Toward Effective Domain Adaptive Retrieval.

This paper studies the problem of unsupervised domain adaptive hashing, which is less-explored but emerging for efficient image retrieval, particularly for cross-domain retrieval. This problem is typically tackled by learning hashing networks with pseudo-labeling and domain alignment techniques. Nevertheless, these approaches usually suffer from overconfident and biased pseudo-labels and inefficient domain alignment without sufficiently exploring semantics, thus failing to achieve satisfactory retrieval performance. To tackle this issue, we present PEACE, a principled framework which holistically explores semantic information in both source and target data and extensively incorporates it for effective domain alignment. For comprehensive semantic learning, PEACE leverages label embeddings to guide the optimization of hash codes for source data. More importantly, to mitigate the effects of noisy pseudo-labels, we propose a novel method to holistically measure the uncertainty of pseudo-labels for unlabeled target data and progressively minimize them through alternative optimization under the guidance of the domain discrepancy. Additionally, PEACE effectively removes domain discrepancy in the Hamming space from two views. In particular, it not only introduces composite adversarial learning to implicitly explore semantic information embedded in hash codes, but also aligns cluster semantic centroids across domains to explicitly exploit label information. Experimental results on several popular domain adaptive retrieval benchmarks demonstrate the superiority of our proposed PEACE compared with various state-of-the-art methods on both single-domain and cross-domain retrieval tasks. Our source codes are available at https://github.com/WillDreamer/PEACE.

Leveraging Imitation Learning on Pose Regulation Problem of a Robotic Fish.

In this article, the pose regulation control problem of a robotic fish is investigated by formulating it as a Markov decision process (MDP). Such a typical task that requires the robot to arrive at the desired position with the desired orientation remains a challenge, since two objectives (position and orientation) may be conflicted during optimization. To handle the challenge, we adopt the sparse reward scheme, i.e., the robot will be rewarded if and only if it completes the pose regulation task. Although deep reinforcement learning (DRL) can achieve such an MDP with sparse rewards, the absence of immediate reward hinders the robot from efficient learning. To this end, we propose a novel imitation learning (IL) method that learns DRL-based policies from demonstrations with inverse reward shaping to overcome the challenge raised by extremely sparse rewards. Moreover, we design a demonstrator to generate various trajectory demonstrations based on one simple example from a nonexpert helper, which greatly reduces the time consumption of collecting robot samples. The simulation results evaluate the effectiveness of our proposed demonstrator and the state-of-the-art (SOTA) performance of our proposed IL method. Furthermore, we deploy the trained IL policy on a physical robotic fish to perform pose regulation in a swimming tank without/with external disturbances. The experimental results verify the effectiveness and robustness of our proposed methods in real world. Therefore, we believe this article is a step forward in the field of biomimetic underwater robot learning.