Deep Learning

視覺提示工程：VIPE 如何改善抽象圖上的物理推理

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在 DeepMind 的測試條件下，抽象草圖可能是影響模型物理推理準確率的因素之一。VIPE 透過自動將輸入圖像轉為寫實風格，讓 Veo 3.1 準確率從 41.3% 躍升至 59.3%。這在受測範圍內指出了一個方向：在花大錢增加算力之前，先搞清楚模型到底看到了什麼。

Visual Prompt Engineering: How VIPE Improves Physical Reasoning on Abstract Images

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Under DeepMind’s test conditions, abstract sketches may be one factor affecting a model’s physical-reasoning accuracy. VIPE automatically converts input images into a photorealistic style, raising Veo 3.1 accuracy on VPCT from 41.3% to 59.3%, while Omni Flash rises from 56.3% to 67.5%. Within the tested scope, this points to a direction: before spending heavily on more compute, first clarify what the model is actually seeing.

GMOS: The Trade-offs of Anchoring Moving Object Segmentation with 3D Geometry

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Traditional moving object segmentation (MOS) relies on 2D aids such as optical flow, without 3D geometric context. GMOS anchors motion directly in RGB video across 3D space and time, introduces the frame-level MOS-I protocol, and builds the GMOS-2K dataset. The paper reports leading results across its evaluated MOS, MOS-I, and UVOS benchmarks, while running about three times faster than earlier multi-object MOS methods under the Tab. 1 evaluation setup.

GMOS：以 3D 幾何錨定動態物件分割的取捨

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傳統動態物件分割（MOS）依賴光流等 2D 輔助，缺乏 3D 幾何資訊。GMOS 直接在 RGB 視頻上錨定 3D 時空，提出 MOS-I 逐幀瞬時運動評估協議並建立 GMOS-2K 資料集；論文報告其在所測 MOS、MOS-I 與 UVOS 基準中達到領先結果，並在 Tab. 1 的設定下比先前多物件 MOS 方法約快三倍。

Chinchilla: Model/Data Ratios on a Budget

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When compute is limited, should you buy a bigger model or feed it more data? Chinchilla gave a counterintuitive answer in 2022: at a fixed training budget, a 70B model trained on 1.4T tokens outperformed a 280B model trained on 300B tokens, using the same compute. That points to an approximate 1:20 parameter-to-token ratio in that setup. If you’re planning an LLM training run, this paper offers a practical way to think about model size, data volume, and where your next dollar creates more learning.