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Toya Oyama

Publications and source records attributed to Toya Oyama.

2 recordsLinked to original sources

SALI: Shot-Aware Late Interaction for Cross-Shot Relation Matching in Text-to-Video Retrieval using Film-Grammar Knowledge

Text-to-video retrieval usually represents a video clip by a single embedding. This embedding often loses important relations between people. E.g., an interaction "Anna confronts Mark" is regularly filmed as alternating shot and reverse shot of both (Fig. 1a). No single shot or averaged embedding over clip shots captures this relation. Thus, we propose SALI (Shot-Aware Late Interaction). It extracts the subject and object from a single-sentence query, and matches the query, its subject and object text embeddings against each visual shot embedding of a video clip. The matching operator is greedy max or optimal transport. A film-grammar penalty in fine-tuning adds a small, consistent shift. Built on CLIP4Clip-meanP, SALI keeps overall recall on par on Condensed Movies and ActivityNet while raising R@1 on multi-shot relation queries by 3 and 12 points, the most among all compared methods, and improves such queries on MSR-VTT at a cost of 1.4 R@1 overall.

cs.CV↗

Image-Space Rule Discovery

Can image-editing models discover visual rules in image space and complete problem-solving end-to-end? We tackle this question in the spirit of a human worksheet test (e.g., an IQ test), using problems that require models to read image-based instructions, recognize the problem, infer the answer, bind it to the correct destination, control output count, suppress unnecessary edits, and preserve the input and format. We introduce WISRD, a Worksheet Image-Space Rule Discovery benchmark with 11 core tasks under eight information conditions, spanning localized marking, filling, copying, counting, and no-edit suppression, together with four supplementary reasoning-stress probes for multi-step spatial manipulation, abstract pattern reasoning, logical inference, and constraint-based problem solving. We identify three key findings as follows. (i) Among the frontier image-editing models evaluated, Nano Banana Pro achieves the highest score. On the shared V0--V3 no-reference subset, the Auto-Strict proxy pass rates are 48.7% for Nano Banana Pro, 13.4\% for Qwen-Image-Edit, 11.5% for FLUX.2 Klein 4B API, 11.3% for FLUX.2 Klein 4B open-weight, and 0.0% for InstructPix2Pix. (ii) Analysis reveals that current image-editing models can partially rely on rendered in-image instructions even when the external prompt is absent or merely generic. (iii) In small supplementary diagnostics, Nano Banana Pro achieves 70.0% on 4-by-4 Sudoku and 22.9% on public RAVEN pattern-discovery items in image space.

cs.CV↗