In this work, we focus on the cross-domain few-shot classification (CDFSC), which is mostly challenged by the low-data problem as well as extreme domain shift between base and novel target classes. Current methods always employ a lightweight backbone and continue to use a linear-probe-like traditional fine-tuning (Trad-FT) paradigm. While for recently emerging large-scale pre-trained model (LPM), which has more parameters with considerable prior knowledge, employing Trad-FT will face significant risks of overfitting and prior knowledge damage. In this paper, we propose semantic-guided robustness tuning (SRT), a novel fine-tuning paradigm including modulus-matching-based image-text mixup (MMIT-Mixup) and robustness-invariance fine-tuning (RI-FT), to address the CDFSC challenge of LPM. Concretely, SRT focuses on achieving robust class-specific representation. It first considers textual information as a robust and domain-invariant conductor, and MMIT-Mixup injects the domain-invariant and class-specific knowledge to obtain domain-invariant prototypes. Then, RI-FT optimizes the distance between features and prototypes to enhance the robustness of visual-encoder. We consider several types of LPMs and conduct extensive experiments, which reveals that SRT is a general solution for LPM’s CDFSC challenge and outperforms the existing methods with a large margin.
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Interaction intention anticipation aims to predict future hand trajectories and interaction hotspots jointly. Existing research often treated trajectory forecasting and interaction hotspots prediction as separate tasks or solely considered the impact of trajectories on interaction hotspots, which led to the accumulation of prediction errors over time. However, a deeper inherent connection exists between hand trajectories and interaction hotspots, which allows for continuous mutual correction between them. Building upon this relationship, a novel Bidirectional pOgressive Transformer (BOT), which introduces a bidirectional progressive mechanism into the anticipation of interaction intention is established. Initially,(BOT) maximizes the utilization of spatial information from the last observation frame through the Spatial-Temporal Reconstruction Module, mitigating conflicts arising from changes of view in the first-person videos. Subsequently, based on two independent prediction branches, a Bidirectional Progressive Enhancement Module is introduced to mutually improve the prediction of hand trajectories and interaction hotspots over time to minimize error accumulation. Finally, acknowledging the intrinsic randomness in human natural behavior, we employ a Trajectory Stochastic Unit and a C-VAE to introduce appropriate uncertainty to trajectories and interaction hotspots, respectively. Our method achieves state-of-the-art results on three benchmark datasets Epic-Kitchens-100, EGO4D, and EGTEA Gaze+, demonstrating superior in complex scenarios.
With the release of large-scale motion datasets with textual annotations, the task of establishing a robust latent space for language and 3D human motion has recently witnessed a surge of interest. Methods have been proposed to convert human motion and texts into features to achieve accurate correspondence between them. However, despite these efforts to align language and motion representations, we claim that the temporal element is often overlooked, especially for compound actions, resulting in chronological inaccuracies. To shed light on the temporal alignment in motion-language latent spaces, we propose Chronologically Accurate Retrieval (CAR) to evaluate the temporal understanding of the models. We decompose textual descriptions into events, and prepare negative text samples by shuffling the order of events in compound action descriptions. We then design a simple task for motion-language models to retrieve the more likely text between the ground truth and its chronologically shuffled version. CAR reveals many cases where current motion-language models fail to distinguish the event chronology of human motion, despite their impressive performance under conventional evaluation metrics. To achieve better temporal alignment between text and motion, we further propose to use these texts with shuffled sequences of events as negative samples to reinforce the motion-language models. We conduct experiments on text-motion retrieval and text-to-motion generation using the reinforced motion-language models, which demonstrate improved performance over conventional approaches, indicating the necessity to consider the temporal elements of motion-language alignment.
Contemporary makeup approaches primarily hinge on unpaired learning paradigms, yet they grapple with the challenges of inaccurate supervision (e.g., face misalignment) and sophisticated facial prompts (including face parsing, and landmark detection). These challenges prohibit low-cost deployment of facial makeup models, especially on mobile devices. To solve above problems, we propose a brand-new learning paradigm, termed "Data Amplify Learning (DAL)," alongside a compact makeup model named "TinyBeauty." The core idea of DAL lies in employing a Diffusion-based Data Amplifier (DDA) to "amplify" limited images for the model training, thereby enabling accurate pixel-to-pixel supervision with merely a handful of annotations. Two pivotal innovations in DDA facilitate the above training approach: (1) A Residual Diffusion Model (RDM) is designed to generate high-fidelity detail and circumvent the detail vanishing problem in the vanilla diffusion models; (2) A Fine-Grained Makeup Module (FGMM) is proposed to achieve precise makeup control and combination while retaining face identity. Coupled with DAL, TinyBeauty necessitates merely $\textbf{80K}$ parameters to achieve a state-of-the-art performance without intricate face prompts. Meanwhile, TinyBeauty achieves a remarkable inference speed of up to \textbf{460 fps} on the iPhone 13. Extensive experiments show that DAL can produce highly competitive makeup models using only \textbf{5} image pairs.
This paper explores the spatio-temporal video grounding (STVG) task, which aims at localizing a particular object corresponding to a given textual description in an untrimmed video. Existing approaches mainly resort to object-level manual annotations as the supervision for addressing this challenging task. Such a paradigm heavily constrains the scalability of processing large-scale unlabeled data. To this end, we present a novel framework that is capable of grounding the target object relying only on the video-sentence correspondence. Specifically, our model re-formulates the original STVG task as two cross-modal alignment sub-problems: region-phrase and frame-sentence. Since the absence of ground-truth alignments during the training stage, we treat them as latent variables and learn to model the joint conditional distribution by reconstructing the interactions of entities in the video. The entire framework can be effectively optimized by the variational Expectation-Maximization (EM) algorithm, which alternates between two updating steps for progressively maximizing the likelihood of query sentence, thereby approximating the real cross-modal assignment. Extensive experiments on two video benchmarks (VidSTG and HC-STVG) further show the effectiveness of the proposed method.
Human motion sensing plays a crucial role in smart systems for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications. To address this, mmWave radars have gained popularity due to their privacy-friendly features. In this work, we propose milliFlow, a novel deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable improvements in human activity recognition and human parsing, and support human body part tracking. To foster further research in this area, we will provide our codebase and dataset for open access.
Recently, learning-based Hyperspectral image (HSI) reconstruction methods have demonstrated promising performance and dominated the mainstream research direction. However, existing learning-based methods still have two issues. 1) Unable to consider both the spatial sparsity and the inter-spectral similarity prior of HSI. 2) Treat all regions equally, ignoring that texture-rich regions and edge regions are more difficult to reconstruct than smooth regions. To address these issues, we propose an uncertainty-driven HSI reconstruction method termed Specformer. Specifically, we first introduce a frequency-wise self-attention (FWSA) and combine it with spatial-wise local-window self-attention (LWSA) with a parallel design to form a Spatial-Frequency (SF) block. LWSA can guide the network to focus on the regions with dense spectral information, and FWSA can capture the inter-spectral similarity. Parallel design helps the network to model cross-window connections, expand its receptive fields while maintaining linear complexity. We use SF-block as the main building block in a multi-scale U-shape network to form our Specformer. In addition, we introduce an uncertainty-driven self-adaptive loss function, which can reinforce the network's attention to challenging regions with rich textures and edges. Comprehensive experiments show that our Specformer significantly outperforms state-of-the-art methods on simulation and real HSI datasets while requiring cheaper computational and memory costs. The code will be publicly available.
In recent years, the neural radiance field (NeRF) model has gained popularity due to its ability to recover complex 3D scenes. Following its success, many approaches proposed different NeRF representations in order to further improve both runtime and performance. One such example is Triplane, in which NeRF is represented using three 2D feature planes. This enables easily using existing 2D neural networks in this framework, e.g., to generate the three planes. Despite its advantage, the triplane representation lagged behind in 3D recovery quality compared to NeRF solutions. In this work, we propose the TriNeRFLet framework, where we learn the wavelet representation of the triplane and regularize it. This approach has multiple advantages: (i) it allows information sharing across scales and regularization of high frequencies; (ii) it facilitates performing learning in a multi-scale fashion; and (iii) it provides a `natural' framework for performing NeRF super-resolution (SR), such that the low-resolution wavelet coefficients are computed from the provided low-resolution multi-view images and the high frequencies are acquired under the guidance of a pre-trained 2D diffusion model. We show the SR approach's advantage on both Blender and LLFF datasets.
Generating multi-camera street-view videos is critical for augmenting autonomous driving datasets, addressing the urgent demand for extensive and varied data. Due to the limitations in diversity and challenges in handling lighting conditions, traditional rendering-based methods are increasingly being supplanted by diffusion-based methods. However, a significant challenge in diffusion-based methods is ensuring that the generated sensor data preserve both intra-world consistency and inter-sensor coherence. To address these challenges, we combine an additional explicit world volume and propose the World Volume-aware Multi-camera Driving Scene Generator (WoVoGen). This system is specifically designed to leverage 4D world volume as a foundational element for video generation. Our model operates in two distinct phases: (i) envisioning the future 4D temporal world volume based on vehicle control sequences, and (ii) generating multi-camera videos, informed by this envisioned 4D temporal world volume and sensor interconnectivity. The incorporation of the 4D world volume empowers WoVoGen not only to generate high-quality street-view videos in response to vehicle control inputs but also to facilitate scene editing tasks.
Earth Observation (EO) presents a unique opportunity to explore self-supervised multimodal learning, given its access to vast and diverse data captured by various sensors. However, current multimodal EO datasets and models often consider modalities from a single data type, either mono-date images or time series, which limits their expressivity. We introduce OmniSat, a novel architecture that exploits the natural alignment between multiple EO modalities to learn expressive multimodal representations without labels. We augment an existing dataset with new modalities to demonstrate the advantages of combining modalities of different natures. We evaluate OmniSat and various state-of-the-art approaches on two relevant downstream tasks: forestry and land cover classification. Our results show that OmniSat can learn rich representations in an unsupervised manner, leading to performance improvements in the semi- and fully-supervised settings, even when only one modality is available at inference. Our code, weights, and dataset are available at https://github.com/gastruc/OmniSat.
We introduce V-Trans4Style, an innovative algorithm tailored for dynamic video content editing needs. It is designed to adapt videos to different production styles like documentaries, dramas, feature films, or a specific YouTube channel's video-making technique. Our algorithm recommends optimal visual transitions to help achieve this flexibility using a more bottom-up approach. We first employ a transformer-based encoder-decoder network to learn recommending temporally consistent and visually seamless sequences of visual transitions using only the input videos. We then introduce a style conditioning module that leverages this model to iteratively adjust the visual transitions obtained from the decoder through activation maximization. We demonstrate the efficacy of our method through experiments conducted on our newly introduced AutoTransition++ dataset. It is a 6k video version of AutoTransition Dataset that additionally categorizes its videos into different production style categories. Our encoder-decoder model outperforms the state-of-the-art transition recommendation method, achieving improvements of 10% to 80% in Recall@K and mean rank values over baseline. Our style conditioning module results in visual transitions that improve the capture of the desired video production style characteristics by an average of around 12% in comparison to other methods when measured with similarity metrics. We hope that our work serves as a foundation for exploring and understanding video production styles further.
Embodied agents must detect and localize objects of interest, e.g., traffic participants for self-driving cars. Supervision in the form of bounding boxes for this task is extremely expensive. As such, prior work has looked at unsupervised object segmentation but, in the absence of annotated boxes, it is unclear how pixels must be grouped into objects and which objects are of interest. This results in over-/under-segmentation and irrelevant objects. Inspired both by the human visual system and by practical applications, we posit that the key missing cue is motion: objects of interest are typically mobile objects. We propose a new approach that learns to detect Mobile Objects from Videos for Embodied agents (MOVE). We begin with pseudo-labels derived from motion segmentation, but introduce a novel training paradigm to progressively discover small objects and static-but-mobile objects that are missed by motion segmentation. As a result, though only learned from unlabeled videos, MOVE can detect and segment mobile objects from a single static image. Empirically, we achieve state-of-the-art performance in unsupervised mobile object detection on Waymo Open, nuScenes, and KITTI Dataset without using any external data or models.
Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. This paper introduces a plug-and-play method to protect NeRF's copyright during its creation. We propose utilizing a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while creating their NeRF. Our plug-and-play property ensures that NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge methods. Our code will be released upon the acceptance of this paper.
Recent progress in large-scale pre-training has led to the development of advanced vision-language models (VLMs) with remarkable proficiency in comprehending and generating multimodal content. Despite the impressive ability to perform complex reasoning for VLMs, current models often struggle to effectively and precisely capture the compositional information on both the image and text sides. To address this, we propose FineMatch, a new aspect-based fine-grained text and image matching benchmark, focusing on text and image mismatch detection and correction. This benchmark introduces a novel task for boosting and evaluating the VLMs’ compositionality for aspect-based fine-grained text and image matching. In this task, the models need to predict the mismatched aspect phrases, identify the class of the aspect, and suggest their corrections for a given image and a text caption with 0 to 3 mismatched aspects. To evaluate the models’ performance on this new task, we propose a new evaluation metric named ITM-IoU for which our experiments show a high correlation to human evaluation. In addition, we also provide a comprehensive experimental analysis of existing mainstream VLMs, including fully supervised learning and in-context learning settings. We have found that models trained on FineMatch demonstrate enhanced proficiency in detecting fine-grained text and image mismatches. Moreover, models (e.g., GPT-4V, Gemini Pro Vision) with strong abilities to perform multimodal in-context learning are not as skilled at fine-grained compositional image and text matching analysis as we might have expected. With FineMatch, we are able to build a system for text-to-image generation hallucination detection and correction.
Instruction-driven image editing allows users to quickly edit an image according to text instructions in a forward pass. Nevertheless, malicious users can easily exploit this technique to create fake images, which could cause a crisis of trust and harm the rights of the original image owners. Watermarking is a common solution to trace such malicious behavior. Unfortunately, instruction-driven image editing can significantly change the watermarked image at the se- mantic level, making it less robust and effective. We propose Robust-Wide, the first robust watermark- ing methodology against instruction-driven image editing. Specifically, we adopt the widely-used encoder-decoder framework for watermark embedding and extraction. To achieve robustness against semantic distortions, we intro- duce a novel Partial Instruction-driven Denoising Sampling Guidance (PIDSG) module, which consists of a large vari- ety of instruction injections and substantial modifications of images at different semantic levels. With PIDSG, the encoder tends to embed the watermark into more robust and semantic-aware areas, which remains in existence even after severe image editing. Experiments demonstrate that Robust-Wide can effectively extract the watermark from the edited image with a low bit error rate of nearly 2.6% for 64-bit watermark messages. Meanwhile, it only induces a neglectable influence on the visual quality and editability of the original images. Moreover, Robust-Wide holds general robustness against different sampling configura- tions and other image editing methods such as ControlNet- InstructPix2Pix, MagicBrush, Inpainting and DDIM Inver- sion.
We tackle the problem of mosaicing bundle adjustment (i.e., simultaneous refinement of camera orientations and scene map) for a purely rotating event camera. We formulate the problem as a regularized non-linear least squares optimization. The objective function is defined using the linearized event generation model in the camera orientations and the panoramic gradient maps of the scene. We show that this BA optimization has an exploitable block-diagonal sparsity structure, so that the problem can be solved efficiently. To the best of our knowledge, this is the first work to leverage such sparsity to speed up the optimization in the context of event-based cameras, without the need to convert events into image-like representations. We evaluate EMBA on both synthetic and real-world datasets to show its effectiveness (50% photometric error decrease), yielding results of unprecedented quality. In addition, we demonstrate EMBA using high spatial resolution event cameras, yielding delicate panoramas in the wild, even without an initial map. We make the source code publicly available.
Large-scale data is crucial for learning realistic and capable driving policies. However, it is impractical to naively scale dataset size with real-data alone. The majority of real driving data is uninteresting for learning, and collecting more to cover the long-tail of scenarios is expensive and potentially unsafe. We propose to use asymmetric self-play to scale learning beyond real world data, with additional challenging, solvable, and realistic synthetic scenarios. In particular, we design two agents—a teacher and a student—with asymmetric objectives so that the teacher learns to propose scenarios that itself can pass but the student fails, and the student learns to solve those scenarios. When applied to traffic simulation, our approach learns realistic policies with significantly lower collision rates across urban, highway, and long-tail scenarios. Our approach also zero-shot transfers to generate more effective training data for learning end-to-end autonomy policies, significantly outperforming alternatives like training on adversarial scenarios or real data alone.
Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires handling diverse ground images captured by users with varying orientations and field of views (FoVs). However, existing learning pipelines are orientation-specific or FoV-specific, requiring separate training for different settings. Such models heavily depend on the North-aligned spatial correspondence and specific FoVs in the training data, compromising the models' robustness in ground view variations. We propose ConGeo, a single- and cross-modal Contrastive method for Geo-localization: it enhances robustness and consistency in feature representations to improve a model's invariance to orientation and its resilience to FoV variations, by enforcing proximity between ground view variations of the same location. As a generic learning objective for cross-view geo-localization, when integrated into state-of-the-art pipelines, ConGeo significantly boosts the performance of three base models on four geo-localization benchmarks for diverse ground view variations and outperforms competing methods that train separate models for each ground view variation.
Image restoration, encompassing tasks such as deblurring, denoising, and super-resolution, remains a pivotal area in computer vision. However, efficiently addressing the spatially varying artifacts of various low-quality images with local adaptiveness and handling their degradations at different scales poses significant challenges. To efficiently tackle these issues, we propose the novel \textit{Efficient Cascaded Multiscale Adaptive} (ECMA) Network. ECMA employs Local Adaptive Module, LAM, which dynamically adjusts convolution kernels across local image regions to efficiently handle varying artifacts. Thus, LAM addresses the local adaptiveness challenge more efficiently than costlier mechanisms like self-attention, due to its less computationally intensive convolutions. To construct a basic ECMA block, three cascading LAMs with convolution kernels from large to small sizes are employed to capture features at different scales. This cascaded multiscale learning effectively handles degradations at different scales, critical for diverse image restoration tasks. Finally, ECMA blocks are stacked in a U-Net architecture to build ECMA networks, which efficiently achieve both local adaptiveness and multiscale processing. Experiments show ECMA's high performance and efficiency, achieving comparable or superior restoration performance to state-of-the-art methods while reducing computational costs by 1.2$\times$ to 9.7$\times$ across various image restoration tasks, e.g., image deblurring, denoising and super-resolution. Our code and models will be released.
Hierarchically Structured Neural Bones for Reconstructing Animatable Objects from Casual Videos
PDF ↗We propose a new framework for creating and easily manipulating 3D models of arbitrary objects using casually captured videos. Our core ingredient is a novel hierarchy deformation model, which captures motions of objects with a tree-structured bones. Our hierarchy system decomposes motions based on the granularity and reveals the correlations between parts without exploiting any prior structural knowledge. We further propose to regularize the bones to be positioned at the basis of motions, centers of parts, sufficiently covering related surfaces of the part. This is achieved by our bone occupancy function, which identifies whether a given 3D point is placed within the bone. Coupling the proposed components, our framework offers several clear advantages: (1) Users can obtain animatable 3D models of the arbitrary objects in improved quality from their casual videos, (2) users can manipulate 3D models in an intuitive manner with minimal costs, and (3) users can interactively add or delete control points as necessary. The experimental results demonstrate the efficacy of our framework on diverse instances, in reconstruction quality, interpretability and easier manipulation.