We present the ilLumination-Aware conditional Image Repainting (LuminAIRe) task to address the unrealistic lighting effects in recent conditional image repainting (CIR) methods. The environment lighting and 3D geometry conditions are explicitly estimated from given background images and parsing masks using a parametric lighting representation and learning-based priors. These 3D conditions are then converted into illumination images through the proposed physically-based illumination rendering and illumination attention module. With the injection of illumination images, physically-correct lighting information is fed into the lighting-realistic generation process and repainted images with harmonized lighting effects in both foreground and background regions can be acquired, whose superiority over the results of state-of-the-art methods is confirmed through extensive experiments. For facilitating and validating the LuminAIRe task, a new dataset CAR-LUMINAIRE with lighting annotations and rich appearance variants is collected.

In this paper, we consider non-smooth stochastic convex optimization with two function evaluations per round under infinite noise variance. In the classical setting when noise has finite variance, an optimal algorithm, built upon the batched accelerated gradient method, was proposed in [17]. This optimality is defined in terms of iteration and oracle complexity, as well as the maximal admissible level of adversarial noise. However, the assumption of finite variance is burdensome and it might not hold in many practical scenarios. To address this, we demonstrate how to adapt a refined clipped version of the accelerated gradient (Stochastic Similar Triangles) method from [35] for a two-point zero-order oracle. This adaptation entails extending the batching technique to accommodate infinite variance -- a non-trivial task that stands as a distinct contribution of this paper.

We show the theoretical energy consumption estimation method of the proposed Spike-driven Transformer in Table 1 of the main text. Compared to the vanilla Transformer counterpart, the spiking version requires information on timesteps T and spike firing rates (R). Therefore, we only need to evaluate the FLOPs of the vanilla Transformer, and T and R are known, we can get the theoretical energy consumption of spike-driven Transformer. FLConv = (kn)2 hn wn cn 1 cn, (S1) where kn is the kernel size, (hn,wn) is the output feature map size, cn 1 and cn are the input and output channel numbers, respectively. The FLOPs of the m-th MLP layer in ANNs are: FLMLP = im om, (S2) where im and om are the input and output dimensions of the MLP layer, respectively.

Discovering achievements with a hierarchical structure in procedurally generated environments presents a significant challenge. This requires an agent to possess a broad range of abilities, including generalization and long-term reasoning. Many prior methods have been built upon model-based or hierarchical approaches, with the belief that an explicit module for long-term planning would be advantageous for learning hierarchical dependencies. However, these methods demand an excessive number of environment interactions or large model sizes, limiting their practicality. In this work, we demonstrate that proximal policy optimization (PPO), a simple yet versatile model-free algorithm, outperforms previous methods when optimized with recent implementation practices. Moreover, we find that the PPO agent can predict the next achievement to be unlocked to some extent, albeit with limited confidence. Based on this observation, we introduce a novel contrastive learning method, called achievement distillation, which strengthens the agent's ability to predict the next achievement. Our method exhibits a strong capacity for discovering hierarchical achievements and shows state-of-the-art performance on the challenging Crafter environment in a sample-efficient manner while utilizing fewer model parameters.

Is an AI version of Mark Zuckerberg - or any boss - a good plan? Feedback has learned that, according to reports, Meta is building an AI version of Mark Zuckerberg to interact with staff. Feedback hopes this doesn't become a trend Feedback has had a number of bosses over the years. One reorganised the company at which we worked in such a way that our job no longer existed. However, none of them was an AI.