In this paper we learn to segment hands and hand-held objects from motion. Our system takes a single RGB image and hand location as input to segment the hand and hand-held object. For learning, we generate responsibility maps that show how well a hand's motion explains other pixels' motion in video. We use these responsibility maps as pseudo-labels to train a weakly-supervised neural network using an attention-based similarity loss and contrastive loss. Our system outperforms alternate methods, achieving good performance on the 100DOH, EPIC-KITCHENS, and HO3D datasets.

Recently, learned image compression (LIC) has garnered increasing interest with its rapidly improving performance surpassing conventional codecs. A key ingredient of LIC is a hyperprior-based entropy model, where the underlying joint probability of the latent image features is modeled as a product of Gaussian distributions from each latent element. Since latents from the actual images are not spatially independent, autoregressive (AR) context based entropy models were proposed to handle the discrepancy between the assumed distribution and the actual distribution. Though the AR-based models have proven effective, the computational complexity is significantly increased due to the inherent sequential nature of the algorithm. In this paper, we present a novel alternative to the AR-based approach that can provide a significantly better trade-off between performance and complexity. To minimize the discrepancy, we introduce a correlation loss that forces the latents to be spatially decorrelated and better fitted to the independent probability model. Our correlation loss is proved to act as a general plug-in for the hyperprior (HP) based learned image compression methods. The performance gain from our correlation loss is'free' in terms of computation complexity for both inference time and decoding time. To our knowledge, our method gives the best trade-off between the complexity and performance: combined with the Checkerboard-CM, it attains 90% and when combined with ChARM-CM, it attains 98% of the AR-based BD-Rate gains yet is around 50 times and 30 times faster than AR-based methods respectively.

This section contains additional details on the object specifications. As mentioned in Section 3, we rely on the PB language to define the structure for each object type that we would like to handle with our model. Our framework supports all basic constructions of the language including nested messages and oneofclauses. For example, in Listing 1b, we can see that a generic Objectcan be either an entityor a constraint. We also use oneoffor objects that may appear in several mutually exclusive configurations (e.g., CircleArcEntityrepresents both arcs and closed circles and for the latter which it does not make sense to specify end points). We handle such constructions by injecting an additional token with the discrete value set to the index of the active field.

For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? While MARL algorithms may be implemented for potentially harmful applications, we do not believe this work uniquely enables such applications. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] In the supplemental material (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? Our allocation proposal network and Q network are illustrated in Figures 7 and 8. Low-level action utility functions and mixing networks are similar to those described in Iqbal et al. [10] with the only 13 difference being a replacement of the RNN layers with standard fully connected layers.

Despite significant progress on multi-agent reinforcement learning (MARL) in recent years, coordination in complex domains remains a challenge. Work in MARL often focuses on solving tasks where agents interact with all other agents and entities in the environment; however, we observe that real-world tasks are often composed of several isolated instances of local agent interactions (subtasks), and each agent can meaningfully focus on one subtask to the exclusion of all else in the environment. In these composite tasks, successful policies can often be decomposed into two levels of decision-making: agents are allocated to specific subtasks and each agent acts productively towards their assigned subtask alone. This decomposed decision making provides a strong structural inductive bias, significantly reduces agent observation spaces, and encourages subtask-specific policies to be reused and composed during training, as opposed to treating each new composition of subtasks as unique. We introduce ALMA, a general learning method for taking advantage of these structured tasks. ALMA simultaneously learns a high-level subtask allocation policy and low-level agent policies. We demonstrate that ALMA learns sophisticated coordination behavior in a number of challenging environments, outperforming strong baselines. ALMA's modularity also enables it to better generalize to new environment configurations. Finally, we find that while ALMA can integrate separately trained allocation and action policies, the best performance is obtained only by training all components jointly.

Note that f(x,c1,c2,) is strongly concave for any (x,c,c) Rd+2.1 2 Impact of the Local Steps: In this section, we run additional experiments to investigate the impact of the local steps K on the training performance. We run FSGDA and SAGDA over the hetergenous "a9a" [40] dataset with the regression model mentioned in Section 4. We fix the local step-size at 0.01, worker number at 100, and choose the number of local update rounds K from the discrete set {2,10,20}. This is due to the fact that the algorithm needs more communication round while K is small, which matches our Corollary 2 and Corollary 3. Impact of the Local Step-size: In this experiment, we choose the value of the local step-sizes from the discrete set {0.0001,0.001,0.01}and As shown in Figure 1(a) and Fig.6(a), larger local step-sizes lead to faster convergence rates. Impact of the Global Step-size: we choose the global step-sizes value from the discrete set {2,5,10} and fix worker number at 100, local update rounds at 10.