In this paper we propose the first generalizable view synthesis approach that specifically targets multi-view stereo-camera images. Since recent stereo matching has demonstrated accurate geometry prediction we introduce stereo matching into novel-view synthesis for high-quality geometry reconstruction. To this end this paper proposes a novel framework dubbed StereoNeRF which integrates stereo matching into a NeRF-based generalizable view synthesis approach. StereoNeRF is equipped with three key components to effectively exploit stereo matching in novel-view synthesis: a stereo feature extractor a depth-guided plane-sweeping and a stereo depth loss. Moreover we propose the StereoNVS dataset the first multi-view dataset of stereo-camera images encompassing a wide variety of both real and synthetic scenes. Our experimental results demonstrate that StereoNeRF surpasses previous approaches in generalizable view synthesis.
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Vision-Language Models (VLMs) are pretrained on large diverse and noisy web-crawled datasets. This underscores the critical need for dataset pruning as the quality of these datasets is strongly correlated with the performance of VLMs on downstream tasks. Using CLIPScore from a pretrained model to only train models using highly-aligned samples is one of the most successful methods for pruning. We argue that this approach suffers from multiple limitations including: false positives and negatives due to CLIP's pretraining on noisy labels. We propose a pruning signal Sieve that employs synthetic captions generated by image-captioning models pretrained on small diverse and well-aligned image-text pairs to evaluate the alignment of noisy image-text pairs. To bridge the gap between the limited diversity of generated captions and the high diversity of alternative text (alt-text) we estimate the semantic textual similarity in the embedding space of a language model pretrained on unlabeled text corpus. Using DataComp a multimodal dataset filtering benchmark when evaluating on 38 downstream tasks our pruning approach surpasses CLIPScore by 2.6% and 1.7% on medium and large scale respectively. In addition on retrieval tasks Sieve leads to a significant improvement of 2.7% and 4.5% on medium and large scale respectively.
The advent of Vision Transformers (ViTs) marks a substantial paradigm shift in the realm of computer vision. ViTs capture the global information of images through self-attention modules which perform dot product computations among patchified image tokens. While self-attention modules empower ViTs to capture long-range dependencies the computational complexity grows quadratically with the number of tokens which is a major hindrance to the practical application of ViTs. Moreover the self-attention mechanism in deep ViTs is also susceptible to the attention saturation issue. Accordingly we argue against the necessity of computing the attention scores in every layer and we propose the Less-Attention Vision Transformer (LaViT) which computes only a few attention operations at each stage and calculates the subsequent feature alignments in other layers via attention transformations that leverage the previously calculated attention scores. This novel approach can mitigate two primary issues plaguing traditional self-attention modules: the heavy computational burden and attention saturation. Our proposed architecture offers superior efficiency and ease of implementation merely requiring matrix multiplications that are highly optimized in contemporary deep learning frameworks. Moreover our architecture demonstrates exceptional performance across various vision tasks including classification detection and segmentation.
Safety and robustness are crucial factors in developing trustworthy autonomous vehicles. One essential aspect of addressing these factors is to equip vehicles with the capability to predict future trajectories for all moving objects in the surroundings and quantify prediction uncertainties. In this paper we propose the Sequential Neural Variational Agent (SeNeVA) a generative model that describes the distribution of future trajectories for a single moving object. Our approach can distinguish Out-of-Distribution data while quantifying uncertainty and achieving competitive performance compared to state-of-the-art methods on the Argoverse 2 and INTERACTION datasets. Specifically a 0.446 meters minimum Final Displacement Error a 0.203 meters minimum Average Displacement Error and a 5.35% Miss Rate are achieved on the INTERACTION test set. Extensive qualitative and quantitative analysis is also provided to evaluate the proposed model. Our open-source code is available at https://github.com/PurdueDigitalTwin/seneva.
A Versatile Framework for Continual Test-Time Domain Adaptation: Balancing Discriminability and Generalizability
PDF ↗Continual test-time domain adaptation (CTTA) aims to adapt the source pre-trained model to a continually changing target domain without additional data acquisition or labeling costs. This issue necessitates an initial performance enhancement within the present domain without labels while concurrently averting an excessive bias toward the current domain. Such bias exacerbates catastrophic forgetting and diminishes the generalization ability to future domains. To tackle the problem this paper designs a versatile framework to capture high-quality supervision signals from three aspects: 1) The adaptive thresholds are employed to determine the reliability of pseudo-labels; 2) The knowledge from the source pre-trained model is utilized to adjust the unreliable one and 3) By evaluating past supervision signals we calculate a diversity score to ensure subsequent generalization. In this way we form a complete supervisory signal generation framework which can capture the current domain discriminative and reserve generalization in future domains. Finally to avoid catastrophic forgetting we design a weighted soft parameter alignment method to explore the knowledge from the source model. Extensive experimental results demonstrate that our method performs well on several benchmark datasets.
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models
PDF ↗Large pre-trained Vision-Language Models (VLMs) like CLIP despite having remarkable generalization ability are highly vulnerable to adversarial examples. This work studies the adversarial robustness of VLMs from the novel perspective of the text prompt instead of the extensively studied model weights (frozen in this work). We first show that the effectiveness of both adversarial attack and defense are sensitive to the used text prompt. Inspired by this we propose a method to improve resilience to adversarial attacks by learning a robust text prompt for VLMs. The proposed method named Adversarial Prompt Tuning (APT) is effective while being both computationally and data efficient. Extensive experiments are conducted across 15 datasets and 4 data sparsity schemes (from 1-shot to full training data settings) to show APT's superiority over hand-engineered prompts and other state-of-the-art adaption methods. APT demonstrated excellent abilities in terms of the in-distribution performance and the generalization under input distribution shift and across datasets. Surprisingly by simply adding one learned word to the prompts APT can significantly boost the accuracy and robustness (epsilon=4/255) over the hand-engineered prompts by +13% and +8.5% on average respectively. The improvement further increases in our most effective setting to +26.4% for accuracy and +16.7% for robustness. Code is available at https://github.com/TreeLLi/APT.
Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth
PDF ↗The absence of real targets to guide the model training is one of the main problems with the makeup transfer task. Most existing methods tackle this problem by synthesizing pseudo ground truths (PGTs). However the generated PGTs are often sub-optimal and their imprecision will eventually lead to performance degradation. To alleviate this issue in this paper we propose a novel Content-Style Decoupled Makeup Transfer (CSD-MT) method which works in a purely unsupervised manner and thus eliminates the negative effects of generating PGTs. Specifically based on the frequency characteristics analysis we assume that the low-frequency (LF) component of a face image is more associated with its makeup style information while the high-frequency (HF) component is more related to its content details. This assumption allows CSD-MT to decouple the content and makeup style information in each face image through the frequency decomposition. After that CSD-MT realizes makeup transfer by maximizing the consistency of these two types of information between the transferred result and input images respectively. Two newly designed loss functions are also introduced to further improve the transfer performance. Extensive quantitative and qualitative analyses show the effectiveness of our CSD-MT method. Our code is available at https://github.com/Snowfallingplum/CSD-MT.
Undoubtedly high-fidelity 3D hair is crucial for achieving realism artistic expression and immersion in computer graphics. While existing 3D hair modeling methods have achieved impressive performance the challenge of achieving high-quality hair reconstruction persists: they either require strict capture conditions making practical applications difficult or heavily rely on learned prior data obscuring fine-grained details in images. To address these challenges we propose MonoHair a generic framework to achieve high-fidelity hair reconstruction from a monocular video without specific requirements for environments. Our approach bifurcates the hair modeling process into two main stages: precise exterior reconstruction and interior structure inference. The exterior is meticulously crafted using our Patch-based Multi-View Optimization PMVO. This method strategically collects and integrates hair information from multiple views independent of prior data to produce a high-fidelity exterior 3D line map. This map not only captures intricate details but also facilitates the inference of the hair's inner structure. For the interior we employ a data-driven multi-view 3D hair reconstruction method. This method utilizes 2D structural renderings derived from the reconstructed exterior mirroring the synthetic 2D inputs used during training. This alignment effectively bridges the domain gap between our training data and real-world data thereby enhancing the accuracy and reliability of our interior structure inference. Lastly we generate a strand model and resolve the directional ambiguity by our hair growth algorithm. Our experiments demonstrate that our method exhibits robustness across diverse hairstyles and achieves state-of-the-art performance. For more results please refer to our project page https://keyuwu-cs.github.io/MonoHair/
Recent innovations on text-to-3D generation have featured Score Distillation Sampling (SDS) which enables the zero-shot learning of implicit 3D models (NeRF) by directly distilling prior knowledge from 2D diffusion models. However current SDS-based models still struggle with intricate text prompts and commonly result in distorted 3D models with unrealistic textures or cross-view inconsistency issues. In this work we introduce a novel Visual Prompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the visual appearance knowledge in 2D visual prompt to boost text-to-3D generation. Instead of solely supervising SDS with text prompt VP3D first capitalizes on 2D diffusion model to generate a high-quality image from input text which subsequently acts as visual prompt to strengthen SDS optimization with explicit visual appearance. Meanwhile we couple the SDS optimization with additional differentiable reward function that encourages rendering images of 3D models to better visually align with 2D visual prompt and semantically match with text prompt. Through extensive experiments we show that the 2D Visual Prompt in our VP3D significantly eases the learning of visual appearance of 3D models and thus leads to higher visual fidelity with more detailed textures. It is also appealing in view that when replacing the self-generating visual prompt with a given reference image VP3D is able to trigger a new task of stylized text-to-3D generation. Our project page is available at https://vp3d-cvpr24.github.io.
The lifting of a 3D structure and camera from 2D landmarks is at the cornerstone of the discipline of computer vision. Traditional methods have been confined to specific rigid objects such as those in Perspective-n-Point (PnP) problems but deep learning has expanded our capability to reconstruct a wide range of object classes (e.g. C3DPO [??] and PAUL [??]) with resilience to noise occlusions and perspective distortions. However all these techniques have been limited by the fundamental need to establish correspondences across the 3D training data significantly limiting their utility to applications where one has an abundance of "in-correspondence" 3D data. Our approach harnesses the inherent permutation equivariance of transformers to manage varying numbers of points per 3D data instance withstands occlusions and generalizes to unseen categories. We demonstrate state-of-the-art performance across 2D-3D lifting task benchmarks. Since our approach can be trained across such a broad class of structures we refer to it simply as a 3D Lifting Foundation Model (3D-LFM) -- the first of its kind.
Semantic scene completion also known as semantic occupancy prediction can provide dense geometric and semantic information for autonomous vehicles which attracts the increasing attention of both academia and industry. Unfortunately existing methods usually formulate this task as a voxel-wise classification problem and treat each voxel equally in 3D space during training. As the hard voxels have not been paid enough attention the performance in some challenging regions is limited. The 3D dense space typically contains a large number of empty voxels which are easy to learn but require amounts of computation due to handling all the voxels uniformly for the existing models. Furthermore the voxels in the boundary region are more challenging to differentiate than those in the interior. In this paper we propose HASSC approach to train the semantic scene completion model with hardness-aware design. The global hardness from the network optimization process is defined for dynamical hard voxel selection. Then the local hardness with geometric anisotropy is adopted for voxel-wise refinement. Besides self-distillation strategy is introduced to make training process stable and consistent. Extensive experiments show that our HASSC scheme can effectively promote the accuracy of the baseline model without incurring the extra inference cost. Source code is available at: https://github.com/songw-zju/HASSC.
We propose a novel self-supervised embedding to learn how actions sound from narrated in-the-wild egocentric videos. Whereas existing methods rely on curated data with known audio-visual correspondence our multimodal contrastive-consensus coding (MC3) embedding reinforces the associations between audio language and vision when all modality pairs agree while diminishing those associations when any one pair does not. We show our approach can successfully discover how the long tail of human actions sound from egocentric video outperforming an array of recent multimodal embedding techniques on two datasets (Ego4D and EPIC-Sounds) and multiple cross-modal tasks.
Recent advances in instruction tuning have led to the development of State-of-the-Art Large Multimodal Models (LMMs). Given the novelty of these models the impact of visual adversarial attacks on LMMs has not been thoroughly examined. We conduct a comprehensive study of the robustness of various LMMs against different adversarial attacks evaluated across tasks including image classification image captioning and Visual Question Answer (VQA). We find that in general LMMs are not robust to visual adversarial inputs. However our findings suggest that context provided to the model via prompts--such as questions in a QA pair--helps to mitigate the effects of visual adversarial inputs. Notably the LMMs evaluated demonstrated remarkable resilience to such attacks on the ScienceQA task with only an 8.10% drop in performance compared to their visual counterparts which dropped 99.73%. We also propose a new approach to real-world image classification which we term query decomposition. By incorporating existence queries into our input prompt we observe diminished attack effectiveness and improvements in image classification accuracy. This research highlights a previously under explored facet of LMM robustness and sets the stage for future work aimed at strengthening the resilience of multimodal systems in adversarial environments.
Once only a few-shot annotated samples are available the performance of learning-based object detection would be heavily dropped. Many few-shot object detection (FSOD) methods have been proposed to tackle this issue by adopting image-level augmentations in linear manners. Nevertheless those handcrafted enhancements often suffer from limited diversity and lack of semantic awareness resulting in unsatisfactory performance. To this end we propose a Semantic-guided Non-linear Instance-level Data Augmentation method (SNIDA) for FSOD by decoupling the foreground and background to increase their diversities respectively. We design a semantic awareness enhancement strategy to separate objects from backgrounds. Concretely masks of instances are extracted by an unsupervised semantic segmentation module. Then the diversity of samples would be improved by fusing instances into different backgrounds. Considering the shortcomings of augmenting images in a limited transformation space of existing traditional data augmentation methods we introduce an object reconstruction enhancement module. The aim of this module is to generate sufficient diversity and non-linear training data at the instance level through a semantic-guided masked autoencoder. In this way the potential of data can be fully exploited in various object detection scenarios. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that the proposed method outperforms baselines by a large margin and achieves new state-of-the-art results under different shot settings.
Extracting keypoint locations from input hand frames known as 3D hand pose estimation is a critical task in various human-computer interaction applications. Essentially the 3D hand pose estimation can be regarded as a 3D point subset generative problem conditioned on input frames. Thanks to the recent significant progress on diffusion-based generative models hand pose estimation can also benefit from the diffusion model to estimate keypoint locations with high quality. However directly deploying the existing diffusion models to solve hand pose estimation is non-trivial since they cannot achieve the complex permutation mapping and precise localization. Based on this motivation this paper proposes HandDiff a diffusion-based hand pose estimation model that iteratively denoises accurate hand pose conditioned on hand-shaped image-point clouds. In order to recover keypoint permutation and accurate location we further introduce joint-wise condition and local detail condition. Experimental results demonstrate that the proposed HandDiff significantly outperforms the existing approaches on four challenging hand pose benchmark datasets. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandDiff.
This paper proposes ConsistDreamer - a novel framework that lifts 2D diffusion models with 3D awareness and 3D consistency thus enabling high-fidelity instruction-guided scene editing. To overcome the fundamental limitation of missing 3D consistency in 2D diffusion models our key insight is to introduce three synergetic strategies that augment the input of the 2D diffusion model to become 3D-aware and to explicitly enforce 3D consistency during the training process. Specifically we design surrounding views as context-rich input for the 2D diffusion model and generate 3D-consistent structured noise instead of image-independent noise. Moreover we introduce self-supervised consistency-enforcing training within the per-scene editing procedure. Extensive evaluation shows that our ConsistDreamer achieves state-of-the-art performance for instruction-guided scene editing across various scenes and editing instructions particularly in complicated large-scale indoor scenes from ScanNet++ with significantly improved sharpness and fine-grained textures. Notably ConsistDreamer stands as the first work capable of successfully editing complex (e.g. plaid/checkered) patterns.
Personalized text-to-image generation models enable users to create images that depict their individual possessions in diverse scenes finding applications in various domains. To achieve the personalization capability existing methods rely on finetuning a text-to-image foundation model on a user's custom dataset which can be non-trivial for general users resource-intensive and time-consuming. Despite attempts to develope finetuning-free methods their generation quality is much lower compared to their finetuning counterparts. In this paper we propose Joint-Image Diffusion (\jedi) an effective technique for learning a finetuning-free personalization model. Our key idea is to learn the joint distribution of multiple related text-image pairs that share a common subject. To facilitate learning we propose a scalable synthetic dataset generation technique. Once trained our model enables fast and easy personalization at test time by simply using reference images as input during the sampling process. Our approach does not require any expensive optimization process or additional modules and can faithfully preserve the identity represented by any number of reference images. Experimental results show that our model achieves state-of-the-art generation quality both quantitatively and qualitatively significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines.
Large language models have achieved great success in recent years so as their variants in vision. Existing vision-language models can describe images in natural languages answer visual-related questions or perform complex reasoning about the image. However it is yet unclear how localization tasks such as word grounding or referring localization can be performed using large language models. In this work we aim to develop a vision-language model that can take locations for example a set of points or boxes as either inputs or outputs. When taking locations as inputs the model performs location-conditioned captioning which generates captions for the indicated object or region. When generating locations as outputs our model regresses pixel coordinates for each output word generated by the language model and thus performs dense word grounding. Our model is pre-trained on the Localized Narrative dataset which contains pixel-word-aligned captioning from human attention. We show our model can be applied to various location-aware vision-language tasks including referring localization location-conditioned captioning and dense object captioning archiving state-of-the-art performance on RefCOCO and Visual Genome.
The ability to learn from context with novel concepts and deliver appropriate responses are essential in human conversations. Despite current Multimodal Large Language Models (MLLMs) and Large Language Models (LLMs) being trained on mega-scale datasets recognizing unseen images or understanding novel concepts in a training-free manner remains a challenge. In-Context Learning (ICL) explores training-free few-shot learning where models are encouraged to "learn to learn" from limited tasks and generalize to unseen tasks. In this work we propose link-context learning (LCL) which emphasizes "reasoning from cause and effect" to augment the learning capabilities of MLLMs. LCL goes beyond traditional ICL by explicitly strengthening the causal relationship between the support set and the query set. By providing demonstrations with causal links LCL guides the model to discern not only the analogy but also the underlying causal associations between data points which empowers MLLMs to recognize unseen images and understand novel concepts more effectively. To facilitate the evaluation of this novel approach we introduce the ISEKAI dataset comprising exclusively of unseen generated image-label pairs designed for link-context learning. Extensive experiments show that our LCL-MLLM exhibits strong link-context learning capabilities to novel concepts over vanilla MLLMs.
Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment
PDF ↗Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript which is time-consuming and hard to be parallelized while training. In this work we aim to escape from this inefficient alignment with massive but redundant frames and instead to directly localize a few action transitions for pseudo segmentation generation where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed.