CVPR 2024 论文列表

Recently diffusion models have emerged as a new powerful generative method for 3D point cloud generation tasks. However few works study the effect of the architecture of the diffusion model in the 3D point cloud resorting to the typical UNet model developed for 2D images. Inspired by the wide adoption of Transformers we study the complementary role of convolution (from UNet) and attention (from Transformers). We discover that their respective importance change according to the timestep in the diffusion process. At early stage attention has an outsized influence because Transformers are found to generate the overall shape more quickly and at later stages when adding fine detail convolution starts having a larger impact on the generated point cloud's local surface quality. In light of this observation we propose a time-varying two-stream denoising model combined with convolution layers and transformer blocks. We generate an optimizable mask from each timestep to reweigh global and local features obtaining time-varying fused features. Experimentally we demonstrate that our proposed method quantitatively outperforms other state-of-the-art methods regarding visual quality and diversity. Code is avaiable github.com/Zhiyuan-R/Tiger-Time-varying-Diffusion-Model-for-Point-Cloud-Generation.

Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g. a woman's photo) and a text description (e.g. "a woman is drinking water."). Existing TI2V frameworks often require costly training on video-text datasets and specific model designs for text and image conditioning. In this paper we propose TI2V-Zero a zero-shot tuning-free method that empowers a pretrained text-to-video (T2V) diffusion model to be conditioned on a provided image enabling TI2V generation without any optimization fine-tuning or introducing external modules. Our approach leverages a pretrained T2V diffusion foundation model as the generative prior. To guide video generation with the additional image input we propose a "repeat-and-slide" strategy that modulates the reverse denoising process allowing the frozen diffusion model to synthesize a video frame-by-frame starting from the provided image. To ensure temporal continuity we employ a DDPM inversion strategy to initialize Gaussian noise for each newly synthesized frame and a resampling technique to help preserve visual details. We conduct comprehensive experiments on both domain-specific and open-domain datasets where TI2V-Zero consistently outperforms a recent open-domain TI2V model. Furthermore we show that TI2V-Zero can seamlessly extend to other tasks such as video infilling and prediction when provided with more images. Its autoregressive design also supports long video generation.

Mitigating hallucinations in large vision-language models (LVLMs) remains an open problem. Recent benchmarks do not address hallucinations in open-ended free-form responses which we term "Type I hallucinations". Instead they focus on hallucinations responding to very specific question formats---typically a multiple-choice response regarding a particular object or attribute---which we term "Type II hallucinations". Additionally such benchmarks often require external API calls to models which are subject to change. In practice we observe that a reduction in Type II hallucinations does not lead to a reduction in Type I hallucinations but rather that the two forms of hallucinations are often anti-correlated. To address this we propose THRONE a novel object-based automatic framework for quantitatively evaluating Type I hallucinations in LVLM free-form outputs. We use public language models (LMs) to identify hallucinations in LVLM responses and compute informative metrics. By evaluating a large selection of recent LVLMs using public datasets we show that an improvement in existing metrics do not lead to a reduction in Type I hallucinations and that established benchmarks for measuring Type I hallucinations are incomplete. Finally we provide a simple and effective data augmentation method to reduce Type I and Type II hallucinations as a strong baseline.

The Diffusion model a prevalent framework for image generation encounters significant challenges in terms of broad applicability due to its extended inference times and substantial memory requirements. Efficient Post-training Quantization (PTQ) is pivotal for addressing these issues in traditional models. Different from traditional models diffusion models heavily depend on the time-step t to achieve satisfactory multi-round denoising. Usually t from the finite set \ 1 \ldots T\ is encoded to a temporal feature by a few modules totally irrespective of the sampling data. However existing PTQ methods do not optimize these modules separately. They adopt inappropriate reconstruction targets and complex calibration methods resulting in a severe disturbance of the temporal feature and denoising trajectory as well as a low compression efficiency. To solve these we propose a Temporal Feature Maintenance Quantization (TFMQ) framework building upon a Temporal Information Block which is just related to the time-step t and unrelated to the sampling data. Powered by the pioneering block design we devise temporal information aware reconstruction (TIAR) and finite set calibration (FSC) to align the full-precision temporal features in a limited time. Equipped with the framework we can maintain the most temporal information and ensure the end-to-end generation quality. Extensive experiments on various datasets and diffusion models prove our state-of-the-art results. Remarkably our quantization approach for the first time achieves model performance nearly on par with the full-precision model under 4-bit weight quantization. Additionally our method incurs almost no extra computational cost and accelerates quantization time by 2.0 xon LSUN-Bedrooms 256 x256 compared to previous works. Our code is publicly available at \href https://github.com/ModelTC/TFMQ-DM https://github.com/ModelTC/TFMQ-DM .

Test Time Adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution with the distinct advantage of not requiring access to training data and processes especially valuable in the context of pre-trained models. However current TTA methods fail to address the fundamental issue: covariate shift i.e. the decreased generalizability can be attributed to the model's reliance on the marginal distribution of the training data which may impair model calibration and introduce confirmation bias. To address this we propose a novel energy-based perspective enhancing the model's perception of target data distributions without requiring access to training data or processes. Building on this perspective we introduce Test-time Energy Adaptation (TEA) which transforms the trained classifier into an energy-based model and aligns the model's distribution with the test data's enhancing its ability to perceive test distributions and thus improving overall generalizability. Extensive experiments across multiple tasks benchmarks and architectures demonstrate TEA's superior generalization performance against state-of-the-art methods. Further in-depth analyses reveal that TEA can equip the model with a comprehensive perception of test distribution ultimately paving the way toward improved generalization and calibration. Code is available at https://github.com/yuanyige/tea.

In this paper we investigate that the normalized coordinate expression is a key factor as reliance on hand-crafted components in query-based detectors for temporal action detection (TAD). Despite significant advancements towards an end-to-end framework in object detection query-based detectors have been limited in achieving full end-to-end modeling in TAD. To address this issue we propose TE-TAD a full end-to-end temporal action detection transformer that integrates time-aligned coordinate expression. We reformulate coordinate expression utilizing actual timeline values ensuring length-invariant representations from the extremely diverse video duration environment. Furthermore our proposed adaptive query selection dynamically adjusts the number of queries based on video length providing a suitable solution for varying video durations compared to a fixed query set. Our approach not only simplifies the TAD process by eliminating the need for hand-crafted components but also significantly improves the performance of query-based detectors. Our TE-TAD outperforms the previous query-based detectors and achieves competitive performance compared to state-of-the-art methods on popular benchmark datasets. Code is available at: https://github.com/Dotori-HJ/TE-TAD.

Training deep models for LiDAR semantic segmentation is challenging due to the inherent sparsity of point clouds. Utilizing temporal data is a natural remedy against the sparsity problem as it makes the input signal denser. However previous multi-frame fusion algorithms fall short in utilizing sufficient temporal information due to the memory constraint and they also ignore the informative temporal images. To fully exploit rich information hidden in long-term temporal point clouds and images we present the Temporal Aggregation Network termed TASeg. Specifically we propose a Temporal LiDAR Aggregation and Distillation (TLAD) algorithm which leverages historical priors to assign different aggregation steps for different classes. It can largely reduce memory and time overhead while achieving higher accuracy. Besides TLAD trains a teacher injected with gt priors to distill the model further boosting the performance. To make full use of temporal images we design a Temporal Image Aggregation and Fusion (TIAF) module which can greatly expand the camera FOV and enhance the present features. Temporal LiDAR points in the camera FOV are used as mediums to transform temporal image features to the present coordinate for temporal multi-modal fusion. Moreover we develop a Static-Moving Switch Augmentation (SMSA) algorithm which utilizes sufficient temporal information to enable objects to switch their motion states freely thus greatly increasing static and moving training samples. Our TASeg ranks 1st on three challenging tracks i.e. SemanticKITTI single-scan track multi-scan track and nuScenes LiDAR segmentation track strongly demonstrating the superiority of our method. Codes are available at https://github.com/LittlePey/TASeg.

The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes. However even though the image and language representations have been aligned by cross-modal models like CLIP we find that the image modality fails to contribute as much as the language in existing multi-modal 3D representation learning methods. This is attributed to the domain shift in the 2D images and the distinct focus of each modality. To more effectively leverage both modalities in the pre-training we introduce TriAdapter Multi-Modal Learning (TAMM) - a novel two-stage learning approach based on three synergistic adapters. First our CLIP Image Adapter mitigates the domain gap between 3D-rendered images and natural images by adapting the visual representations of CLIP for synthetic image-text pairs. Subsequently our Dual Adapters decouple the 3D shape representation space into two complementary sub-spaces: one focusing on visual attributes and the other for semantic understanding which ensure a more comprehensive and effective multi-modal pre-training. Extensive experiments demonstrate that TAMM consistently enhances 3D representations for a wide range of 3D encoder architectures pre-training datasets and downstream tasks. Notably we boost the zero-shot classification accuracy on Objaverse-LVIS from 46.8% to 50.7% and improve the 5-way 10-shot linear probing classification accuracy on ModelNet40 from 96.1% to 99.0%. Project page: https://alanzhangcs.github.io/tamm-page.

Humans commonly work with multiple objects in daily life and can intuitively transfer manipulation skills to novel objects by understanding object functional regularities. However existing technical approaches for analyzing and synthesizing hand-object manipulation are mostly limited to handling a single hand and object due to the lack of data support. To address this we construct TACO an extensive bimanual hand-object-interaction dataset spanning a large variety of tool-action-object compositions for daily human activities. TACO contains 2.5K motion sequences paired with third-person and egocentric views precise hand-object 3D meshes and action labels. To rapidly expand the data scale we present a fully automatic data acquisition pipeline combining multi-view sensing with an optical motion capture system. With the vast research fields provided by TACO we benchmark three generalizable hand-object-interaction tasks: compositional action recognition generalizable hand-object motion forecasting and cooperative grasp synthesis. Extensive experiments reveal new insights challenges and opportunities for advancing the studies of generalizable hand-object motion analysis and synthesis. Our data and code are available at https://taco2024.github.io.

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.

Visual sound source localization poses a significant challenge in identifying the semantic region of each sounding source within a video. Existing self-supervised and weakly supervised source localization methods struggle to accurately distinguish the semantic regions of each sounding object particularly in multi-source mixtures. These methods often rely on audio-visual correspondence as guidance which can lead to substantial performance drops in complex multi-source localization scenarios. The lack of access to individual source sounds in multi-source mixtures during training exacerbates the difficulty of learning effective audio-visual correspondence for localization. To address this limitation in this paper we propose incorporating the text modality as an intermediate feature guide using tri-modal joint embedding models (e.g. AudioCLIP) to disentangle the semantic audio-visual source correspondence in multi-source mixtures. Our framework dubbed T-VSL begins by predicting the class of sounding entities in mixtures. Subsequently the textual representation of each sounding source is employed as guidance to disentangle fine-grained audio-visual source correspondence from multi-source mixtures leveraging the tri-modal AudioCLIP embedding. This approach enables our framework to handle a flexible number of sources and exhibits promising zero-shot transferability to unseen classes during test time. Extensive experiments conducted on the MUSIC VGGSound and VGGSound-Instruments datasets demonstrate significant performance improvements over state-of-the-art methods. Code is released at https://github.com/enyac-group/T-VSL/tree/main.

In typical medical image classification problems labeled data is scarce while unlabeled data is more available. Semi-supervised learning and self-supervised learning are two different research directions that can improve accuracy by learning from extra unlabeled data. Recent methods from both directions have reported significant gains on traditional benchmarks. Yet past benchmarks do not focus on medical tasks and rarely compare self- and semi- methods together on an equal footing. Furthermore past benchmarks often handle hyperparameter tuning suboptimally. First they may not tune hyperparameters at all leading to underfitting. Second when tuning does occur it often unrealistically uses a labeled validation set that is much larger than the training set. Therefore currently published rankings might not always corroborate with their practical utility This study contributes a systematic evaluation of self- and semi- methods with a unified experimental protocol intended to guide a practitioner with scarce overall labeled data and a limited compute budget. We answer two key questions: Can hyperparameter tuning be effective with realistic-sized validation sets? If so when all methods are tuned well which self- or semi-supervised methods achieve the best accuracy? Our study compares 13 representative semi- and self-supervised methods to strong labeled-set-only baselines on 4 medical datasets. From 20000+ GPU hours of computation we provide valuable best practices to resource-constrained practitioners: hyperparameter tuning is effective and the semi-supervised method known as MixMatch delivers the most reliable gains across 4 datasets.

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. 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.

Vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However understanding fine-grained visual-linguistic concepts such as attributes and inter-object relationships remains a significant challenge. While several benchmarks aim to evaluate VLMs in finer granularity their primary focus remains on the linguistic aspect neglecting the visual dimension. Here we highlight the importance of evaluating VLMs from both a textual and visual perspective. We introduce a progressive pipeline to synthesize images that vary in a specific attribute while ensuring consistency in all other aspects. Utilizing this data engine we carefully design a benchmark SPEC to diagnose the comprehension of object size position existence and count. Subsequently we conduct a thorough evaluation of four leading VLMs on SPEC. Surprisingly their performance is close to random guess revealing significant limitations. With this in mind we propose a simple yet effective approach to optimize VLMs in fine-grained understanding achieving significant improvements on SPEC without compromising the zero-shot performance. Results on two additional fine-grained benchmarks also show consistent improvements further validating the transferability of our approach. Code and data are available at https://github.com/wjpoom/SPEC.

Remarkable strides have been made in reconstructing static scenes or human bodies from monocular videos. Yet the two problems have largely been approached independently without much synergy. Most visual SLAM methods can only reconstruct camera trajectories and scene structures up to scale while most HMR methods reconstruct human meshes in metric scale but fall short in reasoning with cameras and scenes. This work introduces Synergistic Camera and Human Reconstruction (SynCHMR) to marry the best of both worlds. Specifically we design Human-aware Metric SLAM to reconstruct metric-scale camera poses and scene point clouds using camera-frame HMR as a strong prior addressing depth scale and dynamic ambiguities. Conditioning on the dense scene recovered we further learn a Scene-aware SMPL Denoiser to enhance world-frame HMR by incorporating spatiotemporal coherency and dynamic scene constraints. Together they lead to consistent reconstructions of camera trajectories human meshes and dense scene point clouds in a common world frame.

Achieving high synchronization in the synthesis of realistic speech-driven talking head videos presents a significant challenge. Traditional Generative Adversarial Networks (GAN) struggle to maintain consistent facial identity while Neural Radiance Fields (NeRF) methods although they can address this issue often produce mismatched lip movements inadequate facial expressions and unstable head poses. A lifelike talking head requires synchronized coordination of subject identity lip movements facial expressions and head poses. The absence of these synchronizations is a fundamental flaw leading to unrealistic and artificial outcomes. To address the critical issue of synchronization identified as the "devil" in creating realistic talking heads we introduce SyncTalk. This NeRF-based method effectively maintains subject identity enhancing synchronization and realism in talking head synthesis. SyncTalk employs a Face-Sync Controller to align lip movements with speech and innovatively uses a 3D facial blendshape model to capture accurate facial expressions. Our HeadSync Stabilizer optimizes head poses achieving more natural head movements. The Portrait-Sync Generator restores hair details and blends the generated head with the torso for a seamless visual experience. Extensive experiments and user studies demonstrate that SyncTalk outperforms state-of-the-art methods in synchronization and realism. We recommend watching the supplementary video: https://ziqiaopeng.github.io/synctalk

Vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However in fashion domain datasets often exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text leading to cases where some textual details are not visible in individual images. This mismatch particularly when non-co-occurring elements are masked undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem we propose Synchronized attentional Masking (SyncMask) which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model ensuring a precise alignment between the two modalities. Additionally we enhance grouped batch sampling with semi-hard negatives effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach outperforming existing methods in three downstream tasks.

Predicting human pose sequences via existing pose estimators often encounters various estimation errors. Motion refinement methods aim to optimize the predicted human pose sequences from pose estimators while ensuring minimal computational overhead and latency. Prior investigations have primarily concentrated on striking a balance between the two objectives i.e. smoothness and precision while optimizing the predicted pose sequences. However it has come to our attention that the tension between these two objectives can provide additional quality cues about the predicted pose sequences. These cues in turn are able to aid the network in optimizing lower-quality poses. To leverage this quality information we propose a motion refinement network termed SynSP to achieve a Synergy of Smoothness and Precision in the sequence refinement tasks. Moreover SynSP can also address multi-view poses of one person simultaneously fixing inaccuracies in predicted poses through heightened attention to similar poses from other views thereby amplifying the resultant quality cues and overall performance. Compared with previous methods SynSP benefits from both pose quality and multi-view information with a much shorter input sequence length achieving state-of-the-art results among four challenging datasets involving 2D 3D and SMPL pose representations in both single-view and multi-view scenes. Github code: https://github.com/InvertedForest/SynSP.