Domain Adaptation (DA) has been widely explored and made significant progress on cross-domain 3D tasks recently. Despite being effective, existing works fail to deal with rapidly changing domains due to the unpredictable test time scenarios and meanwhile fast response time requirement. Thus, we explore a new task named test-time domain adaptive 3D object detection and propose RegTTA3D, a pseudo-label-based test-time adaptative 3D object detection method. By investigating the factor that limits the detection accuracy, we find that regression is essential in this task. To make better regression, we first design a noise-consistency pseudo-label generation process to filter pseudo-labels with instability under noise interference and obtain reliable pseudo-labels. Then, confidence-guided regression refinement is introduced, which uses the box regression results of high-confidence boxes to supervise boxes with relatively low confidence, further making the predicted box size gradually approach the distribution of the target domain. Finally, to better update the regression layer and alleviate the class-imbalance issue, a class-balance EMA updating strategy is proposed. Experimental results on multiple cross-domain scenarios including cross-beam, crosslocation, and cross-weather demonstrate that Reg-TTA3D can achieve comparable or even better performance compared to unsupervised domain adaptation works by only updating less than 0.1% parameters within less than 1% time. Codes will be public.
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Automatic estimation of 3D human pose from monocular RGB images is a challenging and unsolved problem in computer vision. In a supervised manner, approaches heavily rely on laborious annotations and present hampered generalization ability due to the limited diversity of 3D pose datasets. To address these challenges, we propose a unified framework that leverages mask as supervision for unsupervised 3D pose estimation. With general unsupervised segmentation algorithms, the proposed model employs skeleton and physique representations that exploit accurate pose information from coarse to fine. Compared with previous unsupervised approaches, we organize the human skeleton in a fully unsupervised way which enables the processing of annotation-free data and provides ready-to-use estimation results. Comprehensive experiments demonstrate our state-of-the-art pose estimation performance on Human3.6M and MPI-INF-3DHP datasets. Further experiments on in-the-wild datasets also illustrate the capability to access more data to boost our model. The code will be publicly available upon publication.
Visual Place Recognition (VPR) is a crucial component of many visual localization pipelines for embodied agents. VPR is often achieved by jointly learning local features and an aggregation method. The current state-of-the-art VPR methods rely on VLAD aggregation, which can be trained to learn a weighted contribution of features through their soft assignment to cluster centers. However, this process has two key limitations. Firstly, the feature-to-cluster weighting does not account for over-represented repetitive structures within a cluster, e.g., shadows or window panes; this phenomenon is also referred to as the `burstiness' problem, classically solved by discounting repetitive features before aggregation. Secondly, feature to cluster comparisons are compute-intensive for state-of-the-art image encoders with high-dimensional local features. This paper addresses these limitations by introducing VLAD-BuFF with two novel contributions: i) a self-similarity based feature discounting mechanism to learn {Bu}rst-aware features within end-to-end VPR training, and ii) {F}ast {F}eature aggregation} by reducing local feature dimensions through a learnable projection initialized through a PCA transform. We benchmark our method on 9 public datasets, where VLAD-BuFF sets a new state of the art and achieves perfect recall on St Lucia for the first time in VPR research. Our method is able to maintain its high recall even for 12x reduced local feature dimensions, thus enabling fast feature aggregation without compromising on recall. Through additional qualitative studies, we show how our proposed weighting method effectively downweights the non-distinctive features. We will make the source code publicly available.
The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?
PDF ↗Large vision-language models (LVLMs), designed to interpret and respond to human instructions, occasionally generate hallucinated or harmful content due to inappropriate instructions. This study uses linear probing to shed light on the hidden knowledge at the output layer of LVLMs. We demonstrate that the logit distributions of the first tokens contain sufficient information to determine whether to respond to the instructions, including recognizing unanswerable visual questions, defending against multi-modal jailbreaking attack, and identifying deceptive questions. Such hidden knowledge is gradually lost in logits of subsequent tokens during response generation. Then, we illustrate a simple decoding strategy at the generation of the first token, effectively improving the generated content. In experiments, we find a few interesting insights: First, the CLIP model already contains a strong signal for solving these tasks, indicating potential bias in the existing datasets. Second, we observe performance improvement by utilizing the first logit distributions on three additional tasks, including indicting uncertainty in math solving, mitigating hallucination, and image classification. Last, with the same training data, simply finetuning LVLMs improve models' performance but is still inferior to linear probing on these tasks.
Existing video-text retrieval methods predominantly focus on designing diverse cross-modal interaction mechanisms between captions and videos. However, those approaches diverge from human learning paradigms, where humans possess the capability to seek and associate knowledge from an open set, rather than rote memorizing all text-video instances. Motivated by this, we attempt to decouple knowledge from retrieval models through multi-grained knowledge stores and identify two significant benefits of our knowledge-decoupling strategy: (1) it ensures a harmonious balance between knowledge memorization and retrieval optimization, thereby improving retrieval performance; and (2) it can promote incorporating diverse open-world knowledge to augment video-text retrieval. To efficiently integrate information from knowledge stores, we further introduce a novel retrieval framework termed KDProR, which utilizes our proposed Expectation-Knowledge-Maximization (EKM) algorithm for optimization. Specifically, in E-step, KDProR obtains relevant contextual semantics from knowledge stores and achieves efficient knowledge injection through interpolation and alignment correction. During the K-step, KDProR calculates the knowledge KNN distribution by indexing the Top-K acquired knowledge to refine the retrieval distribution, and in M-step, KDProR optimizes the retrieval model by maximizing the likelihood of the objective. Extensive experiments on various benchmarks prove that KDProR significantly outperforms previous state-of-the-art methods across all metrics. Remarkably, KDProR can uniformly and efficiently incorporate diverse open-world knowledge and is compatible with different interaction mechanisms and architectures.
Traditional single-task image restoration methods excel in handling specific degradation types but struggle with multiple degradations. To address this limitation, we propose Grouped Restoration with Image Degradation Similarity (GRIDS), a novel approach that harmonizes the competing objectives inherent in multiple-degradation restoration. We first introduce a quantitative method for assessing relationships between image degradations using statistical modeling of deep degradation representations. This analysis facilitates the strategic grouping of similar tasks, enhancing both the efficiency and effectiveness of the restoration process. Based on the degradation similarity, GRIDS divides restoration tasks into optimal groups, where tasks within the same group are highly correlated. For instance, GRIDS effectively groups 11 degradation types into 4 cohesive groups. Trained models within each group show significant improvements, with an average improvement of 0.09dB over single-task upper bound models and 2.24dB over the mix-training baseline model. GRIDS incorporates an adaptive model selection mechanism for inference, automatically selecting the appropriate grouped-training model based on the input degradation. This mechanism is particularly useful for real-world scenarios with unknown degradations as it does not rely on explicit degradation classification modules. Furthermore, our method can predict model generalization ability without the need for network inference, providing valuable insights for practitioners.
Compositional Zero-Shot Learning (CZSL) aims to classify unseen state-object compositions using seen primitives. Previous methods commonly map an identical primitive from different compositions to the same area within embedding space, aiming to establish primitive representation or assess decoding proficiency. However, relying solely on the intersection area of primitive concepts might overlook nuanced semantics due to conditional variance, thereby limiting the model's capacity to generalize to unseen compositions. In contrast, our approach constructs primitive representations by considering the union area of primitives. We propose a Multiple Representation of Single Primitive learning framework (termed MRSP) for CZSL, which captures composition-relevant features through a state-object-composition three-branch cross-attention architecture. Specifically, the input image feature cross-attends to multiple state, object, and composition features and the prediction scores are adaptively adjusted by combining the output of each branch. Extensive experiments on three benchmarks in both closed-world and open-world settings showcase the superior effectiveness of MRSP.
Exploring Vulnerabilities in Spiking Neural Networks: Direct Adversarial Attacks on Raw Event Data
PDF ↗In the field of computer vision, event-based Dynamic Vision Sensors (DVSs) have emerged as a significant complement to traditional pixel-based imaging due to their low power consumption and high temporal resolution. These sensors, particularly when combined with spiking neural networks (SNNs), offer a promising direction for energy-efficient and fast-reacting vision systems. Typically, DVS data are converted into grid-based formats for processing with SNNs, with this transformation process often being an opaque step in the pipeline. As a result, the grid representation becomes an intermediate yet inaccessible stage during the implementation of attacks, highlighting the importance of attacking raw event data. Existing attack methodologies predominantly target grid-based representations, hindered by the complexity of three-valued optimization and the broad optimization space associated with raw event data. Our study addresses this gap by introducing a novel adversarial attack approach that directly targets raw event data. We tackle the inherent challenges of three-valued optimization and the need to preserve data sparsity through a strategic amalgamation of methods: 1) Treating Discrete Event Values as Probabilistic Samples: This allows for continuous optimization by considering discrete event values as probabilistic space samples. 2) Focusing on Specific Event Positions: We prioritize specific event positions that merge original data with additional target label data, enhancing attack precision. 3) Employing a Sparsity Norm: To retain the original data's sparsity, a sparsity norm is utilized, ensuring the adversarial data's comparability. Our empirical findings demonstrate the effectiveness of our combined approach, achieving noteworthy success in targeted attacks and highlighting vulnerabilities in models based on raw event data.
Vectorized high-definition (HD) maps contain detailed information about surrounding road elements, which are crucial for various downstream tasks in modern autonomous vehicles, such as motion planning and vehicle control. Recent works attempt to directly detect the vectorized HD map as a point set prediction task, achieving notable detection performance improvements. However, these methods usually overlook and fail to analyze the important inner-instance correlations between predicted points, impeding further advancements. To address this issue, we investigate the utilization of inner-instance information for vectorized high-definition mapping through transformers, and propose a powerful system named \textbf{InsMapper}, which effectively harnesses inner-instance information with three exquisite designs, including hybrid query generation, inner-instance query fusion, and inner-instance feature aggregation. The first two modules can better initialize queries for line detection, while the last one refines predicted line instances. InsMapper is highly adaptable and can be seamlessly modified to align with the most recent HD map detection frameworks. Extensive experimental evaluations are conducted on the challenging NuScenes and Argoverse 2 datasets, where InsMapper surpasses the previous state-of-the-art method, demonstrating its effectiveness and generality.
Arbitrary neural style transfer aims to stylise a content image by referencing a provided style image. Despite various efforts to achieve both content preservation and style transferability, learning effective representations for this task remains challenging since the redundancy of content and style features leads to unpleasant image artefacts. In this paper, we learn compact neural representations for style transfer motivated from an information theoretical perspective. In particular, we enforce compressive representations across sequential modules of a reversible flow network in order to reduce feature redundancy without losing content preservation capability. We use a Barlow twins loss to reduce channel dependency and thus to provide better content expressiveness, and optimise the Jensen-Shannon divergence of style representations between reference and target images to avoid under- and over-stylisation. We demonstrate the effectiveness of our proposed method in comparison to other state-of-the-art style transfer methods.
The employment of the event-based synthetic aperture imaging (E-SAI) technique, which has the capability to capture high-frequency light intensity variations, has facilitated its extensive application on scene de-occlusion reconstruction tasks. However, existing methods usually require prior information and have strict restriction of camera motion on SAI acquisition methods. This paper proposes a novel end-to-end refocus-free variable E-SAI de-occlusion image reconstruction approach REDIR, which can align the global and local features of the variable event data and effectively achieve high-resolution imaging of pure event streams. To further improve the reconstruction of the occluded target, we propose a perceptual mask-gated connection module to interlink information between modules, and incorporate a spatial-temporal attention mechanism into the SNN block to enhance target extraction ability of the model. Through extensive experiments, our model achieves state-of-the-art reconstruction quality on the traditional E-SAI dataset without prior information, while verifying the effectiveness of the variable event data feature registration method on our newly introduced V-ESAI dataset, which obviates the reliance on prior knowledge and extends the applicability of SAI acquisition methods by incorporating focus changes, lens rotations, and non-uniform motion.
Pixel-Aware Stable Diffusion for Realistic Image Super-Resolution and Personalized Stylization
PDF ↗Diffusion models have demonstrated impressive performance in various image generation, editing, enhancement and translation tasks. In particular, the pre-trained text-to-image stable diffusion models provide a potential solution to the challenging realistic image super-resolution (Real-ISR) and image stylization problems with their strong generative priors. However, the existing methods along this line often fail to keep faithful pixel-wise image structures. If extra skip connections are used to reproduce details, additional training in image space will be required, limiting the application to tasks in latent space such as image stylization. In this work, we propose a pixel-aware stable diffusion (PASD) network to achieve robust Real-ISR and personalized image stylization. Specifically, a pixel-aware cross attention module is introduced to enable diffusion models perceiving image local structures in pixel-wise level, while a degradation removal module is used to extract degradation insensitive features to guide the diffusion process together with image high level information. An adjustable noise schedule is introduced to further improve the image restoration results. By simply replacing the base diffusion model with a stylized one, PASD can generate diverse stylized images without collecting pairwise training data, and by shifting the base model with an aesthetic one, PASD can bring old photos back to life. Extensive experiments in a variety of image enhancement and stylization tasks demonstrate the effectiveness of our proposed PASD approach.
Vision Transformers (ViT) have emerged as the de-facto choice for numerous industry grade vision solutions. But their inference cost can be prohibitive for many settings, as they compute self-attention in each layer which suffers from quadratic computational complexity in the number of tokens. On the other hand, spatial information in images and spatio-temporal information in videos is usually sparse. In this work, we introduce LookupViT, that aims to exploit this information sparsity to reduce the cost of ViT inference. LookupViT provides a novel general purpose vision transformer block that operates by compressing information from higher resolution tokens to a fixed number of tokens. These few compressed tokens undergo meticulous processing, while the higher-resolution tokens are passed through computationally cheaper layers. Information sharing between these two token sets is enabled through a bidirectional cross-attention mechanism. The approach offers multiple advantages - (a) easy to implement on standard ML accelerators (GPUs/TPUs) via standard high-level operators, (b) applicable to standard ViT and its variants, thus generalizes to various tasks, (c) can handle different tokenization and attention approaches. LookupViT also offers flexibility for the compressed tokens, enabling performance-computation trade-offs in a single trained model. LookupViT's effectiveness on multiple domains - (a) for image-classification (ImageNet-1K and ImageNet-21K), (b) video classification (Kinetics400 and Something-Something V2), (c) image captioning (COCO-Captions) with a frozen encoder. LookupViT provides 2x reduction in FLOPs while upholding or improving accuracy across these domains. In addition, LookupViT also demonstrates out-of-the-box robustness on corrupted image classification (ImageNet-C), improving by more than 4% over ViT.
Think2Drive: Efficient Reinforcement Learning by Thinking with Latent World Model for Autonomous Driving (in CARLA-v2)
PDF ↗Real-world autonomous driving (AD) especially urban driving involves many corner cases. The lately released AD simulator CARLA v2 adds 39 common events in the driving scene, and provides more quasi-realistic testbed compared to CARLA v1. It poses new challenges to the community and so far no literature has reported any success on the new scenarios in V2 as existing works mostly have to rely on specific rules for planning yet they cannot cover the more complex cases in CARLA v2. In this work, we take the initiative of directly training a planner and the hope is to handle the corner cases flexibly and effectively, which we believe is also the future of AD. To our best knowledge, we develop the first model-based RL method named Think2Drive for AD, with a world model to learn the transitions of the environment, and then it acts as a neural simulator to train the planner. This paradigm significantly boosts the training efficiency due to the low dimensional state space and parallel computing of tensors in the world model. As a result, Think2Drive is able to run in an expert-level proficiency in CARLA v2 within 3 days of training on a single A6000 GPU, and to our best knowledge, so far there is no reported success (100\% route completion) on CARLA v2. We also propose CornerCaseRepo, a benchmark that supports the evaluation of driving models by scenarios. Additionally, we propose a new and balanced metric to evaluate the performance by route completion, infraction number, and scenario density, so that the driving score could give more information about the actual driving performance.
Frontier-enhanced Topological Memory with Improved Exploration Awareness for Embodied Visual Navigation
PDF ↗We present a novel graph memory structure for navigation, called Frontier-enhanced Topological Memory (FTM). Most prior research primarily focuses on maintaining memory representations for explored areas. In contrast, our approach incorporates ghost nodes into the topological map to characterize unexplored but visible regions. The ghost nodes are generated using a geometric method and serve to indicate the geometrically explorable frontiers, thereby promoting agent's exploration and target search. We employ an online-trained implicit representation method to predict perceptual features for ghost nodes based on previous observations. In addition, we develop a Multi-Stage Memory Extraction module (MSME) that can effectively utilize the FTM. It focuses particularly on task-specific information and generates actions end-to-end. By using FTM, the agent can improve its capacity for environmental cognition and memory utilization. We evaluate the proposed approach on visual navigation in the photo-realistic Gibson environment. Experimental results conclusively demonstrate that the proposed navigation framework with FTM boosts the agent's exploration awareness and enhances the performance in image-goal navigation tasks.
3D object detection is one of the most important components in any Self-Driving stack, but current state-of-the-art (SOTA) lidar object detectors require costly & slow manual annotation of 3D bounding boxes to perform well. Recently, several methods emerged to generate pseudo ground truth without human supervision, however, all of these methods have various drawbacks: Some methods require sensor rigs with full camera coverage and accurate calibration, partly supplemented by an auxiliary optical flow engine. Others require expensive high-precision localization to find objects that disappeared over multiple drives. We introduce a novel self-supervised method to train SOTA lidar object detection networks which works on unlabeled sequences of lidar point clouds only, which we call trajectory-regularized self-training. It utilizes a SOTA self-supervised lidar scene flow network under the hood to generate, track, and iteratively refine pseudo ground truth. We demonstrate the effectiveness of our approach for multiple SOTA object detection networks across multiple real-world datasets. Code will be released upon acceptance of this paper.
SLEDGE is the first generative simulator for vehicle motion planning trained on real-world driving logs. Its core component is a learned model that is able to generate agent bounding boxes and lane graphs. The model's outputs serve as an initial state for traffic simulation. The unique properties of the entities to be generated for SLEDGE, such as their connectivity and variable count per scene, render the naive application of most modern generative models to this task non-trivial. Therefore, together with a systematic study of existing lane graph representations, we introduce a novel raster-to-vector autoencoder (RVAE). It encodes agents and the lane graph into distinct channels in a rasterized latent map. This facilitates both lane-conditioned agent generation and combined generation of lanes and agents with a Diffusion Transformer. Using generated entities in SLEDGE enables greater control over the simulation, e.g. upsampling turns or increasing traffic density. Further, SLEDGE can support 500m long routes, a capability not found in existing data-driven simulators like nuPlan. It presents new challenges for planning algorithms, evidenced by failure rates of over 40\% for PDM, the winner of the 2023 nuPlan challenge, when tested on hard routes and dense traffic generated by our model. Compared to nuPlan, SLEDGE requires 500$\times$ less storage to set up (<4GB), making it a more accessible option and helping with democratizing future research in this field.
Open-world 3D generation has recently attracted considerable attention. While many single-image-to-3D methods have yielded visually appealing outcomes, they often lack sufficient controllability and tend to produce hallucinated regions that may not align with users' expectations. In this paper, we explore an important scenario in which the input consists of one or a few unposed 2D images of a single object, with little or no overlap. We propose a novel method, \ours, to reconstruct a 3D textured mesh and estimate the relative camera poses for these sparse-view images. \ours distills knowledge from 2D diffusion models and finetunes them to implicitly deduce the 3D spatial relationships between the sparse views. The diffusion model is trained to jointly predict surrogate representations for camera poses and multi-view images of the object under known poses, integrating all information from the input sparse views. These predictions are then leveraged to accomplish 3D reconstruction and pose estimation, and the reconstructed 3D model can be used to further refine the camera poses of input views. Through extensive experiments on three datasets, we demonstrate that our method not only significantly outperforms baseline methods in terms of 3D reconstruction quality and pose prediction accuracy but also exhibits strong efficiency. It requires only about 20 seconds to produce a textured mesh and camera poses for the input views.
Recent years have witnessed the success of large text-to-image diffusion models and their remarkable potential to generate high-quality images. The further pursuit of enhancing the editability of images has sparked significant interest in the downstream task of inpainting a novel object described by a text prompt within a designated region in the image. Nevertheless, the problem is not trivial from two aspects: 1) Solely relying on one single U-Net to align text prompt and visual object across all the denoising timesteps is insufficient to generate desired objects; 2) The controllability of object generation is not guaranteed in the intricate sampling space of diffusion model. In this paper, we propose to decompose the typical single-stage object inpainting into two cascaded processes: 1) semantic pre-inpainting that infers the semantic features of desired objects in a multi-modal feature space; 2) high-fieldity object generation in diffusion latent space that pivots on such inpainted semantic features. To achieve this, we cascade a Transformer-based semantic inpainter and an object inpainting diffusion model, leading to a novel CAscaded Transformer-Diffusion (CAT-Diffusion) framework for text-guided object inpainting. Technically, the semantic inpainter is trained to predict the semantic features of the target object conditioning on unmasked context and text prompt. The outputs of the semantic inpainter then act as the informative visual prompts to guide high-fieldity object generation through a reference adapter layer, leading to controllable object inpainting. Extensive evaluations on OpenImages-V6 and MSCOCO validate the superiority of CAT-Diffusion against the state-of-the-art methods.
Recently, zero-shot image captioning has gained increasing attention, where only text data is available for training. The remarkable progress in text-to-image diffusion model presents the potential to resolve this task by employing synthetic image-caption pairs generated by this pre-trained prior. Nonetheless, the defective details in the salient regions of the synthetic images introduce semantic misalignment between the synthetic image and text, leading to compromised results. To address this challenge, we propose a novel Patch-wise Cross-modal feature Mix-up (PCM) mechanism to adaptively mitigate the unfaithful contents in a fine-grained manner during training, which can be integrated into most of encoder-decoder frameworks, introducing our PCM-Net. Specifically, for each input image, salient visual concepts in the image are first detected considering the image-text similarity in CLIP space. Next, the patch-wise visual features of the input image are selectively fused with the textual features of the salient visual concepts, leading to a mixed-up feature map with less defective content. Finally, a visual-semantic encoder is exploited to refine the derived feature map, which is further incorporated into the sentence decoder for caption generation. Additionally, to facilitate the model training with synthetic data, a novel CLIP-weighted cross-entropy loss is devised to prioritize the high-quality image-text pairs over the low-quality counterparts. Extensive experiments on MSCOCO and Flickr30k datasets demonstrate the superiority of our PCM-Net compared with state-of-the-art VLMs-based approaches. It is noteworthy that our PCM-Net ranks first in both in-domain and cross-domain zero-shot image captioning. The synthetic dataset SynthImgCap and code are available at https://jianjieluo.github.io/SynthImgCap.