Recent text-to-image diffusion models have shown significant advancements in high-quality image synthesis. Represented by Stable Diffusion, these models have been applied in various fields, such as art design, digital development, personalization, etc. However, they still fail to generate images that properly comply with the given text prompts, especially when encountering multiple subjects. Current approaches for multi-subject synthesis focus on latent code optimization at specific timesteps,yet yielding compromised text-image alignment. In this paper, we explore the generation of diffusion models through the lens of both noise initialization and denoising process, and propose an effective two-stage Attention Refinement and Inheritance (ARI) approach. Firstly, we update the initial noise by refining the cross-attention maps between different subject tokens, employing strategies of separation, energization, and centralization. Then, we introduce attention-level interpolation as an inheritancemechanism to explicitly regulate the denoising process. This enables the image generation to maintain high correspondence with the refined initial attention, thereby achieving a more controllable synthesis. Extensive experiments show that our approach achieves more faithful alignment between generated images and text prompts compared with state-of-the-art multi-subject image synthesis methods.

Given a text query, partially relevant video retrieval (PRVR) aims to retrieve untrimmed videos containing relevant moments, wherein event modeling is crucial for partitioning the video into smaller temporal events that partially correspond to the text. Previous methods typically segment videos into a fixed number of equal-length clips, resulting in ambiguous event boundaries. Additionally, they rely on mean pooling to compute event representations, inevitably introducing undesired misalignment. To address these, we propose an Uneven Event Modeling (UEM) framework for PRVR. We first introduce the Progressive-Grouped Video Segmentation (PGVS) module, to iteratively formulate events in light of both temporal dependencies and semantic similarity between consecutive frames, enabling clear event boundaries. Furthermore, we also propose the Context-Aware Event Refinement (CAER) module to refine the event representation conditioned the text’s cross-attention. This enables event representations to focus on the most relevant frames for a given text, facilitating more precise text-video alignment. Extensive experiments demonstrate that our method achieves state-of-the-art performance on two PRVR benchmarks. Code is available at https://github.com/Sasa77777779/UEM.git.

Extensive advancements have been made in person ReID through the mining of semantic information. Nevertheless, existing methods that utilize semantic-parts from a single image modality do not explicitly achieve this goal. Whiteness the impressive capabilities in multimodal understanding of Vision Language Foundation Model CLIP, a recent two-stage CLIP-based method employs automated prompt engineering to obtain specific textual labels for classifying pedestrians. However, we note that the predefined soft prompts may be inadequate in expressing the entire visual context and struggle to generalize to unseen classes. This paper presents an end-to-end Prompt-driven Semantic Guidance (PromptSG) framework that harnesses the rich semantics inherent in CLIP. Specifically, we guide the model to attend to regions that are semantically faithful to the prompt. To provide personalized language descriptions for specific individuals, we propose learning pseudo tokens that represent specific visual contexts. This design not only facilitates learning fine-grained attribute information but also can inherently leverage language prompts during inference. Without requiring additional labeling efforts, our PromptSG achieves state-of-the-art by over 10% on MSMTI7 and nearly 5% on the Market-I50I benchmark. The codes will be available at h t tps: / / gi th ub. com/ YzXian16/PromptSG

Traditional person re-identification (ReID) methods trained on static data are ill-suited to real-world dynamic surveillance systems. Recently, a more desirable setting "Camera Incremental Person ReID (CIPR)", has been proposed to continually adapt to new cameras and accumulate knowledge. However, prior work on relation distillation heavily constrains intra-class relations for all identities, while under-exploring the credibility of different identities in knowledge transfer. Besides, their rehearsal-free setting sidesteps privacy concerns but compromises performance. In this paper, we present a novel framework, P2-ARD, designed specifically for CIPR. Firstly, we propose an innovative Adaptive Relation Distillation loss that automatically selects more crucial identities for distillation. Additionally, we introduce the privacy-preserving replay scheme to effectively retain semantic information while ensuring the privacy of the identity. Finally, we incorporate a cycle-consistent correlation method to address the class overlap issue in CIPR. Extensive experiments demonstrate our method outperforming the state-of-the-art.

Incremental learning for person re-identification (ReID) aims to develop models that can be trained with a continuous data stream, which is a more practical setting for real-world applications. However, the existing incremental ReID methods make two strong assumptions that the cameras are fixed and the new-emerging data is class-disjoint from previous classes. This is unrealistic as previously observed pedestrians may re-appear and be captured again by new cameras. In this paper, we investigate person ReID in an unexplored scenario named Camera Incremental Person ReID (CIPR), which advances existing lifelong person ReID by taking into account the class overlap issue. Specifically, new data collected from new cameras may probably contain an unknown proportion of identities seen before. This subsequently leads to the lack of cross-camera annotations for new data due to privacy concerns. To address these challenges, we propose a novel framework ExtendOVA. First, to handle the class overlap issue, we introduce an instance-wise seen-class identification module to discover previously seen identities at the instance level. Then, we propose a criterion for selecting confident ID-wise candidates and also devise an early learning regularization term to correct noise issues in pseudo labels. Furthermore, to compensate for the lack of previous data, we resort prototypical memory bank to create surrogate features, along with a cross-camera distillation loss to further retain the inter-camera relationship. The comprehensive experimental results on multiple benchmarks show that ExtendOVA significantly outperforms the state-of-the-arts with remarkable advantages.