Denoising Diffusion Probabilistic Models (DDPMs) have shown success in robust 3D object detection tasks. Existing methods often rely on the score matching from 3D boxes or pre-trained diffusion priors. However, they typically require multi-step iterations in inference, which limits efficiency. To address this, we propose a Robust single-stage fully Sparse 3D object Detection Network with a Detachable Latent Framework (DLF) of DDPMs, named RSDNet. Specifically, RSDNet learns the denoising process in latent feature spaces through lightweight denoising networks like multi-level denoising autoencoders (DAEs). This enables RSDNet to effectively understand scene distributions under multi-level perturbations, achieving robust and reliable detection. Meanwhile, we reformulate the noising and denoising mechanisms of DDPMs, enabling DLF to construct multi-type and multi-level noise samples and targets, enhancing RSDNet robustness to multiple perturbations. Furthermore, a semantic-geometric conditional guidance is introduced to perceive the object boundaries and shapes, alleviating the center feature missing problem in sparse representations, enabling RSDNet to perform in a fully sparse detection pipeline. Moreover, the detachable denoising network design of DLF enables RSDNet to perform single-step detection in inference, further enhancing detection efficiency. Extensive experiments on public benchmarks show that RSDNet can outperform existing methods, achieving state-of-the-art detection.

Jingchun Lian, Lingyu Liu, Yaxiong Wang, Yujiao Wu, Lianwei Wu, Li Zhu, Zhedong Zheng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Yuchen Zhang, Yaxiong Wang, Kecheng Han, Yujiao Wu, Lianwei Wu, Li Zhu, Zhedong Zheng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

This article targets to enhance the diffusion-based text-to-video generation by improving the two input prompts, including the noise and the text. Accommodated with this goal, we propose POS, a P rompt O ptimization S uite to boost text-to-video models. POS is motivated by two observations: (1) Video generation shows instability in terms of noise . Given the same text, different noises lead to videos that differ significantly in terms of both frame quality and temporal consistency. This observation implies that there exists an optimal noise matched to each textual input; To capture the potential noise, we propose an optimal noise approximator to approach the potential optimal noise. Particularly, the optimal noise approximator initially searches a video that closely relates to the text prompt and then inverts it into the noise space to serve as an improved noise prompt for the textual input. (2) Improving the text prompt via LLMs often causes semantic deviation . Many existing text-to-vision works have utilized LLMs to improve the text prompts for generation enhancement. However, existing methods often neglect the semantic alignment between the original text and the rewritten one. In response to this issue, we design a semantic-preserving rewriter to impose constraints in both rewriting and denoising phrases to preserve semantic consistency. Extensive experiments on popular benchmarks show that our POS can improve the text-to-video models with a clear margin.

Enabling Large Language Models (LLMs) to understand the 3D physical world is an emerging yet challenging research direction. Current strategies for processing point clouds typically downsample the scene or divide it into smaller parts for separate analysis. However, both approaches risk losing key local details or global contextual information. In this paper, we introduce PerLA, a 3D language assistant designed to be more perceptive to both details and context, making visual representations more informative for the LLM. PerLA captures high-resolution (local) details in parallel from different point cloud areas and integrates them with (global) context obtained from a lower-resolution whole point cloud. We present a novel algorithm that preserves point cloud locality through the Hilbert curve and effectively aggregates local-to-global information via cross-attention and a graph neural network. Lastly, we introduce a novel loss for local representation consensus to promote training stability. PerLA outperforms state-of-the-art 3D language assistants, with gains of up to +1.34 CiDEr on ScanQA for question answering, and +4.22 on ScanRefer and +3.88 on Nr3D for dense captioning. Project page: https://gfmei.github.io/PerLA

Recent progress in aligning images with texts has achieved remarkable results, however, existing models tend to serve general queries and often fall short when dealing with detailed query requirements. In this paper, we work towards Entity-centric Image-Text Matching (EITM), a finer-grained image-text matching task that aligns texts and images centered around specific entities. The main challenge in EITM lies in bridging the substantial semantic gap between entity-related information in texts and images, which is more pronounced than in general image-text matching problems. To address this challenge, we adopt CLIP as our foundational model and devise a Multimodal Attentive Experts (MMAE)-based contrastive learning to adapt CLIP into an expert for EITM problem. Particularly, the core of our multimodal attentive experts learning is to generate explanation texts by Large Language Models (LLMs) as bridging clues. In specific, we first employ off-the-shelf LLMs to generate explanatory text. This text, along with the original image and text, is then fed into our Multimodal Attentive Experts module to narrow the semantic gap within a unified semantic space. Upon the enriched feature representations generated by MMAE, we have further developed an effective Gated Integrative Image-text Matching (GI-ITM) strategy. GI-ITM utilizes an adaptive gating mechanism to combine features from MMAE, followed by applying image-text matching constraints to enhance the alignment precision. Our method has been extensively evaluated on three social media news benchmarks: N24News, VisualNews, and GoodNews. The experimental results demonstrate that our approach significantly outperforms competing methods. Our code is available at: https://github.com/wangyxxjtu/ETE.

Composed image retrieval (CIR) is the task of searching target images using an image-text pair as a query. Given the straightforward relation of query pair-target image, the dominant methods follow the learning paradigm of common image-text retrieval and simply model this problem as the query-target matching problem. Particularly, the common practice first encodes the multi-modal query into one feature and then aligns it with the target image. However, such a learning paradigm only explores the naive relation in the triplets. We argue that CIR triplets encompass additional associations besides the primary query-target relation, which is overlooked in existing works. In this paper, we disclose two new relations residing in the triplets by viewing the triplet as a graph node. In analogy with the graph node, we mine two associations of text-bridged image alignment and complementary text reasoning. The text-bridged image alignment considers composed image retrieval as a specialized form of image retrieval, where the query text acts as a bridge between the query image and the target one, and a hinge-based cross attention is proposed to incorporate this relation into the network learning. On the other hand, the association of complementary text reasoning regards composed image retrieval as a specific type of cross-modal retrieval, where the composite two images are used to reason the complementary text. To integrate these views effectively, a twin attention-based compositor is designed. By combining these two types of complementary associations with the explicit query pair-target image relation, we establish a comprehensive set of constraints for composed image retrieval. With the above designs, we finally developed our CaLa, a Complementary Association Learning framework for Augmenting Composed Image Retrieval. Experimental evaluations are conducted on the widely-used CIRR and FashIionIQ benchmarks with multiple backbones to validate the effectiveness of our CaLa. The results demonstrate the superiority of our method in the composed image retrieval task. Our code and models are available at https://github.com/Chiangsonw/CaLa

In this paper, we introduce a large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, and explore the feasibility of performing pre-training on both attribute recognition and image-text matching tasks in one stone. In particular, MALS contains 1,510,330 image-text pairs, which is about 37.5 × larger than prevailing CUHK-PEDES, and all images are annotated with 27 attributes. Considering the privacy concerns and annotation costs, we leverage the off-the-shelf diffusion models to generate the dataset. To verify the feasibility of learning from the generated data, we develop a new joint Attribute Prompt Learning and Text Matching Learning (APTM) framework, considering the shared knowledge between attribute and text. As the name implies, APTM contains an attribute prompt learning stream and a text matching learning stream. (1) The attribute prompt learning leverages the attribute prompts for image-attribute alignment, which enhances the text matching learning. (2) The text matching learning facilitates the representation learning on fine-grained details, and in turn, boosts the attribute prompt learning. Extensive experiments validate the effectiveness of the pre-training on MALS, achieving state-of-the-art retrieval performance via APTM on three challenging real-world benchmarks. In particular, APTM achieves a consistent improvement of +6.96 %, +7.68%, and +16.95% Recall@1 accuracy on CUHK-PEDES, ICFG-PEDES, and RSTPReid datasets by a clear margin, respectively. The dataset, model, and code are available at https://github.com/Shuyu-XJTU/APTM.

Lung cancer is the leading cause of cancer death worldwide. The critical reason for the deaths is delayed diagnosis and poor prognosis. With the accelerated development of deep learning techniques, it has been successfully applied extensively in many real-world applications, including health sectors such as medical image interpretation and disease diagnosis. By combining more modalities that being engaged in the processing of information, multimodal learning can extract better features and improve the predictive ability. The conventional methods for lung cancer survival analysis normally utilize clinical data and only provide a statistical probability. To improve the survival prediction accuracy and help prognostic decision-making in clinical practice for medical experts, we for the first time propose a multimodal deep learning framework for non-small cell lung cancer (NSCLC) survival analysis, named DeepMMSA. This framework leverages CT images in combination with clinical data, enabling the abundant information held within medical images to be associate with lung cancer survival information. We validate our model on the data of 422 NSCLC patients from The Cancer Imaging Archive (TCIA). Experimental results support our hypothesis that there is an underlying relationship between prognostic information and radiomic images. Besides, quantitative results show that our method could surpass the state-of-the-art methods by 4% on concordance.