecon to yield a coherent and accurate panoptic 3D reconstruction. Experiments on ScanNetV2 demonstrate a very competitive geometry reconstruction result compared with state-of-the-art reconstruction methods as well as promising 3D panoptic segmentation result with only RGB input while being real-time. Code is available at: https://github.com/Riser6/PanoRecon.
We present the pioneering Large Visual Motion Model (LVMM) meticulously engineered to analyze the intrinsic dynamics encapsulated within real-world imagery. Our model fortified with a wealth of prior knowledge extracted from billions of image pairs demonstrates promising results in predicting a diverse spectrum of scene dynamics. As a result it can infuse any generic image with authentic dynamic effects enhancing its visual allure.
In contrast to extensive studies on general vision pre-training for scalable visual autonomous driving remains seldom explored. Visual autonomous driving applications require features encompassing semantics 3D geometry and temporal information simultaneously for joint perception prediction and planning posing dramatic challenges for pre-training. To resolve this we bring up a new pre-training task termed as visual point cloud forecasting - predicting future point clouds from historical visual input. The key merit of this task captures the synergic learning of semantics 3D structures and temporal dynamics. Hence it shows superiority in various downstream tasks. To cope with this new problem we present ViDAR a general model to pre-train downstream visual encoders. It first extracts historical embeddings by the encoder. These representations are then transformed to 3D geometric space via a novel Latent Rendering operator for future point cloud prediction. Experiments show significant gain in downstream tasks e.g. 3.1% NDS on 3D detection  10% error reduction on motion forecasting and  15% less collision rate on planning.
Compared with transferable untargeted attacks transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples posing a greater threat to security-critical tasks. In the meanwhile 3D adversarial samples due to their potential of multi-view robustness can more comprehensively identify weaknesses in existing deep learning systems possessing great application value. However the field of transferable targeted 3D adversarial attacks remains vacant. The goal of this work is to develop a more effective technique that could generate transferable targeted 3D adversarial examples filling the gap in this field. To achieve this goal we design a novel framework named TT3D that could rapidly reconstruct from few multi-view images into Transferable Targeted 3D textured meshes. While existing mesh-based texture optimization methods compute gradients in the high-dimensional mesh space and easily fall into local optima leading to unsatisfactory transferability and distinct distortions TT3D innovatively performs dual optimization towards both feature grid and Multi-layer Perceptron (MLP) parameters in the grid-based NeRF space which significantly enhances black-box transferability while enjoying naturalness.
Experimental results show that TT3D not only exhibits superior cross-model transferability but also maintains considerable adaptability across different renders and vision tasks. More importantly we produce 3D adversarial examples with 3D printing techniques in the real world and verify their robust performance under various scenarios.
We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore to overcome the limitation of scarce high-quality lightstage data we have developed a self-supervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism.
We present DIRECT-3D a diffusion-based 3D generative model for creating high-quality 3D assets (represented by Neural Radiance Fields) from text prompts. Unlike recent 3D generative models that rely on clean and well-aligned 3D data limiting them to single or few-class generation our model is directly trained on extensive noisy and unaligned `in-the-wild' 3D assets mitigating the key challenge (i.e. data scarcity) in large-scale 3D generation. In particular DIRECT-3D is a tri-plane diffusion model that integrates two innovations: 1) A novel learning framework where noisy data are filtered and aligned automatically during the training process. Specifically after an initial warm-up phase using a small set of clean data an iterative optimization is introduced in the diffusion process to explicitly estimate the 3D pose of objects and select beneficial data based on conditional density. 2) An efficient 3D representation that is achieved by disentangling object geometry and color features with two separate conditional diffusion models that are optimized hierarchically. Given a prompt input our model generates high-quality high-resolution realistic and complex 3D objects with accurate geometric details in seconds.
2) An efficient 3D representation that is achieved by disentangling object geometry and color features with two separate conditional diffusion models that are optimized hierarchically. Given a prompt input our model generates high-quality high-resolution realistic and complex 3D objects with accurate geometric details in seconds. We achieve state-of-the-art performance in both single-class generation and text-to-3D generation. We also demonstrate that DIRECT-3D can serve as a useful 3D geometric prior of objects for example to alleviate the well-known Janus problem in 2D-lifting methods such as DreamFusion.
Understanding data visualizations like charts and plots requires reasoning about both visual elements and numerics. Although strong in extractive questions current chart visual question answering (chart VQA) models suffer on complex reasoning questions. In this work we address the lack of reasoning ability by data augmentation. We leverage Large Language Models (LLMs) which have shown to have strong reasoning ability as an automatic data annotator that generates question-answer annotations for chart images. The key innovation in our method lies in the Synthesize Step-by-Step strategy: our LLM-based data generator learns to decompose the complex question into step-by-step sub-questions (rationales) which are then used to derive the final answer using external tools i.e. Python. This step-wise generation procedure is trained on synthetic data generated using a template-based QA generation pipeline. Experimental results highlight the significance of the proposed step-by-step generation. By training with the LLM-augmented data (LAMENDA) we significantly enhance the chart VQA models achieving the state-of-the-art accuracy on the ChartQA and PlotQA datasets.
Experimental results highlight the significance of the proposed step-by-step generation. By training with the LLM-augmented data (LAMENDA) we significantly enhance the chart VQA models achieving the state-of-the-art accuracy on the ChartQA and PlotQA datasets. In particular our approach improves the accuracy of the previous state-of-the-art approach from 38% to 54% on the human-written questions in the ChartQA dataset which needs strong reasoning. We hope our work underscores the potential of synthetic data and encourages further exploration of data augmentation using LLMs for reasoning-heavy tasks.
Recently leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However previous works that employ LLMs/MLLMs for document understanding have not fully explored and utilized the document layout information which is vital for precise document understanding. In this paper we propose LayoutLLM an LLM/MLLM based method for document understanding. The core of LayoutLLM is a layout instruction tuning strategy which is specially designed to enhance the comprehension and utilization of document layouts. The proposed layout instruction tuning strategy consists of two components: Layout-aware Pre-training and Layout-aware Supervised Fine-tuning. To capture the characteristics of document layout in Layout-aware Pre-training three groups of pre-training tasks corresponding to document-level region-level and segment-level information are introduced. Furthermore a novel module called layout chain-of-thought (LayoutCoT) is devised to enable LayoutLLM to focus on regions relevant to the question and generate accurate answers. LayoutCoT is effective for boosting the performance of document understanding. Meanwhile it brings a certain degree of interpretability which could facilitate manual inspection and correction.
LayoutCoT is effective for boosting the performance of document understanding. Meanwhile it brings a certain degree of interpretability which could facilitate manual inspection and correction. Experiments on standard benchmarks show that the proposed LayoutLLM significantly outperforms existing methods that adopt open-source 7B LLMs/MLLMs for document understanding.
Visual-language foundation models like CLIP learn generalized representations that enable zero-shot open-set classification. Few-shot adaptation methods based on prompt tuning have been shown to further improve performance on downstream datasets. However these methods do not fare well in the taxonomic open set (TOS) setting where the classifier is asked to make prediction from label set across different levels of semantic granularity. Frequently they infer incorrect labels at coarser taxonomic class levels even when the inference at the leaf level (original class labels) is correct. To address this problem we propose a prompt tuning technique that calibrates the hierarchical consistency of model predictions. A set of metrics of hierarchical consistency the Hierarchical Consistent Accuracy (HCA) and the Mean Treecut Accuracy (MTA) are first proposed to evaluate TOS model performance. A new Prompt Tuning for Hierarchical Consistency (ProTeCt) technique is then proposed to calibrate classification across label set granularities. Results show that ProTeCt can be combined with existing prompt tuning methods to significantly improve TOS classification without degrading the leaf level classification performance.
Adapters provide an efficient and lightweight mechanism for adapting trained transformer models to a variety of different tasks. However they have often been found to be outperformed by other adaptation mechanisms including low-rank adaptation. In this paper we provide an in-depth study of adapters their internal structure as well as various implementation choices. We uncover pitfalls for using adapters and suggest a concrete improved adapter architecture called Adapter+ that not only outperforms previous adapter implementations but surpasses a number of other more complex adaptation mechanisms in several challenging settings. Despite this our suggested adapter is highly robust and unlike previous work requires little to no manual intervention when addressing a novel scenario. Adapter+ reaches state-of-the-art average accuracy on the VTAB benchmark even without a per-task hyperparameter optimization.
Featurizing microscopy images for use in biological research remains a significant challenge especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond.
In this paper we delve into the creation of one-shot hand avatars attaining high-fidelity and drivable hand representations swiftly from a single image. With the burgeoning domains of the digital human the need for quick and personalized hand avatar creation has become increasingly critical. Existing techniques typically require extensive input data and may prove cumbersome or even impractical in certain scenarios. To enhance accessibility we present a novel method OHTA (One-shot Hand avaTAr) that enables the creation of detailed hand avatars from merely one image. OHTA tackles the inherent difficulties of this data-limited problem by learning and utilizing data-driven hand priors. Specifically we design a hand prior model initially employed for 1) learning various hand priors with available data and subsequently for 2) the inversion and fitting of the target identity with prior knowledge. OHTA demonstrates the capability to create high-fidelity hand avatars with consistent animatable quality solely relying on a single image. Furthermore we illustrate the versatility of OHTA through diverse applications encompassing text-to-avatar conversion hand editing and identity latent space manipulation.
We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions. SAM presents strong generalizability to segment anything while is short for semantic understanding. By introducing a lightweight query-based feature mixer we align the region-specific features with the embedding space of language models for later caption generation. As the number of trainable parameters is small (typically in the order of tens of millions) it costs less computation less memory usage and less communication bandwidth resulting in both fast and scalable training. To address the scarcity problem of regional caption data we propose to first pre-train our model on objection detection and segmentation tasks. We call this step weak supervision pretraining since the pretraining data only contains category names instead of full-sentence descriptions. The weak supervision pretraining allows us to leverage many publicly available object detection and segmentation datasets. We conduct extensive experiments to demonstrate the superiority of our method and validate each design choice. This work serves as a stepping stone towards scaling up regional captioning data and sheds light on exploring efficient ways to augment SAM with regional semantics.
We investigate a new task in human motion prediction which is predicting motions under unexpected physical perturbation potentially involving multiple people. Compared with existing research this task involves predicting less controlled unpremeditated and pure reactive motions in response to external impact and how such motions can propagate through people. It brings new challenges such as data scarcity and predicting complex interactions. To this end we propose a new method capitalizing differentiable physics and deep neural networks leading to an explicit Latent Differentiable Physics (LDP) model. Through experiments we demonstrate that LDP has high data efficiency outstanding prediction accuracy strong generalizability and good explainability. Since there is no similar research a comprehensive comparison with 11 adapted baselines from several relevant domains is conducted showing LDP outperforming existing research both quantitatively and qualitatively improving prediction accuracy by as much as 70% and demonstrating significantly stronger generalization.
Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors these methods often lead to geometric anomalies and multi-view inconsistency. Recently researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets. To harness the advantages of both approaches we propose Bidirectional Diffusion (BiDiff) a unified framework that incorporates both a 3D and a 2D diffusion process to preserve both 3D fidelity and 2D texture richness respectively. Moreover as a simple combination may yield inconsistent generation results we further bridge them with novel bidirectional guidance. In addition our method can be used as an initialization of optimization-based models to further improve the quality of 3D model and efficiency of optimization reducing the process from 3.4 hours to 20 minutes. Experimental results have shown that our model achieves high-quality diverse and scalable 3D generation. Project website https://bidiff.github.io/.