GenAI4ELab/papercli-papers huggingface dataset:
Contrastive Transformer Cross-Modal Hashing for Video-Text Retrieval
Authors: Xiaobo Shen; Qianxin Huang; Long Lan; Yuhui Zheng
Venue: IJCAI 2024 (main)

Abstract: As video-based social networks continue to grow exponentially, there is a rising interest in video retrieval using natural language. Cross-modal hashing, which learns compact hash code for encoding multi-modal data, has proven to be widely effective in large-scale cross-modal retrieval, e.g., image-text retrieval, primarily due to its computation and storage efficiency. However, when applied to video-text retrieval, existing cross-modal hashing methods generally extract features at the frame- or word-level for videos and texts individually, thereby ignoring their long-term dependencies. To address this issue, we propose Contrastive Transformer Cross-Modal Hashing (CTCH), a novel approach designed for video-text retrieval task. CTCH employs bidirectional transformer encoder to encode video and text and leverages their long-term dependencies. CTCH further introduces supervised multi-modality contrastive loss that effectively exploits inter-modality and intra-modality similarities among videos and texts. The experimental results on three video benchmark datasets demonstrate that CTCH outperforms the state-of-the-arts in video-text retrieval tasks.
Conflict-Alleviated Gradient Descent for Adaptive Object Detection
Authors: Wenxu Shi; Bochuan Zheng
Venue: IJCAI 2024 (main)

Abstract: Unsupervised domain adaptive object detection (DAOD) aims to adapt detectors from a labeled source domain to an unlabelled target domain. Existing DAOD works learn feature representations with both class discriminative and domain invariant by jointly minimizing the loss across domain alignment and detection tasks. However, joint resolution of different tasks may lead to conflicts, with one contributing factor being gradient conflicts during optimization. If left untouched, such disagreement may degrade adaptation performance. In this work, we propose an efficient optimization strategy named Conflict-Alleviated Gradient descent (CAGrad) which aims to alleviate the conflict between two tasks (i.e., alignment and classification). Particularly, we alter the gradients by projecting each onto the normal plane of the other. The projection operation changes conflicting gradients from obtuse angles to acute angles, thus alleviating the conflict and achieving gradient harmonization. We further validate our theoretical analysis and methods on several domain adaptive object detection tasks, including cross-camera, weather, scene, and synthetic to real-world adaptation. Extensive experiments on multiple DAOD benchmarks demonstrate the effectiveness and superiority of our CAGrad.
Motion-Aware Heatmap Regression for Human Pose Estimation in Videos
Authors: Inpyo Song; Jongmin Lee; Moonwook Ryu; Jangwon Lee
Venue: IJCAI 2024 (main)

Abstract: We present an approach to solving 2D human pose estimation in videos. The problem of human pose estimation in videos differs from estimating human poses in static images since videos contain a lot of motion related information. Thus, we investigate how to utilize by the information of the human body movements across in a sequence of video frames for estimating human poses in videos. To do this, we introduce a novel heatmap regression method what we call motion-aware heatmap regression. Our approach computes motion vectors in joint keypoints from adjacent frames. We then design a new style of heatmap that we call Motion-Aware Heatmaps to reflect the motion uncertainty of each joint point. Unlike traditional heatmaps, our motion-aware heatmaps not only consider the current joint locations but also account how joints move over time. Furthermore, we introduce a simple yet effective framework designed to incorporate motion information into heatmap regression. We evaluate our motion-aware heatmap regression on PoseTrack(2018, 21) and Sub-JHMDB datasets. Our results validate that the proposed motion-aware heatmaps significantly improve the precision of human pose estimation in videos, particularly in challenging scenarios such as videos like sports game footage with substantial human motions. (Code and related materials are available at https://github.com/Songinpyo/MTPose.)
Learning a Spiking Neural Network for Efficient Image Deraining
Authors: Tianyu Song; Guiyue Jin; Pengpeng Li; Kui Jiang; Xiang Chen; Jiyu Jin
Venue: IJCAI 2024 (main)

Abstract: Recently, spiking neural networks (SNNs) have demonstrated substantial potential in computer vision tasks. In this paper, we present an Efficient Spiking Deraining Network, called ESDNet. Our work is motivated by the observation that rain pixel values will lead to a more pronounced intensity of spike signals in SNNs. However, directly applying deep SNNs to image deraining task still remains a significant challenge. This is attributed to the information loss and training difficulties that arise from discrete binary activation and complex spatiotemporal dynamics. To this end, we develop a spiking residual block to convert the input into spike signals, then adaptively optimize the membrane potential by introducing attention weights to adjust spike responses in a data-driven manner, alleviating information loss caused by discrete binary activation. By this way, our ESDNet can effectively detect and analyze the characteristics of rain streaks by learning their fluctuations. This also enables better guidance for the deraining process and facilitates high-quality image reconstruction. Instead of relying on the ANN-SNN conversion strategy, we introduce a gradient proxy strategy to directly train the model for overcoming the challenge of training. Experimental results show that our approach gains comparable performance against ANN-based methods while reducing energy consumption by 54%. The code source is available at https://github.com/MingTian99/ESDNet.
SPGNet: A Shape-prior Guided Network for Medical Image Segmentation
Authors: Zhengxuan Song; Xun Liu; Wenhao Zhang; Yongyi Gong; Tianyong Hao; Kun Zeng
Venue: IJCAI 2024 (main)

Abstract: Given the intricacy and variability of anatomical structures in medical images, some methods employ shape priors to constrain segmentation. However, limited by the representational capability of these priors, existing approaches often struggle to capture diverse target structure morphologies. To address this, we propose SPGNet to guide segmentation by fully exploiting category-specific shape knowledge. The key idea is to enable the network to perceive data shape distributions by learning from statistical shape models. We uncover shape relationships via clustering and obtain statistical prior knowledge using principal component analysis. Our dual-path network comprises a segmentation path and a shape-prior path that collaboratively discern and harness shape prior distribution to improve segmentation robustness. The shape-prior path further serves to refine shapes iteratively by cropping features from the segmentation path, guiding the segmentation path and directing attention specifically to the edges of shapes which could be most significantly susceptible to segmentation error. We demonstrate superior performance on chest X-ray and breast ultrasound benchmarks.
RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAM
Authors: Ziying Song; Guoxing Zhang; Lin Liu; Lei Yang; Shaoqing Xu; Caiyan Jia; Feiyang Jia; Li Wang
Venue: IJCAI 2024 (main)

Abstract: Multi-modal 3D object detectors are dedicated to exploring secure and reliable perception systems for autonomous driving (AD). Although achieving state-of-the-art (SOTA) performance on clean benchmark datasets, they tend to overlook the complexity and harsh conditions of real-world environments. With the emergence of visual foundation models (VFMs), opportunities and challenges are presented for improving the robustness and generalization of multi-modal 3D object detection in AD. Therefore, we propose RoboFusion, a robust framework that leverages VFMs like SAM to tackle out-of-distribution (OOD) noise scenarios. We first adapt the original SAM for AD scenarios named SAM-AD. To align SAM or SAM-AD with multi-modal methods, we then introduce AD-FPN for upsampling the image features extracted by SAM. We employ wavelet decomposition to denoise the depth-guided images for further noise reduction and weather interference. At last, we employ self-attention mechanisms to adaptively reweight the fused features, enhancing informative features while suppressing excess noise. In summary, RoboFusion significantly reduces noise by leveraging the generalization and robustness of VFMs, thereby enhancing the resilience of multi-modal 3D object detection. Consequently, RoboFusion achieves SOTA performance in noisy scenarios, as demonstrated by the KITTI-C and nuScenes-C benchmarks. Code is available at https://github.com/adept-thu/RoboFusion.
A Consistency and Integration Model with Adaptive Thresholds for Weakly Supervised Object Localization
Authors: Hao Su; Meng Yang
Venue: IJCAI 2024 (main)

Abstract: Weakly Supervised Object Localization (WSOL) is a challenging task, which aims to learn object localization with less costly image-level labels. Existing convolution neural network (CNN) based methods tend to focus on discriminative regions of objects, while transformer-based methods overemphasize deep global features powerful for classification and lack the capability to perceive object details, leading to prediction results far from the object boundary. In this paper, we propose a novel Consistency and Integration Model with Adaptive Thresholds (CIAT) that exploits the spatial-semantic consistency between shallow and deep features to activate more object regions and detects the object regions adaptively in different images. First, we introduce a simple plug-and-play consistency and integration module of shallow-deep features (CISD), which utilizes shallow features efficiently to enhance the entire object perception. Then, we design an online adaptive threshold (OAT) based on Bayesian decision theory, which computes a reasonable segmentation threshold adaptive for the localization map of each image, making the predicted bounding box closer to the ground truth. Extensive experiments on two widely used CUB-200-2011 and ILSVRC datasets verify the effectiveness of our methods.
CLR-Face: Conditional Latent Refinement for Blind Face Restoration Using Score-Based Diffusion Models
Authors: Maitreya Suin; Rama Chellappa
Venue: IJCAI 2024 (main)

Abstract: Recent generative methods have shown promising blind face restoration performance. They usually project the degraded images to the latent space and then decode high-quality faces either by single-stage latent optimization or directly from the encoding. Generating fine-grained facial details faithful to inputs remains challenging. Most existing methods produce either overly smooth outputs or alter the identity. This could be attributed to the typical trade-off between quality and resolution in the latent space. If the latent is highly compressed, the decoded output is more robust to degradations but shows worse fidelity. On the other hand, a more flexible latent space can capture intricate details better, but is extremely difficult to optimize for highly degraded faces. We introduce a diffusion-based-prior inside a VQGAN architecture that focuses on learning the distribution over uncorrupted latent embeddings. We iteratively recover the clean embedding conditioning on the degraded counterpart. Furthermore, to ensure the reverse diffusion trajectory does not deviate from the underlying identity, we train a separate Identity Recovery Network and use its output to constrain the reverse diffusion. Specifically, using a learnable latent mask, we add gradients from a face-recognition network to a subset of latent features that correlates with the finer identity-related details in the pixel space, leaving the other features untouched. Disentanglement between perception and fidelity in the latent space allows us to achieve the best of both worlds. We perform extensive evaluations on multiple real and synthetic datasets to validate our approach.
FineFMPL: Fine-grained Feature Mining Prompt Learning for Few-Shot Class Incremental Learning
Authors: Hongbo Sun; Jiahuan Zhou; Xiangteng He; Jinglin Xu; Yuxin Peng
Venue: IJCAI 2024 (main)

Abstract: Few-Shot Class Incremental Learning (FSCIL) aims to continually learn new classes with few training samples without forgetting already learned old classes. Existing FSCIL methods generally fix the backbone network in incremental sessions to achieve a balance between suppressing forgetting old classes and learning new classes. However, the fixed backbone network causes insufficient learning of new classes from a few samples. Benefiting from the powerful visual and textual understanding ability of Vision-Language (VL) pre-training models, we propose a Fine-grained Feature Mining Prompt Learning (FineFMPL) approach to adapt the VL model to FSCIL, which comprehensively learns and memorizes fine-grained discriminative information of emerging classes. Concretely, the visual probe prompt is firstly proposed to guide the image encoder of VL model to extract global-level coarse-grained features and object-level fine-grained features, and visual prototypes are preserved based on image patch significance, which contains the discriminative characteristics exclusive to the class. Secondly, the textual context prompt is constructed by cross-modal mapping of visual prototypes, feeding into the text encoder of VL model to memorize the class information as textual prototypes. Finally, integrating visual and textual prototypes based on fine-grained feature mining into the model improves the recognition performance of all classes in FSCIL. Extensive experiments on three benchmark datasets demonstrate that our FineFMPL achieves new state-of-the-art. The code is available at https://github.com/PKU-ICST-MIPL/FineFMPL_IJCAI2024.
Who Looks like Me: Semantic Routed Image Harmonization
Authors: Jinsheng Sun; Chao Yao; Xiaokun Wang; Yu Guo; Yalan Zhang; Xiaojuan Ban
Venue: IJCAI 2024 (main)

Abstract: Image harmonization, aiming to seamlessly blend extraneous foreground objects with background images, is a promising and challenging task.Ensuring a synthetic image appears realistic requires maintaining consistency in visual characteristics, such as texture and style, across global and semantic regions.In this paper, We approach image harmonization as a semantic routed style transfer problem, and propose an imageharmonization model by routing semantic similarity explicitly to enhance the consistency of appearance characteristics.To refine calculate the similarity between the composed foreground and background instance, we propose an InstanceSimilarity Evaluation Module(ISEM).To harness analogous semantic information effectively, we further introduceStyle Transfer Block(STB) to establish fine-grained foreground-background semantic correlation.Our method has achieved excellent experimental results on existing datasets and our model outperforms the state-of-the-art by a margin of 0.45 dB on iHarmony4 dataset.
Dynamic Brightness Adaptation for Robust Multi-modal Image Fusion
Authors: Yiming Sun; Bing Cao; Pengfei Zhu; Qinghua Hu
Venue: IJCAI 2024 (main)

Abstract: Infrared and visible image fusion aim to integrate modality strengths for visually enhanced, informative images. Visible imaging in real-world scenarios is susceptible to dynamic environmental brightness fluctuations, leading to texture degradation. Existing fusion methods lack robustness against such brightness perturbations, significantly compromising the visual fidelity of the fused imagery. To address this challenge, we propose the Brightness Adaptive multimodal dynamic fusion framework (BA-Fusion), which achieves robust image fusion despite dynamic brightness fluctuations. Specifically, we introduce a Brightness Adaptive Gate (BAG) module, which is designed to dynamically select features from brightness-related channels for normalization, while preserving brightness-independent structural information within the source images. Furthermore, we propose a brightness consistency loss function to optimize the BAG module. The entire framework is tuned via alternating training strategies. Extensive experiments validate that our method surpasses state-of-the-art methods in preserving multi-modal image information and visual fidelity, while exhibiting remarkable robustness across varying brightness levels. Our code is available: https://github.com/SunYM2020/BA-Fusion.
Multi-Granularity Graph-Convolution-Based Method for Weakly Supervised Person Search
Authors: Haichun Tai; De Cheng; Jie Li; Nannan Wang; Xinbo Gao
Venue: IJCAI 2024 (main)

Abstract: One-step Weakly Supervised Person Search (WSPS) jointly performs pedestrian detection and person Re-IDentification (ReID) only with bounding box annotations, which makes the traditional person ReID problem more suitable and efficient for real-world applications. However, this task is very challenging due to the following reasons: 1) large feature gap between person ReID and general object detection tasks when learning shared representations; 2) difficult pseudo identity estimation for each person image with unrefined raw detection and dramatic scale changes. To address above issues, we propose a multi-granularity graph convolution framework to jointly optimize the aligned task features, as well as to assist the pseudo label estimation. Specifically, the multi-granularity feature alignment module (MFA) in the designed two-branch framework, employs cluster-level bi-directional interaction of various granularity information to narrow down the large feature gap. Further, upon the MFA module, we introduce the multi-granularity graph-convolution-based pseudo-label estimation module, to enhance feature representations for distinguishing diverse identities. Extensive experimental results demonstrate the effectiveness of the proposed method, and show superior performances to state-of-the art methods by a large margin on CUHK-SYSU and PRW datasets.
Expressiveness is Effectiveness: Self-supervised Fashion-aware CLIP for Video-to-Shop Retrieval
Authors: Likai Tian; Zhengwei Yang; Zechao Hu; Hao Li; Yifang Yin; Zheng Wang
Venue: IJCAI 2024 (main)

Abstract: The rise of online shopping and social media has spurred the Video-to-Shop Retrieval (VSR) task, which involves identifying fashion items (e.g., clothing) in videos and matching them with identical products provided by stores. In real-world scenarios, human movement in dynamic video scenes can cause substantial morphological alterations of fashion items with aspects of occlusion, shifting viewpoints (parallax), and partial visibility (truncation). This results in those high-quality frames being overwhelmed by a vast of redundant ones, which makes the retrieval less effectiveness. To this end, this paper introduces a framework, named Self-supervised Fashion-aware CLIP (SF-CLIP), for effective VSR. The SF-CLIP enables the discovery of salient frames with high fashion expressiveness via generating pseudo-labels from three key aspects of fashion expressiveness to assess occlusion, parallax, and truncation. With such pseudo-labels, the ability of CLIP is expanded to facilitate the discovery of salient frames. Furthermore, to encompass the comprehensive representations among salient frames, a dual-branch graph-based fusion module is proposed to extract and integrate inter-frame features. Extensive experiments demonstrate the superiority of SF-CLIP over the state-of-the-arts.
Image Retrieval with Self-Supervised Divergence Minimization and Cross-Attention Classification
Authors: Vivek Trivedy; Longin Jan Latecki
Venue: IJCAI 2024 (main)

Abstract: Common approaches to image retrieval include contrastive methods and specialized loss functions such as ranking losses and entropy regularizers. We present DMCAC (Divergence Minimization with Cross-Attention Classification), a novel image retrieval method that offers a new perspective on this training paradigm. We use self-supervision with a novel divergence loss framework alongside a simple data flow adjustment that minimizes a distribution over a database directly during training. We show that jointly learning a query representation over a database is a competitive and often improved alternative to traditional contrastive methods for image retrieval. We evaluate our method across several model configurations and four datasets, achieving state-of-the-art performance in multiple settings. We also conduct a thorough set of ablations that show the robustness of our method across full vs. approximate retrieval and different hyperparameter configurations.
OSIC: A New One-Stage Image Captioner Coined
Authors: Bo Wang; Zhao Zhang; Mingbo Zhao; Xiaojie Jin; Mingliang Xu; Meng Wang
Venue: IJCAI 2024 (main)

Abstract: Mainstream image captioning models are usually two-stage captioners, i.e., encoding the region features by a pre-trained detector and then feeding them into a language model to generate the captions. However, such a two-stage procedure will lead to a task-based information gap that decreases the performance, because the region features in the detection task are suboptimal representations and cannot provide all the necessary information for subsequent captions generation. Besides, the region features are usually represented from the last layer of the detectors that lose the local details of images. In this paper, we propose a novel One-Stage Image Captioner (OSIC) with dynamic multi-sight learning, which directly transforms the images into descriptive sentences in one stage for eliminating the information gap. Specifically, to obtain rich features, multi-level features are captured by Swin Transformer, and then fed into a novel dynamic multi-sight embedding module to exploit both the global structure and local texture of input images. To enhance the global modeling capacity of the visual encoder, we propose a new dual-dimensional refining to non-locally model the features interaction. As a result, OSIC can directly obtain rich semantic information to improve the captioner. Extensive comparisons on the benchmark MS-COCO, Flickr8K and Flickr30K datasets verified the superior performance of our method.
FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on
Authors: Chenhui Wang; Tao Chen; Zhihao Chen; Zhizhong Huang; Taoran Jiang; Qi Wang; Hongming Shan
Venue: IJCAI 2024 (main)

Abstract: Despite their impressive generative performance, latent diffusion model-based virtual try-on (VTON) methods lack faithfulness to crucial details of the clothes, such as style, pattern, and text. To alleviate these issues caused by the diffusion stochastic nature and latent supervision, we propose a novel Faithful Latent Diffusion Model for VTON, termed FLDM-VTON. FLDM-VTON improves the conventional latent diffusion process in three major aspects. First, we propose incorporating warped clothes as both the starting point and local condition, supplying the model with faithful clothes priors. Second, we introduce a novel clothes flattening network to constrain generated try-on images, providing clothes-consistent faithful supervision. Third, we devise a clothes-posterior sampling for faithful inference, further enhancing the model performance over conventional clothes-agnostic Gaussian sampling. Extensive experimental results on the benchmark VITON-HD and Dress Code datasets demonstrate that our FLDM-VTON outperforms state-of-the-art baselines and is able to generate photo-realistic try-on images with faithful clothing details.