ECCV 2024 Papers

"Conventional multi-label recognition methods often focus on label confidence, frequently overlooking the pivotal role of partial order relations consistent with human preference. To resolve these issues, we introduce a novel method for multimodal label relevance ranking, named Label Relevance Ranking with Proximal Policy Optimization (LR2 PPO), which effectively discerns partial order relations among labels. LR2 PPO first utilizes partial order pairs in the target domain to train a reward model, which aims to capture human preference intrinsic to the specific scenario. Furthermore, we meticulously design state representation and a policy loss tailored for ranking tasks, enabling LR2 PPO to boost the performance of label relevance ranking model and largely reduce the requirement of partial order annotation for transferring to new scenes. To assist in the evaluation of our approach and similar methods, we further propose a novel benchmark dataset, LRMovieNet, featuring multimodal labels and their corresponding partial order data. Extensive experiments demonstrate that our LR2 PPO algorithm achieves state-of-the-art performance, proving its effectiveness in addressing the multimodal label relevance ranking problem. Codes and the proposed LRMovieNet dataset are publicly available at https://github.com/ChazzyGordon/ LR2PPO."

"We address a novel cross-domain few-shot learning task (CD-FSL) with multimodal input and unlabeled target data for egocentric action recognition. This paper simultaneously tackles two critical challenges associated with egocentric action recognition in CD-FSL settings: (1) the extreme domain gap in egocentric videos (, daily life vs. industrial domain) and (2) the computational cost for real-world applications. We propose MM-CDFSL, a domain-adaptive and computationally efficient approach designed to enhance adaptability to the target domain and improve inference cost. To address the first challenge, we propose the incorporation of multimodal distillation into the student RGB model using teacher models. Each teacher model is trained independently on source and target data for its respective modality. Leveraging only unlabeled target data during multimodal distillation enhances the student model’s adaptability to the target domain. We further introduce ensemble masked inference, a technique that reduces the number of input tokens through masking. In this approach, ensemble prediction mitigates the performance degradation caused by masking, effectively addressing the second issue. Our approach outperformed the state-of-the-art CD-FSL approaches with a substantial margin on multiple egocentric datasets, improving by an average of 6.12/6.10 points for 1-shot/5-shot settings while achieving 2.2 times faster inference speed. Project page: https://masashi-hatano. github.io/MM-CDFSL/"

"The field of text-to-image generation has witnessed substantial advancements in the preceding years, allowing the generation of high-quality images based solely on text prompts. However, accurately describing objects through text alone is challenging, necessitating the integration of additional modalities like coordinates and images for more precise image generation. Existing methods often require fine-tuning or only support using single object as the constraint, leaving the zero-shot image generation from multi-object multi-modal prompts as an unresolved challenge. In this paper, we propose MultiGen, a novel method designed to address this problem. Given an image-text pair, we obtain object-level text, coordinates and images, and integrate the information into an “augmented token” for each object. The augmented tokens serve as additional conditions and are trained alongside text prompts in the diffusion model, enabling our model to handle multi-object multi-modal prompts. To manage the absence of modalities during inference, we leverage a coordinate model and a feature model to generate object-level coordinates and features based on text prompts. Consequently, our method can generate images from text prompts alone or from various combinations of multi-modal prompts. Through extensive qualitative and quantitative experiments, we demonstrate that our method not only outperforms existing methods but also enables a wide range of tasks."

"Machine Unlearning removes specific knowledge about training data samples from an already trained model. It has significant practical benefits, such as purging private, inaccurate, or outdated information from trained models without the need for complete re-training. Unlearning within a multimodal setting presents unique challenges due to the complex dependencies between different data modalities and the expensive cost of training on large multimodal datasets and architectures. This paper presents the first machine unlearning approach for multimodal data and models, titled , which is designed to decouple associations between unimodal data points during unlearning without losing the overall representation strength of the trained model. advocates for three key properties for effective multimodal unlearning: (a): , which effectively decouples the association between individual unimodal data points marked for deletion, rendering them as unrelated data points, (b): , which retains the multimodal representation post-unlearning, and (c): , which retains the unimodal representation post-unlearning. is efficient to train and is not constrained by using a strongly convex loss–a common restriction among existing baselines. Experiments on two architectures and four datasets, including image-text and graph-text datasets, show that gains an average improvement of 17.6 points over best performing baseline in unlearning multimodal samples, can maintain the multimodal and unimodal knowledge of the original model post unlearning, and can provide better protection to unlearned data against adversarial attacks1 . 1 Code and data is available at https://github.com/CLU-UML/MultiDelete"

"Object detection in aerial images is a longstanding yet challenging task. Despite the significant advancements in recent years, most works still show unsatisfactory performance due to the scale variation of objects. A standard strategy to address this problem is multi-scale training, aiming to learn scale-invariant feature representations. Albeit achieving inspiring improvements, such a multi-scale strategy is impractical for real application as inference time increases considerably. Besides, the original images are resized to different scales and subsequently trained separately, lacking information interaction across different scales. This paper presents a novel method called multi-scale cross distillation (MSCD) to address the issues mentioned above. MSCD combines the merits of multi-scale training and knowledge distillation, enabling single-scale inference to achieve comparable or superior performance than multi-scale inference. Specifically, we first construct a parallel multi-branch architecture, in which each branch shares the same parameters yet takes images with different scales as input. Furthermore, we design an adaptive cross-scale distillation module that adaptively integrates the knowledge of different branches into one. Thus, the detectors trained with MSCD only require single-scale inference. Extensive experiments demonstrate the effectiveness of MSCD. Without bells and whistles, MSCD can facilitate prevalent two-stage detectors to outperform corresponding single-scale models by ∼5 and ∼7 mAP improvement on DOTA and DIOR-R datasets, respectively."

"Recent advancements in multi-modal pre-training for 3D point clouds have demonstrated promising results by aligning heterogeneous features across 3D shapes and their corresponding 2D images and language descriptions. However, current straightforward solutions often overlook intricate structural relations among samples, potentially limiting the full capabilities of multi-modal learning. To address this issue, we introduce Multi-modal Relation Distillation (MRD), a tri-modal pre-training framework, which is designed to effectively distill reputable large Vision-Language Models (VLM) into 3D backbones. MRD aims to capture both intra-relations within each modality as well as cross-relations between different modalities and produce more discriminative 3D shape representations. Notably, MRD achieves significant improvements in downstream zero-shot classification tasks and cross-modality retrieval tasks, delivering new state-of-the-art performance."

"Multi-modal crowd counting involves estimating crowd density from both visual and thermal/depth images. This task is challenging due to the significant gap between these distinct modalities. In this paper, we propose a novel approach by introducing an auxiliary broker modality and on this basis frame the task as a triple-modal learning problem. We devise a fusion-based method to generate this broker modality, leveraging a non-diffusion, lightweight counterpart of modern denoising diffusion-based fusion models. Additionally, we identify and address the ghosting effect caused by direct cross-modal image fusion in multi-modal crowd counting. Through extensive experimental evaluations on popular multi-modal crowd counting datasets, we demonstrate the effectiveness of our method, which introduces only 4 million additional parameters, yet achieves promising results. The code is available at https://github.com/HenryCilence/Broker-Modality-Crowd-Counting."

"3D referring expression comprehension (3DREC) and segmentation (3DRES) have overlapping objectives, indicating their potential for collaboration. However, existing collaborative approaches predominantly depend on the results of one task to make predictions for the other, limiting effective collaboration. We argue that employing separate branches for 3DREC and 3DRES tasks enhances the model’s capacity to learn specific information for each task, enabling them to acquire complementary knowledge. Thus, we propose the MCLN framework, which includes independent branches for 3DREC and 3DRES tasks. This enables dedicated exploration of each task and effective coordination between the branches. Furthermore, to facilitate mutual reinforcement between these branches, we introduce a Relative Superpoint Aggregation (RSA) module and an Adaptive Soft Alignment (ASA) module. These modules significantly contribute to the precise alignment of prediction results from the two branches, directing the module to allocate increased attention to key positions. Comprehensive experimental evaluation demonstrates that our proposed method achieves state-of-the-art performance on both the 3DREC and 3DRES tasks, with an increase of 2.05% in Acc@0.5 for 3DREC and 3.96% in mIoU for 3DRES. Our code is available at https://github.com/qzp2018/MCLN."

"The existing works on object-level language grounding with 3D objects mostly focus on improving performance by utilizing the off-the-shelf pre-trained models to capture features, such as viewpoint selection or geometric priors. However, they have failed to consider exploring the cross-modal representation of language-vision alignment in the cross-domain field. To answer this problem, we propose a novel method called Domain Adaptation for Language Grounding (DA4LG) with 3D objects. Specifically, the proposed DA4LG consists of a visual adapter module with multi-task learning to realize vision-language alignment by comprehensive multimodal feature representation. Experimental results demonstrate that DA4LG competitively performs across visual and non-visual language descriptions, independent of the completeness of observation. DA4LG achieves state-of-the-art performance in the single-view setting and multi-view setting with the accuracy of 83.8% and 86.8% respectively in the language grounding benchmark SNARE. The simulation experiments show the well-practical and generalized performance of DA4LG compared to the existing methods. Our project is available at https://sites.google.com/view/da4lg."

"In this paper, we aim to establish an automatic, scalable pipeline for denoising the large-scale instructional dataset and construct a high-quality video-text dataset with multiple descriptive steps supervision, named HowToStep. We make the following contributions: (i) improving the quality of sentences in dataset by upgrading ASR systems to reduce errors from speech recognition and prompting a large language model to transform noisy ASR transcripts into descriptive steps; (ii) proposing a Transformer-based architecture with all texts as queries, iteratively attending to the visual features, to temporally align the generated steps to corresponding video segments. To measure the quality of our curated datasets, we train models for the task of multi-sentence grounding on it, i.e., given a long-form video, and associated multiple sentences, to determine their corresponding timestamps in the video simultaneously, as a result, the model shows superior performance on a series of multi-sentence grounding tasks, surpassing existing state-of-the-art methods by a significant margin on three public benchmarks, namely, 9.0% on HT-Step, 5.1% on HTM-Align and 1.9% on CrossTask. All codes, models, and the resulting dataset have been publicly released."

"Besides a 3D mesh, Human Mesh Recovery (HMR) methods usually need to estimate a camera for computing 2D reprojection loss. Previous approaches may encounter the following problem: both the mesh and camera are not correct but the combination of them can yield a low reprojection loss. To alleviate this problem, we define multiple RoIs (region of interest) containing the same human and propose a multiple-RoI-based HMR method. Our key idea is that with multiple RoIs as input, we can estimate multiple local cameras and have the opportunity to design and apply additional constraints between cameras to improve the accuracy of the cameras and, in turn, the accuracy of the corresponding 3D mesh. To implement this idea, we propose a RoI-aware feature fusion network by which we estimate a 3D mesh shared by all RoIs as well as local cameras corresponding to the RoIs. We observe that local cameras can be converted to the camera of the full image through which we construct a local camera consistency loss as the additional constraint imposed on local cameras. Another benefit of introducing multiple RoIs is that we can encapsulate our network into a contrastive learning framework and apply a contrastive loss to regularize the training of our network. Experiments demonstrate the effectiveness of our multi-RoI HMR method and superiority to recent prior arts. Our code is available at https://github.com/CptDiaos/Multi-RoI."

"Human Pose Forecasting is a major problem in human intention comprehension that can be addressed through learning the historical poses via deep methods. However, existing methods often lack the modeling of the person’s role in the event in multi-person scenes. This leads to limited performance in complicated scenes with variant interactions happening at the same time. In this paper, we introduce the Interaction-Aware Pose Forecasting Transformer (IAFormer) framework to better learn the interaction features. With the key insight that the event often involves only part of the people in the scene, we designed the Interaction Perceptron Module (IPM) to evaluate the human-to-event interaction level. With the interaction evaluation, the human-independent features are extracted with the attention mechanism for interaction-aware forecasting. In addition, an Interaction Prior Learning Module (IPLM) is presented to learn and accumulate prior knowledge of high-frequency interactions, encouraging semantic pose forecasting rather than simple trajectory pose forecasting. We conduct experiments using datasets such as CMU-Mocap, UMPM, CHI3D, Human3.6M, and synthesized crowd datasets. The results demonstrate that our method significantly outperforms state-of-the-art approaches considering scenarios with varying numbers of people. Code is available at purplehttps: //github.com/ArcticPole/IAFormer"

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"Unsupervised visible-infrared person re-identification (USL-VI-ReID) is a promising yet highly challenging retrieval task. The key challenges in USL-VI-ReID are to accurately generate pseudo-labels and establish pseudo-label correspondences across modalities without relying on any prior annotations. Recently, clustered pseudo-label methods have gained more attention in USL-VI-ReID. However, most existing methods don’t fully exploit the intra-class nuances, as they simply utilize a single memory that represents an identity to establish cross-modality correspondences, resulting in noisy cross-modality correspondences. To address the problem, we propose a Multi-Memory Matching (MMM) framework for USL-VI-ReID. We first design a simple yet effective Cross-Modality Clustering (CMC) module to generate the pseudo-labels through clustering together both two modality samples. To associate cross-modality clustered pseudo-labels, we design a Multi-Memory Learning and Matching (MMLM) module, ensuring that optimization explicitly focuses on the nuances of individual perspectives and establishes reliable cross-modality correspondences. Finally, we design a Soft Cluster-level Alignment (SCA) loss to narrow the modality gap while mitigating the effect of noisy pseudo-labels through a soft many-to-many alignment strategy. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the reliability of the established cross-modality correspondences and the effectiveness of MMM."

"Contrastive Language Image Pre-training (CLIP) has recently demonstrated success across various tasks due to superior feature representation empowered by image-text contrastive learning. However, the instance discrimination method used by CLIP can hardly encode the semantic structure of training data. To handle this limitation, cluster discrimination has been proposed through iterative cluster assignment and classification. Nevertheless, most cluster discrimination approaches only define a single pseudo-label for each image, neglecting multi-label signals in the image. In this paper, we propose a novel Multi-Label Cluster Discrimination method named MLCD to enhance representation learning. In the clustering step, we first cluster the large-scale LAION-400M dataset into one million centers based on off-the-shelf embedding features. Considering that natural images frequently contain multiple visual objects or attributes, we select the multiple closest centers as auxiliary class labels. In the discrimination step, we design a novel multi-label classification loss, which elegantly separates losses from positive classes and negative classes, and alleviates ambiguity on decision boundary. We validate the proposed multi-label cluster discrimination method with experiments on different scales of models and pre-training datasets. Experimental results show that our method achieves state-of-the-art performance on multiple downstream tasks including linear probe, zero-shot classification, and image-text retrieval."

"We present , a strong model for multi-person 3D human mesh recovery from a single RGB image. Predictions encompass the whole body, , including hands and facial expressions, using the SMPL-X parametric model and 3D location in the camera coordinate system. Our model detects people by predicting coarse 2D heatmaps of person locations, using features produced by a standard Vision Transformer (ViT) backbone. It then predicts their whole-body pose, shape and 3D location using a new cross-attention module called the Human Prediction Head (HPH), with one query attending to the entire set of features for each detected person. As direct prediction of fine-grained hands and facial poses in a single shot, , without relying on explicit crops around body parts, is hard to learn from existing data, we introduce , the dataset, containing humans close to the camera with diverse hand poses. We show that incorporating it into the training data further enhances predictions, particularly for hands. also optionally accounts for camera intrinsics, if available, by encoding camera ray directions for each image token. This simple design achieves strong performance on whole-body and body-only benchmarks simultaneously: a ViT-S backbone on 448×448 images already yields a fast and competitive model, while larger models and higher resolutions obtain state-of-the-art results."

"Current end-to-end Scene Graph Generation (SGG) relies solely on visual representations to separately detect sparse relations and entities in an image. This leads to the issue where the predictions of entities do not contribute to the prediction of relations, necessitating post-processing to assign corresponding subjects and objects to the predicted relations. In this paper, we introduce a sparse relationship matrix that bridges entity detection and relation detection. Our approach not only eliminates the need for relation matching, but also leverages the semantics and positional information of predicted entities to enhance relation prediction. Specifically, a multi-granularity sparse relationship matrix prediction network is proposed, which utilizes three gated pooling modules focusing on filtering negative samples at different granularities, thereby obtaining a sparse relationship matrix containing entity pairs most likely to form relations. Finally, a set of sparse, most probable subject-object pairs can be constructed and used for relation decoding. Experimental results on multiple datasets demonstrate that our method achieves a new state-of-the-art overall performance. Our code is available at https://github.com/wanglei0618/Mg-RMPN."

"This work introduces MotionLCM, extending controllable motion generation to a real-time level. Existing methods for spatial-temporal control in text-conditioned motion generation suffer from significant runtime inefficiency. To address this issue, we first propose the motion latent consistency model (MotionLCM) for motion generation, building upon the latent diffusion model [?]. By adopting one-step (or few-step) inference, we further improve the runtime efficiency of the motion latent diffusion model for motion generation. To ensure effective controllability, we incorporate a motion ControlNet within the latent space of MotionLCM and enable explicit control signals (e.g., initial poses) in the vanilla motion space to control the generation process directly, similar to controlling other latent-free diffusion models [?, ?] for motion generation. By employing these techniques, our approach can generate human motions with text and control signals in real-time. Experimental results demonstrate the remarkable generation and controlling capabilities of MotionLCM while maintaining real-time runtime efficiency."

"Large-scale pre-trained diffusion models have exhibited remarkable capabilities in diverse video generations. Given a set of video clips of the same motion concept, the task of Motion Customization is to adapt existing text-to-video diffusion models to generate videos with this motion. Adaptation methods have been developed for customizing appearance like subject or style, yet under-explored for motion. It is straightforward to extend mainstream adaption methods for motion customization, including full model tuning and Low-Rank Adaptions (LoRAs). However, the motion concept learned by these methods is often coupled with the limited appearances in the training videos, making it difficult to generalize the customized motion to other appearances. To overcome this challenge, we propose MotionDirector, with a dual-path LoRAs architecture to decouple the learning of appearance and motion. Further, we design a novel appearance-debiased temporal loss to mitigate the influence of appearance on the temporal training objective. Experimental results show the proposed method can generate videos of diverse appearances for the customized motions. Our method also supports various downstream applications, such as the mixing of different videos with their appearance and motion respectively, and animating a single image with customized motions. The project website is at: MotionDirector."