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A Large-Scale Study on the Accuracy vs Cost Trade-offs of Training and Evaluation Settings in Fine-Grained Image Recognition
Edwin Arkel Rios, Augusto Christian Surya, Oswin Gosal +5
Prior work on fine-grained image recognition (FGIR) has established the importance of the backbone selection, but has neglected the accuracy-vs-cost trade-offs under different trai…
Fine-Grained Image Recognition from Scratch with Teacher-Guided Data Augmentation
Edwin Arkel Rios, Fernando Mikael, Oswin Gosal +4
Fine-grained image recognition (FGIR) aims to distinguish visually similar sub-categories within a broader class, such as identifying bird species. While most existing FGIR methods…
Cross-Layer Cache Aggregation for Token Reduction in Ultra-Fine-Grained Image Recognition
Edwin Arkel Rios, Jansen Christopher Yuanda, Vincent Leon Ghanz +3
Ultra-fine-grained image recognition (UFGIR) is a challenging task that involves classifying images within a macro-category. While traditional FGIR deals with classifying different…
FlashSloth: Lightning Multimodal Large Language Models via Embedded Visual Compression
Bo Tong, Bokai Lai, Yiyi Zhou +5
Despite a big leap forward in capability, multimodal large language models (MLLMs) tend to behave like a sloth in practical use, i.e., slow response and large latency. Recent effor…
Down-Sampling Inter-Layer Adapter for Parameter and Computation Efficient Ultra-Fine-Grained Image Recognition
Edwin Arkel Rios, Femiloye Oyerinde, Min-Chun Hu +1
Ultra-fine-grained image recognition (UFGIR) categorizes objects with extremely small differences between classes, such as distinguishing between cultivars within the same species,…
Global-Local Similarity for Efficient Fine-Grained Image Recognition with Vision Transformers
Edwin Arkel Rios, Min-Chun Hu, Bo-Cheng Lai
Fine-grained recognition involves the classification of images from subordinate macro-categories, and it is challenging due to small inter-class differences. To overcome this, most…