Jiangtao Wen

Yuxing Han, Yunan Ding, Jiangtao Wen et al.

Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval, especially in an age where an immense volume of video content is constantly being generated. Traditional methods require video decompression to extract pixel-level features like color, texture, and motion, thereby increasing computational and storage demands. Moreover, these methods often suffer from performance degradation in low-quality videos. We present a novel approach that examines only the post-compression bitstream of a video to perform classification, eliminating the need for bitstream. We validate our approach using a custom-built data set comprising over 29,000 YouTube video clips, totaling 6,000 hours and spanning 11 distinct categories. Our preliminary evaluations indicate precision, accuracy, and recall rates well over 80%. The algorithm operates approximately 15,000 times faster than real-time for 30fps videos, outperforming traditional Dynamic Time Warping (DTW) algorithm by six orders of magnitude.

Jun Wu, Yirong Xiong, Jiangtao Wen et al.

Despite rapid advancements in the research and deployment of large language models (LLMs), the statistical distribution of model parameters, as well as their influence on initialization, training dynamics, and downstream efficiency, has received surprisingly little attention. A recent work introduced BackSlash, a training-time compression algorithm. It first demonstrated that pre-trained LLM parameters follow generalized Gaussian distributions (GGDs) better. By optimizing GG priors during training, BackSlash can reduce parameters by up to 90\% with minimal performance loss. Building on this foundational insight, we propose a unified, end-to-end framework for LLM optimization based on the GG model. Our contributions are threefold: (1) GG-based initialization scheme that aligns with the statistical structure of trained models, resulting in faster convergence and improved accuracy; (2) DeepShape, a post-training regularization method that reshapes weight distributions to match a GG profile, improving compressibility with minimized degradation in performance; and (3) RF8, a compact and hardware-efficient 8-bit floating-point format designed for GG-distributed-initialized BackSlash training, enabling low-cost inference without compromising accuracy. Experiments across diverse model architectures show that our framework consistently yields smaller and faster models that match or outperform standard training baselines. By grounding LLM development in principled statistical modeling, this work forges a new path toward efficient, scalable, and hardware-aware AI systems. The code is available on our project page: https://huggingface.co/spaces/shifeng3711/gg_prior.

Jisheng Li, Qi Dai, Jiangtao Wen

Consistent in-focus input imagery is an essential precondition for machine vision systems to perceive the dynamic environment. A defocus blur severely degrades the performance of vision systems. To tackle this problem, we propose a deep-learning-based framework estimating the kernel scale and orientation of the defocus blur to adjust lens focus rapidly. Our pipeline utilizes 3D ConvNet for a variable number of input hypotheses to select the optimal slice from the input stack. We use random shuffle and Gumbel-softmax to improve network performance. We also propose to generate synthetic defocused images with various asymmetric coded apertures to facilitate training. Experiments are conducted to demonstrate the effectiveness of our framework.

Jisheng Li, Yuze He, Yubin Hu et al.

Omnidirectional video is an essential component of Virtual Reality. Although various methods have been proposed to generate content that can be viewed with six degrees of freedom (6-DoF), existing systems usually involve complex depth estimation, image in-painting or stitching pre-processing. In this paper, we propose a system that uses a 3D ConvNet to generate a multi-sphere images (MSI) representation that can be experienced in 6-DoF VR. The system utilizes conventional omnidirectional VR camera footage directly without the need for a depth map or segmentation mask, thereby significantly simplifying the overall complexity of the 6-DoF omnidirectional video composition. By using a newly designed weighted sphere sweep volume (WSSV) fusing technique, our approach is compatible with most panoramic VR camera setups. A ground truth generation approach for high-quality artifact-free 6-DoF contents is proposed and can be used by the research and development community for 6-DoF content generation.

Jiangyue Xia, Anyi Rao, Qingqiu Huang et al.

The task of searching certain people in videos has seen increasing potential in real-world applications, such as video organization and editing. Most existing approaches are devised to work in an offline manner, where identities can only be inferred after an entire video is examined. This working manner precludes such methods from being applied to online services or those applications that require real-time responses. In this paper, we propose an online person search framework, which can recognize people in a video on the fly. This framework maintains a multimodal memory bank at its heart as the basis for person recognition, and updates it dynamically with a policy obtained by reinforcement learning. Our experiments on a large movie dataset show that the proposed method is effective, not only achieving remarkable improvements over online schemes but also outperforming offline methods.

Yanghao Li, Bichuan Guo, Jiangtao Wen et al.

Denoisers trained with synthetic data often fail to cope with the diversity of unknown noises, giving way to methods that can adapt to existing noise without knowing its ground truth. Previous image-based method leads to noise overfitting if directly applied to video denoisers, and has inadequate temporal information management especially in terms of occlusion and lighting variation, which considerably hinders its denoising performance. In this paper, we propose a general framework for video denoising networks that successfully addresses these challenges. A novel twin sampler assembles training data by decoupling inputs from targets without altering semantics, which not only effectively solves the noise overfitting problem, but also generates better occlusion masks efficiently by checking optical flow consistency. An online denoising scheme and a warping loss regularizer are employed for better temporal alignment. Lighting variation is quantified based on the local similarity of aligned frames. Our method consistently outperforms the prior art by 0.6-3.2dB PSNR on multiple noises, datasets and network architectures. State-of-the-art results on reducing model-blind video noises are achieved. Extensive ablation studies are conducted to demonstrate the significance of each technical components.

Minhao Tang, Jiangtao Wen, Yuxing Han

The High Efficiency Video Coding (HEVC/H.265) standard doubles the compression efficiency of the widely used H.264/AVC standard. For practical applications, rate control (RC) algorithms for HEVC need to be developed. Based on the R-Q, R-$ρ$ or R-$λ$ models, rate control algorithms aim at encoding a video clip/segment to a target bit rate accurately with high video quality after compression. Among the various models used by HEVC rate control algorithms, the R-$λ$ model performs the best in both coding efficiency and rate control accuracy. However, compared with encoding with a fixed quantization parameter (QP), even the best rate control algorithm [1] still under-performs when comparing the video quality achieved at identical average bit rates. In this paper, we propose a novel generalized rate-distortion-$λ$ (R-D-$λ$) model for the relationship between rate (R), distortion (D) and the Lagrangian multiplier ($λ$) in rate-distortion (RD) optimized encoding. In addition to the well designed hierarchical initialization and coefficient update scheme, a new model based rate allocation scheme composed of amortization, smooth window and consistency control is proposed for a better rate allocation. Experimental results implementing the proposed algorithm in the HEVC reference software HM-16.9 show that the proposed rate control algorithm is able to achieve an average of BDBR saving of 6.09%, 3.15% and 4.03% for random access (RA), low delay P (LDP) and low delay B (LDB) configurations respectively as compared with the R-$λ$ model based RC algorithm [1] implemented in HM. The proposed algorithm also outperforms the state-of-the-art algorithms, while rate control accuracy and encoding speed are hardly impacted.

Zhihong Shao, Minlie Huang, Jiangtao Wen et al.

Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.

Bichuan Guo, Jiangtao Wen, Yuxing Han

The quality assessment of light field images presents new challenges to conventional compression methods, as the spatial quality is affected by the optical distortion of capturing devices, and the angular consistency affects the performance of dynamic rendering applications. In this paper, we propose a two-pass encoding system for pseudo-temporal sequence based light field image compression with a novel frame level bit allocation framework that optimizes spatial quality and angular consistency simultaneously. Frame level rate-distortion models are estimated during the first pass, and the second pass performs the actual encoding with optimized bit allocations given by a two-step convex programming. The proposed framework supports various encoder configurations. Experimental results show that comparing to the anchor HM 16.16 (HEVC reference software), the proposed two-pass encoding system on average achieves 11.2% to 11.9% BD-rate reductions for the all-intra configuration, 15.8% to 32.7% BD-rate reductions for the random-access configuration, and 12.1% to 15.7% BD-rate reductions for the low-delay configuration. The resulting bit errors are limited, and the total time cost is less than twice of the one-pass anchor. Comparing with our earlier low-delay configuration based method, the proposed system improves BD-rate reduction by 3.1% to 8.3%, reduces the bit errors by more than 60%, and achieves more than 12x speed up.

Bichuan Guo, Yuxing Han, Jiangtao Wen

The widely used adaptive HTTP streaming requires an efficient algorithm to encode the same video to different resolutions. In this paper, we propose a fast block structure determination algorithm based on the AV1 codec that accelerates high resolution encoding, which is the bottle-neck of multiple resolutions encoding. The block structure similarity across resolutions is modeled by the fineness of frame detail and scale of object motions, this enables us to accelerate high resolution encoding based on low resolution encoding results. The average depth of a block's co-located neighborhood is used to decide early termination in the RDO process. Encoding results show that our proposed algorithm reduces encoding time by 30.1%-36.8%, while keeping BD-rate low at 0.71%-1.04%. Comparing to the state-of-the-art, our method halves performance loss without sacrificing time savings.