We introduce OpenLEAF, a new benchmark designed for the emerging open-domain interleaved image-text generation task. This task aims to generate arbitrarily interleaved multimodal content from input queries. It goes beyond commonly seen single-modality image or text generation, thereby enabling various novel applications by creating content such as visual storybooks and how-to instructions. Despite the importance of this task, no established benchmark exists due to the challenges in defining evaluation scenarios and formulating effective metrics. To introduce and facilitate the new task of interleaved image-text generation, we create a new dataset covering queries with various input-output formats and 10 different application scenarios. We also propose a novel evaluation pipeline named "detection-summarization-scoring," which breaks down the evaluation into multiple reasoning steps. This pipeline leverages large multimodal models (LMMs) to thoroughly evaluate ten aspects of the generated content, which leads to the final rating. With experiments on a proposed agent system, we demonstrate that our evaluation method aligns closely with human judgments, thus together with the dataset, offering the research community a valuable benchmark for exploring interleaved image-text generation.
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In this paper, we present the One-shot In-context Part Segmentation (OIParts) framework, designed to tackle the challenges of part segmentation by leveraging visual foundation models (VFMs). Existing training-based one-shot part segmentation methods that utilize VFMs encounter difficulties when faced with scenarios where the one-shot image and test image exhibit significant variance in appearance and perspective, or when the object in the test image is partially visible. We argue that training on the one-shot example often leads to overfitting, thereby compromising the model's generalization capability. Our framework offers a novel approach to part segmentation that is training-free, flexible, and data-efficient, requiring only a single in-context example for precise segmentation with superior generalization ability. By thoroughly exploring the complementary strengths of VFMs, specifically DINOv2 and Stable Diffusion, we introduce an adaptive channel selection approach by minimizing the intra-class distance for better exploiting these two features, thereby enhancing the discriminatory power of the extracted features for the fine-grained parts. We have achieved remarkable segmentation performance across diverse object categories. The OIParts framework not only eliminates the need for extensive labeled data but also demonstrates superior generalization ability. Through comprehensive experimentation on three benchmark datasets, we have demonstrated the superiority of our proposed method over existing part segmentation approaches in one-shot settings. Code is available at https://github.com/dai647/OIParts.
In various domains such as transportation, resource management, and weather forecasting, there is an urgent need for methods that can provide predictions over a sufficiently long time horizon to encompass the period required for decision-making and implementation. Compared to traditional time series forecasting, ultra-long time series forecasting requires enhancing the model's ability to infer long time series, while maintaining inference costs within an acceptable range. To address this challenge, we propose the Boundary-Aware Periodicity-based sparsification strategy for Ultra-Long time series forecasting (BAP-UL).This method effectively captures periodic features in time series and reorganizes inputs and outputs into shorter sub-sequences for improved prediction accuracy. In the paper, we investigate several commonly used benchmark datasets and demonstrate that the proposed method can yield comparable performance across them.
Image-to-image translation is defined as the process of learning a mapping between images from a source domain and images from a target domain. The probabilistic structure that maps a fixed initial state to a pinned terminal state through a standard Wiener process is a Brownian bridge. In this paper, we propose a score-based Stochastic Differential Equation (SDE) approach via the Brownian bridges, termed the Amenable Brownian Bridges (A-Bridges), to image-to-image translation tasks as an unconditional diffusion model. Our framework embraces a large family of Brownian bridge models, while the discretization of the linear A-Bridge exploits its advantage that provides the explicit solution in a closed form and thus facilitates the model training. Our model enables the accelerated sampling and has achieved record-breaking performance in sample quality and diversity on benchmark datasets following the guidance of its SDE structure.
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on various visual-language understanding and generation tasks. However, MLLMs occasionally generate content inconsistent with the given images, which is known as "hallucination". Prior works primarily center on evaluating hallucination using standard, unperturbed benchmarks, which overlook the prevalent occurrence of perturbed inputs in real-world scenarios-such as image cropping or blurring-that are critical for a comprehensive assessment of MLLMs' hallucination. In this paper, to bridge this gap, we propose Hallu-PI, the first benchmark designed to evaluate Hallucination in MLLMs within Perturbed Inputs. Specifically, Hallu-PI consists of seven perturbed scenarios, containing 1,260 perturbed images from 11 object types. Each image is accompanied by detailed annotations, which include fine-grained hallucination types, such as existence, attribute, and relation. We equip these annotations with a rich set of questions, making Hallu-PI suitable for both discriminative and generative tasks. Extensive experiments on 12 mainstream MLLMs, such as GPT-4V and Gemini-Pro Vision, demonstrate that these models exhibit significant hallucinations on Hallu-PI, which is not observed in unperturbed scenarios. Furthermore, our research reveals a severe bias in MLLMs' ability to handle different types of hallucinations. We also design two baselines specifically for perturbed scenarios, namely Perturbed-Reminder and Perturbed-ICL. We hope that our study will bring researchers' attention to the limitations of MLLMs when dealing with perturbed inputs, and spur further investigations to address this issue. Our code and datasets are publicly available at https://github.com/NJUNLP/Hallu-PI.
Text-driven 3D indoor scene generation aims to automatically generate and arrange the objects, which form a 3D scene that accurately captures the semantics detailed in the given text description. Recent works have shown the potential to generate 3D scenes guided by specific object categories and room layouts but lack a robust mechanism to maintain consistent spatial relationships in alignment with the provided text description during the 3D scene generation. Besides, the annotations of the object and relationships of the 3D scenes are usually time- and cost-consuming, which are not easily obtained for the model training. Thus, in this paper, we conduct a dataset and benchmark for assessing spatial relations in text-driven 3D scene generation, which contains a comprehensive collection of 3D scenes, including textual descriptions, annotating object spatial relations, and providing both template and free-form natural language descriptions. We also provide a pseudo description feature generation method to address the 3D scenes without language annotations. We design an aligned latent space for spatial relation in 3D scenes and text description, in which we can sample the features according to the spatial relation for the few-shot learning. We also propose new metrics to investigate the ability of the approach to generate correct spatial relationships among objects.
Model compression and distillation techniques have become essential for deploying deep learning models efficiently. However, existing methods often encounter challenges related to model generalization and scalability for harnessing the expertise of pre-trained large models. This paper introduces CoTuning, a novel framework designed to enhance the generalization ability of neural networks by leveraging collaborative learning between large and small models. CoTuning overcomes the limitations of traditional compression and distillation techniques by introducing strategies for knowledge exchange and simultaneous optimization. Our framework comprises an adapter-based co-tuning mechanism between cloud and edge models, a scale-shift projection for feature alignment, and a novel collaborative knowledge distillation mechanism for domain-agnostic tasks. Extensive experiments conducted on various benchmark datasets demonstrate the effectiveness of CoTuning in improving model generalization while maintaining computational efficiency and scalability. The proposed framework exhibits a significant advancement in model compression and distillation, with broad implications for research in the collaborative evolution of large-small models.
Interpretable and robust medical diagnoses are essential traits for practicing clinicians. Most computer-augmented diagnostic systems suffer from three major problems: non-interpretability, limited modality analysis, and narrow focus. Existing frameworks can either deal with multimodality to some extent but suffer from non-interpretability or partially interpretable but provide a limited modality and multifaceted capabilities. Our work aims to integrate all these aspects in one complete framework to fully utilize the full spectrum of information offered by multiple modalities and facets. We propose our solution via our novel architecture VR-DiagNet, consisting of a planner and a classifier, optimized iteratively and cohesively. VR-DiagNet simulates the perceptual process of clinicians via the use of volumetric imaging information integrated with radiomic features modality; at the same time, it recreates human thought processes via a customized Monte Carlo Tree Search (MCTS) which constructs a volume-tailored experience tree to identify slices of interest (SoIs) in our multi-slice perception space. We conducted extensive experiments across two diagnostic tasks comprising six public medical volumetric benchmark datasets. Our findings showcase superior performance, as evidenced by heightened accuracy and area under the curve (AUC) metrics, reduced computational overhead, and expedited convergence while conclusively illustrating the immense value of integrating volumetric and radiomic modalities for our current problem setup.
Event extraction (EE) is a critical direction in the field of information extraction, laying an important foundation for the construction of structured knowledge bases. EE from text has received ample research and attention for years, yet there can be numerous real-world applications that require direct information acquisition from speech signals, online meeting minutes, interview summaries, press releases, etc. While EE from speech has remained under-explored, this paper fills the gap by pioneering a SpeechEE, defined as detecting the event predicates and arguments from a given audio speech. To benchmark the SpeechEE task, we first construct a large-scale high-quality dataset. Based on textual EE datasets under the sentence, document, and dialogue scenarios, we convert texts into speeches through both manual real-person narration and automatic synthesis, empowering the data with diverse scenarios, languages, domains, ambiences, and speaker styles. Further, to effectively address the key challenges in the task, we tailor an E2E SpeechEE system based on the encoder-decoder architecture, where a novel Shrinking Unit module and a retrieval-aided decoding mechanism are devised. Extensive experimental results on all SpeechEE subsets demonstrate the efficacy of the proposed model, offering a strong baseline for the task. At last, being the first work on this topic, we shed light on key directions for future research. Our codes and the benchmark datasets are open at https://SpeechEE.github.io/
The emergence of video captioning makes it possible to automatically generate natural language description for a given video. However, generating detailed video descriptions that incorporate domain-specific information remains an unsolved challenge, holding significant research and application value, particularly in domains such as sports commentary generation. Moreover, sports event commentary goes beyond being a mere game report, it involves entertaining, metaphorical, and emotional descriptions. To promote the field of sports commentary automatic generation, in this paper, we introduce a novel dataset, the Basketball Highlight Commentary (BH-Commentary), comprising approximately 4K basketball highlight videos with groundtruth commentaries from professional commentators. In addition, we propose an end-to-end framework as a benchmark for basketball highlight commentary generation task, in which a lightweight and effective prompt strategy is designed to enhance alignment fusion among visual and textual features. Experimental results on the BH-Commentary dataset demonstrate the validity of the dataset and the effectiveness of the proposed benchmark for sports highlight commentary generation.
Text to Motion Retrieval (TMR) is an emerging task to retrieve relevant motion sequences with the nature language description. The dominant approach learns a joint embedding space to measure global-level similarities. However, simple global embeddings are insufficient to represent complicated motion and textual details, such as the movement of specific body parts and the coordination among these body parts. In addition, most of the motion variations occur subtly and locally, resulting in semantic vagueness among these motions, which further presents considerable challenges in precisely aligning motion sequences with texts. To address these challenges, we propose a novel Modal-Enhanced Semantic Modeling (MESM) method, focusing on fine-grained alignment through enhanced modal semantics. Specifically, we develop a prompt-enhanced textual module (PTM) to generate detailed descriptions of specific body part movements, which comprehensively captures the fine-grained textual semantics for precise matching. We employ a skeleton-enhanced motion module (SMM) to effectively enhance the model's capability to represent intricate motions. This module leverages a graph convolutional network to meticulously model the intricate spatial dependencies among relevant body parts. To improve the sensitivity to the subtle motions, we further propose a text-driven semantics interaction module (TSIM). The TSIM assigns motion features into a set of aggregated descriptors and employs cross-attention to aggregate discriminative motion embeddings guided by text, enabling precise semantic alignment between subtle motions and corresponding texts. Extensive experiments conducted on two widely used benchmark datasets, HumanML3D and KIT-ML, demonstrate the effectiveness of our proposed method. Our approach outperforms existing state-of-the-art retrieval methods, achieving significant Rsum improvements of 24.28% on HumanML3D and 25.80% on KIT-ML.
Most existing methods for text-based person retrieval focus on text-to-image person retrieval. Nevertheless, due to the lack of dynamic information provided by isolated frames, the performance is hampered when the person is obscured or variable motion details are missed in isolated frames. To overcome this, we propose a novel Text-to-Video Person Retrieval (TVPR) task. Since there is no dataset or benchmark that describes person videos with natural language, we construct a large-scale cross-modal person video dataset containing detailed natural language annotations, termed as Text-to-Video Person Reidentification (TVPReid) dataset. In this paper, we introduce a Multielement Feature Guided Fragments Learning (MFGF) strategy, which leverages the cross-modal text-video representations to provide strong text-visual and text-motion matching information to tackle uncertain occlusion conflicting and variable motion details. Specifically, we establish two potential cross-modal spaces for text and video feature collaborative learning to progressively reduce the semantic difference between text and video. To evaluate the effectiveness of the proposed MFGF, extensive experiments have been conducted on TVPReid dataset. To the best of our knowledge, MFGF is the first successful attempt to use video for text-based person retrieval task and has achieved state-of-the-art performance on TVPReid dataset. The TVPReid dataset will be publicly available to benefit future research.
Current state-of-the-art video quality assessment (VQA) models typically integrate various perceptual features to comprehensively represent video quality degradation. These models either directly concatenate features or fuse different perceptual scores while ignoring the domain gaps between cross-aware features, thus failing to adequately learn the correlations and interactions between different perceptual features. To this end, we analyze the independent effects and information gaps of quality-and semantic-aware features on video quality. Based on an analysis of the spatial and temporal differences between two aware features, we propose a semantic-Aware and quality-Aware Interaction Network (A2INet) for blind VQA. For spatial gaps, we introduce a cross-aware guided interaction module to enhance the interaction between semantic-and quality-aware features in a local-to-global manner. Considering temporal discrepancies, we design a cross-aware temporal modeling module to further perceive temporal content variation and quality saliency information, and perceptual features are regressed into quality score by a temporal network and a temporal pooling. Extensive experiments on six benchmark VQA datasets show that our model achieves state-of-the-art performance, and ablation studies further validate the effectiveness of each module. We also present a simple video sampling strategy to balance the effectiveness and efficiency of the model. The code for the proposed method will be released at https://github.com/JianjunXiang/A2INet.
D3U-Net: Dual-Domain Collaborative Optimization Deep Unfolding Network for Image Compressive Sensing
Deep unfolding network (DUN) is a powerful technique for image compressive sensing that bridges the gap between optimization methods and deep networks. However, DUNs usually rely heavily on single-domain information, overlooking the inter-domain dependencies. Therefore, such DUNs often face the following challenges: 1) information loss due to the inefficient representation within a single domain, and 2) limited robustness due to the absence of inter-domain dependencies. To overcome these challenges, we propose a deep unfolding framework D^3U-Net that establishes a dual-domain collaborative optimization scheme. This framework introduces both visual representations from the image domain and multi-resolution analysis provided by the wavelet domain. Such dual-domain representations constrain the feasible region within the solution space more accurately. Specifically, we design a consistency-difference collaborative mechanism to capture inter-domain dependencies effectively. This mechanism not only enhances the fidelity of reconstruction but also enriches the depth and breadth of extracted features, improving the overall robustness and reconstruction quality. Moreover, we develop an inter-stage transmission pathway to minimize the information loss during transmission while broadcasting multi-scale features in a frequency-adaptive manner. Extensive experimental results on various benchmark datasets show the superior performance of our method.
The increasing use of smartphones for capturing documents in various real-world conditions has underscored the need for robust document localization technologies. Current challenges in this domain include handling diverse document types, complex backgrounds, and varying photographic conditions such as low contrast and occlusion. However, there currently are no publicly available datasets containing these complex scenarios and few methods demonstrate their capabilities on these complex scenes. To address these issues, we create a new comprehensive real-world document localization benchmark dataset which contains the complex scenarios mentioned above and propose a novel Real-world Document Localization Network (RDLNet) for locating targeted documents in the wild. The RDLNet consists of an innovative light-SAM encoder and a masked attention decoder. Utilizing light-SAM encoder, the RDLNet transfers the mighty generalization capability of SAM to the document localization task. In the decoding stage, the RDLNet exploits the masked attention and object query method to efficiently output the triple-branch predictions consisting of corner point coordinates, instance-level segmentation area and categories of different documents without extra post-processing. We compare the performance of RDLNet with other state-of-the-art approaches for real-world document localization on multiple benchmarks, the results of which reveal that the RDLNet remarkably outperforms contemporary methods, demonstrating its superiority in terms of both accuracy and practicability.
Cartoon animal parsing aims to segment the body parts such as heads, arms, legs and tails of cartoon animals. Different from previous parsing tasks, cartoon animal parsing faces new challenges, including irregular body structures, abstract drawing styles and diverse animal categories. Existing methods have difficulties when addressing these challenges caused by the spatial and structural properties of cartoon animals. To address these challenges, a novel spatial learning and structural modeling network, named CAPNet, is proposed for cartoon animal parsing. It aims to address the critical problems of spatial perception, structure modeling and spatial-structural consistency learning. A spatial-aware learning module integrates deformable convolutions to learn spatial features of diverse cartoon animals. The multi-task edge and center point prediction mechanism is incorporated to capture the intricate spatial patterns. A structural modeling method is proposed to model the complex structural representations of cartoon animals, which integrates a graph neural network with a shape-aware relation learning module. To mitigate the significant differences among animals, a spatial and structural consistency learning strategy is proposed to capture and learn feature correlations across different animal species. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed approach, which outperforms the state-of-the-art methods.
Generating diverse plausible outputs from a single input is crucial for addressing visual ambiguities, exemplified in medical imaging where experts may provide varying semantic segmentation annotations for the same image.Existing methods handles ambiguous segmentation relying on probabilistic modeling and extensive multi-output annotated data while often struggles with limited ambiguously labeled datasets common in real-world applications.To surmount the challenge, we propose P²SAM, a novel framework that leverages the Segment Anything Model (SAM)'s prior knowledge for ambiguous object segmentation. By transforming SAM's sensitivity to prompts into an advantage, we introduce a prior probabilistic space for prompts.Experimental results show that P²SAM significantly enhances medical segmentation precision and diversity using minimal ambiguously annotated samples. Benchmarking against state-of-the-art methods demonstrates superior performance with just 5.5% of the training data (+12% Dmax). This approach marks a significant advancement towards deploying probabilistic models in data-limited real-world scenarios.
Engaging in conversational recommendations within a specific scenario represents a promising paradigm in the real world. Scenario-relevant situations often affect conversations and recommendations from two closely related aspects: varying the appealingness of items to users, namely situated item representation, and shifting user interests in the targeted items, namely situated user preference. We highlight that considering those situational factors is crucial, as this aligns with the realistic conversational recommendation process in the physical world. However, it is challenging yet under-explored. In this work, we are pioneering to bridge this gap and introduce a novel setting: Situated Conversational Recommendation Systems (SCRS). We observe an emergent need for high-quality datasets, and building one from scratch requires tremendous human effort. To this end, we construct a new benchmark, named SCREEN, via a role-playing method based on multimodal large language models. We take two multimodal large language models to play the roles of a user and a recommender, simulating their interactions in a co-observed scene. Our SCREEN comprises over 20k dialogues across 1.5k diverse situations, providing a rich foundation for exploring situational influences on conversational recommendations. Based on the SCREEN, we propose three worth-exploring subtasks and evaluate several representative baseline models. Our evaluations suggest that the benchmark is high quality, establishing a solid experimental basis for future research. The code and data are available at https://github.com/DongdingLin/SCREEN.
One-shot object detection (OSOD) uses a query patch to identify the same category of object in a target image. As the OSOD setting, the target images are required to contain the object category of the query patch, and the image styles (domains) of the query patch and target images are always similar. However, in practical application, the above requirements are not commonly satisfied. Therefore, we propose a new problem namely Cross-Domain Object Search (CDOS), where the object categories of the query patch and target image are decoupled, and the image styles between them may also be significantly different. For this problem, we develop a new method, which incorporates both foreground-background contrastive learning heads and a domain-generalized feature augmentation technique. This makes our method effectively handle the object category gap and domain distribution gap, between the query patch and target image in the training and testing datasets. We further build a new benchmark for the proposed CDOS problem, on which our method shows significant performance improvements over the comparison methods.
PC2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval
PDF ↗In the realm of cross-modal retrieval, seamlessly integrating diverse modalities within multimedia remains a formidable challenge, especially given the complexities introduced by noisy correspondence learning (NCL). Such noise often stems from mismatched data pairs, which is a significant obstacle distinct from traditional noisy labels. This paper introduces Pseudo-Classification based Pseudo-Captioning (PC2) framework to address this challenge. PC2 offers a threefold strategy: firstly, it establishes an auxiliary ''pseudo-classification'' task that interprets captions as categorical labels, steering the model to learn image-text semantic similarity through a non-contrastive mechanism. Secondly, unlike prevailing margin-based techniques, capitalizing on PC2's pseudo-classification capability, we generate pseudo-captions to provide more informative and tangible supervision for each mismatched pair. Thirdly, the oscillation of pseudo-classification is borrowed to assistant the correction of correspondence. In addition to technical contributions, we develop a realistic NCL dataset called Noise of Web (NoW), which could be a new powerful NCL benchmark where noise exists naturally. Empirical evaluations of PC2 showcase marked improvements over existing state-of-the-art robust cross-modal retrieval techniques on both simulated and realistic datasets with various NCL settings. The contributed dataset and source code are released at https://github.com/alipay/PC2-NoiseofWeb.