Video prediction is a challenging spatiotemporal prediction task that generates future frames based on historical observations. Although recently proposed deep learning-based methods significantly outperform legacy approaches, there still exist gaps ...

Deep network-based point cloud geometry compression is becoming more crucial and attractive due to constantly expanding 3D applications. The current strategy employing holistic point clouds as input imposes limitations on the compressed point cloud size ...

Given an untrimmed video and a text query, Video Moment Retrieval (VMR) aims at retrieving a specific moment where the video content is semantically related to the text query. Conventional VMR methods rely on video-text paired data or specific temporal ...

Micro-videos, as an increasingly popular form of user-generated content (UGC), naturally include diverse multimodal cues. However, in pursuit of consistent representations, existing methods neglect the simultaneous consideration of exploring modality ...

We study the task of image retrieval with text feedback, where a reference image and modification text are composed to retrieve the desired target image. To accomplish this goal, existing methods always get the multimodal representations through different ...

Owing to the capacity of performing full-time target searches, cross-modality vehicle re-identification based on unmanned aerial vehicles (UAV) is gaining more attention in both video surveillance and public security. However, this promising and ...

Due to the limited number of stable image feature descriptors and the simplistic concatenation approach to hash generation, existing hashing methods have not achieved a satisfactory balance between robustness and discrimination. To this end, a novel ...

Screen content coding (SCC) in Versatile Video Coding (VVC) improves the coding efficiency of screen content videos (SCVs) significantly but results in high computational complexity due to the quad-tree plus multi-type tree (QTMT) structure of the coding ...

Based on deterministic single-point embedding, most extant image-text retrieval methods only focus on the match of ground truth while suffering from one-to-many correspondence, where besides annotated positives, many similar instances of another modality ...

The Diffusion model has a strong ability to generate wild images. However, the model can just generate inaccurate images with the guidance of text, which makes it very challenging to directly apply the text-guided generative model for virtual try-on ...

Pre-training is playing an increasingly important role in learning generic feature representation for Person Re-identification (ReID). We argue that a high-quality ReID representation should have three properties, namely, multi-level awareness, occlusion ...

The successful application of semantic segmentation technology in the real world has been among the most exciting achievements in the computer vision community over the past decade. Although the long-tailed phenomenon has been investigated in many fields, ...

Recently, person re-identification has gained significant attention from both academic and industry fields due to its potential applications in surveillance and security. However, the security of re-identification systems has not been widely investigated, ...

Recent advanced image restoration (IR) methods typically stack homogeneous operators hierarchically in the UNet architecture. To achieve higher accuracy, these models are now going deeper and more complex, making them resource-intensive. After ...

Given an audio-visual pair, audio-visual segmentation (AVS) aims to locate sounding sources by predicting pixel-wise maps. Previous methods assume that each sound component in an audio signal always has a visual counterpart in the image. However, this ...

Currently, there is a growing scholarly and industrial interest in micro-video-centric research. Within these domains, multi-label learning has emerged as a fundamental yet attractive subject. Existing methods primarily place emphasis on feature ...

Cross-modality vessel re-identification (ReID) presents a formidable challenge in the domain of maritime surveillance, necessitating the development of robust methodologies to accurately match vessels across disparate imaging modalities. This paper ...

The recently proposed MaskFormer [Cheng et al. (2021)] gives a refreshed perspective on the task of semantic segmentation: it shifts from the popular pixel-level classification paradigm to a mask-level classification method. In essence, it generates ...

Single domain generalization (single-DG) is a realistic yet challenging domain generalization scenario where a model trained on a single domain generalization scenario where a model trained on a single domain generalizes well to multiple unseen domains. ...

Deeplearning-based methods have significantly influenced the blind image quality assessment (BIQA) field, however, these methods often require training using large amounts of human rating data. In contrast, traditional knowledge-based methods are cost-...