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Yuang Ai, Xiaoqiang Zhou, Huaibo Huang, Lei Zhang, Ran He

Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (SR) by accessing both the source and target data. Considering privacy policies or transmission restrictions of source data in practical scenarios we propose a SOurce-free Domain Adaptation framework for image SR (SODA-SR) to address this issue i.e. adapt a source-trained model to a target domain with only unlabeled target data. SODA-SR leverages the source-trained model to generate refined pseudo-labels for teacher-student learning. To better utilize pseudo-labels we propose a novel wavelet-based augmentation method named Wavelet Augmentation Transformer (WAT) which can be flexibly incorporated with existing networks to implicitly produce useful augmented data. WAT learns low-frequency information of varying levels across diverse samples which is aggregated efficiently via deformable attention. Furthermore an uncertainty-aware self-training mechanism is proposed to improve the accuracy of pseudo-labels with inaccurate predictions being rectified by uncertainty estimation. To acquire better SR results and avoid overfitting pseudo-labels several regularization losses are proposed to constrain target LR and SR images in the frequency domain. Experiments show that without accessing source data SODA-SR outperforms state-of-the-art UDA methods in both synthetic->real and real->real adaptation settings and is not constrained by specific network architectures.

Ji Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, Song Han

Visual language models (VLMs) rapidly progressed with the recent success of large language models. There have been growing efforts on visual instruction tuning to extend the LLM with visual inputs but lacks an in-depth study of the visual language pre-training process where the model learns to perform joint modeling on both modalities. In this work we examine the design options for VLM pre-training by augmenting LLM towards VLM through step-by-step con- trollable comparisons. We introduce three main findings: (1) freezing LLMs during pre-training can achieve decent zero-shot performance but lack in-context learning capabil- ity which requires unfreezing the LLM; (2) interleaved pre- training data is beneficial whereas image-text pairs alone are not optimal; (3) re-blending text-only instruction data to image-text data during instruction fine-tuning not only remedies the degradation of text-only tasks but also boosts VLM task accuracy. With an enhanced pre-training recipe we build VILA a Visual Language model family that consis- tently outperforms the state-of-the-art models e.g. LLaVA- 1.5 across main benchmarks without bells and whistles. Multi-modal pre-training also helps unveil appealing prop- erties of VILA including multi-image reasoning enhanced in-context learning and better world knowledge. VILA is also deployable on Jetson Orin for on-device VLM.

Rishubh Parihar, Abhijnya Bhat, Abhipsa Basu, Saswat Mallick, Jogendra Nath Kundu, R. Venkatesh Babu

Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. These models are widely used for data augmentation and creative applications. However DMs reflect the biases present in the training datasets. This is especially concerning in the context of faces where the DM prefers one demographic subgroup vs others (eg. female vs male). In this work we present a method for debiasing DMs without relying on additional reference data or model retraining. Specifically we propose Distribution Guidance which enforces the generated images to follow the prescribed attribute distribution. To realize this we build on the key insight that the latent features of denoising UNet hold rich demographic semantics and the same can be leveraged to guide debiased generation. We train Attribute Distribution Predictor (ADP) - a small mlp that maps the latent features to the distribution of attributes. ADP is trained with pseudo labels generated from existing attribute classifiers. The proposed Distribution Guidance with ADP enables us to do fair generation. Our method reduces bias across single/multiple attributes and outperforms the baseline by a significant margin for unconditional and text-conditional diffusion models. Further we present a downstream task of training a fair attribute classifier by augmenting the training set with our generated data.

Yingbo Zhou, Yutong Ye, Pengyu Zhang, Xian Wei, Mingsong Chen

Few-shot image generation as an important yet challenging visual task still suffers from the trade-off between generation quality and diversity. According to the principle of feature-matching learning existing fusion-based methods usually fuse different features by using similarity measurements or attention mechanisms which may match features inaccurately and lead to artifacts in the texture and structure of generated images. In this paper we propose an exact Fusion via Feature Distribution matching Generative Adversarial Network (F2DGAN) for few-shot image generation. The rationale behind this is that feature distribution matching is much more reliable than feature matching to explore the statistical characters in image feature space for limited real-world data. To model feature distributions from only a few examples for feature fusion we design a novel variational feature distribution matching fusion module to perform exact fusion by empirical cumulative distribution functions. Specifically we employ a variational autoencoder to transform deep image features into distributions and fuse different features exactly by applying histogram matching. Additionally we formulate two effective losses to guide the matching process for better fitting our fusion strategy. Extensive experiments compared with state-of-the-art methods on three public datasets demonstrate the superiority of F2DGAN for few-shot image generation in terms of generation quality and diversity and the effectiveness of data augmentation in downstream classification tasks.

Qucheng Peng, Ce Zheng, Chen Chen

3D human pose data collected in controlled laboratory settings present challenges for pose estimators that generalize across diverse scenarios. To address this domain generalization is employed. Current methodologies in domain generalization for 3D human pose estimation typically utilize adversarial training to generate synthetic poses for training. Nonetheless these approaches exhibit several limitations. First the lack of prior information about the target domain complicates the application of suitable augmentation through a single pose augmentor affecting generalization on target domains. Moreover adversarial training's discriminator tends to enforce similarity between source and synthesized poses impeding the exploration of out-of-source distributions. Furthermore the pose estimator's optimization is not exposed to domain shifts limiting its overall generalization ability. To address these limitations we propose a novel framework featuring two pose augmentors: the weak and the strong augmentors. Our framework employs differential strategies for generation and discrimination processes facilitating the preservation of knowledge related to source poses and the exploration of out-of-source distributions without prior information about target poses. Besides we leverage meta-optimization to simulate domain shifts in the optimization process of the pose estimator thereby improving its generalization ability. Our proposed approach significantly outperforms existing methods as demonstrated through comprehensive experiments on various benchmark datasets.

Aysim Toker, Marvin Eisenberger, Daniel Cremers, Laura Leal-Taixé

In recent years semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet a prevalent limitation of supervised learning techniques remains the need for extensive manual annotations by experts. In this work we explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. The main idea is to learn the joint data manifold of images and labels leveraging recent advancements in denoising diffusion probabilistic models. To the best of our knowledge we are the first to generate both images and corresponding masks for satellite segmentation. We find that the obtained pairs not only display high quality in fine-scale features but also ensure a wide sampling diversity. Both aspects are crucial for earth observation data where semantic classes can vary severely in scale and occurrence frequency. We employ the novel data instances for downstream segmentation as a form of data augmentation. In our experiments we provide comparisons to prior works based on discriminative diffusion models or GANs. We demonstrate that integrating generated samples yields significant quantitative improvements for satellite semantic segmentation -- both compared to baselines and when training only on the original data.

Jiasen Lu, Christopher Clark, Sangho Lee, Zichen Zhang, Savya Khosla, Ryan Marten, Derek Hoiem, Aniruddha Kembhavi

We present Unified-IO 2 a multimodal and multi-skill unified model capable of following novel instructions. Unified-IO 2 can use text images audio and/or videos as input and can generate text image or audio outputs which is accomplished in a unified way by tokenizing these different inputs and outputs into a shared semantic space that can then be processed by a single encoder-decoder transformer model. Unified-IO 2 is trained from scratch on a custom-built multimodal pre-training corpus and then learns an expansive set of skills through fine-tuning on over 120 datasets including datasets for segmentation object detection image editing audio localization video tracking embodied AI and 3D detection. To facilitate instruction-following we add prompts and other data augmentations to these tasks to allow Unified-IO 2 to generalize these skills to new tasks zero-shot. Unified-IO 2 is the first model to be trained on such a diverse and wide-reaching set of skills and unify three separate generation capabilities. Unified-IO 2 achieves state-of-the-art performance on the multi-task GRIT benchmark and achieves strong results on 30 diverse datasets including SEED-Bench image and video understanding TIFA image generation VQA 2.0 ScienceQA VIMA robotic manipulation VGG-Sound and Kinetics-Sounds and can perform unseen tasks and generate free-form responses. We release our model and code to facilitate future work.

Jiequan Cui, Beier Zhu, Xin Wen, Xiaojuan Qi, Bei Yu, Hanwang Zhang

In this paper we present an empirical study on image recognition unfairness i.e. extreme class accuracy disparity on balanced data like ImageNet. We demonstrate that classes are not equal and unfairness is prevalent for image classification models across various datasets network architectures and model capacities. Moreover several intriguing properties of fairness are identified. First the unfairness lies in problematic representation rather than classifier bias distinguished from long-tailed recognition. Second with the proposed concept of Model Prediction Bias we investigate the origins of problematic representation during training optimization. Our findings reveal that models tend to exhibit greater prediction biases for classes that are more challenging to recognize. It means that more other classes will be confused with harder classes. Then the False Positives (FPs) will dominate the learning in optimization thus leading to their poor accuracy. Further we conclude that data augmentation and representation learning algorithms improve overall performance by promoting fairness to some degree in image classification.

Kumara Kahatapitiya, Anurag Arnab, Arsha Nagrani, Michael S. Ryoo

Vision-Language models (VLMs) have excelled in the image-domain--- especially in zero-shot settings--- thanks to the availability of vast pretraining data (i.e. paired image-text samples). However for videos such paired data is not as abundant. Therefore video-VLMs are usually designed by adapting pretrained image-VLMs to the video-domain instead of training from scratch. All such recipes rely on augmenting visual embeddings with temporal information (i.e. image --> video) often keeping text embeddings unchanged or even being discarded. In this paper we argue the contrary that better video-VLMs can be designed by focusing more on augmenting text rather than visual information. More specifically we introduce Video-conditioned Text Representations (VicTR): a form of text embeddings optimized w.r.t. visual embeddings creating a more-flexible contrastive latent space. Our model can further make use of freely-available semantic information in the form of visually-grounded auxiliary text (e.g. object or scene information). We evaluate our model on few-shot zero-shot (HMDB-51 UCF-101) short-form (Kinetics-400) and long-form (Charades) activity recognition benchmarks showing strong performance among video-VLMs.

Jia-Hao Wu, Fu-Jen Tsai, Yan-Tsung Peng, Chung-Chi Tsai, Chia-Wen Lin, Yen-Yu Lin

Image deblurring aims to remove undesired blurs from an image captured in a dynamic scene. Much research has been dedicated to improving deblurring performance through model architectural designs. However there is little work on data augmentation for image deblurring. Since continuous motion causes blurred artifacts during image exposure we aspire to develop a groundbreaking blur augmentation method to generate diverse blurred images by simulating motion trajectories in a continuous space. This paper proposes Implicit Diffusion-based reBLurring AUgmentation (ID-Blau) utilizing a sharp image paired with a controllable blur condition map to produce a corresponding blurred image. We parameterize the blur patterns of a blurred image with their orientations and magnitudes as a pixel-wise blur condition map to simulate motion trajectories and implicitly represent them in a continuous space. By sampling diverse blur conditions ID-Blau can generate various blurred images unseen in the training set. Experimental results demonstrate that ID-Blau can produce realistic blurred images for training and thus significantly improve performance for state-of-the-art deblurring models. The source code is available at https://github.com/plusgood-steven/ID-Blau.

Zhaowen Li, Yousong Zhu, Zhiyang Chen, Zongxin Gao, Rui Zhao, Chaoyang Zhao, Ming Tang, Jinqiao Wang

Current self-supervised methods can primarily be categorized into contrastive learning and masked image modeling. Extensive studies have demonstrated that combining these two approaches can achieve state-of-the-art performance. However these methods essentially reinforce the global consistency of contrastive learning without taking into account the conflicts between these two approaches which hinders their generalizability to arbitrary scenarios. In this paper we theoretically prove that MAE serves as a patch-level contrastive learning where each patch within an image is considered as a distinct category. This presents a significant conflict with global-level contrastive learning which treats all patches in an image as an identical category. To address this conflict this work abandons the non-generalizable global-level constraints and proposes explicit patch-level contrastive learning as a solution. Specifically this work employs the encoder of MAE to generate dual-branch features which then perform patch-level learning through a decoder. In contrast to global-level data augmentation in contrastive learning our approach leverages patch-level feature augmentation to mitigate interference from global-level learning. Consequently our approach can learn heterogeneous representations from a single image while avoiding the conflicts encountered by previous methods. Massive experiments affirm the potential of our method for learning from arbitrary scenarios.

Jiaxuan Li, Duc Minh Vo, Akihiro Sugimoto, Hideki Nakayama

Large language models (LLMs)-based image captioning has the capability of describing objects not explicitly observed in training data; yet novel objects occur frequently necessitating the requirement of sustaining up-to-date object knowledge for open-world comprehension. Instead of relying on large amounts of data and/or scaling up network parameters we introduce a highly effective retrieval-augmented image captioning method that prompts LLMs with object names retrieved from External Visual--name memory (EVCap). We build ever-changing object knowledge memory using objects' visuals and names enabling us to (i) update the memory at a minimal cost and (ii) effortlessly augment LLMs with retrieved object names by utilizing a lightweight and fast-to-train model. Our model which was trained only on the COCO dataset can adapt to out-of-domain without requiring additional fine-tuning or re-training. Our experiments conducted on benchmarks and synthetic commonsense-violating data show that EVCap with only 3.97M trainable parameters exhibits superior performance compared to other methods based on frozen pre-trained LLMs. Its performance is also competitive to specialist SOTAs that require extensive training.

Fengda Zhang, Qianpei He, Kun Kuang, Jiashuo Liu, Long Chen, Chao Wu, Jun Xiao, Hanwang Zhang

Facial Attribute Classification (FAC) holds substantial promise in widespread applications. However FAC models trained by traditional methodologies can be unfair by exhibiting accuracy inconsistencies across varied data subpopulations. This unfairness is largely attributed to bias in data where some spurious attributes (e.g. Male) statistically correlate with the target attribute (e.g. Smiling). Most of existing fairness-aware methods rely on the labels of spurious attributes which may be unavailable in practice. This work proposes a novel generation-based two-stage framework to train a fair FAC model on biased data without additional annotation. Initially we identify the potential spurious attributes based on generative models. Notably it enhances interpretability by explicitly showing the spurious attributes in image space. Following this for each image we first edit the spurious attributes with a random degree sampled from a uniform distribution while keeping target attribute unchanged. Then we train a fair FAC model by fostering model invariance to these augmentation. Extensive experiments on three common datasets demonstrate the effectiveness of our method in promoting fairness in FAC without compromising accuracy. Codes are in https://github.com/heqianpei/DiGA.

Lanyun Zhu, Tianrun Chen, Deyi Ji, Jieping Ye, Jun Liu

This paper proposes LLaFS the first attempt to leverage large language models (LLMs) in few-shot segmentation. In contrast to the conventional few-shot segmentation methods that only rely on the limited and biased information from the annotated support images LLaFS leverages the vast prior knowledge gained by LLM as an effective supplement and directly uses the LLM to segment images in a few-shot manner. To enable the text-based LLM to handle image-related tasks we carefully design an input instruction that allows the LLM to produce segmentation results represented as polygons and propose a region-attribute table to simulate the human visual mechanism and provide multi-modal guidance. We also synthesize pseudo samples and use curriculum learning for pretraining to augment data and achieve better optimization. LLaFS achieves state-of-the-art results on multiple datasets showing the potential of using LLMs for few-shot computer vision tasks.

João Carreira, Michael King, Viorica Patraucean, Dilara Gokay, Catalin Ionescu, Yi Yang, Daniel Zoran, Joseph Heyward, Carl Doersch, Yusuf Aytar 等

We introduce a framework for online learning from a single continuous video stream - the way people and animals learn without mini-batches data augmentation or shuffling. This poses great challenges given the high correlation between consecutive video frames and there is very little prior work on it. Our framework allows us to do a first deep dive into the topic and includes a collection of streams and tasks composed from two existing video datasets plus methodology for performance evaluation that considers both adaptation and generalization. We employ pixel-to-pixel modelling as a practical and flexible way to switch between pre-training and single-stream evaluation as well as between arbitrary tasks without ever requiring changes to models and always using the same pixel loss. Equipped with this framework we obtained large single-stream learning gains from pre-training with a novel family of future prediction tasks found that momentum hurts and that the pace of weight updates matters. The combination of these insights leads to matching the performance of IID learning with batch size 1 when using the same architecture and without costly replay buffers.

Yu Mitsuzumi, Akisato Kimura, Hisashi Kashima

Source-free Domain Adaptation (SFDA) is an emerging and challenging research area that addresses the problem of unsupervised domain adaptation (UDA) without source data. Though numerous successful methods have been proposed for SFDA a theoretical understanding of why these methods work well is still absent. In this paper we shed light on the theoretical perspective of existing SFDA methods. Specifically we find that SFDA loss functions comprising discriminability and diversity losses work in the same way as the training objective in the theory of self-training based on the expansion assumption which shows the existence of the target error bound. This finding brings two novel insights that enable us to build an improved SFDA method comprising 1) Model Training with Auto-Adjusting Diversity Constraint and 2) Augmentation Training with Teacher-Student Framework yielding a better recognition performance. Extensive experiments on three benchmark datasets demonstrate the validity of the theoretical analysis and our method.

Chih-Hui Ho, Kuan-Chuan Peng, Nuno Vasconcelos

Anomaly detection (AD) aims to identify defective images and localize their defects (if any). Ideally AD models should be able to detect defects over many image classes; without relying on hard-coded class names that can be uninformative or inconsistent across datasets; learn without anomaly supervision; and be robust to the long-tailed distributions of real-world applications. To address these challenges we formulate the problem of long-tailed AD by introducing several datasets with different levels of class imbalance and metrics for performance evaluation. We then propose a novel method LTAD to detect defects from multiple and long-tailed classes without relying on dataset class names. LTAD combines AD by reconstruction and semantic AD modules. AD by reconstruction is implemented with a transformer-based reconstruction module. Semantic AD is implemented with a binary classifier which relies on learned pseudo class names and a pretrained foundation model. These modules are learned over two phases. Phase 1 learns the pseudo-class names and a variational autoencoder (VAE) for feature synthesis that augments the training data to combat long-tails. Phase 2 then learns the parameters of the reconstruction and classification modules of LTAD. Extensive experiments using the proposed long-tailed datasets show that LTAD substantially outperforms the state-of-the-art methods for most forms of dataset imbalance. The long-tailed dataset split is available at https://zenodo.org/records/10854201

Jun Wang, Yuzhe Qin, Kaiming Kuang, Yigit Korkmaz, Akhilan Gurumoorthy, Hao Su, Xiaolong Wang

We introduce CyberDemo a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world handling diverse physical and visual conditions. Regardless of its affordability and convenience in data collection CyberDemo outperforms baseline methods in terms of success rates across various tasks and exhibits generalizability with previously unseen objects. For example it can rotate novel tetra-valve and penta-valve despite human demonstrations only involving tri-valves. Our research demonstrates the significant potential of simulated human demonstrations for real world dexterous manipulation tasks. More details can be found at https://cyber-demo.github.io/

Bruce A. Maxwell, Sumegha Singhania, Avnish Patel, Rahul Kumar, Heather Fryling, Sihan Li, Haonan Sun, Ping He, Zewen Li

The design of deep network architectures and training methods in computer vision has been well-explored. However in almost all cases the images have been used as provided with little exploration of pre-processing steps beyond normalization and data augmentation. Virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors; differences and sums correspond to ratios and products. Features in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.

Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari, Mohammad Bagher Soltani, Mohammad Azizmalayeri, Jafar Habibi, Mohammad Sabokrou, Mohammad Hossein Rohban

Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality which can be interpreted as generalization across various distributions of training or test data. More precisely for novelty detection distribution shifts may occur in the training set or the test set. Shifts in the training set refer to cases where we train a novelty detector on a new dataset and expect strong transferability. Conversely distribution shifts in the test set indicate the methods' performance when the trained model encounters a shifted test sample. We experimentally show that existing methods falter in maintaining universality which stems from their rigid inductive biases. Motivated by this we aim for more generalized techniques that have more adaptable inductive biases. In this context we leverage the fact that contrastive learning provides an efficient framework to easily switch and adapt to new inductive biases through the proper choice of augmentations in forming the negative pairs. We propose a novel probabilistic auto-negative pair generation method AutoAugOOD along with contrastive learning to yield a universal novelty detector method. Our experiments demonstrate the superiority of our method under different distribution shifts in various image benchmark datasets. Notably our method emerges universality in the lens of adaptability to different setups of novelty detection including one-class unlabeled multi-class and labeled multi-class settings.