ICCV 2023 论文列表

We propose a self-supervised method for learning motion-focused video representations. Existing approaches minimize distances between temporally augmented videos, which maintain high spatial similarity. We instead propose to learn similarities between videos with identical local motion dynamics but an otherwise different appearance. We do so by adding synthetic motion trajectories to videos which we refer to as tubelets. By simulating different tubelet motions and applying transformations, such as scaling and rotation, we introduce motion patterns beyond what is present in the pretraining data. This allows us to learn a video representation that is remarkably data efficient: our approach maintains performance when using only 25% of the pretraining videos. Experiments on 10 diverse downstream settings demonstrate our competitive performance and generalizability to new domains and fine-grained actions.

Video segmentation aims to segment and track every pixel in diverse scenarios accurately. In this paper, we present Tube-Link, a versatile framework that addresses multiple core tasks of video segmentation with a unified architecture. Our framework is a near-online approach that takes a short subclip as input and outputs the corresponding spatial-temporal tube masks. To enhance the modeling of cross-tube relationships, we propose an effective way to perform tube-level linking via attention along the queries. In addition, we introduce temporal contrastive learning to instance-wise discriminative features for tube-level association. Our approach offers flexibility and efficiency for both short and long video inputs, as the length of each subclip can be varied according to the needs of datasets or scenarios. Tube-Link outperforms existing specialized architectures by a significant margin on five video segmentation datasets. Specifically, it achieves almost 13% relative improvements on VIPSeg and 4% improvements on KITTI-STEP over the strong baseline Video K-Net. When using a ResNet50 backbone on Youtube-VIS-2019 and 2021, Tube-Link boosts IDOL by 3% and 4%, respectively. Code is available at https://github.com/lxtGH/Tube-Link.

Face Image Quality Assessment (FIQA) lays the foundation for ensuring the stability and accuracy of face recognition systems. However, existing FIQA methods mainly formulate quality relationships within the training set to yield quality scores, ignoring the generalization problem caused by ethnic quality bias between the training and test sets. Domain adaptation presents a potential solution to mitigate the bias, but if FIQA is treated essentially as a regression task, it will be limited by the challenge of feature scaling in transfer learning. Additionally, how to guarantee source risk is also an issue due to the lack of ground-truth labels of the source domain for FIQA. This paper presents the first attempt in the field of FIQA to address these challenges with a novel Ethnic-Quality-Bias Mitigating (EQBM) framework. Specifically, to eliminate the restriction of scalar regression, we first compute the Likert-scale quality probability distributions as source domain annotations. Furthermore, we design an easy-to-hard training scheduler based on the inter-domain uncertainty and intra-domain quality margin as well as the ranking-based domain adversarial network to enhance the effectiveness of transfer learning and further reduce the source risk in domain adaptation. Extensive experiments demonstrate that the EQBM significantly mitigates the quality bias and improves the generalization capability of FIQA across races on different datasets.

One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence. Previous works designed expansion operators to scale up pretrained models to the target model before training. Yet, model functionality is difficult to preserve when scaling a transformer in all dimensions at once. Moreover, maintaining the pretrained optimizer states for weights is critical for model scaling, whereas the new weights added during expansion lack these states in pretrained models. To address these issues, we propose TripLe, which partially scales a model before training, while growing the rest of the new parameters during training by copying both the warmed-up weights with the optimizer states from existing weights. As such, the new parameters introduced during training will obtain their training states. Furthermore, through serializing the scaling of model width and depth, the functionality of each expansion can be preserved. We evaluate TripLe in both single-trial model scaling and multi-trial neural architecture search (NAS). Due to the fast training convergence of TripLe, the proxy accuracy from TripLe better reveals the model quality compared to from-scratch training in multi-trial NAS. Experiments show that TripLe outperforms both from-scratch training and knowledge distillation (KD) in both training time and task performance. TripLe can also be combined with KD to achieve an even higher task accuracy. For NAS, the model obtained from TripLe outperforms DeiT-B in task accuracy with 69% reduction in parameter size and FLOPs.

Despite the tremendous progress in neural radiance fields (NeRF), we still face a dilemma of the trade-off between quality and efficiency, e.g., MipNeRF presents fine-detailed and anti-aliased renderings but takes days for training, while Instant-ngp can accomplish the reconstruction in a few minutes but suffers from blurring or aliasing when rendering at various distances or resolutions due to ignoring the sampling area. To this end, we propose a novel Tri-Mip encoding (a la "mipmap") that enables both instant reconstruction and anti-aliased high-fidelity rendering for neural radiance fields. The key is to factorize the pre-filtered 3D feature spaces in three orthogonal mipmaps. In this way, we can efficiently perform 3D area sampling by taking advantage of 2D pre-filtered feature maps, which significantly elevates the rendering quality without sacrificing efficiency. To cope with the novel Tri-Mip representation, we propose a cone-casting rendering technique to efficiently sample anti-aliased 3D features with the Tri-Mip encoding considering both pixel imaging and observing distance. Extensive experiments on both synthetic and real-world datasets demonstrate our method achieves state-of-the-art rendering quality and reconstruction speed while maintaining a compact representation that reduces 25% model size compared against Instant-ngp. Code is available at the project webpage: https: //wbhu.github.io/projects/Tri-MipRF

We study inferring a tree-structured representation from a single image for object shading. Prior work typically uses the parametric or measured representation to model shading, which is neither interpretable nor easily editable. We propose using the shade tree representation, which combines basic shading nodes and compositing methods to factorize object surface shading. The shade tree representation enables novice users who are unfamiliar with the physical shading process to edit object shading in an efficient and intuitive manner. A main challenge in inferring the shade tree is that the inference problem involves both the discrete tree structure and the continuous parameters of the tree nodes. We propose a hybrid approach to address this issue. We introduce an auto-regressive inference model to generate a rough estimation of the tree structure and node parameters, and then we fine-tune the inferred shade tree through an optimization algorithm. We show experiments on synthetic images, captured reflectance, real images, and non-realistic vector drawings, allowing downstream applications such as material editing, vectorized shading, and relighting. Project website: https://chen-geng.com/inv-shade-trees.

Generating accurate pseudo-labels under the supervision of image categories is a crucial step in Weakly Supervised Semantic Segmentation (WSSS). In this work, we propose a Mat-Label pipeline that provides a fresh way to treat WSSS pseudo-labels generation as an image matting task. By taking a trimap as input which specifies the foreground, background and unknown regions, the image matting task outputs an object mask with fine edges. The intuition behind our Mat-Label is that generating trimap is much easier than generating pseudo-labels directly under weakly supervised setting. Although current CAM-based methods are off-the-shelf solutions for generating a trimap, they suffer from cross-category and foreground-background pixel prediction confusion. To solve this problem, we develop a Double Decoupled Class Activation Map (D2CAM) for Mat-Label to generate a high-quality trimap. By drawing on the idea of metric learning, we explicitly model class activation map with category decoupling and foreground-background decoupling. We also design two simple yet effective refinement constraints for D2CAM to stabilize optimization and eliminate non-exclusive activation. Extensive experiments validate that our Mat-Label achieves substantial and consistent performance gains compared to current state-of-the-art WSSS approaches. Our code is available at supplementary material.

This paper presents a learning-based method for transparent surface estimation from a single view polarization image. Existing shape from polarization(SfP) methods have the difficulty in estimating transparent shape since the inherent transmission interference heavily reduces the reliability of physics-based prior. To address this challenge, we propose the concept of physics-based prior confidence, which is inspired by the characteristic that the transmission component in the polarization image has more noise than reflection. The confidence is used to determine the contribution of the interfered physics-based prior. Then, we build a network(TransSfP) with multi-branch architecture to avoid the destruction of relationships between different hierarchical inputs. To train and test our method, we construct a dataset for transparent shape from polarization with paired polarization images and ground-truth normal maps. Extensive experiments and comparisons demonstrate the superior accuracy of our method.

The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections. However, generating road network poses a significant challenge due to the conflicting underlying combination of Euclidean (e.g., road landmarks location) and non-Euclidean (e.g., road topological connectivity) structures. Existing methods struggle to merge the two types of data domains effectively, but few of them address it properly. Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence. Further than modeling an auto-regressive sequence-to-sequence Transformer model to understand RoadNet Sequence, we decouple the dependency of RoadNet Sequence into a mixture of auto-regressive and non-autoregressive dependency. Building on this, our proposed non-autoregressive sequence-to-sequence approach leverages non-autoregressive dependencies while fixing the gap towards auto-regressive dependencies, resulting in success on both efficiency and accuracy. Extensive experiments on nuScenes dataset demonstrate the superiority of RoadNet Sequence representation and the non-autoregressive approach compared to existing state-of-the-art alternatives.

Image-to-text generation aims to describe images using natural language. Recently, zero-shot image captioning based on pre-trained vision-language models (VLMs) and large language models (LLMs) has made significant progress. However, we have observed and empirically demonstrated that these methods are susceptible to modality bias induced by LLMs and tend to generate descriptions containing objects (entities) that do not actually exist in the image but frequently appear during training (i.e., object hallucination). In this paper, we propose ViECap, a transferable decoding model that leverages entity-aware decoding to generate descriptions in both seen and unseen scenarios. ViECap incorporates entity-aware hard prompts to guide LLMs' attention toward the visual entities present in the image, enabling coherent caption generation across diverse scenes. With entity-aware hard prompts, ViECap is capable of maintaining performance when transferring from in-domain to out-of-domain scenarios. Extensive experiments demonstrate that ViECap sets a new state-of-theart cross-domain (transferable) captioning and performs competitively in-domain captioning compared to previous VLMs-based zero-shot methods. Our code is available at: https://github.com/FeiElysia/ViECap

Visual Transformers (ViTs) and Convolutional Neural Networks (CNNs) are the two primary backbone structures extensively used in various vision tasks. Generating transferable adversarial examples for ViTs is difficult due to ViTs' superior robustness, while transferring adversarial examples across ViTs and CNNs is even harder, since their structures and mechanisms for processing images are fundamentally distinct. In this work, we propose a novel attack method named Momentum Integrated Gradients (MIG), which not only attacks ViTs with high success rate, but also exhibits impressive transferability across ViTs and CNNs. Specifically, we use integrated gradients rather than gradients to steer the generation of adversarial perturbations, inspired by the observation that integrated gradients of images demonstrate higher similarity across models in comparison to regular gradients. Then we acquire the accumulated gradients by combining the integrated gradients from previous iterations with the current ones in a momentum manner and use their sign to modify the perturbations iteratively. We conduct extensive experiments to demonstrate that adversarial examples obtained using MIG show stronger transferability, resulting in significant improvements over state-of-the-art methods for both CNN and ViT models.

This work aims for transferring a Transformer-based image compression codec from human perception to machine perception without fine-tuning the codec. We propose a transferable Transformer-based image compression framework, termed TransTIC. Inspired by visual prompt tuning, TransTIC adopts an instance-specific prompt generator to inject instance-specific prompts to the encoder and task-specific prompts to the decoder. Extensive experiments show that our proposed method is capable of transferring the base codec to various machine tasks and outperforms the competing methods significantly. To our best knowledge, this work is the first attempt to utilize prompting on the low-level image compression task.

Cooperation between vehicles and infrastructure is vital to enhancing the safety of autonomous driving. Two significant and contradictory challenges now stand in the collaborative perception: fusion accuracy and communication bandwidth. Previous intermediate fusion methods that transmit features balance the accuracy and bandwidth compared with early fusion and late fusion, but usually have problems with feature alignment and domain gaps, and the bandwidth usage still falls short of the industrial application standard to our best knowledge. In this paper, we propose TransIFF, an instance-level feature fusion framework with transformers that can effectively reduce bandwidth usage. Furthermore, it can align the domain gaps between vehicle and infrastructure features, and improve the robustness of feature fusion, leading to a high cooperative perception accuracy. TransIFF is composed of three components: a vehicle-side network, an infrastructure-side network, and a vehicle-infrastructure fusion network. Initially, the vehicle-side and infrastructure-side networks independently generate instance-level features. Subsequently, the infrastructure-side instance-level features are transmitted to the vehicles, significantly reducing the communication bandwidth usage. Finally, in the vehicle-infrastructure fusion network, Cross-Domain Adaptation (CDA) module is designed to align the feature domains, followed by Feature Magnet (FM) module which can adaptively fuse the instance features and achieve a robust feature fusion. TransIFF yields state-of-the-art performance on the widely used real-world vehicle-infrastructure cooperative benchmark DAIR-V2X, achieving 59.62% AP with only 2^12 bytes bandwidth consumption.

In this paper, we focus on the task of generalizable neural human rendering which trains conditional Neural Radiance Fields (NeRF) from multi-view videos of different characters. To handle the dynamic human motion, previous methods have primarily used a SparseConvNet (SPC)-based human representation to process the painted SMPL. However, such SPC-based representation i) optimizes under the volatile observation space which leads to the pose-misalignment between training and inference stages, and ii) lacks the global relationships among human parts that is critical for handling the incomplete painted SMPL. Tackling these issues, we present a brand-new framework named TransHuman, which learns the painted SMPL under the canonical space and captures the global relationships between human parts with transformers. Specifically, TransHuman is mainly composed of Transformer-based Human Encoding (TransHE), Deformable Partial Radiance Fields (DPaRF), and Fine-grained Detail Integration (FDI). TransHE first processes the painted SMPL under the canonical space via transformers for capturing the global relationships between human parts. Then, DPaRF binds each output token with a deformable radiance field for encoding the query point under the observation space. Finally, the FDI is employed to further integrate fine-grained information from reference images. Extensive experiments on ZJU-MoCap and H36M show that our TransHuman achieves a significantly new state-of-the-art performance with high efficiency. Project page: https://pansanity666.github.io/TransHuman/

Vision Transformers (ViTs) have demonstrated powerful representation ability in various visual tasks thanks to their intrinsic data-hungry nature. However, we unexpectedly find that ViTs perform vulnerably when applied to face recognition (FR) scenarios with extremely large datasets. We investigate the reasons for this phenomenon and discover that the existing data augmentation approach and hard sample mining strategy are incompatible with ViTs-based FR backbone due to the lack of tailored consideration on preserving face structural information and leveraging each local token information. To remedy these problems, this paper proposes a superior FR model called TransFace, which employs a patch-level data augmentation strategy named DPAP and a hard sample mining strategy named EHSM. Specially, DPAP randomly perturbs the amplitude information of dominant patches to expand sample diversity, which effectively alleviates the overfitting problem in ViTs. EHSM utilizes the information entropy in the local tokens to dynamically adjust the importance weight of easy and hard samples during training, leading to a more stable prediction. Experiments on several benchmarks demonstrate the superiority of our TransFace. Code and models are available at https://github.com/DanJun6737/TransFace.

3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. With the commonly used tracking-by-detection paradigm, 3D MOT has made important progress in recent years. However, these methods only use the detection boxes of the current frame to obtain trajectory-box association results, which makes it impossible for the tracker to recover objects missed by the detector. In this paper, we present TrajectoryFormer, a novel point-cloud-based 3D MOT framework. To recover the missed object by detector, we generates multiple trajectory hypotheses with hybrid candidate boxes, including temporally predicted boxes and currentframe detection boxes, for trajectory-box association. The predicted boxes can propagate object's history trajectory information to the current frame and thus the network can tolerate short-term miss detection of the tracked objects. We combine long-term object motion feature and short-term object appearance feature to create per-hypothesis feature embedding, which reduces the computational overhead for spatial-temporal encoding. Additionally, we introduce a Global-Local Interaction Module to conduct information interaction among all hypotheses and models their spatial relations, leading to accurate estimation of hypotheses. Our TrajectoryFormer achieves state-of-the-art performance on the Waymo 3D MOT benchmarks.

Pedestrian trajectory prediction is an essentially connecting link to understanding human behavior. Recent works achieve state-of-the-art performance gained from the hand-designed post-processing, e.g., clustering. However, this post-processing suffers from expensive inference time and neglects the probability of the predicted trajectory disturbing downstream safety decisions. In this paper, we present Trajectory Unified TRansformer, called TUTR, which unifies the trajectory prediction components, social interaction and multimodal trajectory prediction, into a transformer encoder-decoder architecture to effectively remove the need for post-processing. Specifically, TUTR parses the relationships across various motion modes by an explicit global prediction and an implicit mode-level transformer encoder. Then, TUTR attends to the social interactions with neighbors by a social-level transformer decoder. Finally, a dual prediction forecasts diverse trajectories and corresponding probabilities in parallel without post-processing. TUTR achieves state-of-the-art accuracy performance and about 10x - 40x inference speed improvements compared with previous well-tuning state-of-the-art methods using post-processing.

Robust pedestrian trajectory forecasting is crucial to developing safe autonomous vehicles. Although previous works have studied adversarial robustness in the context of trajectory forecasting, some significant issues remain unaddressed. In this work, we try to tackle these crucial problems. Firstly, the previous definitions of robustness in trajectory prediction are ambiguous. We thus provide formal definitions for two kinds of robustness, namely label robustness and pure robustness. Secondly, as previous works fail to consider robustness about all points in a disturbance interval, we utilise a probably approximately correct (PAC) framework for robustness verification. Additionally, this framework can not only identify potential counterexamples, but also provides interpretable analyses of the original methods. Our approach is applied using a prototype tool named TrajPAC. With TrajPAC, we evaluate the robustness of four state-of-the-art trajectory prediction models -- Trajectron++, MemoNet, AgentFormer, and MID -- on trajectories from five scenes of the ETH/UCY dataset and scenes of the Stanford Drone Dataset. Using our framework, we also experimentally study various factors that could influence robustness performance.

Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers. One key issue is to generate consistent trajectory predictions without colliding. To overcome the challenge, we propose an efficient masked autoencoder for trajectory prediction (Traj-MAE) that better represents the complicated behaviors of agents in the driving environment. Specifically, our Traj-MAE employs diverse masking strategies to pre-train the trajectory encoder and map encoder, allowing for the capture of social and temporal information among agents while leveraging the effect of environment from multiple granularities. To address the catastrophic forgetting problem that arises when pre-training the network with multiple masking strategies, we introduce a continual pre-training framework, which can help Traj-MAE learn valuable and diverse information from various strategies efficiently. Our experimental results in both multi-agent and single-agent settings demonstrate that Traj-MAE achieves competitive results with state-of-the-art methods and significantly outperforms our baseline model. Project page: https://jiazewang.com/projects/trajmae.html.