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Found 4 papers, 3 papers with code
Exploration of Class Center for Fine-Grained Visual Classification
In particular, our method achieves state-of-the-art performance on the FGVC-Aircraft and CUB-200-2011 datasets.
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
Based on this benchmark, we conduct extensive experiments with two well-known LLMs (English and Chinese) and four quantization algorithms to investigate this topic in-depth, yielding several counter-intuitive and valuable findings, e. g., models quantized using a calibration set with the same distribution as the test data are not necessarily optimal.
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
Therefore, instead of utilizing the same setting for all samples, we propose to predict a particular denoising step for each sample by evaluating the difference between image contents and the priors extracted from diffusion models.
Ranked #2 on
Anomaly Detection
on MPDD
CA-PMG: Channel attention and progressive multi-granularity training network for fine-grained visual classification
For each feature map, the channel attention module is proposed to explore channel-wise correlation.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
