Kaifan Zhang

Yuqi Ji, Junjie Ke, Lihuo He et al.

Affordance detection aims to jointly address the fundamental "what-where-how" challenge in embodied AI by understanding "what" an object is, "where" the object is located, and "how" it can be used. However, most affordance learning methods focus solely on "how" objects can be used while neglecting the "what" and "where" aspects. Other affordance detection methods treat object detection and affordance learning as two independent tasks, lacking effective interaction and real-time capability. To overcome these limitations, we introduce YOLO Affordance (YOLOA), a real-time affordance detection model that jointly handles these two tasks via a large language model (LLM) adapter. Specifically, YOLOA employs a lightweight detector consisting of object detection and affordance learning branches refined through the LLM Adapter. During training, the LLM Adapter interacts with object and affordance preliminary predictions to refine both branches by generating more accurate class priors, box offsets, and affordance gates. Experiments on our relabeled ADG-Det and IIT-Heat benchmarks demonstrate that YOLOA achieves state-of-the-art accuracy (52.8 / 73.1 mAP on ADG-Det / IIT-Heat) while maintaining real-time performance (up to 89.77 FPS, and up to 846.24 FPS for the lightweight variant). This indicates that YOLOA achieves an excellent trade-off between accuracy and efficiency.

Kaifan Zhang, Lihuo He, Xin Jiang et al.

Electroencephalogram (EEG) signals have attracted significant attention from researchers due to their non-invasive nature and high temporal sensitivity in decoding visual stimuli. However, most recent studies have focused solely on the relationship between EEG and image data pairs, neglecting the valuable ``beyond-image-modality" information embedded in EEG signals. This results in the loss of critical multimodal information in EEG. To address this limitation, we propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals. Specifically, CognitionCapturer trains Modality Expert Encoders for each modality to extract cross-modal information from the EEG modality. Then, it introduces a diffusion prior to map the EEG embedding space to the CLIP embedding space, followed by using a pretrained generative model, the proposed framework can reconstruct visual stimuli with high semantic and structural fidelity. Notably, the framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities. Through extensive experiments, we demonstrate that CognitionCapturer outperforms state-of-the-art methods both qualitatively and quantitatively. Code: https://github.com/XiaoZhangYES/CognitionCapturer.

Kaifan Zhang, Lihuo He, Junjie Ke et al.

Visual stimuli reconstruction from EEG remains challenging due to fidelity loss and representation shift. We propose CognitionCapturerPro, an enhanced framework that integrates EEG with multi-modal priors (images, text, depth, and edges) via collaborative training. Our core contributions include an uncertainty-weighted similarity scoring mechanism to quantify modality-specific fidelity and a fusion encoder for integrating shared representations. By employing a simplified alignment module and a pre-trained diffusion model, our method significantly outperforms the original CognitionCapturer on the THINGS-EEG dataset, improving Top-1 and Top-5 retrieval accuracy by 25.9% and 10.6%, respectively. Code is available at: https://github.com/XiaoZhangYES/CognitionCapturerPro.

Lukun Wu, Jie Li, Ziqi Ren et al.

Decoding visual features from EEG signals is a central challenge in neuroscience, with cross-modal alignment as the dominant approach. We argue that the relationship between visual and brain modalities is fundamentally asymmetric, characterized by two critical gaps: a Fidelity Gap (stemming from EEG's inherent noise and signal degradation, vs. vision's high-fidelity features) and a Semantic Gap (arising from EEG's shallow conceptual representation, vs. vision's rich semantic depth). Previous methods often overlook this asymmetry, forcing alignment between the two modalities as if they were equal partners and thereby leading to poor generalization. To address this, we propose the adaptive teaching paradigm. This paradigm empowers the ``teacher" modality (vision) to dynamically shrink and adjust its knowledge structure under task guidance, tailoring its semantically dense features to match the ``student" modality (EEG)'s capacity. We implement this paradigm with the ShrinkAdapter, a simple yet effective module featuring a residual-free design and a bottleneck structure. Through extensive experiments, we validate the underlying rationale and effectiveness of our paradigm. Our method achieves a top-1 accuracy of 60.2\% on the zero-shot brain-to-image retrieval task, surpassing previous state-of-the-art methods by a margin of 9.8\%. Our work introduces a new perspective for asymmetric alignment: the teacher must shrink and adapt to bridge the vision-brain gap.

Jiaofen Nan, Gaodeng Fan, Kaifan Zhang et al.

In the field of medical image analysis, image registration is a crucial technique. Despite the numerous registration models that have been proposed, existing methods still fall short in terms of accuracy and interpretability. In this paper, we present MsMorph, a deep learning-based image registration framework aimed at mimicking the manual process of registering image pairs to achieve more similar deformations, where the registered image pairs exhibit consistency or similarity in features. By extracting the feature differences between image pairs across various as-pects using gradients, the framework decodes semantic information at different scales and continuously compen-sates for the predicted deformation field, driving the optimization of parameters to significantly improve registration accuracy. The proposed method simulates the manual approach to registration, focusing on different regions of the image pairs and their neighborhoods to predict the deformation field between the two images, which provides strong interpretability. We compared several existing registration methods on two public brain MRI datasets, including LPBA and Mindboggle. The experimental results show that our method consistently outperforms state of the art in terms of metrics such as Dice score, Hausdorff distance, average symmetric surface distance, and non-Jacobian. The source code is publicly available at https://github.com/GaodengFan/MsMorph

Jili Xia, Lihuo He, Fei Gao et al.

Recently, AI-generated images (AIGIs) created by given prompts (initial prompts) have garnered widespread attention. Nevertheless, due to technical nonproficiency, they often suffer from poor perception quality and Text-to-Image misalignment. Therefore, assessing the perception quality and alignment quality of AIGIs is crucial to improving the generative model's performance. Existing assessment methods overly rely on the initial prompts in the task prompt design and use the same prompts to guide both perceptual and alignment quality evaluation, overlooking the distinctions between the two tasks. To address this limitation, we propose a novel quality assessment method for AIGIs named TSP-MGS, which designs task-specific prompts and measures multi-granularity similarity between AIGIs and the prompts. Specifically, task-specific prompts are first constructed to describe perception and alignment quality degrees separately, and the initial prompt is introduced for detailed quality perception. Then, the coarse-grained similarity between AIGIs and task-specific prompts is calculated, which facilitates holistic quality awareness. In addition, to improve the understanding of AIGI details, the fine-grained similarity between the image and the initial prompt is measured. Finally, precise quality prediction is acquired by integrating the multi-granularity similarities. Experiments on the commonly used AGIQA-1K and AGIQA-3K benchmarks demonstrate the superiority of the proposed TSP-MGS.