GenAI4ELab/papercli-papers huggingface dataset:
A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint
Authors: Xiaofeng Cong; Jie Gui; Jing Zhang; Junming Hou; Hao Shen
Venue: CVPR 2024 (main)

Abstract: Existing research based on deep learning has extensively explored the problem of daytime image dehazing. However few studies have considered the characteristics of nighttime hazy scenes. There are two distinctions between nighttime and daytime haze. First there may be multiple active colored light sources with lower illumination intensity in nighttime scenes which may cause haze glow and noise with localized coupled and frequency inconsistent characteristics. Second due to the domain discrepancy between simulated and real-world data unrealistic brightness may occur when applying a dehazing model trained on simulated data to real-world data. To address the above two issues we propose a semi-supervised model for real-world nighttime dehazing. First the spatial attention and frequency spectrum filtering are implemented as a spatial-frequency domain information interaction module to handle the first issue. Second a pseudo-label-based retraining strategy and a local window-based brightness loss for semi-supervised training process is designed to suppress haze and glow while achieving realistic brightness. Experiments on public benchmarks validate the effectiveness of the proposed method and its superiority over state-of-the-art methods. The source code and Supplementary Materials are placed in the https://github.com/Xiaofeng-life/SFSNiD.
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts
Authors: Yuzheng Wang; Dingkang Yang; Zhaoyu Chen; Yang Liu; Siao Liu; Wenqiang Zhang; Lihua Zhang; Lizhe Qi
Venue: CVPR 2024 (main)

Abstract: Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Existing methods commonly avoid relying on private data by utilizing synthetic or sampled data. However a long-overlooked issue is that the severe distribution shifts between their substitution and original data which manifests as huge differences in the quality of images and class proportions. The harmful shifts are essentially the confounder that significantly causes performance bottlenecks. To tackle the issue this paper proposes a novel perspective with causal inference to disentangle the student models from the impact of such shifts. By designing a customized causal graph we first reveal the causalities among the variables in the DFKD task. Subsequently we propose a Knowledge Distillation Causal Intervention (KDCI) framework based on the backdoor adjustment to de-confound the confounder. KDCI can be flexibly combined with most existing state-of-the-art baselines. Experiments in combination with six representative DFKD methods demonstrate the effectiveness of our KDCI which can obviously help existing methods under almost all settings e.g. improving the baseline by up to 15.54% accuracy on the CIFAR-100 dataset.
Fine-Grained Bipartite Concept Factorization for Clustering
Authors: Chong Peng; Pengfei Zhang; Yongyong Chen; Zhao Kang; Chenglizhao Chen; Qiang Cheng
Venue: CVPR 2024 (main)

Abstract: In this paper we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph which provides comprehensive and complementary neighbor information of the data. The factor matrices are learned with bipartite graph partitioning which exploits explicit cluster structure of the data and is more geared towards clustering application. We develop an effective and efficient optimization algorithm for our method and provide elegant theoretical results about the convergence. Extensive experimental results confirm the effectiveness of the proposed method.
Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding
Authors: Chaolei Tan; Jianhuang Lai; Wei-Shi Zheng; Jian-Fang Hu
Venue: CVPR 2024 (main)

Abstract: Video Paragraph Grounding (VPG) is an emerging task in video-language understanding which aims at localizing multiple sentences with semantic relations and temporal order from an untrimmed video. However existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. In this work we introduce and explore Weakly-Supervised Video Paragraph Grounding (WSVPG) to eliminate the need of temporal annotations. Different from previous weakly-supervised grounding frameworks based on multiple instance learning or reconstruction learning for two-stage candidate ranking we propose a novel siamese learning framework that jointly learns the cross-modal feature alignment and temporal coordinate regression without timestamp labels to achieve concise one-stage localization for WSVPG. Specifically we devise a Siamese Grounding TRansformer (SiamGTR) consisting of two weight-sharing branches for learning complementary supervision. An Augmentation Branch is utilized for directly regressing the temporal boundaries of a complete paragraph within a pseudo video and an Inference Branch is designed to capture the order-guided feature correspondence for localizing multiple sentences in a normal video. We demonstrate by extensive experiments that our paradigm has superior practicability and flexibility to achieve efficient weakly-supervised or semi-supervised learning outperforming state-of-the-art methods trained with the same or stronger supervision.
Language-Driven Anchors for Zero-Shot Adversarial Robustness
Authors: Xiao Li; Wei Zhang; Yining Liu; Zhanhao Hu; Bo Zhang; Xiaolin Hu
Venue: CVPR 2024 (main)

Abstract: Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks. Previous researches mainly focus on improving adversarial robustness in the fully supervised setting leaving the challenging domain of zero-shot adversarial robustness an open question. In this work we investigate this domain by leveraging the recent advances in large vision-language models such as CLIP to introduce zero-shot adversarial robustness to DNNs. We propose LAAT a Language-driven Anchor-based Adversarial Training strategy. LAAT utilizes the features of a text encoder for each category as fixed anchors (normalized feature embeddings) for each category which are then employed for adversarial training. By leveraging the semantic consistency of the text encoders LAAT aims to enhance the adversarial robustness of the image model on novel categories. However naively using text encoders leads to poor results. Through analysis we identified the issue to be the high cosine similarity between text encoders. We then design an expansion algorithm and an alignment cross-entropy loss to alleviate the problem. Our experimental results demonstrated that LAAT significantly improves zero-shot adversarial robustness over state-of-the-art methods. LAAT has the potential to enhance adversarial robustness by large-scale multimodal models especially when labeled data is unavailable during training.
Deep Equilibrium Diffusion Restoration with Parallel Sampling
Authors: Jiezhang Cao; Yue Shi; Kai Zhang; Yulun Zhang; Radu Timofte; Luc Van Gool
Venue: CVPR 2024 (main)

Abstract: Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images achieving promising performance. Due to the inherent property of diffusion models most existing methods need long serial sampling chains to restore HQ images step-by-step resulting in expensive sampling time and high computation costs. Moreover such long sampling chains hinder understanding the relationship between inputs and restoration results since it is hard to compute the gradients in the whole chains. In this work we aim to rethink the diffusion model-based IR models through a different perspective i.e. a deep equilibrium (DEQ) fixed point system called DeqIR. Specifically we derive an analytical solution by modeling the entire sampling chain in these IR models as a joint multivariate fixed point system. Based on the analytical solution we can conduct parallel sampling and restore HQ images without training. Furthermore we compute fast gradients via DEQ inversion and found that initialization optimization can boost image quality and control the generation direction. Extensive experiments on benchmarks demonstrate the effectiveness of our method on typical IR tasks and real-world settings.
LEOD: Label-Efficient Object Detection for Event Cameras
Authors: Ziyi Wu; Mathias Gehrig; Qing Lyu; Xudong Liu; Igor Gilitschenski
Venue: CVPR 2024 (main)

Abstract: Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However it is costly to fully label event streams for supervised training due to their high temporal resolution. To reduce this cost we present LEOD the first method for label-efficient event-based detection. Our approach unifies weakly- and semi-supervised object detection with a self-training mechanism. We first utilize a detector pre-trained on limited labels to produce pseudo ground truth on unlabeled events. Then the detector is re-trained with both real and generated labels. Leveraging the temporal consistency of events we run bi-directional inference and apply tracking-based post-processing to enhance the quality of pseudo labels. To stabilize training against label noise we further design a soft anchor assignment strategy. We introduce new experimental protocols to evaluate the task of label-efficient event-based detection on Gen1 and 1Mpx datasets. LEOD consistently outperforms supervised baselines across various labeling ratios. For example on Gen1 it improves mAP by 8.6% and 7.8% for RVT-S trained with 1% and 2% labels. On 1Mpx RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels. LEOD maintains its effectiveness even when all labeled data are available reaching new state-of-the-art results. Finally we show that our method readily scales to improve larger detectors as well. Code is released at https://github.com/Wuziyi616/LEOD.
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Authors: Andrew H. Song; Richard J. Chen; Tong Ding; Drew F.K. Williamson; Guillaume Jaume; Faisal Mahmood
Venue: CVPR 2024 (main)

Abstract: Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However the slide representations resulting from this approach are highly tailored to specific clinical tasks which limits their expressivity and generalization particularly in scenarios with limited data. Instead we hypothesize that morphological redundancy in tissue can be leveraged to build a task-agnostic slide representation in an unsupervised fashion. To this end we introduce PANTHER a prototype-based approach rooted in the Gaussian mixture model that summarizes the set of WSI patches into a much smaller set of morphological prototypes. Specifically each patch is assumed to have been generated from a mixture distribution where each mixture component represents a morphological exemplar. Utilizing the estimated mixture parameters we then construct a compact slide representation that can be readily used for a wide range of downstream tasks. By performing an extensive evaluation of PANTHER on subtyping and survival tasks using 13 datasets we show that 1) PANTHER outperforms or is on par with supervised MIL baselines and 2) the analysis of morphological prototypes brings new qualitative and quantitative insights into model interpretability. The code is available at https://github.com/mahmoodlab/Panther.
Fooling Polarization-Based Vision using Locally Controllable Polarizing Projection
Authors: Zhuoxiao Li; Zhihang Zhong; Shohei Nobuhara; Ko Nishino; Yinqiang Zheng
Venue: CVPR 2024 (main)

Abstract: Polarization is a fundamental property of light that encodes abundant information regarding surface shape material illumination and viewing geometry. The computer vision community has witnessed a blossom of polarization-based vision applications such as reflection removal shape-from-polarization (SfP) transparent object segmentation and color constancy partially due to the emergence of single-chip mono/color polarization sensors that make polarization data acquisition easier than ever. However is polarization-based vision vulnerable to adversarial attacks? If so is that possible to realize these adversarial attacks in the physical world without being perceived by human eyes? In this paper we warn the community of the vulnerability of polarization-based vision which can be more serious than RGB-based vision. By adapting a commercial LCD projector we achieve locally controllable polarizing projection which is successfully utilized to fool state-of-the-art polarization-based vision algorithms for glass segmentation and SfP. Compared with existing physical attacks on RGB-based vision which always suffer from the trade-off between attack efficacy and eye conceivability the adversarial attackers based on polarizing projection are contact-free and visually imperceptible since naked human eyes can rarely perceive the difference of viciously manipulated polarizing light and ordinary illumination. This poses unprecedented risks on polarization-based vision for which due attentions should be paid and counter measures be considered.
Dense Optical Tracking: Connecting the Dots
Authors: Guillaume Le Moing; Jean Ponce; Cordelia Schmid
Venue: CVPR 2024 (main)

Abstract: Recent approaches to point tracking are able to recover the trajectory of any scene point through a large portion of a video despite the presence of occlusions. They are however too slow in practice to track every point observed in a single frame in a reasonable amount of time. This paper introduces DOT a novel simple and efficient method for solving this problem. It first extracts a small set of tracks from key regions at motion boundaries using an off-the-shelf point tracking algorithm. Given source and target frames DOT then computes rough initial estimates of a dense flow field and visibility mask through nearest-neighbor interpolation before refining them using a learnable optical flow estimator that explicitly handles occlusions and can be trained on synthetic data with ground-truth correspondences. We show that DOT is significantly more accurate than current optical flow techniques outperforms sophisticated "universal" trackers like OmniMotion and is on par with or better than the best point tracking algorithms like CoTracker while being at least two orders of magnitude faster. Quantitative and qualitative experiments with synthetic and real videos validate the promise of the proposed approach. Code data and videos showcasing the capabilities of our approach are available in the project webpage: https://16lemoing.github.io/dot .
A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning
Authors: Xiaoyang Xu; Mengda Yang; Wenzhe Yi; Ziang Li; Juan Wang; Hongxin Hu; Yong Zhuang; Yaxin Liu
Venue: CVPR 2024 (main)

Abstract: Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces a new semi-honest Data Reconstruction Attack on SL named Feature-Oriented Reconstruction Attack (FORA). In contrast to prior works FORA relies on limited prior knowledge specifically that the server utilizes auxiliary samples from the public without knowing any client's private information. This allows FORA to conduct the attack stealthily and achieve robust performance. The key vulnerability exploited by FORA is the revelation of the model representation preference in the smashed data output by victim client. FORA constructs a substitute client through feature-level transfer learning aiming to closely mimic the victim client's representation preference. Leveraging this substitute client the server trains the attack model to effectively reconstruct private data. Extensive experiments showcase FORA's superior performance compared to state-of-the-art methods. Furthermore the paper systematically evaluates the proposed method's applicability across diverse settings and advanced defense strategies.
DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection
Authors: Yuhao Sun; Lingyun Yu; Hongtao Xie; Jiaming Li; Yongdong Zhang
Venue: CVPR 2024 (main)

Abstract: With the rapid development of face recognition (FR) systems the privacy of face images on social media is facing severe challenges due to the abuse of unauthorized FR systems. Some studies utilize adversarial attack techniques to defend against malicious FR systems by generating adversarial examples. However the generated adversarial examples i.e. the protected face images tend to suffer from subpar visual quality and low transferability. In this paper we propose a novel face protection approach dubbed DiffAM which leverages the powerful generative ability of diffusion models to generate high-quality protected face images with adversarial makeup transferred from reference images. To be specific we first introduce a makeup removal module to generate non-makeup images utilizing a fine-tuned diffusion model with guidance of textual prompts in CLIP space. As the inverse process of makeup transfer makeup removal can make it easier to establish the deterministic relationship between makeup domain and non-makeup domain regardless of elaborate text prompts. Then with this relationship a CLIP-based makeup loss along with an ensemble attack strategy is introduced to jointly guide the direction of adversarial makeup domain achieving the generation of protected face images with natural-looking makeup and high black-box transferability. Extensive experiments demonstrate that DiffAM achieves higher visual quality and attack success rates with a gain of 12.98% under black-box setting compared with the state of the arts. The code will be available at https://github.com/HansSunY/DiffAM.
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision Transformers
Authors: K L Navaneet; Soroush Abbasi Koohpayegani; Essam Sleiman; Hamed Pirsiavash
Venue: CVPR 2024 (main)

Abstract: Recently there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based on the input instance. We show that such models can be vulnerable to a universal adversarial patch attack where the attacker optimizes for a patch that when pasted on any image can increase the compute and power consumption of the model. We run experiments with three different efficient vision transformer methods showing that in some cases the attacker can increase the computation to the maximum possible level by simply pasting a patch that occupies only 8% of the image area. We also show that a standard adversarial training defense method can reduce some of the attack's success. We believe adaptive efficient methods will be necessary for the future to lower the power usage of expensive deep models so we hope our paper encourages the community to study the robustness of these methods and develop better defense methods for the proposed attack. Code is available at: https://github.com/UCDvision/SlowFormer.
TULIP: Transformer for Upsampling of LiDAR Point Clouds
Authors: Bin Yang; Patrick Pfreundschuh; Roland Siegwart; Marco Hutter; Peyman Moghadam; Vaishakh Patil
Venue: CVPR 2024 (main)

Abstract: LiDAR Upsampling is a challenging task for the perception systems of robots and autonomous vehicles due to the sparse and irregular structure of large-scale scene contexts. Recent works propose to solve this problem by converting LiDAR data from 3D Euclidean space into an image super-resolution problem in 2D image space. Although their methods can generate high-resolution range images with fine-grained details the resulting 3D point clouds often blur out details and predict invalid points. In this paper we propose TULIP a new method to reconstruct high-resolution LiDAR point clouds from low-resolution LiDAR input. We also follow a range image-based approach but specifically modify the patch and window geometries of a Swin-Transformer-based network to better fit the characteristics of range images. We conducted several experiments on three public real-world and simulated datasets. TULIP outperforms state-of-the-art methods in all relevant metrics and generates robust and more realistic point clouds than prior works.
How to Configure Good In-Context Sequence for Visual Question Answering
Authors: Li Li; Jiawei Peng; Huiyi Chen; Chongyang Gao; Xu Yang
Venue: CVPR 2024 (main)

Abstract: Inspired by the success of Large Language Models in dealing with new tasks via In-Context Learning (ICL) in NLP researchers have also developed Large Vision-Language Models (LVLMs) with ICL capabilities. However when implementing ICL using these LVLMs researchers usually resort to the simplest way like random sampling to configure the in-context sequence thus leading to sub-optimal results. To enhance the ICL performance in this study we use Visual Question Answering (VQA) as case study to explore diverse in-context configurations to find the powerful ones. Additionally through observing the changes of the LVLM outputs by altering the in-context sequence we gain insights into the inner properties of LVLMs improving our understanding of them. Specifically to explore in-context configurations we design diverse retrieval methods and employ different strategies to manipulate the retrieved demonstrations. Through exhaustive experiments on three VQA datasets: VQAv2 VizWiz and OK-VQA we uncover three important inner properties of the applied LVLM and demonstrate which strategies can consistently improve the ICL VQA performance. Our code is provided in: https: //github.com/GaryJiajia/OFv2_ICL_VQA.
Gaussian Shell Maps for Efficient 3D Human Generation
Authors: Rameen Abdal; Wang Yifan; Zifan Shi; Yinghao Xu; Ryan Po; Zhengfei Kuang; Qifeng Chen; Dit-Yan Yeung; Gordon Wetzstein
Venue: CVPR 2024 (main)

Abstract: Efficient generation of 3D digital humans is important in several industries including virtual reality social media and cinematic production. 3D generative adversarial networks (GANs) have demonstrated state-of-the-art (SOTA) quality and diversity for generated assets. Current 3D GAN architectures however typically rely on volume representations which are slow to render thereby hampering the GAN training and requiring multi-view-inconsistent 2D upsamplers. Here we introduce Gaussian Shell Maps (GSMs) as a framework that connects SOTA generator network architectures with emerging 3D Gaussian rendering primitives using an articulable multi shell--based scaffold. In this setting a CNN generates a 3D texture stack with features that are mapped to the shells. The latter represent inflated and deflated versions of a template surface of a digital human in a canonical body pose. Instead of rasterizing the shells directly we sample 3D Gaussians on the shells whose attributes are encoded in the texture features. These Gaussians are efficiently and differentiably rendered. The ability to articulate the shells is important during GAN training and at inference time to deform a body into arbitrary user-defined poses. Our efficient rendering scheme bypasses the need for view-inconsistent upsamplers and achieves high-quality multi-view consistent renderings at a native resolution of 512 x512 pixels. We demonstrate that GSMs successfully generate 3D humans when trained on single-view datasets including SHHQ and DeepFashion.