GenAI4ELab/papercli-papers huggingface dataset:
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Authors: Jiawang Bai; Kuofeng Gao; Shaobo Min; Shu-Tao Xia; Zhifeng Li; Wei Liu
Venue: CVPR 2024 (main)

Abstract: Contrastive Vision-Language Pre-training known as CLIP has shown promising effectiveness in addressing downstream image recognition tasks. However recent works revealed that the CLIP model can be implanted with a downstream-oriented backdoor. On downstream tasks one victim model performs well on clean samples but predicts a specific target class whenever a specific trigger is present. For injecting a backdoor existing attacks depend on a large amount of additional data to maliciously fine-tune the entire pre-trained CLIP model which makes them inapplicable to data-limited scenarios. In this work motivated by the recent success of learnable prompts we address this problem by injecting a backdoor into the CLIP model in the prompt learning stage. Our method named BadCLIP is built on a novel and effective mechanism in backdoor attacks on CLIP i.e. influencing both the image and text encoders with the trigger. It consists of a learnable trigger applied to images and a trigger-aware context generator such that the trigger can change text features via trigger-aware prompts resulting in a powerful and generalizable attack. Extensive experiments conducted on 11 datasets verify that the clean accuracy of BadCLIP is similar to those of advanced prompt learning methods and the attack success rate is higher than 99% in most cases. BadCLIP is also generalizable to unseen classes and shows a strong generalization capability under cross-dataset and cross-domain settings. The code is available at https://github.com/jiawangbai/BadCLIP.
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
Authors: Jaeha Kim; Junghun Oh; Kyoung Mu Lee
Venue: CVPR 2024 (main)

Abstract: In real-world scenarios image recognition tasks such as semantic segmentation and object detection often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However due to the ill-posed property of SR it is challenging for typical SR methods to restore task-relevant high-frequency contents which may dilute the advantage of utilizing the SR method. Therefore in this paper we propose Super-Resolution for Image Recognition (SR4IR) that effectively guides the generation of SR images beneficial to achieving satisfactory image recognition performance when processing LR images. The critical component of our SR4IR is the task-driven perceptual (TDP) loss that enables the SR network to acquire task-specific knowledge from a network tailored for a specific task. Moreover we propose a cross-quality patch mix and an alternate training framework that significantly enhances the efficacy of the TDP loss by addressing potential problems when employing the TDP loss. Through extensive experiments we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task including semantic segmentation object detection and image classification. The implementation code is available at https://github.com/JaehaKim97/SR4IR.
PELA: Learning Parameter-Efficient Models with Low-Rank Approximation
Authors: Yangyang Guo; Guangzhi Wang; Mohan Kankanhalli
Venue: CVPR 2024 (main)

Abstract: Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Recent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to the fixed backbone model. This strategy however leads to more challenges in loading large models for downstream fine-tuning with limited resources. In this paper we propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. To this end we first employ low-rank approximation to compress the original large model and then devise a feature distillation module and a weight perturbation regularization module. These modules are specifically designed to enhance the low-rank model. In particular we update only the low-rank model while freezing the backbone parameters during pre-training. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks. The proposed method achieves both efficiencies in terms of required parameters and computation time while maintaining comparable results with minimal modifications to the backbone architecture. Specifically when applied to three vision-only and one vision-language Transformer models our approach often demonstrates a merely 0.6-point decrease in performance while reducing the original parameter size by 1/3 to 2/3.
XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
Authors: Xuanchi Ren; Jiahui Huang; Xiaohui Zeng; Ken Museth; Sanja Fidler; Francis Williams
Venue: CVPR 2024 (main)

Abstract: We present XCube a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of voxels with a finest effective resolution of up to 1024^3 in a feed-forward fashion without time-consuming test-time optimization. To achieve this we employ a hierarchical voxel latent diffusion model which generates progressively higher resolution grids in a coarse-to-fine manner using a custom framework built on the highly efficient VDB data structure. Apart from generating high-resolution objects we demonstrate the effectiveness of XCube on large outdoor scenes at scales of 100m x 100m with a voxel size as small as 10cm. We observe clear qualitative and quantitative improvements over past approaches. In addition to unconditional generation we show that our model can be used to solve a variety of tasks such as user-guided editing scene completion from a single scan and text-to-3D.
PixelRNN: In-pixel Recurrent Neural Networks for End-to-end-optimized Perception with Neural Sensors
Authors: Haley M. So; Laurie Bose; Piotr Dudek; Gordon Wetzstein
Venue: CVPR 2024 (main)

Abstract: Conventional image sensors digitize high-resolution images at fast frame rates producing a large amount of data that needs to be transmitted off the sensor for further processing. This is challenging for perception systems operating on edge devices because communication is power inefficient and induces latency. Fueled by innovations in stacked image sensor fabrication emerging sensor--processors offer programmability and processing capabilities directly on the sensor. We exploit these capabilities by developing an efficient recurrent neural network architecture PixelRNN that encodes spatio-temporal features on the sensor using purely binary operations. PixelRNN reduces the amount of data to be transmitted off the sensor by factors up to 256 compared to the raw sensor data while offering competitive accuracy for hand gesture recognition and lip reading tasks. We experimentally validate PixelRNN using a prototype implementation on the SCAMP-5 sensor--processor platform.
Reconstruction-free Cascaded Adaptive Compressive Sensing
Authors: Chenxi Qiu; Tao Yue; Xuemei Hu
Venue: CVPR 2024 (main)

Abstract: Scene-aware Adaptive Compressive Sensing (ACS) has constituted a persistent pursuit holding substantial promise for the enhancement of Compressive Sensing (CS) performance. Cascaded ACS furnishes a proficient multi-stage framework for adaptively allocating the CS sampling based on previous CS measurements. However reconstruction is commonly required for analyzing and steering the successive CS sampling which bottlenecks the ACS speed and impedes the practical application in time-sensitive scenarios. Addressing this challenge we propose a reconstruction-free cascaded ACS method which requires NO reconstruction during the adaptive sampling process. A lightweight Score Network (ScoreNet) is proposed to directly determine the ACS allocation with previous CS measurements and a differentiable adaptive sampling module is proposed for end-to-end training. For image reconstruction we propose a Multi-Grid Spatial-Attention Network (MGSANet) that could facilitate efficient multi-stage training and inferencing. By introducing the reconstruction-fidelity supervision outside the loop of the multi-stage sampling process ACS can be efficiently optimized and achieve high imaging fidelity. The effectiveness of the proposed method is demonstrated with extensive quantitative and qualitative experiments compared with the state-of-the-art CS algorithms.
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
Authors: Xidong Wu; Shangqian Gao; Zeyu Zhang; Zhenzhen Li; Runxue Bao; Yanfu Zhang; Xiaoqian Wang; Heng Huang
Venue: CVPR 2024 (main)

Abstract: Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise making their widespread adoption challenging. To address the limitation the Only-Train-Once (OTO) and OTOv2 are proposed to eliminate the need for additional fine-tuning steps by directly training and compressing a general DNN from scratch. Nevertheless the static design of optimizers (in OTO) can lead to convergence issues of local optima. In this paper we proposed the Auto-Train-Once (ATO) an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. During the model training phase our approach not only trains the target model but also leverages a controller network as an architecture generator to guide the learning of target model weights. Furthermore we developed a novel stochastic gradient algorithm that enhances the coordination between model training and controller network training thereby improving pruning performance. We provide a comprehensive convergence analysis as well as extensive experiments and the results show that our approach achieves state-of-the-art performance across various model architectures (including ResNet18 ResNet34 ResNet50 ResNet56 and MobileNetv2) on standard benchmark datasets (CIFAR-10 CIFAR-100 and ImageNet).
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
Authors: Qinghe Ma; Jian Zhang; Lei Qi; Qian Yu; Yinghuan Shi; Yang Gao
Venue: CVPR 2024 (main)

Abstract: Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods address one of these issues separately. However the coexistence of limited annotation and domain shift is quite common which motivates us to introduce a novel and challenging scenario: Mixed Domain Semi-supervised medical image Segmentation (MiDSS). In this scenario we handle data from multiple medical centers with limited annotations available for a single domain and a large amount of unlabeled data from multiple domains. We found that the key to solving the problem lies in how to generate reliable pseudo labels for the unlabeled data in the presence of domain shift with labeled data. To tackle this issue we employ Unified Copy-Paste (UCP) between images to construct intermediate domains facilitating the knowledge transfer from the domain of labeled data to the domains of unlabeled data. To fully utilize the information within the intermediate domain we propose a symmetric Guidance training strategy (SymGD) which additionally offers direct guidance to unlabeled data by merging pseudo labels from intermediate samples. Subsequently we introduce a Training Process aware Random Amplitude MixUp (TP-RAM) to progressively incorporate style-transition components into intermediate samples. Compared with existing state-of-the-art approaches our method achieves a notable 13.57% improvement in Dice score on Prostate dataset as demonstrated on three public datasets. Our code is available at https://github.com/MQinghe/MiDSS
DUSt3R: Geometric 3D Vision Made Easy
Authors: Shuzhe Wang; Vincent Leroy; Yohann Cabon; Boris Chidlovskii; Jerome Revaud
Venue: CVPR 2024 (main)

Abstract: Multi-view stereo reconstruction (MVS) in the wild requires to first estimate the camera intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain yet they are mandatory to triangulate corresponding pixels in 3D space which is at the core of all best performing MVS algorithms. In this work we take an opposite stance and introduce DUSt3R a radically novel paradigm for Dense and Unconstrained Stereo 3D Reconstruction of arbitrary image collections operating without prior information about camera calibration nor viewpoint poses. We cast the pairwise reconstruction problem as a regression of pointmaps relaxing the hard constraints of usual projective camera models. We show that this formulation smoothly unifies the monocular and binocular reconstruction cases. In the case where more than two images are provided we further propose a simple yet effective global alignment strategy that expresses all pairwise pointmaps in a common reference frame. We base our network architecture on standard Transformer encoders and decoders allowing us to leverage powerful pretrained models. Our formulation directly provides a 3D model of the scene as well as depth information but interestingly we can seamlessly recover from it pixel matches focal lengths relative and absolute cameras. Extensive experiments on all these tasks showcase how DUSt3R effectively unifies various 3D vision tasks setting new performance records on monocular & multi-view depth estimation as well as relative pose estimation. In summary DUSt3R makes many geometric 3D vision tasks easy. Code and models at https://github.com/naver/dust3r
From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding
Authors: Yong-Lu Li; Xiaoqian Wu; Xinpeng Liu; Zehao Wang; Yiming Dou; Yikun Ji; Junyi Zhang; Yixing Li; Xudong Lu; Jingru Tan; Cewu Lu
Venue: CVPR 2024 (main)

Abstract: Action understanding matters for intelligent agents and has attracted long-term attention. It can be formed as the mapping from the action physical space to the semantic space. Typically researchers built action datasets according to idiosyncratic choices to define classes and push the envelope of benchmarks respectively. Thus datasets are incompatible with each other like "Isolated Islands" due to semantic gaps and various class granularities e.g. do housework in dataset A and wash plate in dataset B. We argue that a more principled semantic space is an urgent need to concentrate the community efforts and enable us to use all datasets together to pursue generalizable action learning. To this end we design a structured action semantic space in view of verb taxonomy hierarchy and covering massive actions. By aligning the classes of previous datasets to our semantic space we gather (image/video/skeleton/MoCap) datasets into a unified database in a unified label system i.e. bridging "isolated islands" into a "Pangea". Accordingly we propose a novel model mapping from the physical space to semantic space to fully use Pangea. In extensive experiments our new system shows significant superiority especially in transfer learning. Our code and data will be made public at https://mvig-rhos.com/pangea.
Bootstrapping Autonomous Driving Radars with Self-Supervised Learning
Authors: Yiduo Hao; Sohrab Madani; Junfeng Guan; Mohammed Alloulah; Saurabh Gupta; Haitham Hassanieh
Venue: CVPR 2024 (main)

Abstract: The perception of autonomous vehicles using radars has attracted increased research interest due its ability to operate in fog and bad weather. However training radar models is hindered by the cost and difficulty of annotating large-scale radar data. To overcome this bottleneck we propose a self-supervised learning framework to leverage the large amount of unlabeled radar data to pre-train radar-only embeddings for self-driving perception tasks. The proposed method combines radar-to-radar and radar-to-vision contrastive losses to learn a general representation from unlabeled radar heatmaps paired with their corresponding camera images. When used for downstream object detection we demonstrate that the proposed self-supervision framework can improve the accuracy of state-of-the-art supervised baselines by 5.8% in mAP. Code is available at https://github.com/yiduohao/Radical.
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples
Authors: Junhao Dong; Piotr Koniusz; Junxi Chen; Z. Jane Wang; Yew-Soon Ong
Venue: CVPR 2024 (main)

Abstract: Adversarially robust knowledge distillation aims to compress large-scale models into lightweight models while preserving adversarial robustness and natural performance on a given dataset. Existing methods typically align probability distributions of natural and adversarial samples between teacher and student models but they overlook intermediate adversarial samples along the "adversarial path" formed by the multi-step gradient ascent of a sample towards the decision boundary. Such paths capture rich information about the decision boundary. In this paper we propose a novel adversarially robust knowledge distillation approach by incorporating such adversarial paths into the alignment process. Recognizing the diverse impacts of intermediate adversarial samples (ranging from benign to noisy) we propose an adaptive weighting strategy to selectively emphasize informative adversarial samples thus ensuring efficient utilization of lightweight model capacity. Moreover we propose a dual-branch mechanism exploiting two following insights: (i) complementary dynamics of adversarial paths obtained by targeted and untargeted adversarial learning and (ii) inherent differences between the gradient ascent path from class c_i towards the nearest class boundary and the gradient descent path from a specific class c_j towards the decision region of c_i (i \neq j). Comprehensive experiments demonstrate the effectiveness of our method on lightweight models under various settings.
USE: Universal Segment Embeddings for Open-Vocabulary Image Segmentation
Authors: Xiaoqi Wang; Wenbin He; Xiwei Xuan; Clint Sebastian; Jorge Piazentin Ono; Xin Li; Sima Behpour; Thang Doan; Liang Gou; Han-Wei Shen; Liu Ren
Venue: CVPR 2024 (main)

Abstract: The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recent vision-based foundation models such as the Segment Anything Model (SAM) have shown superior performance in generating class-agnostic image segments. The main challenge in open-vocabulary image segmentation now lies in accurately classifying these segments into text-defined categories. In this paper we introduce the Universal Segment Embedding (USE) framework to address this challenge. This framework is comprised of two key components: 1) a data pipeline designed to efficiently curate a large amount of segment-text pairs at various granularities and 2) a universal segment embedding model that enables precise segment classification into a vast range of text-defined categories. The USE model can not only help open-vocabulary image segmentation but also facilitate other downstream tasks (e.g. querying and ranking). Through comprehensive experimental studies on semantic segmentation and part segmentation benchmarks we demonstrate that the USE framework outperforms state-of-the-art open-vocabulary segmentation methods.
Functional Diffusion
Authors: Biao Zhang; Peter Wonka
Venue: CVPR 2024 (main)

Abstract: We propose functional diffusion a generative diffusion model focused on infinite-dimensional function data samples. In contrast to previous work functional diffusion works on samples that are represented by functions with a continuous domain. Functional diffusion can be seen as an extension of classical diffusion models to an infinite-dimensional domain. Functional diffusion is very versatile as images videos audio 3D shapes deformations etc. can be handled by the same framework with minimal changes. In addition functional diffusion is especially suited for irregular data or data defined in non-standard domains. In our work we derive the necessary foundations for functional diffusion and propose a first implementation based on the transformer architecture. We show generative results on complicated signed distance functions and deformation functions defined on 3D surfaces.
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement
Authors: Zhuorong Li; Daiwei Yu; Lina Wei; Canghong Jin; Yun Zhang; Sixian Chan
Venue: CVPR 2024 (main)

Abstract: Adversarial training (AT) is currently one of the most effective ways to obtain the robustness of deep neural networks against adversarial attacks. However most AT methods suffer from robust overfitting i.e. a significant generalization gap in adversarial robustness between the training and testing curves. In this paper we first identify a connection between robust overfitting and the excessive memorization of noisy labels in AT from a view of gradient norm. As such label noise is mainly caused by a distribution mismatch and improper label assignments we are motivated to propose a label refinement approach for AT. Specifically our Self-Guided Label Refinement first self-refines a more accurate and informative label distribution from over-confident hard labels and then it calibrates the training by dynamically incorporating knowledge from self-distilled models into the current model and thus requiring no external teachers. Empirical results demonstrate that our method can simultaneously boost the standard accuracy and robust performance across multiple benchmark datasets attack types and architectures. In addition we also provide a set of analyses from the perspectives of information theory to dive into our method and suggest the importance of soft labels for robust generalization.
Weakly Supervised Monocular 3D Detection with a Single-View Image
Authors: Xueying Jiang; Sheng Jin; Lewei Lu; Xiaoqin Zhang; Shijian Lu
Venue: CVPR 2024 (main)

Abstract: Monocular 3D detection (M3D) aims for precise 3D object localization from a single-view image which usually involves labor-intensive annotation of 3D detection boxes. Weakly supervised M3D has recently been studied to obviate the 3D annotation process by leveraging many existing 2D annotations but it often requires extra training data such as LiDAR point clouds or multi-view images which greatly degrades its applicability and usability in various applications. We propose SKD-WM3D a weakly supervised monocular 3D detection framework that exploits depth information to achieve M3D with a single-view image exclusively without any 3D annotations or other training data. One key design in SKD-WM3D is a self-knowledge distillation framework which transforms image features into 3D-like representations by fusing depth information and effectively mitigates the inherent depth ambiguity in monocular scenarios with little computational overhead in inference. In addition we design an uncertainty-aware distillation loss and a gradient-targeted transfer modulation strategy which facilitate knowledge acquisition and knowledge transfer respectively. Extensive experiments show that SKD-WM3D surpasses the state-of-the-art clearly and is even on par with many fully supervised methods.