Real-world data usually suffers from severe class imbalance and long-tailed distributions, where minority classes are significantly underrepresented compared to the majority ones. Recent research prefers to utilize multi-expert architectures to mitigate the model uncertainty on the minority, where collaborative learning is employed to aggregate the knowledge of experts, i.e., online distillation. In this article, we observe that the knowledge transfer between experts is imbalanced in terms of class distribution, which results in limited performance improvement of the minority classes. To address it, we propose a re-weighted distillation loss by comparing two classifiers' predictions, which are supervised by online distillation and label annotations, respectively. We also emphasize that feature-level distillation will significantly improve model performance and increase feature robustness. Finally, we propose an Effective Collaborative Learning (ECL) framework that integrates a contrastive proxy task branch to further improve feature quality. Quantitative and qualitative experiments on four standard datasets demonstrate that ECL achieves state-of-the-art performance and the detailed ablation studies manifest the effectiveness of each component in ECL.

Deep hashing has shown promising performance in large-scale image retrieval. The hashing process utilizes Deep Neural Networks (DNNs) to embed images into compact continuous latent codes, then map them into binary codes by hashing function for efficient retrieval. Recent approaches perform metric loss and quantization loss to supervise the two procedures that cluster samples with the same categories and alleviate semantic information loss after binarization in the end-to-end training framework. However, we observe the incompatible conflict that the optimal cluster positions are not identical to the ideal hash positions because of the different objectives of the two loss terms, which lead to severe ambiguity and error-hashing after the binarization process. To address the problem, we borrow the Theory of Minimum-Distance Bounds for Binary Linear Codes to design the inflection point that depends on the hash bit length and category numbers and thereby propose Hashing-guided Hinge Function (HHF) to explicitly enforce the termination of metric loss to prevent the negative pairs unlimited alienated. Such modification is proven effective and essential for training, which contributes to proper intra- and inter-distances for clusters and better hash positions for accurate image retrieval simultaneously. Extensive experiments in CIFAR-10, CIFAR-100, ImageNet, and MS-COCO justify that HHF consistently outperforms existing techniques and is robust and flexible to transplant into other methods. Code is available athttps://github.com/JerryXu0129/HHF.

Most existing weakly supervised aspect detection algorithms utilize pre-trained language models as their backbone networks by constructing discriminative tasks with seed words. Once the number of seed words decreases, the performance of current models declines significantly. Recently, prompt tuning has been proposed to bridge the gap of objective forms in pre-training and fine-tuning, which is hopeful of alleviating the above challenge. However, directly applying the existing prompt-based methods to this task not only fails to effectively use large amounts of unlabeled data, but also may cause serious over-fitting problems. In this paper, we propose a lightweight teacher-student network (PTS) based on prompts to solve the above two problems. Concretely, the student network is a hybrid prompt-based classification model to detect aspects, which innovatively compounds hand-crafted prompts and auto-generated prompts. The teacher network comprehensively considers the representation of the sentence and the masked aspect token in the template to guide classification. To utilize unlabeled data and seed words intelligently, we train the teacher and student network alternately. Furthermore, in order to solve the problem that the uneven quality of training data obviously affects the iterative efficiency of PTS, we design a general dynamic data selection strategy to feed the most pertinent data into the current model. Experimental results show that even given the minimum seed words, PTS significantly outperforms previous state-of-the-art methods on three widely used benchmarks.

Image retrieval has become an increasingly appealing technique with broad multimedia application prospects, where deep hashing serves as the dominant branch towards low storage and efficient retrieval. In this paper, we carried out in-depth investigations on metric learning in deep hashing for establishing a powerful metric space in multi-label scenarios, where the pair loss suffers high computational overhead and converge difficulty, while the proxy loss is theoretically incapable of expressing the profound label dependencies and exhibits conflicts in the constructed hypersphere space. To address the problems, we propose a novel metric learning framework with Hybrid Proxy-Pair Loss (HyP$^2$ Loss) that constructs an expressive metric space with efficient training complexity w.r.t. the whole dataset. The proposed HyP$^2$ Loss focuses on optimizing the hypersphere space by learnable proxies and excavating data-to-data correlations of irrelevant pairs, which integrates sufficient data correspondence of pair-based methods and high-efficiency of proxy-based methods. Extensive experiments on four standard multi-label benchmarks justify the proposed method outperforms the state-of-the-art, is robust among different hash bits and achieves significant performance gains with a faster, more stable convergence speed. Our code is available at https://github.com/JerryXu0129/HyP2-Loss.