The imbalanced modality problem in multimodal learning is a vicious phenomenon that leads to the sub-optimization of the modalities due to the gradient conflicts among the modalities and the model’s preference for the easier learning modalities. Recent studies have been dedicated to modulating the gradient from the uni-modal perspective, boosting the learning of the single modality. Nevertheless, they overlook the similarity between multimodal gradient optimization and multi-objective learning, while overemphasizing competition between modalities’ gradients to improve optimization. Therefore, we perceive the imbalanced multimodal optimization as Multi-Objective Optimization, and propose a novel training method: Social Optimum Assisted Gradient Modulation (SOA-GM). In detail, we implemented social Optimum to guide the multimodal model to achieve the trade-off state in which the collaborations of modalities can be maximized. We also proposed envy to reduce the model’s preference to a particular modality. Finally, experiments across multiple datasets indicate our superior and extendable method performance on multimodal learning.

Recent advancements in Large Vision-Language Models (LVLMs) have attracted considerable attention due to their impressive performance across various downstream tasks. However, these tasks predominantly emphasize third-person perspectives and LVLMs demonstrate inadequate capability in reasoning from a first-person perspective. To mitigate these limitations, we introduce Causal Intervention with Active Learning (CIAL), an innovative approach designed to augment the first-person reasoning capabilities of LVLMs. Specifically, our method first incorporates an Active Learning-driven Knowledge Extraction (ALKE) scheme, which utilizes LVLMs themselves to automatically and autonomously acquire knowledge related to egocentric perspectives. Then, to optimize the model’s response in conjunction with the scene, a Knowledge-guided Causal Intervention (KCI) module is employed, thereby LVLMs can integrate both knowledge and certainty scores for inference. Comprehensive experiments conducted on the EgoThink benchmark demonstrate that our CIAL method significantly improves the models’ ability to understand and reason in egocentric contexts. Our anonymous code is available at https://github.com/running-alpaca/CIAL/.

Event retrieval involves searching for specific events from untrimmed video galleries and has garnered significant attention in recent years. However, most existing works follow a text-based video retrieval paradigm only, limited by two main drawbacks: (1) The episodic information presented in described events is not fully perceived, leading to declines in retrieval performance facing variable query intentions. (2) Current models are prone to returning false positive results with similar semantics, as simple text queries can hardly accurately describe the target video content users seek. In this paper, we propose a novel event retrieval framework termed Composed Query-Based Event Retrieval (CQBER). Specifically, we first construct two CQBER benchmark datasets, namely ActivityNet-CQ and TVR-CQ, which cover TV shows and open-world scenarios, respectively. Additionally, we propose an initial CQBER method, termed Multimodal Episodic Perceptron (MEP), which excavates complete query semantics from both observed static visual cues and various descriptions. Extensive experiments demonstrate that our proposed framework significantly boosts event retrieval accuracy across different existing methods. Our code and datasets are available at https://github.com/VincentVanNF/CQBER.

Multimodal learning, which has been given great significance recently, may face the challenge of the imbalanced multimodal phenomenon, which leads to the insufficient optimization of both multimodal and unimodal objectives. The core problem lies in the optimization conflicts between the above optimization objectives, resulting in the diverse updating directions and strengths that cause antagonism between them. In this paper, we mathematically analyze the optimization processes of imbalanced multimodal learning in the hyperspaces from a novel geometric perspective. Additionally, based on our theoretical analysis, we defined the volumes of the gradients constructed parallel polyhedron in the hyperspace to quantify the misalignment between the optimization objectives. Subsequently, we proposed the Geometric Gradient Divergence Modulation (GGDM), which leverages the volumes of gradient polyhedron to perform gradient modulation, encouraging alignment among gradients and promoting a synergistic optimization effect. Lastly, we evaluate our GGDM on five widely used multimodal benchmarks, where RGB image, optical flow, text, image, video and audio are involved. Our method achieved state-of-the-art performance compared to other imbalanced multimodal learning methods. Our code is available at: https://github.com/ConstantineWayne/GGDM.

Numerous researches have proved that deep neural networks (DNNs) can fit almost everything even given data with noisy labels, and result in poor generalization performance. However, recent studies suggest that DNNs tend to gradually memorize the data, moving from correct data to mislabeled data. Inspired by this finding, we propose a novel method namedDynamic Loss Thresholding (DLT). During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds. Specifically, DLT compares the loss value of each sample with the current loss threshold. Samples with smaller losses can be considered as clean samples with higher probability and vice versa. Then, DLT discards the potentially corrupted labels and further leverages self-training semi-supervised learning techniques. Experiments on CIFAR-10/100, WebVision and Clothing1M demonstrate substantial improvements over recent state-of-the-art methods. In addition, we investigate two real-world problems. Firstly, we propose a novel approach to estimate the noise rates of datasets based on the loss difference between the early and late training stages of DNNs. Secondly, we explore the effect of hard samples (which are difficult to be distinguished) on the process of learning from noisy labels.

Deep neural networks have shown promising results on a wide variety of tasks using large-scale and well-annotated training datasets. However, data collected from real-world applications can suffer from two prevalent biases, i.e., long-tailed class distribution and label noise. Previous efforts on long-tailed learning and label-noise learning can only address a single type of data bias, leading to a severe deterioration of their performance. In this paper, we propose a distance-based sample selection algorithm called Stochastic Feature Averaging (SFA), which fits a Gaussian using the exponential running average of class centroids to capture uncertainty in representation space due to label noise and data scarcity. With SFA, we detect noisy samples based on their distances to class centroids sampled from this Gaussian distribution. Based on the identified clean samples, we then propose to train an auxiliary balanced classifier to improve the generalization for the minority class and facilitate the update of Gaussian parameters. Extensive experimental results show that SFA can enhance the performance of existing methods on both simulated and real-world datasets. Further, we propose to combine SFA with the sample-selection approach, distribution-robust, and noise-robust loss functions, resulting in significant improvement in performance over the baselines. Our code is available at https://github.com/HotanLee/SFA

Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels. Due to the exponential number of possible label sets to be considered by the predictive model, it is commonly assumed that label correlations should be well exploited to design an effective multi-label learning approach. On the other hand, class-imbalance stands as an intrinsic property of multi-label data which significantly affects the generalization performance of the multi-label predictive model. For each class label, the number of training examples with positive labeling assignment is generally much less than those with negative labeling assignment. To deal with the class-imbalance issue for multi-label learning, a simple yet effective class-imbalance aware learning strategy called cross-coupling aggregation (COCOA) is proposed in this article. Specifically, COCOA works by leveraging the exploitation of label correlations as well as the exploration of class-imbalance simultaneously. For each class label, a number of multiclass imbalance learners are induced by randomly coupling with other labels, whose predictions on the unseen instance are aggregated to determine the corresponding labeling relevancy. Extensive experiments on 18 benchmark datasets clearly validate the effectiveness of COCOA against state-of-the-art multi-label learning approaches especially in terms of imbalance-specific evaluation metrics.