$R^2$-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding

Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries. Most existing VTG models are built upon frame-wise final-layer CLIP features, aided by additional temporal backbones (e.g., SlowFast) with sophisticated temporal reasoning mechanisms. In this work, we claim that CLIP itself already shows great potential for fine-grained spatial-temporal modeling, as each layer offers distinct yet useful information under different granularity levels. Motivated by this, we propose Reversed Recurrent Tuning ($R^2$-Tuning), a parameter- and memory-efficient transfer learning framework for video temporal grounding. Our method learns a lightweight $R^2$ Block containing only 1.5% of the total parameters to perform progressive spatial-temporal modeling. Starting from the last layer of CLIP, $R^2$ Block recurrently aggregates spatial features from earlier layers, then refines temporal correlation conditioning on the given query, resulting in a coarse-to-fine scheme. $R^2$-Tuning achieves state-of-the-art performance across three VTG tasks (i.e., moment retrieval, highlight detection, and video summarization) on six public benchmarks (i.e., QVHighlights, Charades-STA, Ego4D-NLQ, TACoS, YouTube Highlights, and TVSum) even without the additional backbone, demonstrating the significance and effectiveness of the proposed scheme. Our code is available at https://github.com/yeliudev/R2-Tuning.

SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control

Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data for autonomous driving applications and present SubjectDrive, the first model proven to scale generative data production in a way that could continuously improve autonomous driving applications. We investigate the impact of scaling up the quantity of generative data on the performance of downstream perception models and find that enhancing data diversity plays a crucial role in effectively scaling generative data production. Therefore, we have developed a novel model equipped with a subject control mechanism, which allows the generative model to leverage diverse external data sources for producing varied and useful data. Extensive evaluations confirm SubjectDrive's efficacy in generating scalable autonomous driving training data, marking a significant step toward revolutionizing data production methods in this field.

SD-DiT: Unleashing the Power of Self-supervised Discrimination in Diffusion Transformer

Diffusion Transformer (DiT) has emerged as the new trend of generative diffusion models on image generation. In view of extremely slow convergence in typical DiT, recent breakthroughs have been driven by mask strategy that significantly improves the training efficiency of DiT with additional intra-image contextual learning. Despite this progress, mask strategy still suffers from two inherent limitations: (a) training-inference discrepancy and (b) fuzzy relations between mask reconstruction & generative diffusion process, resulting in sub-optimal training of DiT. In this work, we address these limitations by novelly unleashing the self-supervised discrimination knowledge to boost DiT training. Technically, we frame our DiT in a teacher-student manner. The teacher-student discriminative pairs are built on the diffusion noises along the same Probability Flow Ordinary Differential Equation (PF-ODE). Instead of applying mask reconstruction loss over both DiT encoder and decoder, we decouple DiT encoder and decoder to separately tackle discriminative and generative objectives. In particular, by encoding discriminative pairs with student and teacher DiT encoders, a new discriminative loss is designed to encourage the inter-image alignment in the self-supervised embedding space. After that, student samples are fed into student DiT decoder to perform the typical generative diffusion task. Extensive experiments are conducted on ImageNet dataset, and our method achieves a competitive balance between training cost and generative capacity.

Detecting Abrupt Change of Channel Covariance Matrix in IRS-Assisted Communication

The knowledge of channel covariance matrices is crucial to the design of intelligent reflecting surface (IRS) assisted communication. However, channel covariance matrices may change suddenly in practice. This letter focuses on the detection of the above change in IRS-assisted communication. Specifically, we consider the uplink communication system consisting of a single-antenna user (UE), an IRS, and a multi-antenna base station (BS). We first categorize two types of channel covariance matrix changes based on their impact on system design: Type I change, which denotes the change in the BS receive covariance matrix, and Type II change, which denotes the change in the IRS transmit/receive covariance matrix. Secondly, a powerful method is proposed to detect whether a Type I change occurs, a Type II change occurs, or no change occurs. The effectiveness of our proposed scheme is verified by numerical results.

Bridging the Gap: Fine-to-Coarse Sketch Interpolation Network for High-Quality Animation Sketch Inbetweening

The 2D animation workflow is typically initiated with the creation of keyframes using sketch-based drawing. Subsequent inbetweens (i.e., intermediate sketch frames) are crafted through manual interpolation for smooth animations, which is a labor-intensive process. Thus, the prospect of automatic animation sketch interpolation has become highly appealing. However, existing video interpolation methods are generally hindered by two key issues for sketch inbetweening: 1) limited texture and colour details in sketches, and 2) exaggerated alterations between two sketch keyframes. To overcome these issues, we propose a novel deep learning method, namely Fine-to-Coarse Sketch Interpolation Network (FC-SIN). This approach incorporates multi-level guidance that formulates region-level correspondence, sketch-level correspondence and pixel-level dynamics. A multi-stream U-Transformer is then devised to characterize sketch inbewteening patterns using these multi-level guides through the integration of both self-attention and cross-attention mechanisms. Additionally, to facilitate future research on animation sketch inbetweening, we constructed a large-scale dataset - STD-12K, comprising 30 sketch animation series in diverse artistic styles. Comprehensive experiments on this dataset convincingly show that our proposed FC-SIN surpasses the state-of-the-art interpolation methods. Our code and dataset will be publicly available.

Tackling Scattering and Reflective Flare in Mobile Camera Systems: A Raw Image Dataset for Enhanced Flare Removal

The increasing prevalence of mobile devices has led to significant advancements in mobile camera systems and improved image quality. Nonetheless, mobile photography still grapples with challenging issues such as scattering and reflective flare. The absence of a comprehensive real image dataset tailored for mobile phones hinders the development of effective flare mitigation techniques. To address this issue, we present a novel raw image dataset specifically designed for mobile camera systems, focusing on flare removal. Capitalizing on the distinct properties of raw images, this dataset serves as a solid foundation for developing advanced flare removal algorithms. It encompasses a wide variety of real-world scenarios captured with diverse mobile devices and camera settings. The dataset comprises over 2,000 high-quality full-resolution raw image pairs for scattering flare and 1,100 for reflective flare, which can be further segmented into up to 30,000 and 2,200 paired patches, respectively, ensuring broad adaptability across various imaging conditions. Experimental results demonstrate that networks trained with synthesized data struggle to cope with complex lighting settings present in this real image dataset. We also show that processing data through a mobile phone's internal ISP compromises image quality while using raw image data presents significant advantages for addressing the flare removal problem. Our dataset is expected to enable an array of new research in flare removal and contribute to substantial improvements in mobile image quality, benefiting mobile photographers and end-users alike.

GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video

3D human pose estimation has been researched for decades with promising fruits. 3D human pose lifting is one of the promising research directions toward the task where both estimated pose and ground truth pose data are used for training. Existing pose lifting works mainly focus on improving the performance of estimated pose, but they usually underperform when testing on the ground truth pose data. We observe that the performance of the estimated pose can be easily improved by preparing good quality 2D pose, such as fine-tuning the 2D pose or using advanced 2D pose detectors. As such, we concentrate on improving the 3D human pose lifting via ground truth data for the future improvement of more quality estimated pose data. Towards this goal, a simple yet effective model called Global-local Adaptive Graph Convolutional Network (GLA-GCN) is proposed in this work. Our GLA-GCN globally models the spatiotemporal structure via a graph representation and backtraces local joint features for 3D human pose estimation via individually connected layers. To validate our model design, we conduct extensive experiments on three benchmark datasets: Human3.6M, HumanEva-I, and MPI-INF-3DHP. Experimental results show that our GLA-GCN implemented with ground truth 2D poses significantly outperforms state-of-the-art methods (e.g., up to around 3%, 17%, and 14% error reductions on Human3.6M, HumanEva-I, and MPI-INF-3DHP, respectively). GitHub: https://github.com/bruceyo/GLA-GCN.

GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human

3D human pose estimation has been researched for decades with promising fruits. 3D human pose lifting is one of the promising research directions toward the task where both estimated pose and ground truth pose data are used for training. Existing pose lifting works mainly focus on improving the performance of estimated pose, but they usually underperform when testing on the ground truth pose data. We observe that the performance of the estimated pose can be easily improved by preparing good quality 2D pose, such as fine-tuning the 2D pose or using advanced 2D pose detectors. As such, we concentrate on improving the 3D human pose lifting via ground truth data for the future improvement of more quality estimated pose data. Towards this goal, a simple yet effective model called Global-local Adaptive Graph Convolutional Network (GLA-GCN) is proposed in this work. Our GLA-GCN globally models the spatiotemporal structure via a graph representation and backtraces local joint features for 3D human pose estimation via individually connected layers. To validate our model design, we conduct extensive experiments on three benchmark datasets: Human3.6M, HumanEva-I, and MPI-INF-3DHP. Experimental results show that our GLA-GCN implemented with ground truth 2D poses significantly outperforms state-of-the-art methods (e.g., up to around 3%, 17%, and 13% error reductions on Human3.6M, HumanEva-I, and MPI-INF-3DHP, respectively).

Streaming 360-degree VR Video with Statistical QoS Provisioning in mmWave Networks from Delay and Rate Perspectives

Millimeter-wave(mmWave) technology has emerged as a promising enabler for unleashing the full potential of 360-degree virtual reality (VR). However, the explosive growth of VR services, coupled with the reliability issues of mmWave communications, poses enormous challenges in terms of wireless resource and quality-of-service (QoS) provisioning for mmWave-enabled 360-degree VR. In this paper, we propose an innovative 360-degree VR streaming architecture that addresses three under-exploited issues: overlapping field-of-views (FoVs), statistical QoS provisioning (SQP), and loss-tolerant active data discarding. Specifically, an overlapping FoV-based optimal joint unicast and multicast (JUM) task assignment scheme is designed to implement the non-redundant task assignments, thereby conserving wireless resources remarkably. Furthermore, leveraging stochastic network calculus, we develop a comprehensive SQP theoretical framework that encompasses two SQP schemes from delay and rate perspectives. Additionally, a corresponding optimal adaptive joint time-slot allocation and active-discarding (ADAPT-JTAAT) transmission scheme is proposed to minimize resource consumption while guaranteeing diverse statistical QoS requirements under loss-intolerant and loss-tolerant scenarios from delay and rate perspectives, respectively. Extensive simulations demonstrate the effectiveness of the designed overlapping FoV-based JUM optimal task assignment scheme. Comparisons with six baseline schemes validate that the proposed optimal ADAPTJTAAT transmission scheme can achieve superior SQP performance in resource utilization, flexible rate control, and robust queue behaviors.

Visual Tuning

Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer. Instead, recent advances can achieve superior performance than full-tuning the whole pre-trained parameters by updating far fewer parameters, enabling edge devices and downstream applications to reuse the increasingly large foundation models deployed on the cloud. With the aim of helping researchers get the full picture and future directions of visual tuning, this survey characterizes a large and thoughtful selection of recent works, providing a systematic and comprehensive overview of existing work and models. Specifically, it provides a detailed background of visual tuning and categorizes recent visual tuning techniques into five groups: prompt tuning, adapter tuning, parameter tuning, and remapping tuning. Meanwhile, it offers some exciting research directions for prospective pre-training and various interactions in visual tuning.