CVPR 2024 论文列表

By leveraging temporal dependency in video sequences multi-frame human pose estimation algorithms have demonstrated remarkable results in complicated situations such as occlusion motion blur and video defocus. These algorithms are predominantly based on heatmaps resulting in high computation and storage requirements per frame which limits their flexibility and real-time application in video scenarios particularly on edge devices. In this paper we develop an efficient and effective video-based human pose regression method which bypasses intermediate representations such as heatmaps and instead directly maps the input to the output joint coordinates. Despite the inherent spatial correlation among adjacent joints of the human pose the temporal trajectory of each individual joint exhibits relative independence. In light of this we propose a novel Decoupled Space-Time Aggregation network (DSTA) to separately capture the spatial contexts between adjacent joints and the temporal cues of each individual joint thereby avoiding the conflation of spatiotemporal dimensions. Concretely DSTA learns a dedicated feature token for each joint to facilitate the modeling of their spatiotemporal dependencies. With the proposed joint-wise local-awareness attention mechanism our method is capable of efficiently and flexibly utilizing the spatial dependency of adjacent joints and the temporal dependency of each joint itself. Extensive experiments demonstrate the superiority of our method. Compared to previous regression-based single-frame human pose estimation methods DSTA significantly enhances performance achieving an 8.9 mAP improvement on PoseTrack2017. Furthermore our approach either surpasses or is on par with the state-of-the-art heatmap-based multi-frame human pose estimation methods. Project page: https://github.com/zgspose/DSTA.

The creation of new datasets often presents new challenges for video recognition and can inspire novel ideas while addressing these challenges. While existing datasets mainly comprise landscape mode videos our paper seeks to introduce portrait mode videos to the research community and highlight the unique challenges associated with this video format. With the growing popularity of smartphones and social media applications recognizing portrait mode videos is becoming increasingly important. To this end we have developed the first dataset dedicated to portrait mode video recognition namely PortraitMode-400. The taxonomy of PortraitMode-400 was constructed in a data-driven manner comprising 400 fine-grained categories and rigorous quality assurance was implemented to ensure the accuracy of human annotations. In addition to the new dataset we conducted a comprehensive analysis of the impact of video format (portrait mode versus landscape mode) on recognition accuracy and spatial bias due to the different formats. Furthermore we designed extensive experiments to explore key aspects of portrait mode video recognition including the choice of data augmentation evaluation procedure the importance of temporal information and the role of audio modality. Building on the insights from our experimental results and the introduction of PortraitMode-400 our paper aims to inspire further research efforts in this emerging research area.

Most video captioning models are designed to process short video clips of few seconds and output text describing low-level visual concepts (e.g. objects scenes atomic actions). However most real-world videos last for minutes or hours and have a complex hierarchical structure spanning different temporal granularities. We propose Video ReCap a recursive video captioning model that can process video inputs of dramatically different lengths (from 1 second to 2 hours) and output video captions at multiple hierarchy levels. The recursive video-language architecture exploits the synergy between different video hierarchies and can process hour-long videos efficiently. We utilize a curriculum learning training scheme to learn the hierarchical structure of videos starting from clip-level captions describing atomic actions then focusing on segment-level descriptions and concluding with generating summaries for hour-long videos. Furthermore we introduce Ego4D-HCap dataset by augmenting Ego4D with 8267 manually collected long-range video summaries. Our recursive model can flexibly generate captions at different hierarchy levels while also being useful for other complex video understanding tasks such as VideoQA on EgoSchema. Data code and models are publicly available at https://sites.google.com/view/vidrecap.

Diffusion models have made significant strides in image generation mastering tasks such as unconditional image synthesis text-image translation and image-to-image conversions. However their capability falls short in the realm of video prediction mainly because they treat videos as a collection of independent images relying on external constraints such as temporal attention mechanisms to enforce temporal coherence. In our paper we introduce a novel model class that treats video as a continuous multi-dimensional process rather than a series of discrete frames. Through extensive experimentation we establish state-of-the-art performance in video prediction validated on benchmark datasets including KTH BAIR Human3.6M and UCF101.

We present VIDIM a generative model for video interpolation which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data VIDIM uses cascaded diffusion models to first generate the target video at low resolution and then generate the high-resolution video conditioned on the low-resolution generated video. We compare VIDIM to previous state-of-the-art methods on video interpolation and demonstrate how such works fail in most settings where the underlying motion is complex nonlinear or ambiguous while VIDIM can easily handle such cases. We additionally demonstrate how classifier-free guidance on the start and end frame and conditioning the superresolution model on the original high-resolution frames without additional parameters unlocks high-fidelity results. VIDIM is fast to sample from as it jointly denoises all the frames to be generated requires less than a billion parameters per diffusion model to produce compelling results and still enjoys scalability and improved quality at larger parameter counts. Please see our project page at vidiminterpolation.github.io.

Video harmonization is an important and challenging task that aims to obtain visually realistic composite videos by automatically adjusting the foreground's appearance to harmonize with the background. Inspired by the short-term and long-term gradual adjustment process of manual harmonization we present a Video Triplet Transformer framework to model three spatio-temporal variation patterns within videos i.e. short-term spatial as well as long-term global and dynamic for video-to-video tasks like video harmonization. Specifically for short-term harmonization we adjust foreground appearance to consist with background in spatial dimension based on the neighbor frames; for long-term harmonization we not only explore global appearance variations to enhance temporal consistency but also alleviate motion offset constraints to align similar contextual appearances dynamically. Extensive experiments and ablation studies demonstrate the effectiveness of our method achieving state-of-the-art performance in video harmonization video enhancement and video demoireing tasks. We also propose a temporal consistency metric to better evaluate the harmonized videos. Code is available at https://github.com/zhenglab/VideoTripletTransformer.

Video Frame Interpolation (VFI) has witnessed a surge in popularity due to its abundant downstream applications. Event-based VFI (E-VFI) has recently propelled the advancement of VFI. Thanks to the high temporal resolution benefits event cameras can bridge the informational void present between successive video frames. Most state-of-the-art E-VFI methodologies follow the conventional VFI paradigm which pivots on motion estimation between consecutive frames to generate intermediate frames through a process of warping and refinement. However this reliance engenders a heavy dependency on the quality and consistency of keyframes rendering these methods susceptible to challenges in extreme real-world scenarios such as missing moving objects and severe occlusion dilemmas. This study proposes a novel E-VFI framework that directly synthesize intermediate frames leveraging event-based reference obviating the necessity for explicit motion estimation and substantially enhancing the capacity to handle motion occlusion. Given the sparse and inherently noisy nature of event data we prioritize the reliability of the event-based reference leading to the development of an innovative event-aware reconstruction strategy for accurate reference generation. Besides we implement a bi-directional event-guided alignment from keyframes to the reference using the introduced E-PCD module. Finally a transformer-based decoder is adopted for prediction refinement. Comprehensive experimental evaluations on both synthetic and real-world datasets underscore the superiority of our approach and its potential to execute high-quality VFI tasks.

Diffusion models have made significant advances in generating high-quality images but their application to video generation has remained challenging due to the complexity of temporal motion. Zero-shot video editing offers a solution by utilizing pre-trained image diffusion models to translate source videos into new ones. Nevertheless existing methods struggle to maintain strict temporal consistency and efficient memory consumption. In this work we propose a novel approach to enhance temporal consistency in generated videos by merging self-attention tokens across frames. By aligning and compressing temporally redundant tokens across frames our method improves temporal coherence and reduces memory consumption in self-attention computations. The merging strategy matches and aligns tokens according to the temporal correspondence between frames facilitating natural temporal consistency in generated video frames. To manage the complexity of video processing we divide videos into chunks and develop intra-chunk local token merging and inter-chunk global token merging ensuring both short-term video continuity and long-term content consistency. Our video editing approach seamlessly extends the advancements in image editing to video editing rendering favorable results in temporal consistency over state-of-the-art methods.

In this paper we propose VidLA an approach for video-language alignment at scale. There are two major limitations of previous video-language alignment approaches. First they do not capture both short-range and long-range temporal dependencies and typically employ complex hierarchical deep network architectures that are hard to integrate with existing pretrained image-text foundation models. To effectively address this limitation we instead keep the network architecture simple and use a set of data tokens that operate at different temporal resolutions in a hierarchical manner accounting for the temporally hierarchical nature of videos. By employing a simple two-tower architecture we are able to initialize our video-language model with pretrained image-text foundation models thereby boosting the final performance. Second existing video-language alignment works struggle due to the lack of semantically aligned large-scale training data. To overcome it we leverage recent LLMs to curate the largest video-language dataset to date with better visual grounding. Furthermore unlike existing video-text datasets which only contain short clips our dataset is enriched with video clips of varying durations to aid our temporally hierarchical data tokens in extracting better representations at varying temporal scales. Overall empirical results show that our proposed approach surpasses state-of-the-art methods on multiple retrieval benchmarks especially on longer videos and performs competitively on classification benchmarks.

Vision-Language models (VLMs) have excelled in the image-domain--- especially in zero-shot settings--- thanks to the availability of vast pretraining data (i.e. paired image-text samples). However for videos such paired data is not as abundant. Therefore video-VLMs are usually designed by adapting pretrained image-VLMs to the video-domain instead of training from scratch. All such recipes rely on augmenting visual embeddings with temporal information (i.e. image --> video) often keeping text embeddings unchanged or even being discarded. In this paper we argue the contrary that better video-VLMs can be designed by focusing more on augmenting text rather than visual information. More specifically we introduce Video-conditioned Text Representations (VicTR): a form of text embeddings optimized w.r.t. visual embeddings creating a more-flexible contrastive latent space. Our model can further make use of freely-available semantic information in the form of visually-grounded auxiliary text (e.g. object or scene information). We evaluate our model on few-shot zero-shot (HMDB-51 UCF-101) short-form (Kinetics-400) and long-form (Charades) activity recognition benchmarks showing strong performance among video-VLMs.

Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality spatially consistent new views. While recent methods for view synthesis based on diffusion have shown great progress achieving consistency among various view estimates and at the same time abiding by the desired camera pose remains a critical problem yet to be solved. In this work we demonstrate a strikingly simple method where we utilize a pre-trained video diffusion model to solve this problem. Our key idea is that synthesizing a novel view could be reformulated as synthesizing a video of a camera going around the object of interest---a scanning video---which then allows us to leverage the powerful priors that a video diffusion model would have learned. Thus to perform novel-view synthesis we create a smooth camera trajectory to the target view that we wish to render and denoise using both a view-conditioned diffusion model and a video diffusion model. By doing so we obtain a highly consistent novel view synthesis outperforming the state of the art.

Recent breakthroughs in vision-language models (VLMs) start a new page in the vision community. The VLMs provide stronger and more generalizable feature embeddings compared to those from ImageNet-pretrained models thanks to the training on the large-scale Internet image-text pairs. However despite the amazing achievement from the VLMs vanilla Vision Transformers (ViTs) remain the default choice for the image encoder. Although pure transformer proves its effectiveness in the text encoding area it remains questionable whether it is also the case for image encoding especially considering that various types of networks are proposed on the ImageNet benchmark which unfortunately are rarely studied in VLMs. Due to small data/model scale the original conclusions of model design on ImageNet can be limited and biased. In this paper we aim at building an evaluation protocol of vision models in the vision-language era under the contrastive language-image pretraining (CLIP) framework. We provide a comprehensive way to benchmark different vision models covering their zero-shot performance and scalability in both model and training data sizes. To this end we introduce ViTamin a new vision models tailored for VLMs. ViTamin-L significantly outperforms ViT-L by 2.0% ImageNet zero-shot accuracy when using the same publicly available DataComp-1B dataset and the same OpenCLIP training scheme. ViTamin-L presents promising results on 60 diverse benchmarks including classification retrieval open-vocabulary detection and segmentation and large multi-modal models. When further scaling up the model size our ViTamin-XL with only 436M parameters attains 82.9% ImageNet zero-shot accuracy surpassing 82.0% achieved by EVA-E that has ten times more parameters (4.4B).

Aiming to advance AI agents large foundation models significantly improve reasoning and instruction execution yet the current focus on vision and language neglects the potential of perceiving diverse modalities in open-world environments. However the success of data-driven vision and language models is costly or even infeasible to be reproduced for rare modalities. In this paper we present ViT-Lens that facilitates efficient omni-modal representation learning by perceiving novel modalities with a pretrained ViT and aligning them to a pre-defined space. Specifically the modality-specific lens is tuned to project any-modal signals to an intermediate embedding space which are then processed by a strong ViT with pre-trained visual knowledge. The encoded representations are optimized toward aligning with the modal-independent space pre-defined by off-the-shelf foundation models. ViT-Lens provides a unified solution for representation learning of increasing modalities with two appealing advantages: (i) Unlocking the great potential of pretrained ViTs to novel modalities effectively with efficient data regime; (ii) Enabling emergent downstream capabilities through modality alignment and shared ViT parameters. We tailor ViT-Lens to learn representations for 3D point cloud depth audio tactile and EEG and set new state-of-the-art results across various understanding tasks such as zero-shot classification. By seamlessly integrating ViT-Lens into Multimodal Foundation Models we enable Any-modality to Text and Image Generation in a zero-shot manner. Code and models are available at https://github.com/TencentARC/ViT-Lens.

Although Vision Transformer (ViT) has achieved significant success in computer vision it does not perform well in dense prediction tasks due to the lack of inner-patch information interaction and the limited diversity of feature scale. Most existing studies are devoted to designing vision-specific transformers to solve the above problems which introduce additional pre-training costs. Therefore we present a plain pre-training-free and feature-enhanced ViT backbone with Convolutional Multi-scale feature interaction named ViT-CoMer which facilitates bidirectional interaction between CNN and transformer. Compared to the state-of-the-art ViT-CoMer has the following advantages: (1) We inject spatial pyramid multi-receptive field convolutional features into the ViT architecture which effectively alleviates the problems of limited local information interaction and single-feature representation in ViT. (2) We propose a simple and efficient CNN-Transformer bidirectional fusion interaction module that performs multi-scale fusion across hierarchical features which is beneficial for handling dense prediction tasks. (3) We evaluate the performance of ViT-CoMer across various dense prediction tasks different frameworks and multiple advanced pre-training. Notably our ViT-CoMer-L achieves 64.3% AP on COCO val2017 without extra training data and 62.1% mIoU on ADE20K val both of which are comparable to state-of-the-art methods. We hope ViT-CoMer can serve as a new backbone for dense prediction tasks to facilitate future research. The code will be released at https://github.com/Traffic-X/ViT-CoMer.

While existing large vision-language multimodal models focus on whole image understanding there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge we introduce a novel multimodal model capable of decoding arbitrary (free-form) visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow'". Our simple design directly overlays visual markers onto the RGB image eliminating the need for complex region encodings yet achieves state-of-the-art performance on region-understanding tasks like Visual7W PointQA and Visual Commonsense Reasoning benchmark. Furthermore we present ViP-Bench a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions enabling future research in this domain. Code data and model are publicly available.

Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI) with giga-pixel size and hierarchical image context in digital pathology. However these methods heavily depend on a substantial number of bag-level labels and solely learn from the original slides which are easily affected by variations in data distribution. Recently vision language model (VLM)-based methods introduced the language prior by pre-training on large-scale pathological image-text pairs. However the previous text prompt lacks the consideration of pathological prior knowledge therefore does not substantially boost the model's performance. Moreover the collection of such pairs and the pre-training process are very time-consuming and source-intensive. To solve the above problems we propose a dual-scale vision-language multiple instance learning (ViLa-MIL) framework for whole slide image classification. Specifically we propose a dual-scale visual descriptive text prompt based on the frozen large language model (LLM) to boost the performance of VLM effectively. To transfer the VLM to process WSI efficiently for the image branch we propose a prototype-guided patch decoder to aggregate the patch features progressively by grouping similar patches into the same prototype; for the text branch we introduce a context-guided text decoder to enhance the text features by incorporating the multi-granular image contexts. Extensive studies on three multi-cancer and multi-center subtyping datasets demonstrate the superiority of ViLa-MIL.

Route planning for navigation under partial observability plays a crucial role in modern robotics and autonomous driving. Existing route planning approaches can be categorized into two main classes: traditional autoregressive and diffusion-based methods. The former often fails due to its myopic nature while the latter either assumes full observability or struggles to adapt to unfamiliar scenarios due to strong couplings with behavior cloning from experts. To address these deficiencies we propose a versatile diffusion-based approach for both 2D and 3D route planning under partial observability. Specifically our value-guided diffusion policy first generates plans to predict actions across various timesteps providing ample foresight to the planning. It then employs a differentiable planner with state estimations to derive a value function directing the agent's exploration and goal-seeking behaviors without seeking experts while explicitly addressing partial observability. During inference our policy is further enhanced by a best-plan-selection strategy substantially boosting the planning success rate. Moreover we propose projecting point clouds derived from RGB-D inputs onto 2D grid-based bird-eye-view maps via semantic segmentation generalizing to 3D environments. This simple yet effective adaption enables zero-shot transfer from 2D-trained policy to 3D cutting across the laborious training for 3D policy and thus certifying our versatility. Experimental results demonstrate our superior performance particularly in navigating situations beyond expert demonstrations surpassing state-of-the-art autoregressive and diffusion-based baselines for both 2D and 3D scenarios.

A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance. However building such a model typically demands a large diverse and fully annotated dataset which is challenging to obtain due to the labor-intensive nature of data curation. To address this challenge we propose a cost-effective alternative that harnesses multi-source data with only partial or sparse segmentation labels for training substantially reducing the cost of developing a versatile model. We devise strategies for model self-disambiguation prior knowledge incorporation and imbalance mitigation to tackle challenges associated with inconsistently labeled multi-source data including label ambiguity and modality dataset and class imbalances. Experimental results on a multi-modal dataset compiled from eight different sources for abdominal structure segmentation have demonstrated the effectiveness and superior performance of our method compared to state-of-the-art alternative approaches. We anticipate that its cost-saving features which optimize the utilization of existing annotated data and reduce annotation efforts for new data will have a significant impact in the field.