16 accepted papers
With the rise of visual-language models, multi-modal ReID retrieves specific targets by integrating different spectra and textual descriptions. Existing methods merely adopt descriptive representation learning for image-text, ignoring the relationships among the intrinsic logical hierarchies of sema
Text-to-visible & infrared person retrieval aims to retrieve the corresponding visible (RGB) and thermal infrared (TIR) images given the text descriptions. Existing methods perform semantic decoupling by aligning RGB and TIR features separately to different attributes, thereby facilitating the align
In the field of multi-spectral object re-identification (ReID), multi-modal knowledge and modal-specific knowledge exhibit complementary advantages when handling hard samples, but existing methods rarely integrate this collaborative information. Knowledge distillation is a direct approach for trans
Multi-modal object re-identification (ReID) aims to retrieve specific targets by leveraging complementary cues from different sensing modalities. Despite recent progress, two key challenges remain: (1) the limited ability to jointly address both modality and viewpoint discrepancies, and (2) the diff
Aerial-Ground Person Re-IDentification (AGPReID) aims to extract identity-discriminative representations from heterogeneous perspectives across different platforms in complex real-world environments. However, existing methods primarily focus on visual appearance modeling and make insufficient use of
Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by combining complementary information from multiple modalities. Existing multi-modal object ReID methods primarily focus on the fusion of heterogeneous features. However, they often overlook the dynamic quality changes in…
With the advancement of generative AI, distinguishing real and AI-generated faces in videos has become increasingly challenging. However, traditional methods struggle to capture local details and temporal dynamics simultaneously, making it difficult to achieve high detection accuracy while maintaini…
Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities. Recently, large-scale pre-trained models like CLIP have demonstrated impressive performance in traditional single-modal ReID tasks. However, they rema…
Multi-modal object Re-IDentification (ReID) has gained considerable attention with the goal of retrieving specific targets across cameras using heterogeneous visual data sources. At present, multi-modal object ReID faces two core challenges: (1) learning robust features under fine-grained local nois…
Previous advances in vehicle re-identification (ReID) are mostly reported under favorable lighting conditions while cross-day-and-night performance is neglected which greatly hinders the development of related traffic intelligence applications. This work instead develops a novel Day-Night Dual-domai…
Multi-modal person re-identification (ReID) seeks to mitigate challenging lighting conditions by incorporating diverse modalities. Most existing multi-modal ReID methods concentrate on leveraging complementary multi-modal information via fusion or interaction. However, the relationships among hetero…
Occluded person re-identification (Re-ID), the task of searching for the same person’s images in occluded environments, has attracted lots of attention in the past decades. Recent approaches concentrate on improving performance on occluded data by data/feature augmentation or using extra models to p…
Existing face stylization methods always acquire the presence of the target (style) domain during the translation process, which violates privacy regulations and limits their applicability in real-world systems. To address this issue, we propose a new method called MODel-drIven Face stYlization (MOD…
Multi-modal person Re-ID introduces more complementary information to assist the traditional Re-ID task. Existing multi-modal methods ignore the importance of modality-specific information in the feature fusion stage. To this end, we propose a novel method to boost modality-specific representations…
To avoid the illumination limitation in visible person re-identification (Re-ID) and the heterogeneous issue in cross-modality Re-ID, we propose to utilize complementary advantages of multiple modalities including visible (RGB), near infrared (NI) and thermal infrared (TI) ones for robust person Re-…
Talking face generation aims to synthesize a face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video via the given speech clip and facial image. Most existing methods mainly focus on either disentangling the information in a single image or l…