Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data. In the computer vision community PEFT has shown effectiveness in image classification but little research has studied its ability for image segmentation. Fine-tuning segmentation models usually require a heavier adjustment of parameters to align the proper projection directions in the parameter space for new scenarios. This raises a challenge to existing PEFT algorithms as they often inject a limited number of individual parameters into each block which prevents substantial adjustment of the projection direction of the parameter space due to the limitation of Hidden Markov Chain along blocks. In this paper we equip PEFT with a cross-block orchestration mechanism to enable the adaptation of the Segment Anything Model (SAM) to various downstream scenarios. We introduce a novel inter-block communication module which integrates a learnable relation matrix to facilitate communication among different coefficient sets of each PEFT block's parameter space. Moreover we propose an intra-block enhancement module which introduces a linear projection head whose weights are generated from a hyper-complex layer further enhancing the impact of the adjustment of projection directions on the entire parameter space. Extensive experiments on diverse benchmarks demonstrate that our proposed approach consistently improves the segmentation performance significantly on novel scenarios with only around 1K additional parameters.
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Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations. In this regard studies on WSSGG have utilized image captions to obtain unlocalized triplets while primarily focusing on grounding the unlocalized triplets over image regions. However they have overlooked the two issues involved in the triplet formation process from the captions: 1) Semantic over-simplification issue arises when extracting triplets from captions where fine-grained predicates in captions are undesirably converted into coarse-grained predicates resulting in a long-tailed predicate distribution and 2) Low-density scene graph issue arises when aligning the triplets in the caption with entity/predicate classes of interest where many triplets are discarded and not used in training leading to insufficient supervision. To tackle the two issues we propose a new approach i.e. Large Language Model for weakly-supervised SGG (LLM4SGG) where we mitigate the two issues by leveraging the LLM's in-depth understanding of language and reasoning ability during the extraction of triplets from captions and alignment of entity/predicate classes with target data. To further engage the LLM in these processes we adopt the idea of Chain-of-Thought and the in-context few-shot learning strategy. To validate the effectiveness of LLM4SGG we conduct extensive experiments on Visual Genome and GQA datasets showing significant improvements in both Recall@K and mean Recall@K compared to the state-of-the-art WSSGG methods. A further appeal is that LLM4SGG is data-efficient enabling effective model training with a small amount of training images.
Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. Different from standard natural image datasets remote sensing data is acquired from various sensor technologies and exhibit diverse range of scale variations as well as modalities. Existing satellite image pre-training methods either ignore the scale information present in the remote sensing imagery or restrict themselves to use only a single type of data modality. In this paper we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities. Our proposed approach named SatMAE++ performs multi-scale pre-training and utilizes convolution based upsampling blocks to reconstruct the image at higher scales making it extensible to include more scales. Compared to existing works the proposed SatMAE++ with multi-scale pre-training is equally effective for both optical as well as multi-spectral imagery. Extensive experiments on six datasets reveal the merits of proposed contributions leading to state-of-the-art performance on all datasets. SatMAE++ achieves mean average precision (mAP) gain of 2.5% for multi-label classification task on BigEarthNet dataset.
We present DiffPortrait3D a conditional diffusion model that is capable of synthesizing 3D-consistent photo-realistic novel views from as few as a single in-the-wild portrait. Specifically given a single RGB input we aim to synthesize plausible but consistent facial details rendered from novel camera views with retained both identity and facial expression. In lieu of time-consuming optimization and fine-tuning our zero-shot method generalizes well to arbitrary face portraits with unposed camera views extreme facial expressions and diverse artistic depictions. At its core we leverage the generative prior of 2D diffusion models pre-trained on large-scale image datasets as our rendering backbone while the denoising is guided with disentangled attentive control of appearance and camera pose. To achieve this we first inject the appearance context from the reference image into the self-attention layers of the frozen UNets. The rendering view is then manipulated with a novel conditional control module that interprets the camera pose by watching a condition image of a crossed subject from the same view. Furthermore we insert a trainable cross-view attention module to enhance view consistency which is further strengthened with a novel 3D-aware noise generation process during inference. We demonstrate state-of-the-art results both qualitatively and quantitatively on our challenging in-the-wild and multi-view benchmarks.
In video super-resolution it is common to use a frame-wise alignment to support the propagation of information over time. The role of alignment is well-studied for low-level enhancement in video but existing works overlook a critical step -- resampling. We show through extensive experiments that for alignment to be effective the resampling should preserve the reference frequency spectrum while minimizing spatial distortions. However most existing works simply use a default choice of bilinear interpolation for resampling even though bilinear interpolation has a smoothing effect and hinders super-resolution. From these observations we propose an implicit resampling-based alignment. The sampling positions are encoded by a sinusoidal positional encoding while the value is estimated with a coordinate network and a window-based cross-attention. We show that bilinear interpolation inherently attenuates high-frequency information while an MLP-based coordinate network can approximate more frequencies. Experiments on synthetic and real-world datasets show that alignment with our proposed implicit resampling enhances the performance of state-of-the-art frameworks with minimal impact on both compute and parameters.
Human hands are highly articulated and versatile at handling objects. Jointly estimating the 3D poses of a hand and the object it manipulates from a monocular camera is challenging due to frequent occlusions. Thus existing methods often rely on intermediate 3D shape representations to increase performance. These representations are typically explicit such as 3D point clouds or meshes and thus provide information in the direct surroundings of the intermediate hand pose estimate. To address this we introduce HOISDF a Signed Distance Field (SDF) guided hand-object pose estimation network which jointly exploits hand and object SDFs to provide a global implicit representation over the complete reconstruction volume. Specifically the role of the SDFs is threefold: equip the visual encoder with implicit shape information help to encode hand-object interactions and guide the hand and object pose regression via SDF-based sampling and by augmenting the feature representations. We show that HOISDF achieves state-of-the-art results on hand-object pose estimation benchmarks (DexYCB and HO3Dv2). Code is available https://github.com/amathislab/HOISDF.
We introduce Posterior Distillation Sampling (PDS) a novel optimization method for parametric image editing based on diffusion models. Existing optimization-based methods which leverage the powerful 2D prior of diffusion models to handle various parametric images have mainly focused on generation. Unlike generation editing requires a balance between conforming to the target attribute and preserving the identity of the source content. Recent 2D image editing methods have achieved this balance by leveraging the stochastic latent encoded in the generative process of diffusion models. To extend the editing capabilities of diffusion models shown in pixel space to parameter space we reformulate the 2D image editing method into an optimization form named PDS. PDS matches the stochastic latents of the source and the target enabling the sampling of targets in diverse parameter spaces that align with a desired attribute while maintaining the source's identity. We demonstrate that this optimization resembles running a generative process with the target attribute but aligning this process with the trajectory of the source's generative process. Extensive editing results in Neural Radiance Fields and Scalable Vector Graphics representations demonstrate that PDS is capable of sampling targets to fulfill the aforementioned balance across various parameter spaces.
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.
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.
From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration
PDF ↗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.
NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-Efficient Scene Representation
PDF ↗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.
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.
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/
Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach
PDF ↗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/.
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.
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
PDF ↗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.
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.
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.
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.
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.