GenAI4ELab/papercli-papers huggingface dataset:
Accept the Modality Gap: An Exploration in the Hyperbolic Space
Authors: Sameera Ramasinghe; Violetta Shevchenko; Gil Avraham; Ajanthan Thalaiyasingam
Venue: CVPR 2024 (main)

Abstract: Recent advancements in machine learning have spotlighted the potential of hyperbolic spaces as they effectively learn hierarchical feature representations. While there has been progress in leveraging hyperbolic spaces in single-modality contexts its exploration in multimodal settings remains under explored. Some recent efforts have sought to transpose Euclidean multimodal learning techniques to hyperbolic spaces by adopting geodesic distance based contrastive losses. However we show both theoretically and empirically that such spatial proximity based contrastive loss significantly disrupts hierarchies in the latent space. To remedy this we advocate that the cross-modal representations should accept the inherent modality gap between text and images and introduce a novel approach to measure cross-modal similarity that does not enforce spatial proximity. Our approach show remarkable capabilities in preserving unimodal hierarchies while aligning the two modalities. Our experiments on a series of downstream tasks demonstrate that better latent structure emerges with our objective function while being superior in text-to-image and image-to-text retrieval tasks.
Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation
Authors: Yuanhong Chen; Yuyuan Liu; Hu Wang; Fengbei Liu; Chong Wang; Helen Frazer; Gustavo Carneiro
Venue: CVPR 2024 (main)

Abstract: Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. The effectiveness of audio-visual learning critically depends on achieving accurate cross-modal alignment between sound and visual objects. Successful audio-visual learning requires two essential components: 1) a challenging dataset with high-quality pixel-level multi-class annotated images associated with audio files and 2) a model that can establish strong links between audio information and its corresponding visual object. However these requirements are only partially addressed by current methods with training sets containing biased audio-visual data and models that generalise poorly beyond this biased training set. In this work we propose a new cost-effective strategy to build challenging and relatively unbiased high-quality audio-visual segmentation benchmarks. We also propose a new informative sample mining method for audio-visual supervised contrastive learning to leverage discriminative contrastive samples to enforce cross-modal understanding. We show empirical results that demonstrate the effectiveness of our benchmark. Furthermore experiments conducted on existing AVS datasets and on our new benchmark show that our method achieves state-of-the-art (SOTA) segmentation accuracy.
Few-Shot Object Detection with Foundation Models
Authors: Guangxing Han; Ser-Nam Lim
Venue: CVPR 2024 (main)

Abstract: Few-shot object detection (FSOD) aims to detect objects with only a few training examples. Visual feature extraction and query-support similarity learning are the two critical components. Existing works are usually developed based on ImageNet pre-trained vision backbones and design sophisticated metric-learning networks for few-shot learning but still have inferior accuracy. In this work we study few-shot object detection using modern foundation models. First vision-only contrastive pre-trained DINOv2 model is used for the vision backbone which shows strong transferable performance without tuning the parameters. Second Large Language Model (LLM) is employed for contextualized few-shot learning with the input of all classes and query image proposals. Language instructions are carefully designed to prompt the LLM to classify each proposal in context. The contextual information include proposal-proposal relations proposal-class relations and class-class relations which can largely promote few-shot learning. We comprehensively evaluate the proposed model (FM-FSOD) in multiple FSOD benchmarks achieving state-of-the-arts performance.
FedMef: Towards Memory-efficient Federated Dynamic Pruning
Authors: Hong Huang; Weiming Zhuang; Chen Chen; Lingjuan Lyu
Venue: CVPR 2024 (main)

Abstract: Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However its application on resource-constrained devices is challenging due to the high demand for computation and memory resources to train deep learning models. Neural network pruning techniques such as dynamic pruning could enhance model efficiency but directly adopting them in FL still poses substantial challenges including post-pruning performance degradation high activation memory usage etc. To address these challenges we propose FedMef a novel and memory-efficient federated dynamic pruning framework. FedMef comprises two key components. First we introduce the budget-aware extrusion that maintains pruning efficiency while preserving post-pruning performance by salvaging crucial information from parameters marked for pruning within a given budget. Second we propose scaled activation pruning to effectively reduce activation memory footprints which is particularly beneficial for deploying FL to memory-limited devices. Extensive experiments demonstrate the effectiveness of our proposed FedMef. In particular it achieves a significant reduction of 28.5% in memory footprint compared to state-of-the-art methods while obtaining superior accuracy.
Seeing the Unseen: Visual Common Sense for Semantic Placement
Authors: Ram Ramrakhya; Aniruddha Kembhavi; Dhruv Batra; Zsolt Kira; Kuo-Hao Zeng; Luca Weihs
Venue: CVPR 2024 (main)

Abstract: Computer vision tasks typically involve describing what is visible in an image (e.g. classification detection segmentation and captioning). We study a visual common sense task that requires understanding 'what is not visible'. Specifically given an image (e.g. of a living room) and a name of an object ("cushion") a vision system is asked to predict semantically-meaningful regions (masks or bounding boxes) in the image where that object could be placed or is likely be placed by humans (e.g. on the sofa). We call this task: Semantic Placement (SP) and believe that such common-sense visual understanding is critical for assitive robots (tidying a house) AR devices (automatically rendering an object in the user's space) and visually-grounded chatbots with common sense. Studying the invisible is hard. Datasets for image description are typically constructed by curating relevant images (e.g. via image search with object names) and asking humans to annotate the contents of the image; neither of those two steps are straightforward for objects not present in the image. We overcome this challenge by operating in the opposite direction: we start with an image of an object in context (which is easy to find online) and remove that object from the image via inpainting. This automated pipeline converts unstructured web data into a paired with/without object dataset. With this proposed data generation pipeline we collect a novel dataset containing  1.3M images across 9 object categories. We then train a SP prediction model called CLIP-UNet on our dataset. The CLIP-UNet outperforms existing VLMs and baselines that combine semantic priors with object detectors generalizes well to real-world and simulated images exhibits semantics-aware reasoning for object placement and enables downstream applications like tidying robots in indoor environments.
Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On
Authors: Xu Yang; Changxing Ding; Zhibin Hong; Junhao Huang; Jin Tao; Xiangmin Xu
Venue: CVPR 2024 (main)

Abstract: Image-based virtual try-on is an increasingly important task for online shopping. It aims to synthesize images of a specific person wearing a specified garment. Diffusion model-based approaches have recently become popular as they are excellent at image synthesis tasks. However these approaches usually employ additional image encoders and rely on the cross-attention mechanism for texture transfer from the garment to the person image which affects the try-on's efficiency and fidelity. To address these issues we propose an Texture-Preserving Diffusion (TPD) model for virtual try-on which enhances the fidelity of the results and introduces no additional image encoders. Accordingly we make contributions from two aspects. First we propose to concatenate the masked person and reference garment images along the spatial dimension and utilize the resulting image as the input for the diffusion model's denoising UNet. This enables the original self-attention layers contained in the diffusion model to achieve efficient and accurate texture transfer. Second we propose a novel diffusion-based method that predicts a precise inpainting mask based on the person and reference garment images further enhancing the reliability of the try-on results. In addition we integrate mask prediction and image synthesis into a single compact model. The experimental results show that our approach can be applied to various try-on tasks e.g. garment-to-person and person-to-person try-ons and significantly outperforms state-of-the-art methods on popular VITON VITON-HD databases. Code is available at https://github.com/Gal4way/TPD.
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
Authors: Zining Chen; Weiqiu Wang; Zhicheng Zhao; Fei Su; Aidong Men; Hongying Meng
Venue: CVPR 2024 (main)

Abstract: Domain Generalization (DG) aims to resolve distribution shifts between source and target domains and current DG methods are default to the setting that data from source and target domains share identical categories. Nevertheless there exists unseen classes from target domains in practical scenarios. To address this issue Open Set Domain Generalization (OSDG) has emerged and several methods have been exclusively proposed. However most existing methods adopt complex architectures with slight improvement compared with DG methods. Recently vision-language models (VLMs) have been introduced in DG following the fine-tuning paradigm but consume huge training overhead with large vision models. Therefore in this paper we innovate to transfer knowledge from VLMs to lightweight vision models and improve the robustness by introducing Perturbation Distillation (PD) from three perspectives including Score Class and Instance (SCI) named SCI-PD. Moreover previous methods are oriented by the benchmarks with identical and fixed splits ignoring the divergence between source domains. These methods are revealed to suffer from sharp performance decay with our proposed new benchmark Hybrid Domain Generalization (HDG) and a novel metric H^ 2 -CV which construct various splits to comprehensively assess the robustness of algorithms. Extensive experiments demonstrate that our method outperforms state-of-the-art algorithms on multiple datasets especially improving the robustness when confronting data scarcity.
SODA: Bottleneck Diffusion Models for Representation Learning
Authors: Drew A. Hudson; Daniel Zoran; Mateusz Malinowski; Andrew K. Lampinen; Andrew Jaegle; James L. McClelland; Loic Matthey; Felix Hill; Alexander Lerchner
Venue: CVPR 2024 (main)

Abstract: We introduce SODA a self-supervised diffusion model designed for representation learning. The model incorporates an image encoder which distills a source view into a compact representation that in turn guides the generation of related novel views. We show that by imposing a tight bottleneck between the encoder and a denoising decoder and leveraging novel view synthesis as a self-supervised objective we can turn diffusion models into strong representation learners capable of capturing visual semantics in an unsupervised manner. To the best of our knowledge SODA is the first diffusion model to succeed at ImageNet linear-probe classification and at the same time it accomplishes reconstruction editing and synthesis tasks across a wide range of datasets. Further investigation reveals the disentangled nature of its emergent latent space that serves as an effective interface to control and manipulate the produced images. All in all we aim to shed light on the exciting and promising potential of diffusion models not only for image generation but also for learning rich and robust representations. See our website at soda-diffusion.github.io.
Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach
Authors: Guoqiang Liang; Kanghao Chen; Hangyu Li; Yunfan Lu; Lin Wang
Venue: CVPR 2024 (main)

Abstract: Event camera has recently received much attention for low-light image enhancement (LIE) thanks to their distinct advantages such as high dynamic range. However current research is prohibitively restricted by the lack of large-scale real-world and spatial-temporally aligned event-image datasets. To this end we propose a real-world (indoor and outdoor) dataset comprising over 30K pairs of images and events under both low and normal illumination conditions. To achieve this we utilize a robotic arm that traces a consistent non-linear trajectory to curate the dataset with spatial alignment precision under 0.03mm. We then introduce a matching alignment strategy rendering 90% of our dataset with errors less than 0.01s. Based on the dataset we propose a novel event-guided LIE approach called EvLight towards robust performance in real-world low-light scenes. Specifically we first design the multi-scale holistic fusion branch to extract holistic structural and textural information from both events and images. To ensure robustness against variations in the regional illumination and noise we then introduce a Signal-to-Noise-Ratio (SNR)-guided regional feature selection to selectively fuse features of images from regions with high SNR and enhance those with low SNR by extracting regional structural information from events. our EvLight significantly surpasses the frame-based methods e.g. Retinexformer by 1.14 dB and 2.62 dB respectively. Code and datasets are available at https://vlislab22.github.io/eg-lowlight/.
Zero-Reference Low-Light Enhancement via Physical Quadruple Priors
Authors: Wenjing Wang; Huan Yang; Jianlong Fu; Jiaying Liu
Venue: CVPR 2024 (main)

Abstract: Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during training and illumination-specific hyper-parameters limiting their ability to handle unseen scenarios. In this paper we propose a new zero-reference low-light enhancement framework trainable solely with normal light images. To accomplish this we devise an illumination-invariant prior inspired by the theory of physical light transfer. This prior serves as the bridge between normal and low-light images. Then we develop a prior-to-image framework trained without low-light data. During testing this framework is able to restore our illumination-invariant prior back to images automatically achieving low-light enhancement. Within this framework we leverage a pretrained generative diffusion model for model ability introduce a bypass decoder to handle detail distortion as well as offer a lightweight version for practicality. Extensive experiments demonstrate our framework's superiority in various scenarios as well as good interpretability robustness and efficiency. Code is available on our project homepage: http://daooshee.github.io/QuadPrior-Website/
LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction
Authors: Bo Zou; Chao Yang; Yu Qiao; Chengbin Quan; Youjian Zhao
Venue: CVPR 2024 (main)

Abstract: Existing methods to fine-tune LLMs like Adapter Prefix-tuning and LoRA which introduce extra modules or additional input sequences to inject new skills or knowledge may compromise the innate abilities of LLMs. In this paper we propose LLaMA-Excitor a lightweight method that stimulates the LLMs' potential to better follow instructions by gradually paying more attention to worthwhile information. Specifically the LLaMA-Excitor does not directly change the intermediate hidden state during the self-attention calculation of the transformer structure. We designed the Excitor block as a bypass module for the similarity score computation in LLMs' self-attention to reconstruct keys and change the importance of values by learnable prompts. LLaMA-Excitor ensures a self-adaptive allocation of additional attention to input instructions thus effectively preserving LLMs' pre-trained knowledge when fine-tuning LLMs on low-quality instruction-following datasets. Furthermore we unify the modeling of multi-modal tuning and language-only tuning extending LLaMA-Excitor to a powerful visual instruction follower without the need for complex multi-modal alignment. Our proposed approach is evaluated in language-only and multi-modal tuning experimental scenarios. Notably LLaMA-Excitor is the only method that maintains basic capabilities while achieving a significant improvement (+6%) on the MMLU benchmark. In the visual instruction tuning we achieve a new state-of-the-art image captioning performance of 157.5 CIDEr on MSCOCO and a comparable performance (88.39%) on ScienceQA to cutting-edge models with more parameters and extensive vision-language pertaining.
NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-Efficient Scene Representation
Authors: Sicheng Li; Hao Li; Yiyi Liao; Lu Yu
Venue: CVPR 2024 (main)

Abstract: The emergence of Neural Radiance Fields (NeRF) has greatly impacted 3D scene modeling and novel-view synthesis. As a kind of visual media for 3D scene representation compression with high rate-distortion performance is an eternal target. Motivated by advances in neural compression and neural field representation we propose NeRFCodec an end-to-end NeRF compression framework that integrates non-linear transform quantization and entropy coding for memory-efficient scene representation. Since training a non-linear transform directly on a large scale of NeRF feature planes is impractical we discover that pre-trained neural 2D image codec can be utilized for compressing the features when adding content-specific parameters. Specifically we reuse neural 2D image codec but modify its encoder and decoder heads while keeping the other parts of the pre-trained decoder frozen. This allows us to train the full pipeline via supervision of rendering loss and entropy loss yielding the rate-distortion balance by updating the content-specific parameters. At test time the bitstreams containing latent code feature decoder head and other side information are transmitted for communication. Experimental results demonstrate our method outperforms existing NeRF compression methods enabling high-quality novel view synthesis with a memory budget of 0.5 MB.
From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration
Authors: Zekun Qian; Ruize Han; Wei Feng; Song Wang
Venue: CVPR 2024 (main)

Abstract: We tackle a new problem of multi-view camera and subject registration in the bird's eye view (BEV) without pre-given camera calibration which promotes the multi-view subject registration problem to a new calibration-free stage. This greatly alleviates the limitation in many practical applications. However this is a very challenging problem since its only input is several RGB images from different first-person views (FPVs) without the BEV image and the calibration of the FPVs while the output is a unified plane aggregated from all views with the positions and orientations of both the subjects and cameras in a BEV. For this purpose we propose an end-to-end framework solving camera and subject registration together by taking advantage of their mutual dependence whose main idea is as below: i) creating a subject view-transform module (VTM) to project each pedestrian from FPV to a virtual BEV ii) deriving a multi-view geometry-based spatial alignment module (SAM) to estimate the relative camera pose in a unified BEV iii) selecting and refining the subject and camera registration results within the unified BEV. We collect a new large-scale synthetic dataset with rich annotations for training and evaluation. Additionally we also collect a real dataset for cross-domain evaluation. The experimental results show the remarkable effectiveness of our method. The code and proposed datasets are available at https://github.com/zekunqian/BEVSee.
Steerers: A Framework for Rotation Equivariant Keypoint Descriptors
Authors: Georg Bökman; Johan Edstedt; Michael Felsberg; Fredrik Kahl
Venue: CVPR 2024 (main)

Abstract: Image keypoint descriptions that are discriminative and matchable over large changes in viewpoint are vital for 3D reconstruction. However descriptions output by learned descriptors are typically not robust to camera rotation. While they can be made more robust by e.g. data aug-mentation this degrades performance on upright images. Another approach is test-time augmentation which incurs a significant increase in runtime. Instead we learn a lin-ear transform in description space that encodes rotations of the input image. We call this linear transform a steerer since it allows us to transform the descriptions as if the im-age was rotated. From representation theory we know all possible steerers for the rotation group. Steerers can be optimized (A) given a fixed descriptor (B) jointly with a de-scriptor or (C) we can optimize a descriptor given a fixed steerer. We perform experiments in these three settings and obtain state-of-the-art results on the rotation invariant im-age matching benchmarks AIMS and Roto-360.
Efficient Dataset Distillation via Minimax Diffusion
Authors: Jianyang Gu; Saeed Vahidian; Vyacheslav Kungurtsev; Haonan Wang; Wei Jiang; Yang You; Yiran Chen
Venue: CVPR 2024 (main)

Abstract: Dataset distillation reduces the storage and computational consumption of training a network by generating a small surrogate dataset that encapsulates rich information of the original large-scale one. However previous distillation methods heavily rely on the sample-wise iterative optimization scheme. As the images-per-class (IPC) setting or image resolution grows larger the necessary computation will demand overwhelming time and resources. In this work we intend to incorporate generative diffusion techniques for computing the surrogate dataset. Observing that key factors for constructing an effective surrogate dataset are representativeness and diversity we design additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models. We present a theoretical model of the process as hierarchical diffusion control demonstrating the flexibility of the diffusion process to target these criteria without jeopardizing the faithfulness of the sample to the desired distribution. The proposed method achieves state-of-the-art validation performance while demanding much less computational resources. Under the 100-IPC setting on ImageWoof our method requires less than one-twentieth the distillation time of previous methods yet yields even better performance. Source code and generated data are available in https://github.com/vimar-gu/MinimaxDiffusion.