Reviewerly/exHarmony huggingface dataset:
Data-Scarce Animal Face Alignment via Bi-Directional Cross-Species Knowledge Transfer

Animal face alignment is challenging due to large intra- and inter-species variations and a scarcity of labeled data. Existing studies circumvent this problem by directly finetuning a human face alignment model or focusing on animal-specific face alignment~(e.g., horse, sheep). In this paper, we propose Cross-Species Knowledge Transfer, Meta-CSKT, for animal face alignment, which consists of a base network and an adaptation network. Two networks continuously complement each other through the bi-directional cross-species knowledge transfer. This is motivated by observing knowledge sharing among animals. Meta-CSKT uses a circuit feedback mechanism to improve the base network with the cognitive differences of the adaptation network between few-shot labeled and large-scale unlabeled data. In addition, we propose a positive example mining method to identify positives, semi-hard positives, and hard negatives in unlabeled data to mitigate the scarcity of labeled data and facilitate Meta-CSKT learning. Experiments show that Meta-CSKT outperforms state-of-the-art methods by a large margin on the horse facial keypoint dataset and Japanese Macaque Species dataset, while achieving comparable results to state-of-the-art methods on large-scale labeled AnimalWeb~(e.g., 18K), using only a few labeled images~(e.g., 40)1.
Follow-me: Deceiving Trackers with Fabricated Paths

Convolutional Neural Networks (CNNs) are vulnerable to adversarial attacks in which visually imperceptible perturbations can deceive CNN-based models. While current research on adversarial attacks in single object tracking exists, it overlooks a critical aspect of manipulating predicted trajectories to follow user-defined paths regardless of the actual location of the targeted object. To address this, we propose the very first white-box attack algorithm that is capable of deceiving victim trackers by compelling them to generate trajectories that adhere to predetermined counterfeit paths. Specifically, we focus on Siamese-based trackers as our victim models. Given an arbitrary counterfeit path, we first decompose it into discrete target locations in each frame, with the assumption of constant velocity. These locations are converted to heatmap anchors, which represent the offset of their location from the target object's location in the previous frame. Later on, we design a novel loss function to minimize the gap between above-mentioned anchors and our predicted ones. Finally, the gradients computed by such loss are used to update the original video, resulting in our adversarial video. To validate our ideas, we design three sets of counterfeit paths as well as novel evaluation metrics to measure the path-following properties. Experiments with two victim models on three publicly available datasets, OTB100, VOT2018, and VOT2016, demonstrate that our algorithm not only outperforms SOTA methods significantly under conventional evaluation metrics, e.g. 90% and 68.4% precision and successful rate drop on OTB100, but also follows the counterfeit paths well, which is beyond any existing attack methods. The source code is available at https://github.com/loushengtao/Follow-me.
VTQAGen: BART-based Generative Model For Visual Text Question Answering

Visual Text Question Answering (VTQA) is a challenging task that requires answering questions pertaining to visual content by combining image understanding and language comprehension. The main objective is to develop models that can accurately provide relevant answers based on complementary information from both images and text, as well as the semantic meaning of the question. Despite ongoing efforts, the VTQA task presents several challenges, including multimedia alignment, multi-step cross-media reasoning, and handling open-ended questions. This paper introduces a novel generative framework called VTQAGen, which leverages a Multi- modal Attention Layer to combine image-text pairs and question inputs, as well as a BART-based model for reasoning and entity extraction from both images and text. The framework incorporates a step-based ensemble method to enhance model performance and generalization ability. VTQAGen utilizes an encoder-decoder generative model based on BART. Faster R-CNN is employed to extract visual regions of interest, while BART's encoder is modified to handle multi-modal interaction. The decoder stage utilizes the shift-predict approach and introduces step-based logits fusion to improve stability and accuracy. In the experiments, the proposed VTQAGen demonstrates superior performance on the testing set, securing second place in the ACM Multimedia Visual Text Question Answer Challenge.
Beyond Domain Gap: Exploiting Subjectivity in Sketch-Based Person Retrieval

Person re-identification (re-ID) requires densely distributed cameras. In practice, the person of interest may not be captured by cameras and therefore need to be retrieved using subjective information (e.g., sketches from witnesses). Previous research defines this case using the sketch as sketch re-identification (Sketch re-ID) and focuses on eliminating the domain gap. Actually, subjectivity is another significant challenge. We model and investigate it by posing a new dataset with multi-witness descriptions. It features two aspects. 1) Large-scale. It contains over 4,763 sketches and 32,668 photos, making it the largest Sketch re-ID dataset. 2) Multi-perspective and multi-style. Our dataset offers multiple sketches for each identity. Witnesses' subjective cognition provides multiple perspectives on the same individual, while different artists' drawing styles provide variation in sketch styles. We further have two novel designs to alleviate the challenge of subjectivity. 1) Fusing subjectivity. We propose a non-local (NL) fusion module that gathers sketches from different witnesses for the same identity. 2) Introducing objectivity. An AttrAlign module utilizes attributes as an implicit mask to align cross-domain features. To push forward the advance of Sketch re-ID, we set three benchmarks (large-scale, multi-style, cross-style). Extensive experiments demonstrate our leading performance in these benchmarks. Dataset and Codes are publicly available at: https://github.com/Lin-Kayla/subjectivity-sketch-reid
Dynamic Contrastive Learning with Pseudo-samples Intervention for Weakly Supervised Joint Video MR and HD

Joint video moment retrieval (MR) and highlight detection (HD) aims to find relevant video moments according to the query text. Existing methods are fully supervised based on manual annotation, and their coarse multi-modal information interactions easily lose details about video and text. In addition, some tasks introduce weakly supervised learning with random masks, while the single masking forces the model to focus on masked words and ignore multi-modal contextual information. In view of this, we attempt weakly supervised joint tasks (MR+HD) and propose Dynamic Contrastive Learning with Pseudo-Sample Intervention (CPI) for better multi-modal video comprehension. First, we design pseudo-samples over random masks for a more efficient contrastive learning manner. We introduce a proportional sampling strategy for pseudo-samples to ensure the semantic difference between the pseudo-samples and the query text. This balances the over-reliance from single random mask to global text semantics and makes the model learn multimodal context from each word fairly. Second, we design dynamic intervention contrastive loss to enhance the core feature-matching ability of the model dynamically. We add pseudo-sample intervention when negative proposals are close to positive proposals. This can help the model overcome the vision confusion phenomenon and achieve semantic similarity instead of word similarity. Extensive experiments demonstrate the effectiveness of CPI and the potential of weakly supervised joint tasks.
Controllable Face Sketch-Photo Synthesis with Flexible Generative Priors

Current face sketch-photo synthesis researches generally embrace an image-to-image (I2I) translation pipeline. However, these methods ignore the one-to-many mapping problem (i.e., multiple plausible photo results can correspond to a single input sketch) in sketch-to-photo synthesis task, resulting in significant performance degradation on diverse datasets. Besides, generating high-quality images on limited data is also a challenge for this task. To address these challenges, we propose a dual-path framework that introduces generative priors to better perform cross-domain reconstruction on limited data. The coarse path uses a layer-swapped pre-trained generator to achieve coarse cross-domain reconstruction, and the refinement path further improves the structure and texture details. To align the feature maps between the two paths, we introduce a spatial feature calibration module. Despite this, our framework still struggles to handle diverse datasets. Thanks to the flexibility of generative priors, we can extend the framework to achieve exemplar-guided I2I translation by incorporating an exemplar with style mixing and a proposed semantic-aware style refinement strategy, which addresses the one-to-many mapping problem in sketch-to-photo synthesis task. Furthermore, our framework can perform cross-domain editing by employing off-the-shelf editing methods based on the latent space, achieving fine-grained control. Extensive experiments on diverse datasets demonstrate the superiority of our framework over other state-of-the-art methods.
DLFusion: Painting-Depth Augmenting-LiDAR for Multimodal Fusion 3D Object Detection

Surround-view cameras combined with image depth transformation to 3D feature space and fusion with point cloud features are highly regarded. The transformation of 2D features into 3D feature space by means of predefined sampling points and depth distribution happens throughout the scene, and this process generates a large number of redundant features. In addition, multimodal feature fusion unified in 3D space often happens in the previous step of the downstream task, ignoring the interactive fusion between different scales. To this end, we design a new framework, focusing on the design that can give 3D geometric perception information to images and unify them into voxel space to accomplish multi-scale interactive fusion, and we mitigate feature alignment between modal features by geometric relationships between voxel features. The method has two main designs. First, a Segmentation-guided Image View Transformation module is used to accurately transform the pixel region containing the object into a 3D pseudo-point voxel space with the help of a depth distribution. This allows subsequent feature fusion to be performed in a unified voxel feature. Secondly, a Voxel-centric Consistent Fusion module is used to alleviate the errors caused by depth estimation, as well as to achieve better feature fusion between unified modalities. Through extensive experiments on the KITTI and nuScenes datasets, we validate the effectiveness of our camera-LIDAR fusion method. Our proposed approach shows competitive performance on both datasets and outperforms state-of-the-art methods in certain classes of 3D object detection benchmarks. https://github.com/no-Name128/DLFusion [code release]
POAR: Towards Open Vocabulary Pedestrian Attribute Recognition

Pedestrian attribute recognition (PAR) aims to predict the attributes of a target pedestrian. Recent methods often address the PAR problem by training a multi-label classifier with predefined attribute classes, but they can hardly exhaust all possible pedestrian attributes in the real world. To tackle this problem, we propose a novel Pedestrian Open-Attribute Recognition (POAR) approach by formulating the problem as a task of image-text search. Our approach employs a Transformer-based Encoder with a Masking Strategy (TEMS) to focus on the attributes of specific pedestrian parts (e.g., head, upper body, lower body, feet, etc.), and introduces a set of attribute tokens to encode the corresponding attributes into visual embeddings. Each attribute category is described as a natural language sentence and encoded by the text encoder. Then, we compute the similarity between the visual and text embeddings to find the best attribute descriptions for the input images. To handle multiple attributes of a single pedestrian, we propose a Many-To-Many Contrastive (MTMC) loss with masked tokens. In addition, we propose a Grouped Knowledge Distillation (GKD) method to minimize the disparity between visual embeddings and unseen attribute text embeddings. We evaluate our proposed method on three PAR datasets with an open-attribute setting. The results demonstrate the effectiveness of our method as a strong baseline for the POAR task. Our code is available at https://github.com/IvyYZ/POAR.
pyUDLF: A Python Framework for Unsupervised Distance Learning Tasks

The representation of multimedia content experienced tremendous advances in the last decades. Mainly supported by deep learning models, impressive results have been obtained. However, despite such advances in representation, the definition of similarity has been neglected. Effectively computing the similarity between representations remains a challenge. Traditional distance functions, such as the Euclidean distance, are not able to properly consider the relevant similarity information encoded in the dataset manifold. In fact, manifolds are essential to perception in many scenarios, such that exploiting the underlying structure of dataset manifolds plays a central role in multimedia content understanding and retrieval. In this paper, we present a framework for unsupervised distance learning which provides easy and uniform access to methods capable of considering the dataset manifold for redefining similarity. Such methods perform context-sensitive similarity learning based on more global measures, capable of improving the effectiveness of retrieval and machine learning tasks. The framework can use distance, similarity, or ranking information both as input and output and compute traditional retrieval effectiveness measures. Implemented as a wrapper in Python, the framework allows integration with a large number of Python libraries while keeping a back-end in C++ for efficiency. The paper also discusses diverse applications of the methods available in the pyUDLF framework, including image re-ranking, video retrieval, person re-ID, and pre-processing of distance measurements for clustering and classification.
Entropy Neural Estimation for Graph Contrastive Learning

Contrastive learning on graphs aims at extracting distinguishable high-level representations of nodes. We theoretically illustrate that the entropy of a dataset is approximated by maximizing the lower bound of the mutual information across different views of a graph, i.e., entropy is estimated by a neural network. Based on this finding, we propose a simple yet effective subset sampling strategy to contrast pairwise representations between views of a dataset. In particular, we randomly sample nodes and edges from a given graph to build the input subset for a view. Two views are fed into a parameter-shared Siamese network to extract the high-dimensional embeddings and estimate the information entropy of the entire graph. For the learning process, we propose to optimize the network using two objectives, simultaneously. Concretely, the input of the contrastive loss consists of positive and negative pairs. Our selection strategy of pairs is different from previous works and we present a novel strategy to enhance the representation ability by selecting nodes based on cross-view similarities. We enrich the diversity of the positive and negative pairs by selecting highly similar samples and totally different data with the guidance of cross-view similarity scores, respectively. We also introduce a cross-view consistency constraint on the representations generated from the different views. We conduct experiments on seven graph benchmarks, and the proposed approach achieves competitive performance compared to the current state-of-the-art methods. The source code is available at https://github.com/kunzhan/M-ILBO.
Self-PT: Adaptive Self-Prompt Tuning for Low-Resource Visual Question Answering

Pretraining and finetuning large vision-language models (VLMs) have achieved remarkable success in visual question answering (VQA). However, finetuning VLMs requires heavy computation, expensive storage costs, and is prone to overfitting for VQA in low-resource settings. Existing prompt tuning methods have reduced the number of tunable parameters, but they cannot capture valid context-aware information during prompt encoding, resulting in 1) poor generalization of unseen answers and 2) lower improvements with more parameters. To address these issues, we propose a prompt tuning method for low-resource VQA named Adaptive Self-Prompt Tuning (Self-PT), which utilizes representations of question-image pairs as conditions to obtain context-aware prompts. To enhance the generalization of unseen answers, Self-PT uses dynamic instance-level prompts to avoid overfitting the correlations between static prompts and seen answers observed during training. To reduce parameters, we utilize hyper-networks and low-rank parameter factorization to make Self-PT more flexible and efficient. The hyper-network decouples the number of parameters and prompt length to generate flexible-length prompts by the fixed number of parameters. While the low-rank parameter factorization decomposes and reparameterizes the weights of the prompt encoder into a low-rank subspace for better parameter efficiency. Experiments conducted on VQA v2, GQA, and OK-VQA with different low-resource settings show that our Self-PT outperforms the state-of-the-art parameter-efficient methods, especially in lower-shot settings, e.g., 6% average improvements cross three datasets in 16-shot. Code is available at https://github.com/NJUPT-MCC/Self-PT.
HCSD-Net: Single Image Desnowing with Color Space Transformation

Single-image desnowing aims at depressing snowflake noises while preserving a clean background. Existing methods usually mask the locations of noises and remove them in RGB color space. In this paper, we rethink this problem by investigating the impacts of color space selection. Theoretical analysis and experiments reveal that the feature of snowflake noises exhibit different distributions in different color spaces. In particular, these noises are barely seen in Hue channel, which inspires us to recover global structure and texture information of the clean background from Hue channel. More low-frequency information is also found in the Hue channel. With these observations, we propose a novel Hybrid-Color-Space-based Desnowing Network (HCSD-Net). The proposed HCSD-Net extracts low-frequency and high-frequency features in Hue channel and RGB color space, respectively. After that, it utilizes a multi-scale fusion module to enhance high-frequency details at a small feature resolution. These details are further used to supervise and supplement the background information. Extensive experiments demonstrate that our proposed HCSD-Net outperforms state-of-the-art methods on various synthetic and real-world desnowing datasets. Codes are available at https://github.com/ttz-rainbow/HCSD-Net.
Probability Distribution Based Frame-supervised Language-driven Action Localization

Frame-supervised language-driven action localization aims to localize action boundaries in untrimmed videos corresponding to the input natural language query, with only a single frame annotation within the target action in training. This task is challenging due to the absence of complete and accurate annotation of action boundaries, hindering visual-language alignment and action boundary prediction. To address this challenge, we propose a novel method that introduces distribution functions to model both the probability of action frame and that of boundary frame. Specifically, we assign each video frame the probability of being the action frame based on the estimated shape parameters of the distribution function, serving as a foreground pseudo-label that guides cross-modal feature learning. Moreover, we model the probabilities of start frame and end frame of the target action using different distribution functions, and then estimate the probability of each action candidate being a positive candidate based on its start and end boundaries, which facilitates predicting action boundaries by exploring more positive terms in training. Experiments on two benchmark datasets demonstrate that our method outperforms existing methods, achieving a gain of more than 10% of R1@ 0.5 on the challenging TACoS dataset. These results emphasize the significance of generating pseudo labels with appropriate probabilities via distribution functions to address the challenge of frame-supervised language-driven action localization.
Cross-Modal and Multi-Attribute Face Recognition: A Benchmark

Face recognition has made significant advances with the development of deep learning and has begun to be deployed in some unrestricted scenarios. Many smartphones, for example, have infrared sensors that allow them to capture clear images even in low-light conditions. Face authentication under complex environmental conditions can thus be accomplished by matching NIR-VIS face images across modalities. However, existing NIR-VIS datasets lack enough variation in face attributes and are insufficient for real-world scenarios. To address the aforementioned issues, we first propose a 300-person NIR-VIS cross-modality face dataset with a variety of attributes. Based on modal information removal, we proposed a NIR-VIS cross-modal face recognition model. We can effectively extract modal information by constraining the similarity distribution of modalities and then using the orthogonal loss to remove modal information from identity features. The method achieves excellent results on our dataset and CASIA NIR-VIS 2.0 dataset.
Uniformly Distributed Category Prototype-Guided Vision-Language Framework for Long-Tail Recognition

Recently, large-scale pre-trained vision-language models have presented benefits for alleviating class imbalance in long-tailed recognition. However, the long-tailed data distribution can corrupt the representation space, where the distance between head and tail categories is much larger than the distance between two tail categories. This uneven feature space distribution causes the model to exhibit unclear and inseparable decision boundaries on the uniformly distributed test set, which lowers its performance. To address these challenges, we propose the uniformly category prototype-guided vision-language framework to effectively mitigate feature space bias caused by data imbalance. Especially, we generate a set of category prototypes uniformly distributed on a hypersphere. Category prototype-guided mechanism for image-text matching makes the features of different classes converge to these distinct and uniformly distributed category prototypes, which maintain a uniform distribution in the feature space, and improve class boundaries. Additionally, our proposed irrelevant text filtering and attribute enhancement module allows the model to ignore irrelevant noisy text and focus more on key attribute information, thereby enhancing the robustness of our framework. In the image recognition fine-tuning stage, to address the positive bias problem of the learnable classifier, we design the class feature prototype-guided classifier, which compensates for the performance of tail classes while maintaining the performance of head classes. Our method outperforms previous vision-language methods for long-tailed learning work by a large margin and achieves state-of-the-art performance.