CVPR 2024 论文列表

Panoptic segmentation combining semantic and instance segmentation stands as a cutting-edge computer vision task. Despite recent progress with deep learning models the dynamic nature of real-world applications necessitates continual learning where models adapt to new classes (plasticity) over time without forgetting old ones (catastrophic forgetting). Current continual segmentation methods often rely on distillation strategies like knowledge distillation and pseudo-labeling which are effective but result in increased training complexity and computational overhead. In this paper we introduce a novel and efficient method for continual panoptic segmentation based on Visual Prompt Tuning dubbed ECLIPSE. Our approach involves freezing the base model parameters and fine-tuning only a small set of prompt embeddings addressing both catastrophic forgetting and plasticity and significantly reducing the trainable parameters. To mitigate inherent challenges such as error propagation and semantic drift in continual segmentation we propose logit manipulation to effectively leverage common knowledge across the classes. Experiments on ADE20K continual panoptic segmentation benchmark demonstrate the superiority of ECLIPSE notably its robustness against catastrophic forgetting and its reasonable plasticity achieving a new state-of-the-art. The code is available at https://github.com/clovaai/ECLIPSE.

Text-to-image (T2I) diffusion models notably the unCLIP models (e.g. DALL-E-2) achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks at the cost of significant computational resources. The unCLIP stack comprises T2I prior and diffusion image decoder. The T2I prior model alone adds a billion parameters compared to the Latent Diffusion Models which increases the computational and high-quality data requirements. We introduce ECLIPSE a novel contrastive learning method that is both parameter and data-efficient. ECLIPSE leverages pre-trained vision-language models (e.g. CLIP) to distill the knowledge into the prior model. We demonstrate that the ECLIPSE trained prior with only 3.3% of the parameters and trained on a mere 2.8% of the data surpasses the baseline T2I priors with an average of 71.6% preference score under resource-limited setting. It also attains performance on par with SOTA big models achieving an average of 63.36% preference score in terms of the ability to follow the text compositions. Extensive experiments on two unCLIP diffusion image decoders Karlo and Kandinsky affirm that ECLIPSE priors consistently deliver high performance while significantly reducing resource dependency. Project page: https://eclipse-t2i.vercel.app/

This paper views the DETR's non-duplicate detection ability as a competition result among object queries. Around each object there are usually multiple queries within which only a single one can win the chance to become the final detection. Such a competition is hard: while some competing queries initially have very close prediction scores their leading query has to dramatically enlarge its score superiority after several decoder layers. To help the leading query stands out this paper proposes EASE-DETR which eases the competition by introducing bias that favours the leading one. EASE-DETR is very simple: in every intermediate decoder layer we identify the "leading / trailing" relationship between any two queries and encode this binary relationship into the following decoder layer to amplify the superiority of the leading one. More concretely the leading query is to be protected from mutual query suppression in the self-attention layer and encouraged to absorb more object features in the cross-attention layer therefore accelerating to win. Experimental results show that EASE-DETR brings consistent and remarkable improvement to various DETRs.

Semantic segmentation has innately relied on extensive pixel-level annotated data leading to the emergence of unsupervised methodologies. Among them leveraging self-supervised Vision Transformers for unsupervised semantic segmentation (USS) has been making steady progress with expressive deep features. Yet for semantically segmenting images with complex objects a predominant challenge remains: the lack of explicit object-level semantic encoding in patch-level features. This technical limitation often leads to inadequate segmentation of complex objects with diverse structures. To address this gap we present a novel approach EAGLE which emphasizes object-centric representation learning for unsupervised semantic segmentation. Specifically we introduce EiCue a spectral technique providing semantic and structural cues through an eigenbasis derived from the semantic similarity matrix of deep image features and color affinity from an image. Further by incorporating our object-centric contrastive loss with EiCue we guide our model to learn object-level representations with intra- and inter-image object-feature consistency thereby enhancing semantic accuracy. Extensive experiments on COCO-Stuff Cityscapes and Potsdam-3 datasets demonstrate the state-of-the-art USS results of EAGLE with accurate and consistent semantic segmentation across complex scenes.

Geometry Problem Solving has drawn growing attention recently due to its application prospects in intelligent education field. However existing methods are still inadequate to meet the needs of practical application suffering from the following limitations: 1) explainability is not ensured which is essential in real teaching scenarios; 2) the small scale and incomplete annotation of existing datasets make it hard for model to comprehend geometric knowledge. To tackle the above problems we propose a novel method called Explainable Geometry Problem Solving (E-GPS). E-GPS first parses the geometric diagram and problem text into unified formal language representations. Then the answer and explainable reasoning and solving steps are obtained by a Top-Down Problem Solver (TD-PS) which innovatively solves the problem from the target and focuses on what is needed. To alleviate the data issues a Bottom-Up Problem Generator (BU-PG) is devised to augment the data set with various well-annotated constructed geometry problems. It enables us to train an enhanced theorem predictor with a better grasp of theorem knowledge which further improves the efficiency of TD-PS. Extensive experiments demonstrate that E-GPS maintains comparable solving performances with fewer steps and provides outstanding explainability.

Text-to-video (T2V) synthesis has gained increasing attention in the community in which the recently emerged diffusion models (DMs) have promisingly shown stronger performance than the past approaches. While existing state-of-the-art DMs are competent to achieve high-resolution video generation they may largely suffer from key limitations (e.g. action occurrence disorders crude video motions) with respect to the intricate temporal dynamics modeling one of the crux of video synthesis. In this work we investigate strengthening the awareness of video dynamics for DMs for high-quality T2V generation. Inspired by human intuition we design an innovative dynamic scene manager (dubbed as Dysen) module which includes (step-1) extracting from input text the key actions with proper time-order arrangement (step-2) transforming the action schedules into the dynamic scene graph (DSG) representations and (step-3) enriching the scenes in the DSG with sufficient and reasonable details. Taking advantage of the existing powerful LLMs (e.g. ChatGPT) via in-context learning Dysen realizes (nearly) human-level temporal dynamics understanding. Finally the resulting video DSG with rich action scene details is encoded as fine-grained spatio-temporal features integrated into the backbone T2V DM for video generating. Experiments on popular T2V datasets suggest that our Dysen-VDM consistently outperforms prior arts with significant margins especially in scenarios with complex actions.

Text-to-image generative models specifically those based on diffusion models like Imagen and Stable Diffusion have made substantial advancements. Recently there has been a surge of interest in the delicate refinement of text prompts. Users assign weights or alter the injection time steps of certain words in the text prompts to improve the quality of generated images. However the success of fine-control prompts depends on the accuracy of the text prompts and the careful selection of weights and time steps which requires significant manual intervention. To address this we introduce the Prompt Auto-Editing (PAE) method. Besides refining the original prompts for image generation we further employ an online reinforcement learning strategy to explore the weights and injection time steps of each word leading to the dynamic fine-control prompts. The reward function during training encourages the model to consider aesthetic score semantic consistency and user preferences. Experimental results demonstrate that our proposed method effectively improves the original prompts generating visually more appealing images while maintaining semantic alignment. Code is available at \href https://github.com/Mowenyii/PAE this https URL .

Multi-Instance Learning (MIL) has shown impressive performance for histopathology whole slide image (WSI) analysis using bags or pseudo-bags. It involves instance sampling feature representation and decision-making. However existing MIL-based technologies at least suffer from one or more of the following problems: 1) requiring high storage and intensive pre-processing for numerous instances (sampling); 2) potential over-fitting with limited knowledge to predict bag labels (feature representation); 3) pseudo-bag counts and prior biases affect model robustness and generalizability (decision-making). Inspired by clinical diagnostics using the past sampling instances can facilitate the final WSI analysis but it is barely explored in prior technologies. To break free these limitations we integrate the dynamic instance sampling and reinforcement learning into a unified framework to improve the instance selection and feature aggregation forming a novel Dynamic Policy Instance Selection (DPIS) scheme for better and more credible decision-making. Specifically the measurement of feature distance and reward function are employed to boost continuous instance sampling. To alleviate the over-fitting we explore the latent global relations among instances for more robust and discriminative feature representation while establishing reward and punishment mechanisms to correct biases in pseudo-bags using contrastive learning. These strategies form the final Dynamic Policy-Driven Adaptive Multi-Instance Learning (PAMIL) method for WSI tasks. Extensive experiments reveal that our PAMIL method outperforms the state-of-the-art by 3.8% on CAMELYON16 and 4.4% on TCGA lung cancer datasets.

We introduce DyNFL a novel neural field-based approach for high-fidelity re-simulation of LiDAR scans in dynamic driving scenes. DyNFL processes LiDAR measurements from dynamic environments accompanied by bounding boxes of moving objects to construct an editable neural field. This field comprising separately reconstructed static background and dynamic objects allows users to modify viewpoints adjust object positions and seamlessly add or remove objects in the re-simulated scene. A key innovation of our method is the neural field composition technique which effectively integrates reconstructed neural assets from various scenes through a ray drop test accounting for occlusions and transparent surfaces. Our evaluation with both synthetic and real-world environments demonstrates that DyNFL substantially improves dynamic scene LiDAR simulation offering a combination of physical fidelity and flexible editing capabilities. Project page: https://shengyuh.github.io/dynfl

This paper introduces a novel human pose estimation approach using sparse inertial sensors addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and model generalization. This method features two innovative components: a pseudo-velocity regression model for dynamic motion capture with inertial sensors and a part-based model dividing the body and sensor data into three regions each focusing on their unique characteristics. The approach demonstrates superior performance over state-of-the-art models across five public datasets notably reducing pose error by 19% on the DIP-IMU dataset thus representing a significant improvement in inertial sensor-based human pose estimation. Our codes are available at https://github.com/dx118/dynaip

Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches involve learning WSIs as instance-bag representations emphasizing significant instances but struggling to capture the interactions between instances. Additionally conventional graph representation methods utilize explicit spatial positions to construct topological structures but restrict the flexible interaction capabilities between instances at arbitrary locations particularly when spatially distant. In response we propose a novel dynamic graph representation algorithm that conceptualizes WSIs as a form of the knowledge graph structure. Specifically we dynamically construct neighbors and directed edge embeddings based on the head and tail relationships between instances. Then we devise a knowledge-aware attention mechanism that can update the head node features by learning the joint attention score of each neighbor and edge. Finally we obtain a graph-level embedding through the global pooling process of the updated head serving as an implicit representation for the WSI classification. Our end-to-end graph representation learning approach has outperformed the state-of-the-art WSI analysis methods on three TCGA benchmark datasets and in-house test sets. Our code is available at https://github.com/WonderLandxD/WiKG.

Point Cloud Registration is a critical and challenging task in computer vision. Recent advancements have predominantly embraced a coarse-to-fine matching mechanism with the key to matching the superpoints located in patches with inter-frame consistent structures. However previous methods still face challenges with ambiguous matching because the interference information aggregated from irrelevant regions may disturb the capture of inter-frame consistency relations leading to wrong matches. To address this issue we propose Dynamic Cues-Assisted Transformer (DCATr). Firstly the interference from irrelevant regions is greatly reduced by constraining attention to certain cues i.e. regions with highly correlated structures of potential corresponding superpoints. Secondly cues-assisted attention is designed to mine the inter-frame consistency relations while more attention is assigned to pairs with high consistent confidence in feature aggregation. Finally a dynamic updating fashion is proposed to facilitate mining richer consistency information further improving aggregated features' distinctiveness and relieving matching ambiguity. Extensive evaluations on indoor and outdoor standard benchmarks demonstrate that DCATr outperforms all state-of-the-art methods.

Point cloud analysis has achieved outstanding performance by transferring point cloud pre-trained models. However existing methods for model adaptation usually update all model parameters i.e. full fine-tuning paradigm which is inefficient as it relies on high computational costs (e.g. training GPU memory) and massive storage space. In this paper we aim to study parameter-efficient transfer learning for point cloud analysis with an ideal trade-off between task performance and parameter efficiency. To achieve this goal we freeze the parameters of the default pre-trained models and then propose the Dynamic Adapter which generates a dynamic scale for each token considering the token significance to the downstream task. We further seamlessly integrate Dynamic Adapter with Prompt Tuning (DAPT) by constructing Internal Prompts capturing the instance-specific features for interaction. Extensive experiments conducted on five challenging datasets demonstrate that the proposed DAPT achieves superior performance compared to the full fine-tuning counterparts while significantly reducing the trainable parameters and training GPU memory by 95% and 35% respectively. Code is available at https://github.com/LMD0311/DAPT.

Despite recent progress in diffusion-based video editing existing methods are limited to short-length videos due to the contradiction between long-range consistency and frame-wise editing. Prior attempts to address this challenge by introducing video-2D representations encounter significant difficulties with large motion- and view-change videos especially in human-centric scenarios. To overcome this we propose to introduce the dynamic Neural Radiance Fields (NeRF) as the innovative video representation where the editing can be performed in the 3D spaces and propagated to the entire video via the deformation field. To provide consistent and controllable editing we propose the image-based video-NeRF editing pipeline with a set of innovative designs including multi-view multi-pose Score Distillation Sampling (SDS) from both the 2D personalized diffusion prior and 3D diffusion prior reconstruction losses text-guided local parts super-resolution and style transfer. Extensive experiments demonstrate that our method dubbed as DynVideo-E significantly outperforms SOTA approaches on two challenging datasets by a large margin of 50% 95% for human preference. Code will be released at https://showlab.github.io/DynVideo-E/.

Recent advancements in dynamic neural radiance field methods have yielded remarkable outcomes. However these approaches rely on the assumption of sharp input images. When faced with motion blur existing dynamic NeRF methods often struggle to generate high-quality novel views. In this paper we propose DyBluRF a dynamic radiance field approach that synthesizes sharp novel views from a monocular video affected by motion blur. To account for motion blur in input images we simultaneously capture the camera trajectory and object Discrete Cosine Transform (DCT) trajectories within the scene. Additionally we employ a global cross-time rendering approach to ensure consistent temporal coherence across the entire scene. We curate a dataset comprising diverse dynamic scenes that are specifically tailored for our task. Experimental results on our dataset demonstrate that our method outperforms existing approaches in generating sharp novel views from motion-blurred inputs while maintaining spatial-temporal consistency of the scene.

State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by passing latent representations between the different modules. In contrast to previous approaches that rely on a unified grid to represent the belief state of the scene we propose dedicated representations to disentangle dynamic agents and static scene elements. This allows us to explicitly compensate for the effect of both ego and object motion between consecutive time steps and to flexibly propagate the belief state through time. Furthermore dynamic objects can not only attend to the input camera images but also directly benefit from the inferred static scene structure via a novel dynamic-static cross-attention. Extensive experiments on the challenging nuScenes benchmark demonstrate the benefits of the proposed dual-stream design especially for modelling highly dynamic agents in the scene and highlight the improved temporal consistency of our approach. Our method titled DualAD not only outperforms independently trained single-task networks but also improves over previous state-of-the-art end-to-end models by a large margin on all tasks along the functional chain of driving.

Automatic web navigation aims to build a web agent that can follow language instructions to execute complex and diverse tasks on real-world websites. Existing work primarily takes HTML documents as input which define the contents and action spaces (i.e. actionable elements and operations) of webpages. Nevertheless HTML documents may not provide a clear task-related context for each element making it hard to select the right (sequence of) actions. In this paper we propose to contextualize HTML elements through their "dual views" in webpage screenshots: each HTML element has its corresponding bounding box and visual content in the screenshot. We build upon the insight---web developers tend to arrange task-related elements nearby on webpages to enhance user experiences---and propose to contextualize each element with its neighbor elements using both textual and visual features. The resulting representations of HTML elements are more informative for the agent to take action. We validate our method on the recently released Mind2Web dataset which features diverse navigation domains and tasks on real-world websites. Our method consistently outperforms the baseline in all the scenarios including cross-task cross-website and cross-domain ones.

Single-pixel imaging (SPI) is a potential computational imaging technique which produces image by solving an ill-posed reconstruction problem from few measurements captured by a single-pixel detector. Deep learning has achieved impressive success on SPI reconstruction. However previous poor reconstruction performance and impractical imaging model limit its real-world applications. In this paper we propose a deep unfolding network with hybrid-attention Transformer on Kronecker SPI model dubbed HATNet to improve the imaging quality of real SPI cameras. Specifically we unfold the computation graph of the iterative shrinkage-thresholding algorithm (ISTA) into two alternative modules: efficient tensor gradient descent and hybrid-attention multi-scale denoising. By virtue of Kronecker SPI the gradient descent module can avoid high computational overheads rooted in previous gradient descent modules based on vectorized SPI. The denoising module is an encoder-decoder architecture powered by dual-scale spatial attention for high- and low-frequency aggregation and channel attention for global information recalibration. Moreover we build a SPI prototype to verify the effectiveness of the proposed method. Extensive experiments on synthetic and real data demonstrate that our method achieves the state-of-the-art performance. The source code and pre-trained models are available at https://github.com/Gang-Qu/HATNet-SPI.