2022-12-02 更新
Hyperbolic Contrastive Learning for Visual Representations beyond Objects
Authors:Songwei Ge, Shlok Mishra, Simon Kornblith, Chun-Liang Li, David Jacobs
Although self-/un-supervised methods have led to rapid progress in visual representation learning, these methods generally treat objects and scenes using the same lens. In this paper, we focus on learning representations for objects and scenes that preserve the structure among them. Motivated by the observation that visually similar objects are close in the representation space, we argue that the scenes and objects should instead follow a hierarchical structure based on their compositionality. To exploit such a structure, we propose a contrastive learning framework where a Euclidean loss is used to learn object representations and a hyperbolic loss is used to encourage representations of scenes to lie close to representations of their constituent objects in a hyperbolic space. This novel hyperbolic objective encourages the scene-object hypernymy among the representations by optimizing the magnitude of their norms. We show that when pretraining on the COCO and OpenImages datasets, the hyperbolic loss improves downstream performance of several baselines across multiple datasets and tasks, including image classification, object detection, and semantic segmentation. We also show that the properties of the learned representations allow us to solve various vision tasks that involve the interaction between scenes and objects in a zero-shot fashion. Our code can be found at \url{https://github.com/shlokk/HCL/tree/main/HCL}.
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One-shot recognition of any material anywhere using contrastive learning with physics-based rendering
Authors:Manuel S. Drehwald, Sagi Eppel, Jolina Li, Han Hao, Alan Aspuru-Guzik
We present MatSim: a synthetic dataset, a benchmark, and a method for computer vision based recognition of similarities and transitions between materials and textures, focusing on identifying any material under any conditions using one or a few examples (one-shot learning). The visual recognition of materials is essential to everything from examining food while cooking to inspecting agriculture, chemistry, and industrial products. In this work, we utilize giant repositories used by computer graphics artists to generate a new CGI dataset for material similarity. We use physics-based rendering (PBR) repositories for visual material simulation, assign these materials random 3D objects, and render images with a vast range of backgrounds and illumination conditions (HDRI). We add a gradual transition between materials to support applications with a smooth transition between states (like gradually cooked food). We also render materials inside transparent containers to support beverage and chemistry lab use cases. We then train a contrastive learning network to generate a descriptor that identifies unfamiliar materials using a single image. We also present a new benchmark for a few-shot material recognition that contains a wide range of real-world examples, including the state of a chemical reaction, rotten/fresh fruits, states of food, different types of construction materials, types of ground, and many other use cases involving material states, transitions and subclasses. We show that a network trained on the MatSim synthetic dataset outperforms state-of-the-art models like Clip on the benchmark, despite being tested on material classes that were not seen during training. The dataset, benchmark, code and trained models are available online.
PDF 11 pages, 8 figures, 1 table. for associated code, see https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX and https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF
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