Multimodal learning aims to integrate diverse data sources to capture more comprehensive information about things, thus enhancing perception and understanding of the real world. However, inherent discrepancies between different modalities often lead to imbalanced optimization during multimodal learning, hindering performance improvement. To address this issue, in this paper, we present a Multimodal Information Balance (MIB) theory, grounded in Information Theory, to reveal that this imbalance arises from the imbalanced retention of complementary information during modality fusion, providing an intuitive and explainable perspective on the issue. Building on this insight, we propose a theoretical MIB criterion to adaptively balance the preservation of complementary information across individual modalities, thereby facilitating multimodal fusion. Using this criterion, we develop an Information-Balanced Multimodal Learning (IBML) framework to mine comprehensive and balanced multimodal information, achieving optimal learning. More specifically, IBML introduces Balance Information Optimization (BIO) module to maximize tractable lower bound objectives derived from the MIB criterion according to the optimization discrepancies across modalities, ensuring balanced retention of complementary information and enhancing information contributions during multimodal fusion. In addition, we present a supplementary and provable Task Complexity Modulation (TCM) module based on the MIB criterion to adjust task complexity discrepancies across input modalities, thus indirectly promoting the balanced preservation of complementary information throughout the learning process. Extensive experiments are conducted on eight multimodal datasets, spanning audio-visual recognition, image-text classification, and 2D-3D recognition, to verify the superiority and effectiveness of IBML.

This paper studies the complex challenge of cross-domain image retrieval under the condition of noisy labels (NCIR), a scenario that not only includes the inherent obstacles of traditional cross-domain image retrieval (CIR) but also requires alleviating the adverse effects of label noise. To address this challenge, this paper introduces a novel Robust Domain Alignment framework (RoDA), specifically designed for the NCIR task. At the heart of RoDA is the Selective Division and Adaptive Learning mechanism (SDAL), a key component crafted to shield the model from overfitting the noisy labels. SDAL effectively learns discriminative knowledge by dividing the dataset into clean and noisy parts, subsequently rectifying the labels for the latter based on information drawn from the clean one. This process involves adaptively weighting the relabeled samples and leveraging both the clean and relabeled data to bootstrap model training. Moreover, to bridge the domain gap further, we introduce the Accumulative Class Center Alignment (ACCA), a novel approach that fosters domain alignment through an accumulative domain loss mechanism.Thanks to SDAL and ACCA, our RoDA demonstrates its superiority in overcoming label noise and domain discrepancies within the NCIR paradigm. The effectiveness and robustness of our RoDA framework are comprehensively validated through extensive experiments across three multi-domain benchmarks.

With the rapid proliferation of 2D and 3D data, driven by advances in virtual environments and AI-generated content, cross-modal 2D-3D retrieval has attracted growing attention. However, it is easy to introduce noisy labels due to the spatial complexity of 3D content. Although various methods have been proposed to address this issue, they still struggle to handle or effectively re-exploit noisy samples. Moreover, existing approaches are prone to error accumulation due to the self-reinforcement of the model during training. To address these issues, we propose a Noise-Robust Cross-modal Learning (NRCL) framework based on the hybrid strategy. Specifically, NRCL introduces a Robust Cross-modal Co-separator (RCC), which separates noisy samples from clean ones by leveraging modality complementarity and adopting a co-teaching paradigm to mitigate potential error accumulation of the single model during training. Besides, a Reliable Soft Rectification (RSR) method is adopted to correct noisy labels by aggregating historical and dual-model predictions, exploiting the discriminative information from noisy samples. Finally, a Robust Cross-modal Prototype Learning (RCPL) is proposed to improve the discriminability of inter-class and alleviate the inherent gaps across modalities in the shared common space, which jointly leverages clean and rectified labels, thereby mitigating the detrimental impact of noisy samples. Extensive experiments are conducted on three 3D multimodal datasets to verify the effectiveness of our method by comparing it with 10 state-of-the-art methods. The code is available at https://github.com/yangaonidaye123/NRCL.

Text-3D Scene Retrieval (T3SR) aims to retrieve relevant scenes using linguistic queries. Although traditional T3SR methods have made significant progress in capturing fine-grained associations, they implicitly assume that query descriptions are information-complete. In practical deployments, however, limited by the capabilities of users and models, it is difficult or even impossible to directly obtain a perfect textual query suiting the entire scene and model, thereby leading to performance degradation. To address this issue, we propose a novel Interactive Text-3D Scene Retrieval Method (IDeal), which promotes the enhancement of the alignment between texts and 3D scenes through continuous interaction. To achieve this, we present an Interactive Retrieval Refinement framework (IRR), which employs a questioner to pose contextually relevant questions to an answerer in successive rounds that either promote detailed probing or encourage exploratory divergence within scenes. Upon the iterative responses received from the answerer, IRR adopts a retriever to perform both feature-level and semantic-level information fusion, facilitating scene-level interaction and understanding for more precise re-rankings. To bridge the domain gap between queries and interactive texts, we propose an Interaction Adaptation Tuning strategy (IAT). IAT mitigates the discriminability and diversity risks among augmented text features that approximate the interaction text domain, achieving contrastive domain adaptation for our retriever. Extensive experimental results on three datasets demonstrate the superiority of IDeal. Code is available at https://github.com/Yangl1nFeng/IDeal.

Grounding target objects in 3D environments via natural language is a fundamental capability for autonomous agents to successfully fulfill user requests. Almost all existing works typically assume that the target object lies within a known scene and focus solely on in-scene localization. In practice, however, agents often encounter unknown or previously visited environments and need to search across a large archive of scenes to ground the described object, thereby invalidating this assumption. To address this, we reveal a novel task called Cross-Scene Spatial Reasoning and Grounding (CSSRG), which aims to locate a described object anywhere across an entire collection of 3D scenes rather than predetermined scenes. Due to the difference from existing 3D visual grounding, CSSRG poses two challenges: the prohibitive cost of exhaustively traversing all scenes and more complex cross-modal spatial alignment. To address the challenges, we propose a Cross-Scene 3D Object Reasoning Framework (CoRe), which adopts a matching-then-grounding pipeline to reduce computational overhead. Specifically, CoRe consists of i) a Robust Text-Scene Aligning (RTSA) module that learns global scene representations for robust alignment between object descriptions and the corresponding 3D scenes, enabling efficient retrieval of candidate scenes; and ii) a Tailored Word-Object Associating (TWOA) module that establishes fine-grained alignment between words and target objects to filter out redundant context, supporting precise object-level reasoning and alignment. Additionally, to benchmark CSSRG, we construct a new CrossScene-RETR dataset and evaluation protocol tailored for cross-scene grounding. Extensive experiments across four multimodal datasets demonstrate that CoRe dramatically reduces computational overhead while showing superiority in both scene retrieval and object grounding. Code is available at https://github.com/Yangl1nFeng/CoRe.

In this paper, we investigate source-free domain adaptation (SFDA) for visible-infrared person re-identification (VI-ReID), aiming to adapt a pre-trained source model to an unlabeled target domain without access to source data. To address this challenging setting, we propose a novel learning paradigm, termed Source-Free Visible-Infrared Person Re-Identification (SVIP), which fully exploits the prior knowledge embedded in the source model to guide target domain adaptation. The proposed framework comprises three key components specifically designed for the source-free scenario: 1) a Source-Guided Contrastive Learning (SGCL) module, which leverages the discriminative feature space of the frozen source model as a reference to perform contrastive learning on the unlabeled target data, thereby preserving discrimination without requiring source samples; 2) a Residual Transfer Learning (RTL) module, which learns residual mappings to adapt the target model’s representations while maintaining the knowledge from the source model; and 3) a Structural Consistency-Guided Cross-modal Alignment (SCCA) module, which enforces reciprocal structural constraints between visible and infrared modalities to identify reliable cross-modal pairs and achieve robust modality alignment without source supervision. Extensive experiments on benchmark datasets demonstrate that SVIP substantially enhances target domain performance and outperforms existing unsupervised VI-ReID methods under source-free settings.

In this paper, we present and study a new instance-level retrieval task: PointCloud-Text Matching (PTM), which aims to identify the exact cross-modal instance that matches a given point-cloud query or text query. PTM has potential applications in various scenarios, such as indoor/urban-canyon localization and scene retrieval. However, there is a lack of suitable and targeted datasets for PTM in practice. To address this issue, we present a new PTM benchmark dataset, namely SceneDepict-3D2T. We observe that the data poses significant challenges due to its inherent characteristics, such as the sparsity, noise, or disorder of point clouds and the ambiguity, vagueness, or incompleteness of texts, which render existing cross-modal matching methods ineffective for PTM. To overcome these challenges, we propose a PTM baseline, namedRobust PointCloud-TextMatching method (RoMa). RoMa consists of two key modules: a Dual Attention Perception module (DAP) and a Robust Negative Contrastive Learning module (RNCL). Specifically, DAP leverages token-level and feature-level attention mechanisms to adaptively focus on useful local and global features, and aggregate them into common representations, thereby reducing the adverse impact of noise and ambiguity. To handle noisy correspondence, RNCL enhances robustness against mismatching by dividing negative pairs into clean and noisy subsets and assigning them forward and reverse optimization directions, respectively. We conduct extensive experiments on our benchmarks and demonstrate the superiority of our RoMa.

Recently, with the advent of Metaverse and AI Generated Content, cross-modal retrieval becomes popular with a burst of 2D and 3D data. However, this problem is challenging given the heterogeneous structure and semantic discrepancies. Moreover, imperfect annotations are ubiquitous given the ambiguous 2D and 3D content, thus inevitably producing noisy labels to degrade the learning performance. To tackle the problem, this paper proposes a robust 2D-3D retrieval framework (RONO) to robustly learn from noisy multimodal data. Specifically, one novel Robust Discriminative Center Learning mechanism (RDCL) is proposed in RONO to adaptively distinguish clean and noisy samples for respectively providing them with positive and negative optimization directions, thus mitigating the negative impact of noisy labels. Besides, we present a Shared Space Consistency Learning mechanism (SSCL) to capture the intrinsic information inside the noisy data by minimizing the cross-modal and semantic discrepancy between common space and label space simultaneously. Comprehensive mathematical analyses are given to theoretically prove the noise tolerance of the proposed method. Furthermore, we conduct extensive experiments on four 3D-model multimodal datasets to verify the effectiveness of our method by comparing it with 15 state-of-the-art methods. Code is available at https://github.com/penghu-cs/RONO.

In recent years, Unsupervised Domain Adaptation (UDA) has emerged as a popular technique for transferring knowledge from a labeled source domain to an unlabeled target domain. However, almost all of the existing approaches implicitly assume that the source domain is correctly labeled, which is expensive or even impossible to satisfy in open-world applications due to ubiquitous imperfect annotations (i.e., noisy labels). In this paper, we reveal that noisy labels interfere with learning from the source domain, thus leading to noisy knowledge being transferred from the source domain to the target domain, termed Dual Noisy Information (DNI). To address this issue, we propose a robust unsupervised domain adaptation framework (ROAD), which prevents the network model from overfitting noisy labels to capture accurate discrimination knowledge for domain adaptation. Specifically, a Robust Adaptive Weighted Learning mechanism (RSWL) is proposed to adaptively assign weights to each sample based on its reliability to enforce the model to focus more on reliable samples and less on unreliable samples, thereby mining robust discrimination knowledge against noisy labels in the source domain. In order to prevent noisy knowledge from misleading domain adaptation, we present a Robust Domain-adapted Prediction Learning mechanism (RDPL) to reduce the weighted decision uncertainty of predictions in the target domain, thus ensuring the accurate knowledge of source domain transfer into the target domain, rather than uncertain knowledge from noise impact. Comprehensive experiments are conducted on three widely-used UDA benchmarks to demonstrate the effectiveness and robustness of our ROAD against noisy labels by comparing it with 13 state-of-the-art methods. Code is available at https://github.com/penghu-cs/ROAD.