most citedA Survey on Interpretability in Visual Recognition

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cs.CV2026

DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding

Yilin Wang, Haochen Shi, Guanyu Chen +4

Food segmentation is essential for applications such as intelligent catering, dietary assessment, and recommendation. However, existing benchmarks fail to capture the complexity of…

cs.CV2026

RFHNet: Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval

Junsong Wang, Weiqing Min, Guorui Sheng +3

Fine-grained food image retrieval is a key task in computational gastronomy, with applications in food traceability, dietary monitoring, and smart catering systems. Although hashin…

cs.CV2026

Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection

Xinyuan Liu, Hang Xu, Zirui Chen +3

Given its ability to reduce annotation costs, weakly supervised learning based on single-point annotations has emerged as a research focus in oriented object detection. Compared wi…

cs.CV20263 cited

A Survey on Interpretability in Visual Recognition

Qiyang Wan, Chengzhi Gao, Ruiping Wang +1

Visual recognition models have achieved unprecedented success in various tasks. While researchers aim to understand the underlying mechanisms of these models, the growing demand fo…

cs.CV2026

Row-Column Separated Attention Based Low-Light Image/Video Enhancement

Chengqi Dong, Zhiyuan Cao, Tuoshi Qi +3

U-Net structure is widely used for low-light image/video enhancement. The enhanced images result in areas with large local noise and loss of more details without proper guidance fo…