Vision-Language Models (VLMs) excel at extracting salient visual features from query images, thus exhibiting promising visual recognition performance. However, VLMs would encounter significant degradation in fine-grained scenarios due to their deficiency in distinguishing nuanced differences among candidate categories. As a remedy, we draw inspiration from the ``System 1 & System 2" cognitive theory of humans, paving the way to achieve fine-grained recognition for VLMs. To be specific, we observe that VLMs naturally align with System 1, quickly identifying candidate categories but leaving easily-confused ones unresolved. Based on the observation, we propose System-2 enhanCed visuAl recogNition (SCAN), a novel plug-and-play approach that makes VLMs aware of nuanced differences. In brief, SCAN first specifies and abstracts the discriminative attributes for the confused candidate categories and query images by resorting to off-the-shelf large foundation models, respectively. After that, SCAN adaptively integrates the salient visual features from System 1 with the nuanced differences derived from System 2, resolving confusion in candidates with estimated uncertainty. Extensive experiments on eight widely used fine-grained recognition benchmarks against 10 state-of-the-art baselines verify the effectiveness and superiority of SCAN.

Due to the complexity of data collection in the real world, Multi-view Representation Learning (MvRL) always encounters the incomplete information challenge, typically manifested as the Sample-missing Problem (SP) and the View-unaligned Problem (VP). Although several methods have been proposed, they fail to find a good trade-off among sample restoration, view alignment, and data diversity preservation. To address this issue, we take and mathematically formulate two sociological concepts for MvRL, i.e., community commonality and community versatility, where the former refers to the identical custom shared within the same community, and the latter refers to the similar but non-identical custom within communities of the same minority. One could find that the community commonality can enhance the compactness of view-specific clusters, and the community versatility can preserve the view diversity. Moreover, combining both of them could facilitate achieving robust MvRL with incomplete information. With the formulations, we propose a novel method dubbed Community-Aware Multi-viEw RepresentAtion learning with incomplete information (CAMERA). In brief, CAMERA employs a novel dual-stream network and an elaborate objective function that theoretically and empirically embraces community commonality and versatility. Extensive experimental results on seven datasets demonstrate that CAMERA remarkably outperforms 24 competitive multi-view learning methods on clustering, classification, and human action recognition tasks.

The success of existing graph matching methods heavily relies on high-quality training data with complete and precise correspondences between keypoints across different graphs. However, this assumption is often violated in real-world scenarios, leading to partial correspondence and noisy correspondence challenges. In brief, partial correspondence arises from viewpoint occlusions, where certain keypoints (i.e., outliers) lack valid counterparts in the target graph, while noisy correspondence refers to both incorrectly established (i.e., false positives) and neglected (i.e., false negatives) correspondences due to annotation error. In this paper, we propose the first unified framework to address both partial and noisy correspondence challenges in graph matching. Specifically, we introduce a dual-expert cooperative framework that integrates Koopmans-Beckmann and Lawler's quadratic assignment programming formulations (KB-QAP and L-QAP) through an align-fuse-refine pipeline. In the alignment stage, the KB-QAP expert aligns keypoints and distinguishes inliers from outliers using a novel quadratic contrastive loss. In the fusion stage, the L-QAP expert employs a graph transformer on the association graph to merge the aligned graphs and incorporates a learnable outlier-rejection mechanism to handle partial correspondences. Finally, by exploiting the different noise resistances of the two experts, we identify and refine the false positive and false negative correspondences, thereby enhancing robustness against noisy correspondence. Extensive experiments on four widely-used graph matching datasets demonstrate the effectiveness of our method against 17 competitive baselines in both partial and noisy correspondence scenarios.

The success of most existing cross-modal retrieval methods heavily relies on the assumption that the given queries follow the same distribution of the source domain. However, such an assumption is easily violated in real-world scenarios due to the complexity and diversity of queries, thus leading to the query shift problem. Specifically, query shift refers to the online query stream originating from the domain that follows a different distribution with the source one. In this paper, we observe that query shift would not only diminish the uniformity (namely, within-modality scatter) of the query modality but also amplify the gap between query and gallery modalities. Based on the observations, we propose a novel method dubbed Test-time adaptation for Cross-modal Retrieval (TCR). In brief, TCR employs a novel module to refine the query predictions (namely, retrieval results of the query) and a joint objective to prevent query shift from disturbing the common space, thus achieving online adaptation for the cross-modal retrieval models with query shift. Expensive experiments demonstrate the effectiveness of the proposed TCR against query shift. Code is available at https://github.com/XLearning-SCU/2025-ICLR-TCR.

Text-to-visual retrieval often struggles with semantic redundancy and granularity mismatches between textual queries and visual content. Unlike existing methods that address these challenges during training, we propose VISual Abstraction (VISA), a test-time approach that enhances retrieval by transforming visual content into textual descriptions using off-the-shelf large models. The generated text descriptions, with their dense semantics, naturally filter out low-level redundant visual information. To further address granularity issues, VISA incorporates a question-answering process, enhancing the text description with the specific granularity information requested by the user. Extensive experiments demonstrate that VISA brings substantial improvements in text-to-image and text-to-video retrieval for both short- and long-context queries, offering a plug-and-play enhancement to existing retrieval systems.

Traditional text-based person ReID assumes that person descriptions from witnesses are complete and provided at once. However, in real-world scenarios, such descriptions are often partial or vague. To address this limitation, we introduce a new task called interactive person re-identification (Inter-ReID). Inter-ReID is a dialogue-based retrieval task that iteratively refines initial descriptions through ongoing interactions with the witnesses. To facilitate the study of this new task, we construct a dialogue dataset that incorporates multiple types of questions by decomposing fine-grained attributes of individuals. We further propose LLaVA-ReID, a question model that generates targeted questions based on visual and textual contexts to elicit additional details about the target person. Leveraging a looking-forward strategy, we prioritize the most informative questions as supervision during training. Experimental results on both Inter-ReID and text-based ReID benchmarks demonstrate that LLaVA-ReID significantly outperforms baselines.

Multimodal learning is pivotal for the advancement of artificial intelligence, enabling machines to integrate complementary information from diverse data sources for holistic perception and understanding. Despite significant progress, existing methods struggle with challenges such as noisy inputs, noisy correspondence, and the inherent uncertainty of multimodal data, limiting their reliability and robustness. To address these issues, this paper presents a novel Probabilistic Multimodal Learning framework (PML) that models each data point as a von Mises-Fisher (vMF) distribution, effectively capturing intrinsic uncertainty and enabling robust fusion. Unlike traditional Gaussian-based models, PML learns directional representation with a concentration parameter to quantify reliability directly, enhancing stability and interpretability. To enhance discrimination, we propose a von Mises-Fisher Prototypical Contrastive Learning paradigm (vMF-PCL), which projects data onto a hypersphere by pulling within-class samples closer to their class prototype while pushing between-class prototypes apart, adaptively learning the reliability estimations. Building upon the estimated reliability, we develop a Reliable Multimodal Fusion mechanism (RMF) that dynamically adjusts the contribution and conflict of each modality, ensuring robustness against noisy data, noisy correspondence, and uncertainty. Extensive experiments on nine benchmarks demonstrate the superiority of PML, consistently outperforming 14 state-of-the-art methods. Code is available at https://github.com/XLearning-SCU/2025-IJCAI-PML.

Vision-Language Pre-training (VLP) has shown promising performance in various tasks by learning a generic image-text representation space. However, most existing VLP methods encounter the Noisy Correspondence (NC) problem which refers to wrongly matched image-text pairs harvested from the wild. In this paper, we empirically study the influence of NC on the VLP model and obtain the following two observations. First, the NC will largely degrade the performance in downstream tasks even via fine-tuning, indicating the necessity of handling NC in the pre-training period. Second, the influence of NC varies in different pre-training objectives, suggesting the objective-customized solution for achieving NC robustness. Based on the above observations, we propose a novel NoisE-robust Vision-languagE pRe-training method (NEVER) to endow the VLP model with robustness against NC. In brief, NEVER first divides the training data into clean and noisy subsets in a progressive and adaptive manner. Then NEVER employs the positive learning (PL) and negative learning (NL) on the splits to enjoy model convergence and noise robustness, respectively. To further handle the false negative in PL and NL, NEVER proposes to smoothen and sharpen the training targets with the predictions from a twin momentum model. Extensive experiments on the various V+L tasks verify the effectiveness of the proposed method.

In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i.e., some intra-cluster samples are wrongly treated as negative pairs. Although promising performance has been achieved by these methods, the false negative issue is still far from addressed and the false positive issue emerges because all in- and out-of-neighborhood samples are simply treated as positive and negative, respectively. To address the issues, we propose a novel robust method, dubbed decoupled contrastive multi-view clustering with high-order random walks (DIVIDE). In brief, DIVIDE leverages random walks to progressively identify data pairs in a global instead of local manner. As a result, DIVIDE could identify in-neighborhood negatives and out-of-neighborhood positives. Moreover, DIVIDE embraces a novel MvC architecture to perform inter- and intra-view contrastive learning in different embedding spaces, thus boosting clustering performance and embracing the robustness against missing views. To verify the efficacy of DIVIDE, we carry out extensive experiments on four benchmark datasets comparing with nine state-of-the-art MvC methods in both complete and incomplete MvC settings. The code is released on https://github.com/XLearning-SCU/2024-AAAI-DIVIDE.

Test-time adaptation (TTA) has emerged as a new paradigm for reconciling distribution shifts across domains without accessing source data. However, existing TTA methods mainly concentrate on uni-modal tasks, overlooking the complexity of multi-modal scenarios. In this paper, we delve into the multi-modal test-time adaptation and reveal a new challenge named reliability bias. Different from the definition of traditional distribution shifts, reliability bias refers to the information discrepancies across different modalities derived from intra-modal distribution shifts. To solve the challenge, we propose a novel method, dubbed REliable fusion and robust ADaptation (READ). On the one hand, unlike the existing TTA paradigm that mainly repurposes the normalization layers, READ employs a new paradigm that modulates the attention between modalities in a self-adaptive way, supporting reliable fusion against reliability bias. On the other hand, READ adopts a novel objective function for robust multi-modal adaptation, where the contributions of confident predictions could be amplified and the negative impacts of noisy predictions could be mitigated. Moreover, we introduce two new benchmarks to facilitate comprehensive evaluations of multi-modal TTA under reliability bias. Extensive experiments on the benchmarks verify the effectiveness of our method against multi-modal reliability bias. The code and benchmarks are available at https://github.com/XLearning-SCU/2024-ICLR-READ.

Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to efficiently narrow the performance gap between pre-training and downstream. There are two important factors for various PEFTs, namely, the accessible data size and fine-tunable parameter size. A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size. However, according to the evaluation of five PEFTs on two downstream vision-language (VL) tasks, we find that such an intuition holds only if the downstream data and task are not consistent with pre-training. For downstream fine-tuning consistent with pre-training, data size no longer affects the performance, while the influence of fine-tunable parameter size is not monotonous. We believe such an observation could guide the choice of training strategy for various PEFTs.

Recently, contrastive multi-view clustering (MvC) has emerged as a promising avenue for analyzing data from heterogeneous sources, typically leveraging the off-the-shelf instances as positives and randomly sampled ones as negatives. In practice, however, this paradigm would unavoidably suffer from the Dual Noisy Correspondence (DNC) problem, where noise compromises the constructions of both positive and negative pairs. Specifically, the complexity of data collection and transmission might mistake some unassociated pairs as positive (namely, false positive correspondence), while the intrinsic one-to-many contrast nature of contrastive MvC would sample some intra-cluster samples as negative (namely, false negative correspondence). To handle this daunting problem, we propose a novel method, dubbed Contextually-spectral based correspondence refinery (CANDY). CANDY dexterously exploits inter-view similarities as \textit{context} to uncover false negatives. Furthermore, it employs a spectral-based module to denoise correspondence, alleviating the negative influence of false positives. Extensive experiments on five widely-used multi-view benchmarks, in comparison with eight competitive multi-view clustering methods, verify the effectiveness of our method in addressing the DNC problem. The code is available at https://github.com/XLearning-SCU/2024-NeurIPS-CANDY.

Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign or impute defective ones, but such preconditions cannot always be satisfied in practice due to the complexity of data collection and transmission. To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples. To be specific, we discover the existence of invariant semantic distribution across different views, which enables SMILE to alleviate the cross-view discrepancy to learn consensus semantics without requiring any paired samples. The resulting consensus semantics remains unaffected by cross-view distribution shifts, making them useful for realigning/imputing defective instances and forming clusters. We demonstrate the effectiveness of SMILE through extensive comparison experiments with 13 state-of-the-art baselines on five benchmarks. Our approach improves the clustering accuracy of NoisyMNIST from 19.3%/23.2% to 82.7%/69.0% when the correspondences/instances are fully incomplete. We will release the code after acceptance.

The success of existing cross-modal retrieval (CMR) methods heavily rely on the assumption that the annotated cross-modal correspondence is faultless. In practice, however, the correspondence of some pairs would be inevitably contaminated during data collection or annotation, thus leading to the so-called Noisy Correspondence (NC) problem. To alleviate the influence of NC, we propose a novel method termed Consistency REfining And Mining (CREAM) by revealing and exploiting the difference between correspondence and consistency. Specifically, the correspondence and the consistency only be coincident for true positive and true negative pairs, while being distinct for false positive and false negative pairs. Based on the observation, CREAM employs a collaborative learning paradigm to detect and rectify the correspondence of positives, and a negative mining approach to explore and utilize the consistency. Thanks to the consistency refining and mining strategy of CREAM, the overfitting on the false positives could be prevented and the consistency rooted in the false negatives could be exploited, thus leading to a robust CMR method. Extensive experiments verify the effectiveness of our method on three image-text benchmarks including Flickr30K, MS-COCO, and Conceptual Captions. Furthermore, we adopt our method into the graph matching task and the results demonstrate the robustness of our method against fine-grained NC problem. The code is available on https://github.com/XLearning-SCU/2024-TIP-CREAM.

In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC). In brief, on the one hand, due to the poor recognizability and viewpoint differences between images, it is inevitable to inaccurately annotate some keypoints with offset and confusion, leading to the mismatch between two associated nodes, i.e., NNC. On the other hand, the noisy node-to-node correspondence will further contaminate the edge-to-edge correspondence, thus leading to ENC. For the BNC challenge, we propose a novel method termed Contrastive Matching with Momentum Distillation. Specifically, the proposed method is with a robust quadratic contrastive loss which enjoys the following merits: i) better exploring the node-to-node and edge-to-edge correlations through a GM customized quadratic contrastive learning paradigm; ii) adaptively penalizing the noisy assignments based on the confidence estimated by the momentum teacher. Extensive experiments on three real-world datasets show the robustness of our model compared with 12 competitive baselines. The code is available at https://github.com/XLearning-SCU/2023-ICCV-COMMON.

In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance commonality refers to that within-cluster instances should share a common pattern, and ii) view versatility refers to that cross-view samples should own view-specific patterns. To this end, we design a novel dual-stream model which employs a dual attention layer and a dual contrastive learning loss to learn view-specific prototypes and model the sample-prototype relationship. When the view is missed, our model performs data recovery using the prototypes in the missing view and the sample-prototype relationship inherited from the observed view. Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC. Extensive experiments demonstrate the superiority of our method on five challenging benchmarks compared with 11 approaches. The code could be accessed from https://pengxi.me.

The success of existing multi-view clustering methods heavily relies on the assumption of view consistency and instance completeness, referred to as the complete information. However, these two assumptions would be inevitably violated in data collection and transmission, thus leading to the so-called Partially View-unaligned Problem (PVP) and Partially Sample-missing Problem (PSP). To overcome such incomplete information challenges, we propose a novel method, termed robuSt mUlti-view clusteRing with incomplEte information (SURE), which solves PVP and PSP under a unified framework. In brief, SURE is a novel contrastive learning paradigm which uses the available pairs as positives and randomly chooses some cross-view samples as negatives. To reduce the influence of the false negatives caused by random sampling, SURE is with a noise-robust contrastive loss that theoretically and empirically mitigates or even eliminates the influence of the false negatives. To the best of our knowledge, this could be the first successful attempt that simultaneously handles PVP and PSP using a unified solution. In addition, this could be one of the first studies on the noisy correspondence problem (i.e., the false negatives) which is a novel paradigm of noisy labels. Extensive experiments demonstrate the effectiveness and efficiency of SURE comparing with 10 state-of-the-art approaches on the multi-view clustering task.

In this paper, we study an untouched problem in visible-infrared person re-identification (VI-ReID), namely, Twin Noise Labels (TNL) which refers to as noisy annotation and correspondence. In brief, on the one hand, it is inevitable to annotate some persons with the wrong identity due to the complexity in data collection and annotation, e.g., the poor recognizability in the infrared modality. On the other hand, the wrongly annotated data in a single modality will eventually contaminate the cross-modal correspondence, thus leading to noisy correspondence. To solve the TNL problem, we propose a novel method for robust VI-ReID, termed DuAlly Robust Training (DART). In brief, DART first computes the clean confidence of annotations by resorting to the memorization effect of deep neural networks. Then, the proposed method rectifies the noisy correspondence with the estimated confidence and further divides the data into four groups for further utilizations. Finally, DART employs a novel dually robust loss consisting of a soft identification loss and an adaptive quadruplet loss to achieve robustness on the noisy annotation and noisy correspondence. Extensive experiments on SYSU-MM01 and RegDB datasets verify the effectiveness of our method against the twin noisy labels compared with five state-of-the-art methods. The code could be accessed from https://github.com/XLearning-SCU/2022-CVPR-DART.

In real-world applications, it is common that only a portion of data is aligned across views due to spatial, temporal, or spatiotemporal asynchronism, thus leading to the so-called Partially View-aligned Problem (PVP). To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss. In brief, for each sample from one view, our method aims to identify its within-category counterparts from other views, and thus the cross-view correspondence could be established. As the contrastive learning needs data pairs as input, we construct positive pairs using the known correspondences and negative pairs using random sampling. To alleviate or even eliminate the influence of the false negatives caused by random sampling, we propose a noise-robust contrastive loss that could adaptively prevent the false negatives from dominating the network optimization. To the best of our knowledge, this could be the first successful attempt of enabling contrastive learning robust to noisy labels. In fact, this work might remarkably enrich the learning paradigm with noisy labels. More specifically, the traditional noisy labels are defined as incorrect annotations for the supervised tasks such as classification. In contrast, this work proposes that the view correspondence might be false, which is remarkably different from the widely-accepted definition of noisy label. Extensive experiments show the promising performance of our method comparing with 10 state-of-the-art multi-view approaches in the clustering and classification tasks. The code will be publicly released at https://pengxi.me.