Multi-head detectors are widely used in the industry for multi-scale detection. These detectors typically employ a convention of using a features-fused-pyramid-neck. However, the features-fused-pyramid-neck encounters feature misalignment when representations from different level hierarchies are forcibly fused point-to-point. To address this issue, we propose an independent hierarchy pyramid (IHP) architecture to evaluate the effectiveness of the features-unfused-pyramid-neck for multi-head detectors. Subsequently, we introduce a soft nearest neighbor interpolation (SNI) to address feature misalignment, utilizing a weight-downscaling factor to minimize the fusion impact on different level features. Furthermore, we introduce spatial window extension for down-sampling (ESD) to retain spatial features and propose an enhanced lightweight convolutional technique. Finally, building upon the above advancements, we design a secondary features alignment solution (SANs) for real-time detection and achieve state-of-the-art results on the Pascal VOC and MS COCO. Code will be released soon.
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The concept of negative prompts, emerging from conditional generation models like Stable Diffusion, allows users to specify what to exclude from the generated images. Despite the widespread use of negative prompts, their intrinsic mechanisms remain largely unexplored. This paper presents the first comprehensive study to uncover how and when negative prompts take effect. Our extensive empirical analysis identifies two primary behaviors of negative prompts. Delayed Effect: The impact of negative prompts is observed after positive prompts render corresponding content. Deletion Through Neutralization: Negative prompts delete concepts from the generated image through a mutual cancellation effect in latent space with positive prompts. These insights reveal significant potential real-world applications; for example, we demonstrate that negative prompts can facilitate object inpainting with minimal alterations to the background via a simple adaptive algorithm. We believe our findings will offer valuable insights for the community in capitalizing on the potential of negative prompts.
Occluded Person Re-Identification (ReID) is a metric learning task that involves matching occluded individuals based on their appearance. While many studies have tackled occlusions caused by objects, multi-person occlusions remain less explored. In this work, we identify and address a critical challenge overlooked by previous occluded ReID methods: the Multi-Person Ambiguity (MPA) arising when multiple individuals are visible in the same bounding box, making it impossible to determine the intended ReID target among the candidates. Inspired by recent work on prompting in vision, we introduce Keypoint Promptable ReID (KPR), a novel formulation of the ReID problem that explicitly complements the input bounding box with a set of semantic keypoints indicating the intended target. Since promptable re-identification is an unexplored paradigm, existing ReID datasets lack the pixel-level annotations necessary for prompting. To bridge this gap and foster further research on this topic, we introduce Occluded PoseTrack-ReID, a novel ReID dataset with keypoints labels, that features strong inter-person occlusions. Furthermore, we release custom keypoint labels for four popular ReID benchmarks. Experiments on person retrieval, but also on pose tracking, demonstrate that our method systematically surpasses previous state-of-the-art approaches on various occluded scenarios.
Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model that generates them. In this work, we study the origin attribution of generated images in a practical setting where only a few images generated by a source model are available and the source model cannot be accessed. The goal is to check if a given image is generated by the source model. We first formulate this problem as a few-shot one-class classification task. To solve the task, we propose OCC-CLIP, a CLIP-based framework for few-shot one-class classification, enabling the identification of an image's source model, even among multiple candidates. Extensive experiments corresponding to various generative models verify the effectiveness of our OCC-CLIP framework. Furthermore, an experiment based on the recently released DALL·E-3 API verifies the real-world applicability of our solution.
Weather forecasting requires both deterministic outcomes for immediate decision-making and probabilistic results for assessing uncertainties. However, deterministic models may not fully capture the spectrum of weather possibilities, and probabilistic forecasting can lack the precision needed for specific planning, presenting significant challenges as the field aims for enhance accuracy and reliability. In this paper, we propose the Deterministic Guidance-based Diffusion Model (DGDM) to exploit the benefits of both deterministic and probabilistic weather forecasting models. DGDM integrates a deterministic branch and a diffusion model as a probabilistic branch to improve forecasting accuracy while providing probabilistic forecasting. In addition, we introduce a sequential variance schedule that predicts from the near future to the distant future. Moreover, we present a truncated diffusion by using the result of the deterministic branch to truncate the reverse process of the diffusion model to control uncertainties. We conduct extensive analyses of DGDM on the Moving MNIST. Furthermore, we evaluate the effectiveness of DGDM on the Pacific Northwest Windstorm (PNW)-Typhoon satellite dataset for regional extreme weather forecasting, as well as on the WeatherBench dataset for global weather forecasting dataset. Experimental results show that DGDM achieves state-of-the-art performance not only in global forecasting but also in regional forecasting scenarios. The code is available at: https://github.com/DongGeun-Yoon/DGDM.
This paper presents a self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequences of 2D skeleton heatmaps as input. Unlike CASA which performs self-attention in the temporal domain only, we feed 2D skeleton heatmaps to a video transformer which performs self-attention both in the spatial and temporal domains for extracting effective spatiotemporal and contextual features. In addition, we introduce simple heatmap augmentation techniques based on 2D skeletons for self-supervised learning. Despite the lack of 3D information, our approach achieves not only higher accuracy but also better robustness against missing and noisy keypoints than CASA. Furthermore, extensive evaluations on three public datasets, i.e., Penn Action, IKEA ASM, and H2O, demonstrate that our approach outperforms previous methods in different fine-grained human activity understanding tasks. Finally, fusing 2D skeleton heatmaps with RGB videos yields the state-of-the-art on all metrics and datasets. To our best knowledge, our work is the first to utilize 2D skeleton heatmap inputs and the first to explore multi-modality fusion for temporal video alignment.
Audio-Visual Segmentation (AVS) aims to achieve pixel-level localization of sound sources in videos, while Audio-Visual Semantic Segmentation (AVSS), as an extension of AVS, further pursues semantic understanding of audio-visual scenes. However, since the AVSS task requires the establishment of audio-visual correspondence and semantic understanding simultaneously, we observe that previous methods have struggled to handle this mashup of objectives in end-to-end training, resulting in insufficient learning and sub-optimization. Therefore, we propose a two-stage training strategy called Stepping Stones, which decomposes the AVSS task into two simple subtasks from localization to semantic understanding, which are fully optimized in each stage to achieve step-by-step global optimization. This training strategy has also proved its generalization and effectiveness on existing methods. To further improve the performance of AVS tasks, we propose a novel framework Adaptive Audio Visual Segmentation, in which we incorporate an adaptive audio query generator and integrate masked attention into the transformer decoder, facilitating the adaptive fusion of visual and audio features. Extensive experiments demonstrate that our methods achieve state-of-the-art results on all three AVS benchmarks. The code will be released soon.
Reaching-and-grasping is a fundamental skill for robotic ma-nipulation, but existing methods usually train models on a specific gripper and cannot be reused on another gripper without retraining. In this paper, we propose a novel method that can learn a unifed policy modelthat can be easily transferred to different dexterous grippers. Our method consists of two stages: a gripper-agnostic policy model that predicts thedisplacements of predefined key points on the gripper, and a gripperspecifc adaptation model that translates these displacements into adjustments for controlling the grippers’ joints.The gripper state and interactions with objects are captured at the finger level using robust geometric representations integrated with a transformer-based network to address variations in gripper morphology and geometry. We evaluateour method on several dexterous grippers and objects of diverse shapes.And the result shows our method signifcantly outperforms the base-line methods. Our method pioneers the transfer of grasp policies acrossdifferent dexterous grippers, and demonstrates the potential of learn-ing generalizable and transferable manipulation skills for various robotichands.
Parameter-efficient fine-tuning methods adjust a small subset of parameters in large models, achieving performance comparable to or even surpassing that of models fine-tuned with the full parameter set, and significantly reducing the time and computational costs associated with the fine-tuning process. Despite the developments of parameter-efficient fine-tuning methods for large models, we observe significant performance disparities across different vision tasks. We attribute this pronounced performance variability to the insufficient robustness of current parameter-efficient fine-tuning methods. In this paper, we propose a robust reparameterization framework for parameter-efficient fine-tuning. This framework has a dynamic training structure and introduces no additional computational overhead during the inference stage. Specifically, we propose Dropout-Mixture Low-Rank Adaptation (DMLoRA), which incorporates multiple up and down branches, to provide the model with a more robust gradient descent path. As training proceeds, DMLoRA gradually drops out branches to achieve a balance between accuracy and regularization. Additionally, we employ a 2-Stage Learning Scalar (LS) strategy to optimize the scale factor for each layer's DMLoRA module. Experimental results demonstrate that our method achieves state-of-the-art performance on the benchmark VTAB-1k and FGVC datasets for parameter-efficient fine-tuning.
Transformer-based methods have achieved remarkable success in various machine learning tasks. How to design efficient test-time adaptation methods for transformer models becomes an important research task. In this work, motivated by the dual-subband wavelet lifting scheme developed in multi-scale signal processing which is able to efficiently separate the input signals into principal components and noise components, we introduce a dual-path token lifting for domain shift correction in test time adaptation. Specifically, we introduce an extra token, referred to as \textit{domain shift token}, at each layer of the transformer network. We then perform dual-path lifting with interleaved token prediction and update between the path of domain shift tokens and the path of class tokens at all network layers. The prediction and update networks are learned in an adversarial manner. Specifically, the task of the prediction network is to learn the residual noise of domain shift which should be largely invariant across all classes and all samples in the target domain. In other words, the predicted domain shift noise should be indistinguishable between all sample classes. On the other hand, the task of the update network is to update the class tokens by removing the domain shift from the input image samples so that input samples become more discriminative between different classes in the feature space. To effectively learn the prediction and update networks with two adversarial tasks, both theoretically and practically, we demonstrate that it is necessary to use smooth optimization for the update network but non-smooth optimization for the prediction network. Experimental results on the benchmark datasets demonstrate that our proposed method significantly improves the online fully test-time domain adaptation performance.
Large language models have evolved data-efficient generalists, benefiting from the universal language interface and large-scale pre-training. However, constructing a data-efficient generalist for dense visual prediction presents a distinct challenge due to the variation in label structures across different tasks. Consequently, generalization to unseen dense prediction tasks in the low-data regime is not straightforward and has received less attention from previous vision generalists. In this study, we explore a universal model that can flexibly adapt to unseen dense label structures with a few examples, enabling it to serve as a data-efficient vision generalist in diverse real-world scenarios. To this end, we base our method on a powerful meta-learning framework and explore several axes to improve its performance and versatility for real-world problems, such as flexible adaptation mechanisms and scalability. We evaluate our model across a spectrum of unseen real-world scenarios where low-shot learning is desirable, including video, 3D, medical, biological, and user-interactive tasks. Equipped with a generic architecture and an effective adaptation mechanism, our model flexibly adapts to all of these tasks with at most 50 labeled images, showcasing a significant advancement over existing data-efficient generalist approaches.
Hyperspectral Imaging (HSI) plays an increasingly critical role in precise vision tasks within remote sensing, capturing a wide spectrum of visual data. Transformer architectures have significantly enhanced HSI task performance, while advancements in Transformer Architecture Search (TAS) have improved model discovery. To harness these advancements for HSI classification, we make the following contributions: i) We propose HyTAS, the first benchmark on transformer architecture search for Hyperspectral imaging, ii) We comprehensively evaluate 12 different methods to identify the optimal transformer over 5 different datasets, iii) We perform an extensive factor analysis on the Hyperspectral transformer search performance, greatly motivating future research in this direction. All benchmark materials included in our supplementary will be publicly available upon publication.
Subspace Prototype Guidance for Mitigating Class Imbalance in Point Cloud Semantic Segmentation
PDF ↗Point cloud semantic segmentation can significantly enhance the perception of an intelligent agent. Nevertheless, the discriminative capability of the segmentation network is influenced by the quantity of samples available for different categories. To mitigate the cognitive bias induced by class imbalance, this paper introduces a novel method, namely subspace prototype guidance (SPG), to guide the training of segmentation network. Specifically, the point cloud is initially separated into independent point sets by category to provide initial conditions for the generation of feature subspaces. The auxiliary branch which consists of an encoder and a projection head maps these point sets into separate feature subspaces. Subsequently, the feature prototypes which are extracted from the current separate subspaces and then combined with prototypes of historical subspaces guide the feature space of main branch to enhance the discriminability of features of minority categories. The prototypes derived from the feature space of main branch are also employed to guide the training of the auxiliary branch, forming a supervisory loop to maintain consistent convergence of the entire network. The experiments conducted on the large public benchmarks (i.e. S3DIS, ScanNet v2, ScanNet200, Toronto-3D) and collected real-world data illustrate that the proposed method significantly improves the segmentation performance and surpasses the state-of-the-art method.
A novel algorithm for video lane detection is proposed in this paper. First, we extract a feature map for a current frame and detect a latent mask for obstacles occluding lanes. Then, we enhance the feature map by developing an occlusion-aware memory-based refinement (OMR) module. It takes the obstacle mask and feature map from the current frame, previous output, and memory information as input, and processes them recursively in a video. Moreover, we apply a novel data augmentation scheme for training the OMR module effectively. Experimental results show that the proposed algorithm outperforms existing techniques on video lane datasets. The source codes will be made publicly available.
We tackle the task of learning dynamic 3D semantic radiance fields given a single monocular video as input. Our learned semantic radiance field captures per-point semantics as well as color and geometric properties for a dynamic 3D scene, enabling the generation of novel views and their corresponding semantics. This enables the segmentation and tracking of a diverse set of 3D semantic entities, specified using a simple and intuitive interface that includes a user click or a text prompt. To this end, we present DGD, a unified 3D representation for both the appearance and semantics of a dynamic 3D scene, building upon the recently proposed dynamic 3D Gaussians representation. Our representation is optimized over time with both color and semantic information. Key to our method is the joint optimization of the appearance and semantic attributes, which jointly affect the geometric properties of the scene. We evaluate our approach in its ability to enable dense semantic 3D object tracking and demonstrate high-quality results that are fast to render, for a diverse set of scenes. Our code will be made available upon acceptance.
Natural images can suffer from various degradation phenomena caused by adverse atmospheric conditions or unique degradation mechanism. Such diversity makes it challenging to design a universal framework for kinds of restoration tasks. Instead of exploring the commonality across different degradation phenomena, existing image restoration methods focus on the modification of network architecture under limited restoration priors. In this work, we first review various degradation phenomena from a frequency perspective as prior. Based on this, we propose an efficient image restoration framework, dubbed SFHformer, which incorporates the Fast Fourier Transform mechanism into Transformer architecture. Specifically, we design a dual domain hybrid structure for multi-scale receptive fields modeling, in which the spatial domain and the frequency domain focuses on local modeling and global modeling, respectively. Moreover, we design unique positional coding and frequency dynamic convolution for each frequency component to extract rich frequency-domain features. Extensive experiments on twenty-three restoration datasets for a range of eight restoration tasks such as deraining, dehazing, deblurring, desnowing and underwater/low-light enhancement, demonstrate that our SFHformer surpasses the state-of-the-art approaches and achieves a favorable trade-off between performance, parameter size and computational cost. We will release the code after acceptance.
In this paper, we propose \textbf{UniCode}, a novel approach within the domain of multimodal large language models (MLLMs) that learns a unified codebook to efficiently tokenize visual, text, and potentially other types of signals. This innovation addresses a critical limitation in existing MLLMs: their reliance on a text-only codebook, which restricts MLLM's ability to generate images and texts in a multimodal context. Towards this end, we propose a language-driven iterative training paradigm, coupled with an in-context pre-training task we term ``image decompression'', enabling our model to interpret compressed visual data and generate high-quality images. The unified codebook empowers our model to extend visual instruction tuning to non-linguistic generation tasks. Moreover, UniCode is adaptable to diverse stacked quantization approaches in order to compress visual signals into a more compact token representation. Despite using significantly fewer parameters and less data during training, Unicode demonstrates exceptional capabilities in visual reconstruction and generation. It also achieves performances comparable to leading MLLMs across a spectrum of VQA benchmarks. Our codes are available at \url{https://anonymous.4open.science/r/UniCode}.
Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery. However, INRs simply map coordinates via a multi-layer perceptron (MLP) to corresponding values, ignoring the inherent semantic information of the data. To leverage semantic priors from the data, we propose a novel Superpixel-informed INR (S-INR). Specifically, we suggest utilizing generalized superpixel instead of pixel as an alternative basic unit of INR for multi-dimensional data (e.g., images and weather data). The coordinates of generalized superpixels are first fed into exclusive attention-based MLPs, and then the intermediate results interact with a shared dictionary matrix. The elaborately designed modules in S-INR allow us to ingenuously exploit the semantic information within and across generalized superpixels. Extensive experiments on various applications validate the effectiveness and efficacy of our S-INR compared to state-of-the-art INR methods.
This paper introduces a novel paradigm termed \textbf{Neural Metamorphosis}~(\textbf{NeuMeta}), which aims to represent a continuous family of networks within a single versatile model. Unlike traditional methods that rely on separate models for different network tasks or sizes, NeuMeta enables an expansive continuum of neural networks that readily morph to fit various needs. The core mechanism is to train a neural implicit function that takes the desired network size and parameter coordinates as inputs, and generates exact corresponding weight values without requiring separate models for different configurations. Specifically, to achieve weight smoothness in a single model, we address the Shortest Hamiltonian Path problem within each neural clique graph. We maintain cross-model consistency by incorporating input noise during training. As such, NeuMeta may dynamically create arbitrary network parameters during the inference stage by sampling on the weight manifold. NeuMeta shows promising results in synthesizing parameters for unseen network configurations. Our extensive tests in image classification, semantic segmentation, and image generation reveal that NeuMeta sustains full-size performance even at a 75% compression rate.
The literature on text-to-image generation is plagued by issues of faithfully composing entities with relations. But there lacks a formal understanding of how entity-relation compositions can be effectively learned. Moreover, the underlying phenomenon space that meaningfully reflects the problem structure is not well-defined, leading to an arms race for larger quantities of data in the hope that generalization emerges out of large scale pretraining. We hypothesize that the underlying phenomenological coverage has not been proportionally scaled up, leading to a skew of the presented phenomenon which harms generalization. We introduce statistical metrics that quantify both the linguistic and visual skew of a dataset for relational learning, and show that generalization failures of text-to-image generation is a direct result of an incomplete or unbalanceed phenomenological coverage. We first perform experiments in a synthetic domain and demonstrate that systematically controlled metrics are strongly predictive of generalization performance. Then we move to natural images and show that simple distribution perturbations in light of our theories boost generalization without enlarging the absolute data size. This work informs an important direction towards quality-enhancing the data diversity or balance orthogonal to scaling up the absolute size. Our discussions point out important open questions on 1) Evaluation of generated entity-relation compositions, 2) Better models for reasoning with abstract relations.