Reviewerly/exHarmony huggingface dataset:
DartBlur: Privacy Preservation with Detection Artifact Suppression

Nowadays, privacy issue has become a top priority when training AI algorithms. Machine learning algorithms are expected to benefit our daily life, while personal information must also be carefully protected from exposure. Facial information is particularly sensitive in this regard. Multiple datasets containing facial information have been taken offline, and the community is actively seeking solutions to remedy the privacy issues. Existing methods for privacy preservation can be divided into blur-based and face replacement-based methods. Owing to the advantages of review convenience and good accessibility, blur-based based methods have become a dominant choice in practice. However, blur-based methods would inevitably introduce training artifacts harmful to the performance of downstream tasks. In this paper, we propose a novel De-artifact Blurring (DartBlur) privacy-preserving method, which capitalizes on a DNN architecture to generate blurred faces. DartBlur can effectively hide facial privacy information while detection artifacts are simultaneously suppressed. We have designed four training objectives that particularly aim to improve review convenience and maximize detection artifact suppression. We associate the algorithm with an adversarial training strategy with a second-order optimization pipeline. Experimental results demonstrate that DartBlur outperforms the existing face-replacement method from both perspectives of review convenience and accessibility, and also shows an exclusive advantage in suppressing the training artifact compared to traditional blur-based methods. Our implementation is available at https://github.com/JaNg2333/DartBlur.
Weak-shot Object Detection through Mutual Knowledge Transfer

Weak-shot Object Detection methods exploit a fully-annotated source dataset to facilitate the detection performance on the target dataset which only contains image-level labels for novel categories. To bridge the gap between these two datasets, we aim to transfer the object knowledge between the source (S) and target (T) datasets in a bi-directional manner. We propose a novel Knowledge Transfer (KT) loss which simultaneously distills the knowledge of objectness and class entropy from a proposal generator trained on the S dataset to optimize a multiple instance learning module on the T dataset. By jointly optimizing the classification loss and the proposed KT loss, the multiple instance learning module effectively learns to classify object proposals into novel categories in the T dataset with the transferred knowledge from base categories in the S dataset. Noticing the predicted boxes on the T dataset can be regarded as an extension for the original annotations on the S dataset to refine the proposal generator in return, we further propose a novel Consistency Filtering (CF) method to reliably remove inaccurate pseudo labels by evaluating the stability of the multiple instance learning module upon noise injections. Via mutually transferring knowledge between the S and T datasets in an iterative manner, the detection performance on the target dataset is significantly improved. Extensive experiments on public benchmarks validate that the proposed method performs favourably against the state-of-the-art methods without increasing the model parameters or inference computational complexity.
SVGformer: Representation Learning for Continuous Vector Graphics using Transformers

Advances in representation learning have led to great success in understanding and generating data in various domains. However, in modeling vector graphics data, the pure data-driven approach often yields unsatisfactory results in downstream tasks as existing deep learning methods often require the quantization of SVG parameters and cannot exploit the geometric properties explicitly. In this paper, we propose a transformer-based representation learning model (SVG-former) that directly operates on continuous input values and manipulates the geometric information of SVG to encode outline details and long-distance dependencies. SVGfomer can be used for various downstream tasks: reconstruction, classification, interpolation, retrieval, etc. We have conducted extensive experiments on vector font and icon datasets to show that our model can capture high-quality representation information and outperform the previous state-of-the-art on downstream tasks significantly.
Blemish-aware and Progressive Face Retouching with Limited Paired Data

Face retouching aims to remove facial blemishes, while at the same time maintaining the textual details of a given input image. The main challenge lies in distinguishing blemishes from the facial characteristics, such as moles. Training an image-to-image translation network with pixel-wise supervision suffers from the problem of expensive paired training data, since professional retouching needs specialized experience and is time-consuming. In this paper, we propose a Blemish-aware and Progressive Face Retouching model, which is referred to as BPFRe. Our framework can be partitioned into two manageable stages to perform progressive blemish removal. Specifically, an encoder-decoder-based module learns to coarsely remove the blemishes at the first stage, and the resulting intermediate features are injected into a generator to enrich local detail at the second stage. We find that explicitly suppressing the blemishes can contribute to an effective collaboration among the components. Toward this end, we incorporate an attention module, which learns to infer a blemish-aware map and further determine the corresponding weights, which are then used to refine the intermediate features transferred from the encoder to the decoder, and from the decoder to the generator. Therefore, BPFRe is able to deliver significant performance gains on a wide range of face retouching tasks. It is worth noting that we reduce the dependence of BPFRe on paired training samples by imposing effective regularization on unpaired ones.
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization

Recent research in robust optimization has shown an overfitting-like phenomenon in which models trained against adversarial attacks exhibit higher robustness on the training set compared to the test set. Although previous work provided theoretical explanations for this phenomenon using a robust PAC-Bayesian bound over the adversarial test error, related algorithmic derivations are at best only loosely connected to this bound, which implies that there is still a gap between their empirical success and our understanding of adversarial robustness theory. To close this gap, in this paper we consider a different form of the robust PAC-Bayesian bound and directly minimize it with respect to the model posterior. The derivation of the optimal solution connects PAC-Bayesian learning to the geometry of the robust loss surface through a Trace of Hessian (TrH) regularizer that measures the surface flatness. In practice, we restrict the TrH regularizer to the top layer only, which results in an analytical solution to the bound whose computational cost does not depend on the network depth. Finally, we evaluate our TrH regularization approach over CIFAR-10/100 and ImageNet using Vision Transformers (ViT) and compare against baseline adversarial robustness algorithms. Experimental results show that TrH regularization leads to improved ViT robustness that either matches or surpasses previous state-of-the-art approaches while at the same time requires less memory and computational cost.
Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage's autoregressive model's ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQVAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. Code will be released at https://github.com/CrossmodalGroup/MaskedVectorQuantization.
VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs

We introduce VIVE3D, a novel approach that extends the capabilities of image-based 3D GANs to video editing and is able to represent the input video in an identity-preserving and temporally consistent way. We propose two new building blocks. First, we introduce a novel GAN inversion technique specifically tailored to 3D GANs by jointly embedding multiple frames and optimizing for the camera parameters. Second, besides traditional semantic face edits (e.g. for age and expression), we are the first to demonstrate edits that show novel views of the head enabled by the inherent prop-erties of 3D GANs and our optical flow-guided compositing technique to combine the head with the background video. Our experiments demonstrate that VIVE3D generates high-fidelity face edits at consistent quality from a range of camera viewpoints which are composited with the original video in a temporally and spatially consistent manner.
Towards Transferable Targeted Adversarial Examples

Transferability of adversarial examples is critical for black-box deep learning model attacks. While most existing studies focus on enhancing the transferability of untargeted adversarial attacks, few of them studied how to generate transferable targeted adversarial examples that can mislead models into predicting a specific class. Moreover, existing transferable targeted adversarial attacks usually fail to sufficiently characterize the target class distribution, thus suffering from limited transferability. In this paper, we propose the Transferable Targeted Adversarial Attack (TTAA), which can capture the distribution information of the target class from both label-wise and feature-wise perspectives, to generate highly transferable targeted adversarial examples. To this end, we design a generative adversarial training framework consisting of a generator to produce targeted adversarial examples, and feature-label dual discriminators to distinguish the generated adversarial examples from the target class images. Specifically, we design the label discriminator to guide the adversarial examples to learn label-related distribution information about the target class. Meanwhile, we design a feature discriminator, which extracts the feature-wise information with strong cross-model consistency, to enable the adversarial examples to learn the transferable distribution information. Furthermore, we introduce the random perturbation dropping to further enhance the transferability by augmenting the diversity of adversarial examples used in the training process. Experiments demonstrate that our method achieves excellent performance on the transferability of targeted adversarial examples. The targeted fooling rate reaches 95.13% when transferred from VGG-19 to DenseNet-121, which significantly outperforms the state-of-the-art methods.
Leveraging per Image-Token Consistency for Vision-Language Pre-training

Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Underutilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://github.com/gyhdog99/epic
3D Video Loops from Asynchronous Input

Looping videos are short video clips that can be looped endlessly without visible seams or artifacts. They provide a very attractive way to capture the dynamism of natural scenes. Existing methods have been mostly limited to 2D representations. In this paper, we take a step forward and propose a practical solution that enables an immersive experience on dynamic 3D looping scenes. The key challenge is to consider the per-view looping conditions from asynchronous input while maintaining view consistency for the 3D representation. We propose a novel sparse 3D video representation, namely Multi-Tile Video (MTV), which not only provides a view-consistent prior, but also greatly reduces memory usage, making the optimization of a 4D volume tractable. Then, we introduce a two-stage pipeline to construct the 3D looping MTV from completely asynchronous multi-view videos with no time overlap. A novel looping loss based on video temporal retargeting algorithms is adopted during the optimization to loop the 3D scene. Experiments of our framework have shown promise in successfully generating and rendering photorealistic 3D looping videos in real time even on mobile devices. The code, dataset, and live demos are available in https://limacv.github.io/VideoLoop3D_web/.
Non-Contrastive Learning Meets Language-Image Pre-Training

Contrastive language-image pre-training (CLIP) serves as a de-facto standard to align images and texts. Nonetheless, the loose correlation between images and texts of webcrawled data renders the contrastive objective data inefficient and craving for a large training batch size. In this work, we explore the validity of non-contrastive language-image pre-training (nCLIP), and study whether nice properties exhibited in visual self-supervised models can emerge. We empirically observe that the non-contrastive objective benefits representation learning while sufficiently underperforming under zero-shot recognition. Based on the above study, we further introduce xCLIP, a multi-tasking framework combining CLIP and nCLIP, and show that nCLIP aids CLIP in enhancing feature semantics. The synergy between two objectives lets xCLIP enjoy the best of both worlds: superior performance in both zero-shot transfer and representation learning. Systematic evaluation is conducted spanning a wide variety of downstream tasks including zero-shot classification, out-of-domain classification, retrieval, visual representation learning, and textual representation learning, showcasing a consistent performance gain and validating the effectiveness of xCLIP. The code and pre-trained models will be publicly available at https://github.com/shallowtoil/xclip.
SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation Network

In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject. However, manually selecting and removing these small and dense distracting regions can be a laborious and time-consuming task. In this paper, we propose an interactive distractor selection method that is optimized to achieve the task with just a single click. Our method surpasses the precision and recall achieved by the traditional method of running panoptic segmentation and then selecting the segments containing the clicks. We also showcase how a transformer-based module can be used to identify more distracting regions similar to the user's click position. Our experiments demonstrate that the model can effectively and accurately segment unknown distracting objects interactively and in groups. By significantly simplifying the photo cleaning and retouching process, our proposed model provides inspiration for exploring rare object segmentation and group selection with a single click. More information can be found at https://github.com/hmchuong/SimpSON.
Latency Matters: Real-Time Action Forecasting Transformer

We present RAFTformer, a realtime action forecasting transformer for latency-aware realworld action forecasting. RAFTformer is a two-stage fully transformer based architecture comprising of a video transformer backbone that operates on high resolution, short-range clips, and a head transformer encoder that temporally aggregates information from multiple short-range clips to span a long-term horizon. Additionally, we propose a novel self-supervised shuffled causal masking scheme as a model level augmentation to improve forecasting fidelity. Finally, we also propose a novel realtime evaluation setting for action fore-casting that directly couples model inference latency to overall forecasting performance and brings forth a hith-erto overlooked trade-off between latency and action fore-casting performance. Our parsimonious network design fa-cilitates RAFTformer inference latency to be 9x smaller than prior works at the same forecasting accuracy. Owing to its two-staged design, RAFTformer uses 94% less training compute and 90% lesser training parameters to outperform prior state-of-the-art baselines by 4.9 points on EGTEA Gaze+ and by 1.4 points on EPIC-Kitchens-100 validation set, as measured by Top-5 recall (T5R) in the offline setting. In the realtime setting, RAFTformer outperforms prior works by an even greater margin of upto 4.4 T5R points on the EPIC-Kitchens-100 dataset. Project Webpage: https://karttikeya.github.io/publication/RAFTformer/.
DyNCA: Real-Time Dynamic Texture Synthesis Using Neural Cellular Automata

Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synthesis process. We propose Dynamic Neural Cellular Automata (DyNCA), a framework for real-time and controllable dynamic texture synthesis. Our method is built upon the recently introduced NCA models and can synthesize infinitely long and arbitrary-sized realistic video textures in real time. We quantitatively and qualitatively evaluate our model and show that our synthesized videos appear more realistic than the existing results. We improve the SOTA DyTS performance by 2 ~ 4 orders of magnitude. Moreover, our model offers several real-time video controls including motion speed, motion direction, and an editing brush tool. We exhibit our trained models in an online interactive demo that runs on local hardware and is accessible on personal computers and smartphones.
AdamsFormer for Spatial Action Localization in the Future

Predicting future action locations is vital for applications like human-robot collaboration. While some computer vision tasks have made progress in predicting human actions, accurately localizing these actions in future frames remains an area with room for improvement. We introduce a new task called spatial action localization in the future (SALF), which aims to predict action locations in both observed and future frames. SALF is challenging because it requires understanding the underlying physics of video observations to predict future action locations accurately. To address SALF, we use the concept of NeuralODE, which models the latent dynamics of sequential data by solving ordinary differential equations (ODE) with neural networks. We propose a novel architecture, AdamsFormer, which extends observed frame features to future time horizons by modeling continuous temporal dynamics through ODE solving. Specifically, we employ the Adams method, a multi-step approach that efficiently uses information from previous steps without discarding it. Our extensive experiments on UCF101-24 and JHMDB-21 datasets demonstrate that our proposed model outperforms existing long-range temporal modeling methods by a significant margin in terms of frame-mAP.
K3DN: Disparity-Aware Kernel Estimation for Dual-Pixel Defocus Deblurring

The dual-pixel (DP) sensor captures a two-view image pair in a single snapshot by splitting each pixel in half. The disparity occurs in defocus blurred regions between the two views of the DP pair, while the in-focus sharp regions have zero disparity. This motivates us to propose a K3DN framework for DP pair deblurring, and it has three modules: i) a disparity-aware deblur module. It estimates a disparity feature map, which is used to query a trainable kernel set to estimate a blur kernel that best describes the spatially-varying blur. The kernel is constrained to be symmetrical per the DP formulation. A simple Fourier transform is performed for deblurring that follows the blur model; ii) a reblurring regularization module. It reuses the blur kernel, performs a simple convolution for reblurring, and regularizes the estimated kernel and disparity feature unsupervisedly, in the training stage; iii) a sharp region preservation module. It identifies in-focus regions that correspond to areas with zero disparity between DP images, aims to avoid the introduction of noises during the deblurring process, and improves image restoration performance. Experiments on four standard DP datasets show that the proposed K3DN outperforms state-of-the-art methods, with fewer parameters and flops at the same time.
Distilling Focal Knowledge from Imperfect Expert for 3D Object Detection

Multi-camera 3D object detection blossoms in recent years and most of state-of-the-art methods are built up on the bird’ s-eye- view (BEV) representations. Albeit remarkable performance, these works suffer from low efficiency. Typically, knowledge distillation can be used for model compression. However, due to unclear 3D geometry reasoning, expert features usually contain some noisy and confusing areas. In this work, we investigate on how to distill the knowledge from an imperfect expert. We propose FD3D, a Focal Distiller for 3D object detection. Specifically, a set of queries are leveraged to locate the instance-level areas for masked feature generation, to intensify feature representation ability in these areas. Moreover, these queries search out the representative fine-grained positions for refined distillation. We verify the effectiveness of our method by applying it to two popular detection models, BEVFormer and DETR3D. The results demonstrate that our method achieves improvements of 4.07 and 3.17 points respectively in terms of NDS metric on nuScenes benchmark. Code is hosted at https://github.com/OpenPerceptionX/BEVPerception-Survey-Recipe.