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Figure 1. 

Representative images from the Cotton80 cultivar classes (three images per class)^[1]. Despite belonging to distinct cultivar categories, samples share near-identical macro-morphology: a defining characteristic of Ultra-FGVC that limits even state-of-the-art supervised methods to below 60% accuracy at K = 50. The figure is original and created by Chenhui Wang. © 2026 Chenhui Wang.

Figure 2. 

Base architecture of a multimodal large language model (MLLM) for few-shot in-context learning. Reference images ^[1] (K categories, n shots each) and an unlabelled query image are encoded by a CLIP-based vision encoder^[33], whose patch features are projected into the language model's token space by a lightweight cross-modal connector (Q-Former^[35] or MLP bridge^[11]). A text prompt encoding the task instruction and category identifiers is tokenised in parallel. Both token streams are consumed jointly by the LLM backbone, which generates a structured output containing the predicted category label. In this work, category identifiers are numeric only (Task 1) or real species names (Task 2); the backbone architecture is otherwise identical across conditions. The figure is original and created by Chenhui Wang. © 2026 Chenhui Wang.

Figure 3. 

Structural comparison of the three prompting paradigms reviewed in this section. (a) Few-shot ICL prepends $ K $ labelled reference image-label pairs $ ({\bf{x}}_k, y_k) $ as in-context demonstrations before the query, enabling task adaptation without gradient updates^[10,43]. (b) Describe-then-classify applies chain-of-thought decomposition: the model first articulates observable visual features, then compares them against candidate classes before committing to a label, reducing confirmation bias^[44,45]. (c) Contrastive prompting extends (b) by requiring explicit enumeration of matching and contradicting evidence per class and a justified elimination of the runner-up^[46,47]. Node shading: dashed grey = reference demonstrations; solid grey = query image; blue = chain-of-thought step; orange = contrastive reasoning step; green = predicted output. The figure is original and created by Chenhui Wang. © 2026 Chenhui Wang.

Figure 4. 

All six prompt variants used in Task 3, shown schematically (part 1 of 2: variants a–c). Image blocks are denoted $ \langle\,\cdot\,\rangle $; K categories with three reference shots each are assumed throughout. The shared JSON output schema is reproduced verbatim in every box so that differences in discriminative content are visible by direct inspection. See Fig. 4 for variants (d)–(f). (part 2 of 2: variants d–f.) Variants differ along three orthogonal dimensions: whether chain-of-thought (CoT) decomposition is applied ([a], [d], [e] vs [b], [c], [f]); whether explicit match/contradiction elimination is required ([d] only); and where task instructions appear relative to the images (preamble: [a], [b], [d]; after all images: [c]; co-located per category: [e]; inline: [f]). The Baseline (a) is the primary reference for all comparisons in Section 4.3; the Minimal (f) estab]lishes the lower bound of prompt engineering contribution. The figure is original and created by Chenhui Wang. © 2026 Chenhui Wang.

Figure 5. 

Full baseline prompt used in Tasks 1–3. Italicised $ K $ and indexed placeholders are instantiated at runtime. The mandatory JSON schema enforces structured output and eliminates free-text parsing as a confound across all six Task 3 variants. In the named condition of Task 2, numeric category identifiers are replaced with real species names; all other elements are identical. The figure is original and created by Chenhui Wang. © 2026 Chenhui Wang.