Image-based fashion design with AI techniques has attracted increasing attention in recent years. We focus on the reference-based fashion design task, where we aim to combine a reference appearance image and a clothing image to generate a new fashion clothing image. Although existing diffusion-based image translation methods have enabled flexible style transfer, it is often difficult to transfer the appearance of the image realistically during reverse diffusion. When the referenced appearance domain greatly differs from the source domain, it often leads to the collapse in the translation. To tackle this issue, we present a novel diffusion model-based unsupervised structureaware transfer method, namely DiffFashion. Our method is free of model tuning and structure-preserving and has high flexibility in transferring from images with large domain gaps. Specifically, based on the optimal transport properties, we keep a shared latent across the clothing image and reference appearance image to bridge the gap between the two domains in the denoising process, and the latent of the reference image is gradually adapted to the clothing domain. Simultaneously, the structure is transferred from the source clothing to the output fashion image with mixed guidance, including pre-trained Vision Transformer (ViT) guidance and a foreground mask guidance, to further preserve the structure and appearance semantics from source and reference images. Our experimental results show that the proposed method outperforms state-of-the-art baseline models, generating more realistic images in the fashion design task. Code and demo can be found at https://github.com/Rem105- 210/DiffFashion .

Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific vision tasks. Yet, existing methods either employ complex spatial-temporal modules or rely heavily on additional perception models to extract temporal features for video understanding, performing well only on short videos. For long videos, the computational complexity and memory costs associated with long-term temporal connections are significantly increased, posing additional challenges. Leveraging the hierarchical memory structure of the Atkinson-Shiffrin memory model, with tokens in Transformers being employed as the carriers of memory in combination, we propose MovieChat within a training-free memory consolidation mechanism to overcome these challenges, which transfers dense frames from short-term memory into sparse tokens in long-term memory by temporally merging adjacent frames. We lift pre-trained large multi-modal models for understanding long videos without additional trainable modules, employing a zero-shot approach. Additionally, in our new version, MovieChat+, we design an enhanced training-free vision-question matching-based memory consolidation mechanism to better anchor predictions to relevant visual content. MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1 K benchmark with 1 K long video, 2 K temporal grounding labels, and 14 K manual annotations.

Deep vision multimodal learning aims at combining deep visual representation learning with other modalities, such as text, sound, and data collected from other sensors. With the fast development of deep learning, vision multimodal learning has gained much interest from the community. This paper reviews the types of architectures used in multimodal learning, including feature extraction, modality aggregation, and multimodal loss functions. Then, we discuss several learning paradigms such as supervised, semi-supervised, self-supervised, and transfer learning. We also introduce several practical challenges such as missing modalities and noisy modalities. Several applications and benchmarks on vision tasks are listed to help researchers gain a deeper understanding of progress in the field. Finally, we indicate that pretraining paradigm, unified multitask framework, missing and noisy modality, and multimodal task diversity could be the future trends and challenges in the deep vision multimodal learning field. Compared with existing surveys, this paper focuses on the most recent works and provides a thorough discussion of methodology, benchmarks, and future trends.

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications.

The current 3D human pose estimators face challenges in adapting to new datasets due to the scarcity of 2D-3D pose pairs in target domain training sets. We present the Multi-Hypothesis Pose Synthesis Domain Adaptation (PoSynDA) framework to overcome this issue without extensive target domain annotation. Utilizing a diffusion-centric structure, PoSynDA simulates the 3D pose distribution in the target domain, filling the data diversity gap. By incorporating a multi-hypothesis network, it creates diverse pose hypotheses and aligns them with the target domain. Target-specific source augmentation obtains the target domain distribution data from the source domain by decoupling the scale and position parameters. The teacher-student paradigm and low-rank adaptation further refine the process. PoSynDA demonstrates competitive performance on benchmarks, such as Human3.6M, MPI-INF-3DHP, and 3DPW, even comparable with the target-trained MixSTE model. This work paves the way for the practical application of 3D human pose estimation1. The source code is available at https://github.com/hbing-l/PoSynDA.