Dynamic Proposals for Efficient Object Detection

Object detection is a basic computer vision task to loccalize and categorize objects in a given image. Most state-of-the-art detection methods utilize a fixed number of proposals as an intermediate representation of object candidates, which is unable to adapt to different computational constraints during inference. In this paper, we propose a simple yet effective method which is adaptive to different computational resources by generating dynamic proposals for object detection. We first design a module to make a single query-based model to be able to inference with different numbers of proposals. Further, we extend it to a dynamic model to choose the number of proposals according to the input image, greatly reducing computational costs. Our method achieves significant speed-up across a wide range of detection models including two-stage and query-based models while obtaining similar or even better accuracy

Evolution of conditional cooperation in collective-risk social dilemma with repeated group interactions

The evolution and long-term sustenance of cooperation has consistently piqued scholarly interest across the disciplines of evolutionary biology and social sciences. Previous theoretical and experimental studies on collective risk social dilemma games have revealed that the risk of collective failure will affect the evolution of cooperation. In the real world individuals usually adjust their decisions based on environmental factors such as risk intensity and cooperation level. However, it is still not well understood how such conditional behaviors affect the evolution of cooperation in repeated group interactions scenario from a theoretical perspective. Here, we construct an evolutionary game model with repeated interactions, in which defectors decide whether to cooperate in subsequent rounds of the game based on whether the risk exceeds their tolerance threshold and whether the number of cooperators exceeds the collective goal in the early rounds of the game. We find that the introduction of conditional cooperation strategy can effectively promote the emergence of cooperation, especially when the risk is low. In addition, the risk threshold significantly affects the evolutionary outcomes, with a high risk promoting the emergence of cooperation. Importantly, when the risk of failure to reach collective goals exceeds a certain threshold, the timely transition from a defective strategy to a cooperative strategy by conditional cooperators is beneficial for maintaining high-level cooperation.Comment: Accepted by Proceedings of the Royal Society B-Biological Science

Incident laser modulation by tool marks on micro-milled KDP crystal surface: Numerical simulation and experimental verification

© 2019 Elsevier Ltd Micro-milling has been accepted as the most promising method to repair the micro-defects on the surface of KH2PO4 (KDP) optics. However, surface tool marks are inevitably introduced during the micro-milling repairing process, and could possess great potential risks in lowering the laser-induced damage threshold of KDP optics. The primary cause of laser damage growth of nonlinear crystals has been considered as its internal light intensification. In this work, how the tool marks impact the incident laser modulation as well as the laser-induced damage resistance of micro-milled KDP optics was theoretically and experimentally investigated. The results indicate that periodic tool marks can cause diffraction effect and result in significant relative light intensity modulation (IRmax), up to 5.6 times higher than that inside smooth crystal surfaces. Although the change trends of IRmax with respect to tool marks on both surfaces of KDP optics are similar, the IRmax induced by the rear-surface tool marks is nearly twice higher than that induced by the front-surface tool marks, which means the rear surface with tool marks are more vulnerable to be damaged. The period of tool marks determines the modulation degree and distribution patterns of light intensity inside KDP crystal while the residual height of tool marks can only slightly regulate the modulation degree of light intensity. The tool marks with a period of 1 μm normally give rise to serious light intensification and should be strictly excluded, while the period of tool marks from 10 μm to 20 μm is conducive to the laser damage resistance of micro-milled KDP optics, which were verified by the tests of transmittance capacity and laser damage resistance, and is supposed to be preferred in the actual repairing process of full-aperture KDP optics

Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Despite the promising progress in multi-modal tasks, current large multi-modal models (LMMs) are prone to hallucinating inconsistent descriptions with respect to the associated image and human instructions. This paper addresses this issue by introducing the first large and diverse visual instruction tuning dataset, named Large-scale Robust Visual (LRV)-Instruction. Our dataset comprises 400k visual instructions generated by GPT4, covering 16 vision-and-language tasks with open-ended instructions and answers. Unlike existing studies that primarily focus on positive instruction samples, we design LRV-Instruction to include both positive and negative instructions for more robust visual instruction tuning. Our negative instructions are designed at three semantic levels: (i) Nonexistent Object Manipulation, (ii) Existent Object Manipulation and (iii) Knowledge Manipulation. To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a stable approach to evaluate visual instruction tuning like human experts. GAVIE does not require human-annotated groundtruth answers and can adapt to diverse instruction formats. We conduct comprehensive experiments to investigate the hallucination of LMMs. Our results demonstrate existing LMMs exhibit significant hallucinations when presented with our negative instructions, particularly Existent Object and Knowledge Manipulation instructions. Moreover, we successfully mitigate hallucination by finetuning MiniGPT4 and mPLUG-Owl on LRV-Instruction while improving performance on several public datasets compared to state-of-the-art methods. Additionally, we observed that a balanced ratio of positive and negative instances in the training data leads to a more robust model.Comment: 40 pages, 32 figures. Under Revie