Reviewerly/exHarmony huggingface dataset:
A Baseline Investigation: Transformer-based Cross-view Baseline for Text-based Person Search

This paper investigates a baseline approach for text-based person search by using a transformer-based framework. Existing methods usually treat the visual and textual features as independent entities for speeding up the model inference process. However, the attention to the same images should be changed according to different texts. In this paper, we use a commonly employed framework with a fused feature as the baseline, which overcomes the misalignment problem introduced by fixed features. A thorough investigation is conducted in this paper. Moreover, we propose Cross-View Matching (CVM) to provide challenging, positive text-image pairs that enable the model to learn cross-view meta-information. Furthermore, we suggest a novel evaluation process to reduce the inference time and GPU memory demand. The experiments are conducted on CUHK-PEDES, ICFG-PEDES, and RSTPReid benchmarks. Through extensive parameter analysis, the potentials of a transformer-based framework are fully explored. Although the proposed scheme is a simple framework, it achieves significant performance improvements compared with other state-of-the-art methods.
Biased-Predicate Annotation Identification via Unbiased Visual Predicate Representation

Panoptic Scene Graph Generation (PSG) translates visual scenes to structured linguistic descriptions, i.e., mapping visual instances to subjects/objects, and their relationships to predicates. However, the annotators' preferences and semantic overlaps between predicates inevitably lead to the semantic mappings of multiple predicates to one relationship, i.e., biased-predicate annotations. As a result, with the contradictory mapping between visual and linguistics, PSG models are struggled to construct clear decision planes among predicates, so as to cause existing poor performances. Obviously, it is essential for the PSG task to tackle this multi-modal contradiction. Therefore, we propose a novel method that utilizes unbiased visual predicate representations for Biased-Annotation Identification (BAI) as a fundamental step for PSG/SGG tasks. Our BAI includes three main steps: predicate representation extraction, predicate representation debiasing, and biased-annotation identification. With flexible biased annotation processing methods, our BAI can act as a fundamental step of dataset debiasing. Experimental results demonstrate that our proposed BAI has achieved state-of-the-art performance, which promotes the performance of benchmark models to various degrees with ingenious biased annotation processing methods. Furthermore, our BAI shows great generalization and effectiveness on multiple datasets. Our codes are released at https://github.com/lili0415/BAI.
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning

As the most essential property in a video, motion information is critical to a robust and generalized video representation. To inject motion dynamics, recent works have adopted frame difference as the source of motion information in video contrastive learning, considering the trade-off between quality and cost. However, existing works align motion features at the instance level, which suffers from spatial and temporal weak alignment across modalities. In this paper, we present a Fine-grained Motion Alignment (FIMA) framework, capable of introducing well-aligned and significant motion information. Specifically, we first develop a dense contrastive learning framework in the spatiotemporal domain to generate pixel-level motion supervision. Then, we design a motion decoder and a foreground sampling strategy to eliminate the weak alignments in terms of time and space. Moreover, a frame-level motion contrastive loss is presented to improve the temporal diversity of the motion features. Extensive experiments demonstrate that the representations learned by FIMA possess great motion-awareness capabilities and achieve state-of-the-art or competitive results on downstream tasks across UCF101, HMDB51, and Diving48 datasets. Code is available at https://github.com/ZMHH-H/FIMA.
Knowledge Decomposition and Replay: A Novel Cross-modal Image-Text Retrieval Continual Learning Method

To enable machines to mimic human cognitive abilities and alleviate the catastrophic forgetting problem in cross-modal image-text retrieval (CMITR), this paper proposes a novel continual learning method, Knowledge Decomposition and Replay (KDR), which emulates the process of knowledge decomposition and replay exhibited by humans in complex and changing environments. KDR has two components: a feature Decomposition-based CMITR Model (DCM) and a cross-task Generic Knowledge Replay strategy (GKR). DCM decomposes text and image features into task-specific and generic knowledge features, mimicking the human cognitive process of knowledge decomposition. Specifically, it employs a generic knowledge features extraction module for all tasks and a task-specific module for each task with a few trainable fully connected layers. Similarly, GKR emulates the human behavior of knowledge replay by utilizing the image-text similarity matrix output from the old task model with inputting the previous samples to induce the learning of the image-text similarity matrix output from the current task model with inputting the previous samples, using knowledge distillation technology. To demonstrate the effect of KDR, we adapted a continual learning dataset Seq-COCO from MSCOCO. Extensive experiments on Seq-COCO showed that KDR reduces catastrophic forgetting and consolidates general knowledge, improving the model's learning ability in CMITR.
CMCU-CSS: Enhancing Naturalness via Commonsense-based Multi-modal Context Understanding in Conversational Speech Synthesis

Conversational Speech Synthesis (CSS) aims to produce speech appropriate for oral communication. However, the complexity of context dependency modeling poses significant challenges in the field of CSS, especially the mutual psychological influence between interlocutors. Previous studies have verified that prior commonsense knowledge helps machines understand subtle psychological information (e.g., feelings and intentions) in spontaneous oral dialogues. Therefore, to enhance context understanding and improve the naturalness of synthesized speech, we propose a novel conversational speech synthesis system (CMCU-CSS) that incorporates the Commonsense-based Multi-modal Context Understanding (CMCU) module to model the dynamic emotional interaction among interlocutors. Specifically, we first utilize three implicit states (intent state, internal state and external state) in CMCU to model the context dependency between inter/intra speakers with the help of commonsense knowledge. Furthermore, we infer emotion vectors from the fusion of these implicit states and multi-modal features to enhance the emotion discriminability of synthesized speech. This is the first attempt to combine commonsense knowledge with conversational speech synthesis, and its effect in terms of emotion discriminability of synthetic speech is evaluated by emotion recognition in conversation task. The results of subjective and objective evaluations demonstrate that the CMCU-CSS model achieves more natural speech with context-appropriate emotion and is equipped with the best emotion discriminability, surpassing that of other conversational speech synthesis models.
Rethinking Voice-Face Correlation: A Geometry View

Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion. In this paper, we aim to investigate the capability of reconstructing the 3D facial shape from voice from a geometry perspective without any semantic information. We propose a voice-anthropometric measurement (AM)-face paradigm, which identifies predictable facial AMs from the voice and uses them to guide 3D face reconstruction. By leveraging AMs as a proxy to link the voice and face geometry, we can eliminate the influence of unpredictable AMs and make the face geometry tractable. Our approach is evaluated on our proposed dataset with ground-truth 3D face scans and corresponding voice recordings, and we find significant correlations between voice and specific parts of the face geometry, such as the nasal cavity and cranium. Our work offers a new perspective on voice-face correlation and can serve as a good empirical study for anthropometry science.
Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation

Video-based scene graph generation (VidSGG) is an approach that aims to represent video content in a dynamic graph by identifying visual entities and their relationships. Due to the inherently biased distribution and missing annotations in the training data, current VidSGG methods have been found to perform poorly on less-represented predicates. In this paper, we propose an explicit solution to address this under-explored issue by supplementing missing predicates that should be included in the ground-truth annotations. Dubbed Trico, our method seeks to supplement the missing predicates that are supposed to appear in the ground-truth annotations, by exploring three complementary spatio-temporal correlations. Guided by these correlations, the missing labels can be effectively supplemented thus achieving an unbiased predicate predictions. We validate the effectiveness of Trico on the most widely used VidSGG datasets, i.e., VidVRD and VidOR. Extensive experiments demonstrate the state-of-the-art performance achieved by Trico, particularly on those tail predicates. The code is available in the supplementary material.
Kernel Dimension Matters: To Activate Available Kernels for Real-time Video Super-Resolution

Real-time video super-resolution requires low latency with high-quality reconstruction. Existing methods mostly use pruning schemes or neglect complicated modules to reduce the calculation complexity. However, the video contains large amounts of temporal redundancies due to the inter-frame correlation, which is rarely investigated in existing methods. The static and dynamic information lies in feature maps and represents the redundant complements and temporal offsets respectively. It is crucial to split channels with dynamic and static information for efficient processing. Thus, this paper proposes a kernel-split strategy to activate available kernels for real-time inference. This strategy focuses on the dimensions of convolutional kernels, including the channel and depth dimensions. Available kernel dimensions are activated according to the split of high-value and low-value channels. Specifically, a multi-channel selection unit is designed to discriminate the importance of channels and filter the high-value channels hierarchically. At each hierarchy, low-dimensional convolutional kernels are activated to reuse the low-value channel and re-parameterized convolutional kernels are employed on the high-value channel to merge the depth dimension. In addition, we design a multiple flow deformable alignment module for a sufficient temporal representation with affordable calculation cost. Experimental results demonstrate that our method outperforms other state-of-the-art (SOTA) ones in terms of reconstruction quality and runtime. Codes will be available at https://github.com/Kimsure/KSNet.
Dense Object Grounding in 3D Scenes

Localizing objects in 3D scenes according to the semantics of a given natural language is a fundamental yet important task in the field of multimedia understanding, which benefits various real-world applications such as robotics and autonomous driving. However, the majority of existing 3D object grounding methods are restricted to a single-sentence input describing an individual object, which cannot comprehend and reason more contextualized descriptions of multiple objects in more practical 3D cases. To this end, we introduce a new challenging task, called 3D Dense Object Grounding (3D DOG), to jointly localize multiple objects described in a more complicated paragraph rather than a single sentence. Instead of naively localizing each sentence-guided object independently, we found that dense objects described in the same paragraph are often semantically related and spatially located in a focused region of the 3D scene. To explore such semantic and spatial relationships of densely referred objects for more accurate localization, we propose a novel Stacked Transformer based framework for 3D DOG, named 3DOGSFormer. Specifically, we first devise a contextual query-driven local transformer decoder to generate initial grounding proposals for each target object. The design of these contextual queries enables the model to capture linguistic semantic relationships of objects in the paragraph in a lightweight manner. Then, we employ a proposal-guided global transformer decoder that exploits the local object features to learn their correlation for further refining initial grounding proposals. In particular, we develop two types of proposal-guided attention layers to encode both explicit and implicit pairwise spatial relations to enhance 3D relation understanding. Extensive experiments on three challenging benchmarks (Nr3D, Sr3D, and ScanRefer) show that our proposed 3DOGSFormer outperforms state-of-the-art 3D single-object grounding methods and their dense-object variants by significant margins.
BLAT: Bootstrapping Language-Audio Pre-training based on AudioSet Tag-guided Synthetic Data

Compared with ample visual-text pre-training research, few works explore audio-text pre-training, mostly due to the lack of sufficient parallel audio-text data. Most existing methods incorporate the visual modality as a pivot for audio-text pre-training, which inevitably induces data noise. In this paper, we propose to utilize audio captioning to generate text directly from audio, without the aid of the visual modality so that potential noise from modality mismatch is eliminated. Furthermore, we propose caption generation under the guidance of AudioSet tags, leading to more accurate captions. With the above two improvements, we curate high-quality, large-scale parallel audio-text data, based on which we perform audio-text pre-training. We comprehensively demonstrate the performance of the pre-trained model on a series of downstream audio-related tasks, including single-modality tasks like audio classification and tagging, as well as cross-modal tasks consisting of audio-text retrieval and audio-based text generation. Experimental results indicate that our approach achieves state-of-the-art zero-shot classification performance on most datasets, suggesting the effectiveness of our synthetic data. The audio encoder also serves as an efficient pattern recognition model by fine-tuning it on audio-related tasks. Synthetic data and pre-trained models are available online1 The code, checkpoints and data are available at https://github.com/wsntxxn/BLAT and https://zenodo.org/record/8218696/.
Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning

Fine-Grained Image Recognition (FGIR) is a fundamental and challenging task in computer vision and multimedia that plays a crucial role in Intellectual Economy and Industrial Internet applications. However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners in the field. To address this gap, we present Hawkeye, a PyTorch-based library for FGIR with deep learning. Hawkeye is designed with a modular architecture, emphasizing high-quality code and human-readable configuration, providing a comprehensive solution for FGIR tasks. In Hawkeye, we have implemented 16 state-of-the-art fine-grained methods, covering 6 different paradigms, enabling users to explore various approaches for FGIR. To the best of our knowledge, Hawkeye represents the first open-source PyTorch-based library dedicated to FGIR. It is publicly available at https://github.com/Hawkeye-FineGrained/Hawkeye/, providing researchers and practitioners with a powerful tool to advance their research and development in the field of FGIR.
Bit-Sliced Implementation of SM4 and New Performance Records

SM4 is a popular block cipher issued by the Office of State Commercial Cryptography Administration (OSCCA) of China. In this paper, we use the bit-slicing technique that has been shown as a powerful strategy to achieve very fast software implementations of SM4. We investigate optimizations on two frontiers. First, we present a more efficient bit-sliced representation for SM4, which enables running 64 blocks in parallel with 256-bit registers. Second, we describe an optimized algorithm for data form transformations, also allowing efficient implementations of SM4 under Counter (CTR) mode and Galois/Counter mode. The above optimizations contribute to a significant performance gain on one core compared with the state-of-the-art results. This work is an extension of the conference paper at Inscrypt 2022, awarded the best paper award.
DeepSVC: Deep Scalable Video Coding for Both Machine and Human Vision

Nowadays, end-to-end video coding for both machine and human vision has become an emerging research topic. In complicated systems such as large-scale internet of video things (IoVT), feature streams and video streams can be separately encoded and delivered for machine judgement and human viewing. In this paper, we propose a deep scalable video codec (DeepSVC) to support three-layer scalability from machine to human vision. First, we design a semantic layer that encodes semantic features extracted from the captured video for machine analysis. This layer employs a conditional semantic compression (CSC) method to remove redundancies between semantic features. Second, we design a structure layer that can be combined with semantic layer to predict the captured video at a low quality. This layer effectively estimates video frames based on semantic layer with an interlayer frame prediction (IFP) network. Third, we design a texture layer that can be combined with the above two layers to reconstruct high-quality video signals. This layer also takes advantage of the IFP network to improve its coding efficiency. In large-scale IoVT systems, DeepSVC can deliver semantic layer for regular use and transmit the other layers on demand. Experimental results indicate that the proposed DeepSVC outperforms popular codecs for machine and human vision. Compared with scalable extension of H.265/HEVC (SHVC), the proposed DeepSVC reduces average bit-per-pixel (bpp) by 25.51%/27.63%/59.87% at the same mAP/PSNR/MS-SSIM. Sourcecode is available at: https://github.com/LHB116/DeepSVC.
HAAN: Human Action Aware Network for Multi-label Temporal Action Detection

The task of multi-label temporal action detection aims to accurately detect dense action instances in untrimmed videos. Previous methods focused on modeling the appearance features of RGB images have struggled to capture the fine details and subtle variations in human actions, resulting in three critical issues: overlapping action confusion, intra-class appearance diversity, and background interferences. These issues have significantly undermined the accuracy and generalization of detection models. To tackle these issues, we propose incorporating the human skeleton into the feature design of the detection model. By utilizing multi-person skeletons, our proposed method can accurately represent various human actions in the scene, balance the salience of overlapping actions, and reduce the impact of changes in human appearance and background interferences on action features. Overall, we propose a novel two-stream human action aware network~(HAAN) for multi-label temporal action detection based on the original RGB frames and the estimated skeleton frames. To leverage the complementary advantages of RGB features and skeleton features, we design a cross-modality fusion module that allows the two features to guide each other and enhance their representation of human actions. On the popular benchmarks MultiTHUMOS and Charades, our HAAN achieves state-of-the-art performance with 56.9% (+5.4%) and 32.1% (+3.3%) mean average precision (mAP) compared to the best available methods. Importantly, HAAN shows superior improvements of +6.83%, +22.35%, and +2.56% on the challenging sample subsets of the three critical issues.
Learning Generalized Representations for Open-Set Temporal Action Localization

Open-set Temporal Action Localization (OSTAL) is a critical and challenging task that aims to recognize and temporally localize human actions in untrimmed videos in open word scenarios. The main challenge in this task is the knowledge transfer from known actions to unknown actions. However, existing methods utilize limited training data and overparameterized deep neural network, which have poor generalization. This paper proposes a novel Generalized OSTAL model (namely GOTAL) to learn generalized representations of actions. GOTAL utilizes a Transformer network to model actions and a open-set detection head to perform action localization and recognition. Benefitting from Transformer's temporal modeling capabilities, GOTAL facilitates the extraction of human motion information from videos to mitigate the effects of irrelevant background data. Furthermore, a sharpness minimization algorithm is used to learn the network parameters of GOTAL, which facilitates the convergence of network parameters towards flatter minima by simultaneously minimizing the training loss value and sharpness of the loss plane. The collaboration of the above components significantly enhances the generalization of the representation. Experimental results demonstrate that GOTAL achieves the state-of-the-art performance on THUMOS14 and ActivityNet1.3 benchmarks, confirming the effectiveness of our proposed method.
Globally-Robust Instance Identification and Locally-Accurate Keypoint Alignment for Multi-Person Pose Estimation

Scenes with a large number of human instances are characterized by significant overlap of the instances with similar appearance, occlusion, and scale variation. We propose GRAPE, a novel method that leverages both Globally Robust human instance identification and locally Accurate keypoint alignment for 2D Pose Estimation. GRAPE predicts instance center and keypoint heatmaps, as global identifications of instance location and scale, and keypoint offset vectors from instance centers, as representations of accurate local keypoint positions. We use Transformer to jointly learn the global and local contexts, which allows us to robustly detect instance centers even in difficult cases such as crowded scenes, and align instance offset vectors with relevant keypoint heatmaps, resulting in refined final poses. GRAPE also predicts keypoint visibility, which is crucial for estimating centers of partially visible instances in crowded scenes. We demonstrate that GRAPE achieves state-of-the-art performance on the CrowdPose, OCHuman, and COCO datasets. The benefit of GRAPE is more apparent on crowded scenes (CrowdPose and OCHuman), where our model significantly outperforms previous methods, especially on hard examples.