Low-shot Visual Recognition by Shrinking and Hallucinating Features

Few-Shot Learning via Saliency-guided Hallucination of Samples

Learning new concepts from a few of samples is a standard challenge in computer vision. The main directions to improve the learning ability of few-shot training models include (i) a robust similarity learning and (ii) generating or hallucinating additional data from the limited existing samples. In this paper, we follow the latter direction and present a novel data hallucination model. Currently, most datapoint generators contain a specialized network (i.e., GAN) tasked with hallucinating new datapoints, thus requiring large numbers of annotated data for their training in the first place. In t…

缩小教师：用于非对称EEG-视觉对齐的自适应教学范式

Shrinking the Teacher: An Adaptive Teaching Paradigm for Asymmetric EEG-Vision Alignment

针对EEG与视觉模态的非对称性（保真度和语义差距），提出自适应教学范式及ShrinkAdapter模块，动态调整视觉“教师”知识结构匹配EEG“学生”能力，零样本脑到图像检索top-1准确率达60.2%，超SOTA 9.8%。

One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

Despite remarkable progress achieved, most neural architecture search (NAS) methods focus on searching for one single accurate and robust architecture. To further build models with better generalization capability and performance, model ensemble is usually adopted and performs better than stand-alone models. Inspired by the merits of model ensemble, we propose to search for multiple diverse models simultaneously as an alternative way to find powerful models. Searching for ensembles is non-trivial and has two key challenges: enlarged search space and potentially more complexity for the searched…

红外域自适应与零样本量化

Infrared Domain Adaptation with Zero-Shot Quantization

本文提出一种针对红外域目标检测模型的零样本量化框架，利用批量归一化统计数据蒸馏校准数据，在无训练数据条件下有效实现模型量化。

Nano Banana Pro是否是低级视觉的全能选手？对14项任务和40个数据集的全面评估

Is Nano Banana Pro a Low-Level Vision All-Rounder? A Comprehensive Evaluation on 14 Tasks and 40 Datasets

本文评估了Nano Banana Pro在14项低级视觉任务上的表现，发现其在主观视觉质量上优于专业模型，但在定量指标上表现欠佳，揭示了生成模型在保持像素一致性方面的挑战。