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Jiaqi Xu, Mengyang Wu, Xiaowei Hu, Chi-Wing Fu, Qi Dou, Pheng-Ann Heng

This paper addresses the limitations of existing adverse weather image restoration methods trained on synthetic data when applied to real-world scenarios. We formulate a semi-supervised learning framework utilizing vision-language models to enhance restoration performance across diverse adverse weather conditions in real-world settings. Our approach involves assessing image clarity and providing semantics using vision-language models on real data, serving as supervision signals for training restoration models. For clearness enhancement, we use real-world data, employing a dual-step strategy with pseudo-labels generated by vision-language models and weather prompt learning. For semantic enhancement, we integrate real-world data by adjusting weather conditions in vision-language model descriptions while preserving semantic meaning. Additionally, we introduce an efficient training strategy to alleviate computational burdens. Our approach achieves superior results in real-world adverse weather image restoration, demonstrated through qualitative and quantitative comparisons with state-of-the-art approaches.

Zhengfeng Lai, Haotian Zhang, Bowen Zhang, Wentao Wu, Haoping Bai, Aleksei Timofeev, Xianzhi Du, Zhe Gan, Jiulong Shan, Chen-Nee Chuah 等

Large-scale web-crawled datasets are fundamental for the success of pre-training vision-language models, such as CLIP. However, the inherent noise and potential irrelevance of web-crawled AltTexts pose challenges in achieving precise image-text alignment. Existing methods utilizing large language models (LLMs) for caption rewriting have shown promise on small, curated datasets like CC3M and CC12M. This study introduces a scalable pipeline for noisy caption rewriting. Unlike recent LLM rewriting techniques, we emphasize the incorporation of visual concepts into captions, termed as Visual-enriched Captions (VeCap). To ensure data diversity, we propose a novel mixed training scheme that optimizes the utilization of AltTexts alongside newly generated VeCap. We showcase the adaptation of this method for training CLIP on large-scale web-crawled datasets, termed VeCLIP. Employing this cost-effective pipeline, we effortlessly scale our dataset up to 300 million samples named VeCap dataset. Our results show significant advantages in image-text alignment and overall model performance. For example, VeCLIP achieves up to +25.2% gain in COCO and Flickr30k retrieval tasks under the 12M setting. For data efficiency, VeCLIP achieves +3% gain while only using 14% of the data employed in the vanilla CLIP and 11% in ALIGN. We also note the VeCap data is complementary with other well curated datasets good for zero-shot classification tasks. When combining VeCap and DFN, our model can achieve strong performance on both of image-text retrieval and zero-shot classification tasks, e.g. 83.1% accuracy@1 on ImageNet zero-shot for a H/14 model.

Zhenyi Wang, Li Shen, junfeng guo, Tiehang Duan, Siyu Luan, Tongliang Liu, Mingchen Gao

The objective of data-free model extraction (DFME) is to acquire a pre-trained black-box model solely through query access, without any knowledge of the training data used for the victim model. Defending against DFME presents a formidable challenge because the distribution of attack query data and the attacker's strategy remain undisclosed to the defender beforehand. However, existing defense methods: (1) are computational and memory inefficient; or (2) can only provide evidence of model theft after the model has already been stolen. To address these limitations, we thus propose a defensive training method, named, Attack-Aware and Uncertainty-Guided (AAUG) defense method against DFME. AAUG is designed to effectively thwart DFME attacks while concurrently enhancing deployment efficiency. The key strategy involves introducing random weight perturbations to the victim model's weights during predictions for various inputs. During defensive training, the weights perturbations are maximized on simulated out-of-distribution (OOD) data to heighten the challenge of model theft, while being minimized on in-distribution (ID) training data to preserve model utility. Additionally, we formulate an attack-aware defensive training objective function, reinforcing the model's resistance to theft attempts. Extensive experiments on defending against both soft-label and hard-label DFME attacks demonstrate the effectiveness of AAUG. In particular, AAUG significantly reduces the accuracy of the clone model and is substantially more efficient than existing defense methods.

Zonghao Guo, Ruyi Xu, Yuan Yao, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, Gao Huang

Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding the visual world. Conventional LMMs process images in fixed sizes and limited resolutions, while recent explorations in this direction are limited in adaptivity, efficiency, and even correctness. In this work, we first take GPT-4V and LLaVA 1.5 as representative examples and expose systematic flaws rooted in their visual encoding strategy. To address the challenges, we present LLaVA-UHD, a large multimodal model that can efficiently perceive images in any aspect ratio and high resolution. LLaVA-UHD includes three key components: (1) An image modularization strategy that divides native-resolution images into smaller variable-sized slices for efficient and extensible encoding, (2) a compression module that further condenses image tokens from visual encoders, and (3) a spatial schema to organize slice tokens for LLMs. Comprehensive experiments show that LLaVA-UHD outperforms established LMMs trained with 2-3 orders of magnitude more data on 9 benchmarks. Notably, our model built on LLaVA-1.5 336x336 supports 6 times larger (i.e., 672x1088) resolution images using only 94% computation, and achieves 6.4 accuracy improvement on TextVQA. All the data and codes will be publicly available to facilitate future research.

Rolandos Alexandros Potamias, Michael Tarasiou, Stylianos Ploumpis, Stefanos Zafeiriou

In the realm of 3D computer vision, parametric models have emerged as a ground-breaking methodology for the creation of realistic and expressive 3D avatars. Traditionally, they rely on Principal Component Analysis (PCA), given its ability to decompose data to an orthonormal space that maximally captures shape variations. However, due to the orthogonality constraints and the global nature of PCA's decomposition, these models struggle to perform localized and disentangled editing of 3D shapes, which severely affects their use in applications requiring fine control such as face sculpting. In this paper, we leverage diffusion models to enable diverse and fully localized edits on 3D meshes, while completely preserving the un-edited regions. We propose an effective diffusion masking training strategy that, by design, facilitates localized manipulation of any shape region, without being limited to predefined regions or to sparse sets of predefined control vertices. Following our framework, a user can explicitly set their manipulation region of choice and define an arbitrary set of vertices as handles to edit a 3D mesh. Compared to the current state-of-the-art our method leads to more interpretable shape manipulations than methods relying on latent code state, greater localization and generation diversity while offering faster inference than optimization based approaches. Code has been included in the supplementary material.

Lixiong Qin, Mei Wang, Xuannan Liu, Yuhang Zhang, Wei Deng, Xiaoshuai Song, Weiran Xu, Weihong Deng

With the comprehensive research conducted on various face analysis tasks, there is a growing interest among researchers to develop a unified approach to face perception. Existing methods mainly discuss unified representation and training, which lack task extensibility and application efficiency. To tackle this issue, we focus on the unified model structure, exploring a face generalist model. As an intuitive design, Naive Faceptor enables tasks with the same output shape and granularity to share the structural design of the standardized output head, achieving improved task extensibility. Furthermore, Faceptor is proposed to adopt a well-designed single-encoder dual-decoder architecture, allowing task-specific queries to represent new-coming semantics. This design enhances the unification of model structure while improving application efficiency in terms of storage overhead. Additionally, we introduce Layer-Attention into Faceptor, enabling the model to adaptively select features from optimal layers to perform the desired tasks. Through joint training on 13 face perception datasets, Faceptor achieves exceptional performance in facial landmark localization, face parsing, age estimation, expression recognition, binary attribute classification, and face recognition, achieving or surpassing specialized methods in most tasks. Our training framework can also be applied to auxiliary supervised learning, significantly improving performance in data-sparse tasks such as age estimation and expression recognition. The code and model will be made publicly available.

Hajin Shim, Changhun Kim, Eunho Yang

3D point clouds captured from real-world sensors frequently encompass noisy points due to various obstacles, such as occlusion, limited resolution, and variations in scale. This poses challenges when deploying pre-trained point cloud recognition models trained on clean point clouds, leading to significant performance degradation. While test-time adaptation (TTA) strategies have shown promising results in addressing this issue in the 2D domain, its application to 3D point clouds remains under-explored. Among TTA methods, an input adaptation approach, which directly converts test instances to the source domain using a pre-trained diffusion model, has been proposed in the 2D domain. Despite its robust TTA performance in practical situations, naively adopting this into the 3D domain may be suboptimal due to the neglect of inherent properties of point clouds, and its prohibitive computational cost. Motivated by such limitations, we propose CloudFixer, a test-time input adaptation method tailored for 3D point clouds, employing pre-trained diffusion model. Specifically, CloudFixer optimizes geometric transformation parameters with carefully designed objectives that leverage the geometric properties of point clouds. We also substantially improve computational efficiency by avoiding backpropagation through the diffusion model or extensive generation process. Furthermore, we propose an online model adaptation strategy by aligning the original model prediction with that of the adapted input. Extensive experiments showcase the superiority of CloudFixer over various TTA baselines, excelling in handling common corruptions and natural distribution shifts across diverse real-world scenarios.

Jacopo Bonato, Marco Cotogni, Luigi Sabetta

In this paper, we introduce Selective-distillation for Class and Architecture-agnostic unleaRning (SCAR), a novel approximate unlearning method. SCAR efficiently eliminates specific information while preserving the model's test accuracy without using a retain set, which is a key component in state-of-the-art approximate unlearning algorithms. Our approach utilizes a modified Mahalanobis distance to guide the unlearning of the feature vectors of the instances to be forgotten, aligning them to the nearest wrong class distribution. Moreover, we propose a distillation-trick mechanism that distills the knowledge of the original model into the unlearning model with out-of-distribution images for retaining the original model's test performance without using any retain set. Importantly, we propose a self-forget version of SCAR that unlearns without having access to the forget set. We experimentally verified the effectiveness of our method, on three public datasets, comparing it with state-of-the-art methods. Our method obtains performance higher than methods that operate without the retain set and comparable w.r.t the best methods that rely on the retain set.

Minzhou Pan, Zhenting Wang, Xin Dong, Vikash Sehwag, Lingjuan Lyu, Xue Lin

In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting. WMD is capable of detecting arbitrary watermarks within a given reference dataset using a clean non-watermarked dataset as a reference, without relying on specific decoding methods or prior knowledge of the watermarking techniques. We develop WMD using foundations of offset learning, where a clean non-watermarked dataset enables us to isolate the influence of only watermarked samples in the reference dataset. Our comprehensive evaluations demonstrate the effectiveness of WMD, significantly outperforming naive detection methods, which only yield AUC scores around 0.5. In contrast, WMD consistently achieves impressive detection AUC scores, surpassing 0.9 in most single-watermark datasets and exceeding 0.7 in more challenging multi-watermark scenarios across diverse datasets and watermarking methods. As invisible watermarks become increasingly prevalent, while specific decoding techniques remain undisclosed, our approach provides a versatile solution and establishes a path toward increasing accountability, transparency, and trust in our digital visual content.

Ziyun Wang, Jinyuan Guo, Kostas Daniilidis

Event cameras are a novel type of biologically inspired vision sensor known for their high temporal resolution, high dynamic range, and low power consumption. Because of these properties, they are well-suited for processing fast motions that require rapid reactions. Although event cameras have recently shown competitive performance in unsupervised optical flow estimation, performance in detecting independently moving objects (IMOs) is lacking behind, although event-based methods would be suited for this task based on their low latency and HDR properties. Previous approaches to event-based IMO segmentation have been heavily dependent on labeled data. However, biological vision systems have developed the ability to avoid moving objects through daily tasks without being given explicit labels. In this work, we propose the first event framework that generates IMO pseudo-labels using geometric constraints. Due to its unsupervised nature, our method can handle an arbitrary number of not predetermined objects and is easily scalable to datasets where expensive IMO labels are not readily available. We evaluate our approach on the EVIMO dataset and show that it performs competitively with supervised methods, both quantitatively and qualitatively.

Mohammad Saeed Ebrahimi Saadabadi, Sahar Rahimi Malakshan, Ali Dabouei, Nasser Nasrabadi

Aiming to enhance Face Recognition (FR) performance on Low-Quality (LQ) inputs, recent studies suggest incorporating synthetic LQ samples into training. Although promising, the quality factors that are considered in these works are general rather than FR-specific, \eg, atmospheric turbulence, blur, resolution, \etc. Motivated by our observation of the vulnerability of current FR models to even mild Face Alignment Errors (FAE) in LQ images, we present a simple yet effective method that considers FAE as another quality factor that is tailored to FR. We seek to improve LQ FR by enhancing FR models' robustness to FAE. To this aim, we formalize the problem as a combination of differentiable spatial transformations and adversarial data augmentation in FR. We perturb the alignment of training samples using a controllable spatial transformation and enrich the training with samples expressing FAE. We demonstrate the benefits of the proposed method by conducting evaluations on IJB-B, IJB-C, IJB-S (+4.3\% Rank1), and TinyFace (+2.63\%).

Shen Jianbing, Wencheng Han

In the area of self-supervised monocular depth estimation, models that utilize rich-resource inputs, such as high-resolution and multi-frame inputs, typically achieve better performance than models that use ordinary single image input. However, these rich-resource inputs may not always be available, limiting the applicability of these methods in general scenarios. In this paper, we propose Rich-resource Prior Depth estimator (RPrDepth), which only requires single input image during the inference phase but can still produce highly accurate depth estimations comparable to rich-resource based methods. Specifically, we consider rich-resource inputs as a prior information and extract rich-resource features from them during the training phase. When estimating the depth for a single-image image, we search for similar pixels from the rich-resource features and use them as prior information to estimate the depth. Experimental results demonstrate that our model outperform other single-image model and can achieve comparable or even better performance than models with rich-resource inputs, only using low-resolution single-image input.

Yuhao Su, Ehsan Elhamifar

Training a temporal action segmentation (TAS) model on long and untrimmed videos requires gathering framewise video annotations, which is very costly. We propose a two-stage active learning framework to efficiently learn a TAS model using only a small amount of video annotations. Our framework consists of three components that work together in each active learning iteration. 1) Using current labeled frames, we learn a TAS model and action prototypes using a novel contrastive learning method. Leveraging prototypes not only enhances the model performance, but also increases the computational efficiency of both video and frame selection for labeling, which are the next components of our framework. 2) Using the currently learned TAS model and action prototypes, we select informative unlabeled videos for annotation. To do so, we find unlabeled videos that have low alignment scores to learned action prototype sequences in labeled videos. 3) To annotate a small subset of informative frames in each selected unlabeled video, we propose a video-aligned summary selection method and an efficient greedy search algorithm. By evaluation on four benchmark datasets (50Salads, GTEA, Breakfast, CrossTask), we show that our method significantly reduces the annotation costs, while consistently surpassing baselines. We further extend our framework to a semi-supervised active learning setting. We show that we can obtain the full supervision performance using only a small amount of labeled frames. To the best of our knowledge, this is the first work studying active learning for TAS.

Dimity Miller, Niko Suenderhauf, Alex Kenna, Keita Mason

Are vision-language models (VLMs) open-set models because they are trained on internet-scale datasets? We answer this question with a clear no -- VLMs introduce closed-set assumptions via their finite query set, making them vulnerable to open-set conditions. We systematically evaluate VLMs for open-set recognition and find they frequently misclassify objects not contained in their query set, leading to alarmingly low precision when tuned for high recall and vice versa. We show that naively increasing the size of the query set to contain more and more classes does not mitigate this problem, but instead causes diminishing task performance and open-set performance. We establish a revised definition of the open-set problem for the age of VLMs, define a new benchmark and evaluation protocol to facilitate standardised evaluation and research in this important area, and evaluate promising baseline approaches based on predictive uncertainty and dedicated negative embeddings on a range of VLM classifiers and object detectors.

Rui Zhao, Huibin Yan, Shuoyao Wang

Due to data collection challenges, the mean-teacher learning paradigm has emerged as a critical approach for cross-domain object detection, especially in adverse weather conditions. Despite making significant progress, existing methods are still plagued by low-quality pseudo-labels in the degraded images. In this paper, we propose a generation-composition paradigm training framework that includes the tiny-object-friendly loss, i.e., IAoU loss with a joint-filtering and student-aware strategy to improve pseudo-labels generation quality and refine the filtering scheme. Specifically, in the generation phase of pseudo-labels, we observe that bounding box regression is essential for feature alignment and develop the IAoU loss to improve the accuracy of bounding box regression, further facilitating subsequent feature alignment. We also find that selecting bounding boxes based solely on classification confidence does not perform well in cross-domain noisy image scenes. Moreover, relying exclusively on predictions from the teacher model could cause the student model to collapse. Accordingly, in the composition phase, we introduce the mean-teacher model with a joint-filtering and student-aware strategy combining classification and regression thresholds from both the student and the teacher models. Our extensive experiments, conducted on synthetic and real-world adverse weather datasets, clearly demonstrate that the proposed method surpasses state-of-the-art benchmarks across all scenarios, particularly achieving a 12.4% improvement of mAP i.e., cityscapes to RTTS.

Licheng Zhong, Hong-Xing Yu, Jiajun Wu, Yunzhu Li

Reconstructing and simulating elastic objects from visual observations is crucial for applications in computer vision and robotics. Existing methods, such as 3D Gaussians, provide modeling for 3D appearance and geometry but lack the ability to simulate physical properties or optimize parameters for heterogeneous objects. We propose Spring-Gaus, a novel framework that integrates 3D Gaussians with physics-based simulation for reconstructing and simulating elastic objects from multi-view videos. Our method utilizes a 3D Spring-Mass model, enabling the optimization of physical parameters at the individual point level while decoupling the learning of physics and appearance. This approach achieves great sample efficiency, enhances generalization, and reduces sensitivity to the distribution of simulation particles. We evaluate Spring-Gaus on both synthetic and real-world datasets, demonstrating accurate reconstruction and simulation of elastic objects. This includes future prediction and simulation under varying initial states and environmental parameters.

Haoqi Wang, Tong Zhang, Mathieu Salzmann

Vision Transformer models trained on large-scale datasets, although effective, often exhibit artifacts in the patch token they extract. While such defects can be alleviated by re-training the entire model with additional classification tokens, the underlying reasons for the presence of these tokens remain unclear. In this paper, we conduct a thorough investigation of this phenomenon, combining theoretical analysis with empirical observations. Our findings reveal that these artifacts originate from the pre-trained network itself, specifically stemming from the leading left singular vector of the network's weights. Furthermore, to mitigate these defects, we propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset, thereby avoiding the need for complete re-training. We validate our method on various downstream tasks, including unsupervised segmentation, classification, and supervised segmentation, demonstrating its effectiveness in improving model performance. Our code and checkpoints will be released.

Yunan LI, Yihao Zhang, Shoude Li, Long Tian, DOU QUAN, Chaoneng Li, Qiguang Miao

Low illumination significantly impacts the performance of learning-based models trained in well-lit conditions. Although current methods alleviate this issue through either image-level enhancement or feature-level adaptation, they often focus solely on the image itself, ignoring how the task-relevant target varies along with different illumination. In this paper, we propose a target-aware representation learning framework designed to improve high-level task performance in low-illumination environments. We first achieve a bi-directional domain alignment from both image appearance and semantic features to bridge data under varying illumination conditions. To better focus on the target itself, we design a target highlighting strategy, incorporated with the saliency mechanism and Temporal Gaussian Mixture Model to emphasize the location and movement of task-relevant targets. We also design a mask token-based representation learning scheme to learn a more robust target-aware feature. Our framework ensures more robust and effective feature representation for high-level vision tasks in low illumination. Extensive experiments conducted on CODaN, ExDark, and ARID datasets validate the effectiveness of our method for both image and video-based tasks, such as classification, detection, and action recognition. The code will be released upon acceptance.

Guangcheng Chen, Yicheng He, Li He, Hong Zhang

Neural implicit surface reconstruction has achieved remarkable progress recently. Despite resorting to complex radiance modeling, state-of-the-art methods still struggle with textureless and specular surfaces. Different from RGB images, polarization images can provide direct constraints on the azimuth angles of the surface normals. In this paper, we present PISR, a novel method that utilizes a geometrically accurate polarimetric loss to refine shape independently of appearance. In addition, PISR smooths surface normals in image space to eliminate severe shape distortions and leverages the hash-grid-based neural signed distance function to accelerate the reconstruction. Experimental results demonstrate that PISR achieves higher accuracy and robustness, with an L1 chamfer distance of 0.5 mm and an F-score of 99.5% at 1mm, while converging 4~ 30x faster than previous polarimetric surface reconstruction methods. The source code and dataset will be released upon acceptance of this paper.

Penglei SUN, Yaoxian Song, Xinglin Pan, Peijie Dong, Xiaofei Yang, Qiang Wang, Zhixu Li, Tiefeng Li, Xiaowen Chu

The existing works on object-level language grounding with 3D objects mostly focus on improving performance by utilizing the off-the-shelf pre-trained models to capture features, such as viewpoint selection or geometric priors. However, they have failed to consider exploring the cross-modal representation of language-vision alignment in the cross-domain field. To answer this problem, we propose a novel method called Domain Adaptation for Language Grounding (DA4LG) with 3D objects. Specifically, the proposed DA4LG consists of a visual adapter module with multi-task learning to realize vision-language alignment by comprehensive multimodal feature representation. Experimental results demonstrate that DA4LG competitively performs across visual and non-visual language descriptions, independent of the completeness of observation. DA4LG achieves state-of-the-art performance in the single-view setting and multi-view setting with the accuracy of 83.8 % and 86.8 % respectively in the language grounding benchmark SNARE. The simulation experiments show the well-practical and generalized performance of DA4LG compared to the existing methods. Our project is available anonymously at https://sites.google.com/view/da4lg.