Reviewerly/exHarmony huggingface dataset:
Multi-Spectral Image Stitching via Spatial Graph Reasoning

Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view~(FOV) scene. The primary challenge of this task is to explore the relations between multi-spectral images for aligning and integrating multi-view scenes. Capitalizing on the strengths of Graph Convolutional Networks (GCNs) in modeling feature relationships, we propose a spatial graph reasoning based multi-spectral image stitching method that effectively distills the deformation and integration of multi-spectral images across different viewpoints. To accomplish this, we embed multi-scale complementary features from the same view position into a set of nodes. The correspondence across different views is learned through powerful dense feature embeddings, where both inter- and intra-correlations are developed to exploit cross-view matching and enhance inner feature disparity. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features, generating informative and reliable wide FOV scenes. Moreover, we release a challenging dataset named ChaMS, comprising both real-world and synthetic sets with significant parallax, providing a new option for comprehensive evaluation. Extensive experiments demonstrate that our method surpasses the state-of-the-arts.
VPA: Fully Test-Time Visual Prompt Adaptation

Textual prompt tuning has demonstrated significant performance improvements in adapting natural language processing models to a variety of downstream tasks by treating hand-engineered prompts as trainable parameters. Inspired by the success of textual prompting, several studies have investigated the efficacy of visual prompt tuning. In this work, we present Visual Prompt Adaptation (VPA), the first framework that generalizes visual prompting with test-time adaptation. VPA introduces a small number of learnable tokens, enabling fully test-time and storage-efficient adaptation without necessitating source-domain information. We examine our VPA design under diverse adaptation settings, encompassing single-image, batched-image, and pseudo-label adaptation. We evaluate VPA on multiple tasks, including out-of-distribution (OOD) generalization, corruption robustness, and domain adaptation. Experimental results reveal that VPA effectively enhances OOD generalization by 3.3% across various models, surpassing previous test-time approaches. Furthermore, we show that VPA improves corruption robustness by 6.5% compared to strong baselines. Finally, we demonstrate that VPA also boosts domain adaptation performance by relatively 5.2%. Our VPA also exhibits marked effectiveness in improving the robustness of zero-shot recognition for vision-language models.
Occluded Skeleton-Based Human Action Recognition with Dual Inhibition Training

Recently, skeleton-based human action recognition has received widespread attention in computer vision community. However, most existing research focuses on improving the recognition accuracy on complete skeleton data, while ignoring the performance on the incomplete skeleton data with occlusion or noise. This paper addresses occluded and noise-robust skeleton-based action recognition and presents a novel Dual Inhibition Training strategy. Specifically, we propose Part-aware and Dual-inhibition Graph Convolutional Network (PDGCN), which comprises of three parts: Input Skeleton Inhibition (ISI), Part-Aware Representation Learning (PARL) and Predicted Score Inhibition (PSI). The ISI and PSI are plug and play modules which could encourage the model to learn discriminative features from diversified body joints by effectively simulating key body part occlusions and random occlusions. The PARL module learns both the global and local representations from the whole body and body parts, respectively, and progressively fuses them during representation learning to enhance the model robustness under occlusions. Finally, we design different settings for occluded skeleton-based human action recognition to deep study this problem and better evaluate different approaches. Our approach achieves state-of-the-art results on different benchmarks and dramatically outperforms the recent skeleton-based action recognition approaches, especially under large-scale temporal occlusion.
Digging into Depth Priors for Outdoor Neural Radiance Fields

Neural Radiance Fields (NeRFs) have demonstrated impressive performance in vision and graphics tasks, such as novel view synthesis and immersive reality. However, the shape-radiance ambiguity of radiance fields remains a challenge, especially in the sparse viewpoints setting. Recent work resorts to integrating depth priors into outdoor NeRF training to alleviate the issue. However, the criteria for selecting depth priors and the relative merits of different priors have not been thoroughly investigated. Moreover, the relative merits of selecting different approaches to use the depth priors is also an unexplored problem. In this paper, we provide a comprehensive study and evaluation of employing depth priors to outdoor neural radiance fields, covering common depth sensing technologies and most application ways. Specifically, we conduct extensive experiments with two representative NeRF methods equipped with four commonly-used depth priors and different depth usages on two widely used outdoor datasets. Our experimental results reveal several interesting findings that can potentially benefit practitioners and researchers in training their NeRF models with depth priors. Project page: https://cwchenwang.github.io/outdoor-nerf-depth
CoCa: A Connectivity-Aware Cascade Framework for Histology Gland Segmentation

Gland segmentation is crucial for computer-aided diagnosis of adenocarcinoma. However, Topologically Critical Areas (TCAs), such as background tissues between two adjacent glands, can easily cause under- or over-connection of gland topological structures that may lead to the opposite diagnostic of the malignancy degree. Therefore, we provide a novel perspective for gland segmentation by incorporating gland connectivity information to locate critical errors within TCAs. We propose a Connectivity-Aware Cascade framework (CoCa) that explicitly encodes gland connectivity information into the network to locate all connectivity errors during training and then leverage attention operations to focus on these errors. Since under- or over-connected glands can change the Betti number (e.g., number of connected components) of glands, we design a Connectivity Refinement Module (CRM) to compare the Betti number of each gland to locate connectivity errors. We propose CoCa-Net to mine the topological relations among different biomedical entities to guide gland prediction. We also use contrastive learning to separate pixel embeddings of different classes within TCAs through our connectivity-aware hard example sampling strategy. Extensive experiments on the GlaS and CRAG datasets demonstrate the effectiveness of CoCa over state-of-the-art methods.
Towards Adaptable Graph Representation Learning: An Adaptive Multi-Graph Contrastive Transformer

Significant progress has been made in graph representation learning in recent years. However, most of these methods model spatial relationships via predefined graphs or decouple spatial-temporal representations, which limits the generalization and effectiveness of the model. To address these issues, we introduce an adaptive multi-graph contrastive transformer (AMGCT) for general spatial-temporal graph representation learning. Specifically, we first propose adaptive multi-graph contrastive learning (AMGCL). Without any expert knowledge, AMGCL can gradually generate adaptive spatial graphs with different topologies to learn spatial representations from different views. Cross-graph contrastive learning further explores potential correlations between different views, making each view's features more discriminative. In addition, to avoid insufficient interaction caused by decoupling spatial-temporal information in existing methods, we design a coupled graph transformer (CGT) to consider spatial relationships at each stage of temporal modeling, explore complementary information between spatial and temporal domains, and obtain more compact spatial-temporal representations. Experimental results on two different spatial-temporal graph datasets and tasks demonstrate that the proposed method achieves excellent performance.
ALDA: An Adaptive Layout Design Assistant for Diverse Posters throughout the Design Process

Layout generation is important in the field of graphic design and has attracted intensive research attention recently. To further prompt human-computer interactions, we construct the ALDA to assist users throughout the design process, which achieves adaptive and diverse content expansions upon only one element for beginners and generates high-quality posters subsequently. Specifically, to obtain diverse contents, we propose aesthetic-aware design graphs (AGs) for effective poster representation and propose a self-constrained blending strategy upon related examples. In addition, we build a novel layout generator to better arrange elements conditioned on our AGs. Finally, we implement ALDA as an online tool with a set of controllable factors to enhance its practicality.
Self-Reference Image Super-Resolution via Pre-trained Diffusion Large Model and Window Adjustable Transformer

Currently, reference-based super-resolution (RefSR) techniques leverage high-resolution (HR) reference images to provide useful content and texture information for low-resolution (LR) images during the super-resolution (SR) process. Nevertheless, it is time-consuming, laborious, and even impossible in some cases to find high-quality reference images. To tackle this problem, we propose a brand-new self-reference image super-resolution approach using a pre-trained diffusion large model and a window adjustable transformer, termed DWTrans. Our proposed method does not require explicitly inputting manually acquired reference images during training and inference. Specifically, we feed the degraded LR images into a pre-trained stable diffusion large model to automatically generate corresponding high-quality self-reference (SRef) images that provide valuable high-frequency details for the LR images in the process of SR. To extract valuable high-frequency information in SRef images, we design a window adjustable transformer with both non-adjustable window layer (NWL) and adjustable window layer (AWL). The NWL learns local features from LR images using a dense window, while the AWL acquires global features from the SRef images using a random sparse window. Furthermore, to fully utilize the high-frequency features in the SRef image, we introduce the adaptive deformable fusion module to adaptively fuse the features of the LR and SRef images. Experimental results validate that our proposed DWTrans outperforms state-of-the-art methods on various benchmark datasets both quantitatively and visually.
Multi-view Self-Expressive Subspace Clustering Network

Advanced deep multi-view subspace clustering methods are based on the self-expressive model, which has achieved impressive performance. However, most existing works have several limitations: 1) They endure high computational complexity when learning a consistent affinity matrix, impeding their capacity to handle large-scale multi-view data; 2) The global and local structure information of multi-view data remains under-explored. To tackle these challenges, we propose a simplistic but comprehensive framework called Multi-view Self-Expressive Subspace Clustering (MSESC) network. Specifically, we design a deep metric network to replace the conventional self-expressive model, which can directly and efficiently produce the intrinsic similarity values of any instance-pairs of all views. Moreover, our method explores global and local structure information from the connectivity of instance-pairs across views and the nearest neighbors of instance-pairs within the view, respectively. By integrating global and local structure information within a unified framework, MSESC can learn a high-quality shared affinity matrix for better clustering performance. Extensive experimental results indicate the superiority of MSESC compared to several state-of-the-art methods.
Object Part Parsing with Hierarchical Dual Transformer

Object part parsing involves segmenting objects into semantic parts, which has drawn great attention recently. The current methods ignore the specific hierarchical structure of the object, which can be used as strong prior knowledge. To address this, we propose the Hierarchical Dual Transformer (HDTR) to explore the contribution of the typical structural priors of the object parts. HDTR first generates the pyramid multi-granularity pixel representations under the supervision of the object part parsing maps at different semantic levels and then assigns each region an initial part embedding. Moreover, HDTR generates an edge pixel representation to extend the capability of the network to capture detailed information. Afterward, we design a Hierarchical Part Transformer to upgrade the part embeddings to their hierarchical counterparts with the assistance of the multi-granularity pixel representations. Next, we propose a Hierarchical Pixel Transformer to infer the hierarchical information from the part embeddings to enrich the pixel representations. Note that both transformer decoders rely on the structural relations between object parts, i.e., dependency, composition, and decomposition relations. The experiments on five large-scale datasets, i.e., LaPa, CelebAMask-HQ, CIHP, LIP and Pascal Animal, demonstrate that our method sets a new state-of-the-art performance for object part parsing.
Rethinking Missing Modality Learning from a Decoding Perspective

Conventional pipeline of multimodal learning consists of three stages, including encoding, fusion, and decoding. Most existing methods under missing modality condition focus on the first stage and aim to learn the modality invariant representation or reconstruct missing features. However, these methods rely on strong assumptions (i.e., all the pre-defined modalities are available for each input sample during training and the number of modalities is fixed). To solve this problem, we propose a simple yet effective method called Interaction Augmented Prototype Decomposition (IPD) for a more general setting, where the number of modalities is arbitrary and there are various incomplete modality conditions happening in both training and inference phases, even there are unseen testing conditions. Different from the previous methods, we improve the decoding stage. Concretely, IPD jointly learns the common and modality-specific task prototypes. Considering that the number of missing modality conditions scales exponentially with the number of modalities O(2n) and different conditions may have implicit interaction, the low-rank partial prototype decomposition with enough theoretical analysis is employed for modality-specific components to reduce the complexity. The decomposition also can promote unseen generalization with the modality factors of existing conditions. To simulate the low-rank setup, we further constrain the explicit interaction of specific modality conditions by employing disentangled contrastive constraints. Extensive results on the newly-created benchmarks of multiple tasks illustrate the effectiveness of our proposed model.
Text-Only Training for Visual Storytelling

Visual storytelling aims to generate a narrative based on a sequence of images, necessitating both vision-language alignment and coherent story generation. Most existing solutions predominantly depend on paired image-text training data, which can be costly to collect and challenging to scale. To address this, we formulate visual storytelling as a visual-conditioned story generation problem and propose a text-only training method that separates the learning of cross-modality alignment and story generation. Our approach specifically leverages the cross-modality pre-trained CLIP model to integrate visual control into a story generator, trained exclusively on text data. Moreover, we devise a training-free visual condition planner that accounts for the temporal structure of the input image sequence while balancing global and local visual content. The distinctive advantage of requiring only text data for training enables our method to learn from external text story data, enhancing the generalization capability of visual storytelling. We conduct extensive experiments on the VIST benchmark, showcasing the effectiveness of our approach in both in-domain and cross-domain settings. Further evaluations on expression diversity and human assessment underscore the superiority of our method in terms of informativeness and robustness.
Cross-Lingual Transfer of Large Language Model by Visually-Derived Supervision Toward Low-Resource Languages

Recent progress on vision and language research has shown that visual supervision improves the performance of large language models (LLMs) in various natural language processing (NLP) tasks. In particular, the Vokenization approach [65] initiated a new way of incorporating visual information into LLM training, demonstrating the potential of visual supervision for NLP tasks in a monolingual (i.e., English) setting. Given the effectiveness of visual information in human communication among people who speak different languages, we tackle an ambitious question in this paper; can we expect that visual supervision contributes to cross-lingual transfer learning from a high-resource language to low-resource languages in NLP tasks? To study this hypothesis, we build a cross-lingual Vokenization model and train a cross-lingual LLM on three languages, English, Urdu, and Swahili, in which the last two are considered low-resource languages. The experimental results demonstrate that our visually-supervised cross-lingual transfer learning method significantly improves the LLM performance in multiple cross-lingual NLP tasks such as XNLI, NER, and TyDiQA tasks for low-resource languages. We also qualitatively and quantitatively demonstrate that the benefit of our approach increases as the linguistic distance between low-and high-resource languages grows larger.
Unsupervised Domain Adaptation for Referring Semantic Segmentation

In this paper, we study the task of referring semantic segmentation in a highly practical setting, in which labeled visual data with corresponding text descriptions are available in the source, but only unlabeled visual data (without text descriptions) are available in the target. It is a challenging task that has many difficulties: (1) how to obtain proper queries for the target domain; (2) how to adapt visual-text joint distribution shifts; (3) how to maintain the original segmentation performance. Thus, we propose a cycle-consistent vision-language matching network to narrow down the domain gap and ease adaptation difficulty. Our model has significant practical applications since they are capable generalising to new data sources without requiring corresponding text annotations. First, a pseudo-text selector is devised to handle the missing modality, through the pre-trained clip model to measure the gap between query features of the source and visual features of the target. Next, a cross-domain segmentation predictor is adopted, which prompts the joint representations to be domain invariant and minimize the discrepancy between two domains. Then, we present a cycle-consistent query matcher to learn discriminative features via reconstructing visual features from masks. Instead of doing the textual comparison, we match the visual features to the pseudo queries. Extensive experiments show the effectiveness of our method.
Synthesizing Videos from Images for Image-to-Video Adaptation

We address the image-to-video adaptation task that aims to leverage labeled images and unlabeled videos for video recognition. There are two major challenges in this task, including the domain discrepancy between the two domains, and the modality gap between the image and video modalities. Existing methods mainly employ a two-stage paradigm by first adopting frame-level adaptation to reduce the domain discrepancy and then learning a spatio-temporal model to bridge the modality gap. In this paper, we provide a new perspective and propose a single-stage method that synthesizes video from the source static image and converts the image-to-video adaptation problem into a video-to-video adaptation problem. With the synthesized video, we present a simple baseline that a spatio-temporal model is trained with cross entropy loss with source labels and the Batch Nuclear norm Maximization loss to encourage the classification responses of target videos maintain the discriminability and diversity. We further propose a new pseudo label generation method that inherits the robustness of class prototype and the effectiveness of the small loss criterion. Based on the constructed baseline and the proposed pseudo label generation method, we train a model that achieves state-of-the-art performances or gets comparable performances on three standard benchmarks. Our codes are publicly available at https://github.com/junbaoZHUO/ST-I2V.
Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identification

Supervised person re-identification assumes that a person has images captured under multiple cameras. However when cameras are placed in distance, a person rarely appears in more than one camera. This paper thus studies person re-ID under such isolated camera supervised (ISCS) setting. Instead of trying to generate fake cross-camera features like previous methods, we explore a novel perspective by making efficient use of the variation in training data. Under ISCS setting, a person only has limited images from a single camera, so the camera bias becomes a critical issue confounding ID discrimination. Cross-camera images are prone to being recognized as different IDs simply by camera style. To eliminate the confounding effect of camera bias, we propose to learn both intra- and inter-camera invariance under a unified framework. First, we construct style-consistent environments via clustering, and perform prototypical contrastive learning within each environment. Meanwhile, strongly augmented images are contrasted with original prototypes to enforce intra-camera augmentation invariance. For inter-camera invariance, we further design a much improved variant of multi-camera negative loss that optimizes the distance of multi-level negatives. The resulting model learns to be invariant to both subtle and severe style variation within and cross-camera. On multiple benchmarks, we conduct extensive experiments and validate the effectiveness and superiority of the proposed method. Code will be available athttps://github.com/Terminator8758/IICI.