ICCV 2023 论文列表

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown great success in achieving high performance on classification tasks with very few time steps. However, how to design a directly-trained SNN for the regression task of object detection still remains a challenging problem. To address this problem, we propose EMS-YOLO, a novel directly-trained SNN framework for object detection, which is the first trial to train a deep SNN with surrogate gradients for object detection rather than ANN-SNN conversion strategies. Specifically, we design a full-spike residual block, EMS-ResNet, which can effectively extend the depth of the directly-trained SNN with low power consumption. Furthermore, we theoretically analyze and prove the EMS-ResNet could avoid gradient vanishing or exploding. The results demonstrate that our approach outperforms the state-of-the-art ANN-SNN conversion methods (at least 500 time steps) in extremely fewer time steps (only 4 time steps). It is shown that our model could achieve comparable performance to the ANN with the same architecture while consuming 5.83x less energy on the frame-based COCO Dataset and the event-based Gen1 Dataset. Our code is available in https://github.com/BICLab/EMS-YOLO.

This paper solves the problem of real-time 6-DoF object tracking from an RGB video. Prior optimization-based methods optimize the object pose by aligning the projected model to the image based on handcrafted features, which are prone to suboptimal solutions. Recent learning-based methods use neural networks to predict the pose, which suffer from limited generalizability or computational efficiency. We propose a learning-based active contour model to make the best use of both worlds. Specifically, given an initial pose, we project the object model to the image plane to obtain the initial contour and use a lightweight network to predict how the contour should move to match the true object boundary, which provides the gradients to optimize the object pose. We also devise an efficient optimization algorithm to train our model end-to-end with pose supervision. Experimental results on semi-synthetic and real-world 6-DoF object tracking datasets demonstrate that our model outperforms state-of-the-art methods by a substantial margin in pose accuracy, while achieving real-time performance on mobile devices. Code is available on our project page: https://zju3dv.github.io/deep_ac/.

In this paper, we realize automatic visual recognition and direction estimation of pointing. We introduce the first neural pointing understanding method based on two key contributions. The first is the introduction of a first-of-its-kind large-scale dataset for pointing recognition and direction estimation, which we refer to as the DP Dataset. DP Dataset consists of more than 2 million frames of 33 people pointing in various styles annotated for each frame with pointing timings and 3D directions. The second is DeePoint, a novel deep network model for joint recognition and 3D direction estimation of pointing. DeePoint is a Transformer-based network which fully leverages the spatio-temporal coordination of the body parts, not just the hands. Through extensive experiments, we demonstrate the accuracy and efficiency of DeePoint. We believe DP Dataset and DeePoint will serve as a sound foundation for visual human intention understanding.

Reconstructing interacting hands from a single RGB image is a very challenging task. On the one hand, severe mutual occlusion and similar local appearance between two hands confuse the extraction of visual features, resulting in the misalignment of estimated hand meshes and the image. On the other hand, there are complex spatial relationship between interacting hands, which significantly increases the solution space of hand poses and increases the difficulty of network learning. In this paper, we propose a decoupled iterative refinement framework to achieve pixel-alignment hand reconstruction while efficiently modeling the spatial relationship between hands. Specifically, we define two feature spaces with different characteristics, namely 2D visual feature space and 3D joint feature space. First, we obtain joint-wise features from the visual feature map and utilize a graph convolution network and a transformer to perform intra- and inter-hand information interaction in the 3D joint feature space, respectively. Then, we project the joint features with global information back into the 2D visual feature space in an obfuscation-free manner and utilize the 2D convolution for pixel-wise enhancement. By performing multiple alternate enhancements in the two feature spaces, our method can achieve an accurate and robust reconstruction of interacting hands. Our method outperforms all existing two-hand reconstruction methods by a large margin on the InterHand2.6M dataset.

The introduction of DETR represents a new paradigm for object detection. However, its decoder conducts classification and box localization using shared queries and cross-attention layers, leading to suboptimal results. We observe that different regions of interest in the visual feature map are suitable for performing query classification and box localization tasks, even for the same object. Salient regions provide vital information for classification, while the boundaries around them are more favorable for box regression. Unfortunately, such spatial misalignment between these two tasks greatly hinders DETR's training. Therefore, in this work, we focus on decoupling localization and classification tasks in DETR. To achieve this, we introduce a new design scheme called spatially decoupled DETR (SD-DETR), which includes a task-aware query generation module and a disentangled feature learning process. We elaborately design the task-aware query initialization process and divide the cross-attention block in the decoder to allow the task-aware queries to match different visual regions. Meanwhile, we also observe that the prediction misalignment problem for high classification confidence and precise localization exists, so we propose an alignment loss to further guide the spatially decoupled DETR training. Through extensive experiments, we demonstrate that our approach achieves a significant improvement in MSCOCO datasets compared to previous work. For instance, we improve the performance of Conditional DETR by 4.5%. By spatially disentangling the two tasks, our method overcomes the misalignment problem and greatly improves the performance of DETR for object detection.

In the real world, a desirable Visual Question Answering model is expected to provide correct answers to new questions and images in a continual setting (recognized as CL-VQA). However, existing works formulate CLVQA from a vision-only or language-only perspective, and straightforwardly apply the uni-modal continual learning (CL) strategies to this multi-modal task, which is improper and suboptimal. On the one hand, such a partial formulation may result in limited evaluations. On the other hand, neglecting the interactions between modalities will lead to poor performance. To tackle these challenging issues, we propose a comprehensive formulation for CL-VQA from the perspective of multi-modal vision-language fusion. Based on our formulation, we further propose MulTi-Modal PRompt LearnIng with DecouPLing bEfore InTeraction (TRIPLET), a novel approach that builds on a pre-trained vision-language model and consists of decoupled prompts and prompt interaction strategies to capture the complex interactions between modalities. In particular, decoupled prompts contain learnable parameters that are decoupled w.r.t different aspects, and the prompt interaction strategies are in charge of modeling interactions between inputs and prompts. Additionally, we build two CL-VQA benchmarks for a more comprehensive evaluation. Extensive experiments demonstrate that our TRIPLET outperforms state-of-the-art methods in both uni-modal and multi-modal continual settings for CL-VQA.

Multi-contrast MRI super-resolution (SR) and reconstruction methods aim to explore complementary information from the reference image to help the reconstruction of the target image. Existing deep learning-based methods usually manually design fusion rules to aggregate the multi-contrast images, fail to model their correlations accurately and lack certain interpretations. Against these issues, we propose a multi-contrast variational network (MC-VarNet) to explicitly model the relationship of multi-contrast images. Our model is constructed based on an intuitive motivation that multi-contrast images have consistent (edges and structures) and inconsistent (contrast) information. We thus build a model to reconstruct the target image and decompose the reference image as a common component and a unique component. In the feature interaction phase, only the common component is transferred to the target image. We solve the variational model and unfold the iterative solutions into a deep network. Hence, the proposed method combines the good interpretability of model-based methods with the powerful representation ability of deep learning-based methods. Experimental results on the multi-contrast MRI reconstruction and SR demonstrate the effectiveness of the proposed model. Especially, since we explicitly model the multi-contrast images, our model is more robust to the reference images with noises and large inconsistent structures. The code is available at https://github.com/lpcccc-cv/MC-VarNet.

Natural image compression has been greatly improved in the deep learning era. However, the compression performance will be heavily degraded if the pretrained encoder is directly applied on screen content image compression. Meanwhile, we observe that parameter-efficient trans-fer learning (PETL) methods have shown great adaptation ability in high-level vision tasks. Therefore, we propose a Dec-Adapter, a pioneering entropy-efficient transfer learning module for the decoder to bridge natural image and screen content compression. The adapter's parameters are learned during encoding and transmitted to the decoder for image-adaptive decoding. Our Dec-Adapter is lightweight, domain-transferable, and architecture-agnostic with generalized performance in bridging the two domains. Experiments demonstrate that our method outperforms all existing methods by a large margin in terms of BD-rate performance on screen content image compression. Specifically, our method achieves over 2 dB gain compared with the baseline when transferred to screen content image com-pression.

Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel view synthesis. However, standard volume rendering approaches struggle with degenerate geometries in the case of limited viewpoint diversity, a common scenario in robotics applications. In this work, we propose to use the multi-view photometric objective from the self-supervised depth estimation literature as a geometric regularizer for volumetric rendering, significantly improving novel view synthesis without requiring additional information. Building upon this insight, we explore the explicit modeling of scene geometry using a generalist Transformer, jointly learning a radiance field as well as depth and light fields with a set of shared latent codes. We demonstrate that sharing geometric information across tasks is mutually beneficial, leading to improvements over single-task learning without an increase in network complexity. Our DeLiRa architecture achieves state-of-the-art results on the ScanNet benchmark, enabling high quality volumetric rendering as well as real-time novel view and depth synthesis in the limited viewpoint diversity setting.

Explicit 3D shape abstraction from a single 2D image is a long-standing problem in computer vision and graphics. By leveraging a set of primitives to represent the target shape, recent methods have achieved promising results. However, these methods either use a relatively larger number of primitives or lack geometric flexibility due to the low-dimensional expressibility of the primitives. In this paper, we propose a novel bi-channel Transformer architecture, integrated with parameterized deformable models, termed DeFormer, to simultaneously estimate global and local deformations. In this way, DeFormer can abstract complex object shapes while using a small number of primitives which offer a broader geometry coverage and finer details. Then, we introduce a force-driven dynamic fitting and a cycle-consistent re-projection loss to optimize the primitive parameters. Extensive experiments on ShapeNet across various settings show that DeFormer achieves better reconstruction accuracy over the state-of-the-art, and visualizes with consistent semantic correspondences for improved interpretability.

State-of-the-art deep neural networks are trained with large amounts (millions or even billions) of data. The expensive computation and memory costs make it difficult to train them on limited hardware resources, especially for recent popular large language models (LLM) and computer vision models (CV). Recent popular dataset distillation methods are thus developed, aiming to reduce the number of training samples via synthesizing small-scale datasets via gradient matching. However, as the gradient calculation is coupled with the specific network architecture, the synthesized dataset is biased and performs poorly when used for training unseen architectures. To address these limitations, we present dataset quantization (DQ), a new framework to compress large-scale datasets into small subsets which can be used for training any neural network architectures. Extensive experiments demonstrate that DQ is able to generate condensed small datasets for training unseen network architectures with state-of-the-art compression ratios for lossless model training. To the best of our knowledge, DQ is the first method that can successfully distill large-scale datasets such as ImageNet-1k with a state-of-the-art compression ratio. Notably, with 60% data from ImageNet and 20% data from Alpaca's instruction tuning data, the models can be trained with negligible or no performance drop for both vision tasks (including classification, semantic segmentation, and object detection) as well as language tasks (including instruction tuning tasks such as BBH and DROP).

Researchers have long tried to minimize training costs in deep learning while maintaining strong generalization across diverse datasets. Emerging research on dataset distillation aims to reduce training costs by creating a small synthetic set that contains the information of a larger real dataset and ultimately achieves test accuracy equivalent to a model trained on the whole dataset. Unfortunately, the synthetic data generated by previous methods are not guaranteed to distribute and discriminate as well as the original training data, and they incur significant computational costs. Despite promising results, there still exists a significant performance gap between models trained on condensed synthetic sets and those trained on the whole dataset. In this paper, we address these challenges using efficient Dataset Distillation with Attention Matching (DataDAM), achieving state-of-the-art performance while reducing training costs. Specifically, we learn synthetic images by matching the spatial attention maps of real and synthetic data generated by different layers within a family of randomly initialized neural networks. Our method outperforms the prior methods on several datasets, including CIFAR10/100, TinyImageNet, ImageNet-1K, and subsets of ImageNet-1K across most of the settings, and achieves improvements of up to 6.5% and 4.1% on CIFAR100 and ImageNet-1K, respectively. We also show that our high-quality distilled images have practical benefits for downstream applications, such as continual learning and neural architecture search.

Data-free knowledge distillation (DFKD) is a promising approach for addressing issues related to model compression, security privacy, and transmission restrictions. Although the existing methods exploiting DFKD have achieved inspiring achievements in coarse-grained classification, in practical applications involving fine-grained classification tasks that require more detailed distinctions between similar categories, sub-optimal results are obtained. To address this issue, we propose an approach called DFKD-FGVC that extends DFKD to fine-grained vision categorization (FGVC) tasks. Our approach utilizes an adversarial distillation framework with attention generator, mixed high-order attention distillation, and semantic feature contrast learning. Specifically, we introduce a spatial-wise attention mechanism to the generator to synthesize fine-grained images with more details of discriminative parts. We also utilize the mixed high-order attention mechanism to capture complex interactions among parts and the subtle differences among discriminative features of the fine-grained categories, paying attention to both local features and semantic context relationships. Moreover, we leverage the teacher and student models of the distillation framework to contrast high-level semantic feature maps in the hyperspace, comparing variances of different categories. We evaluate our approach on three widely-used FGVC benchmarks (Aircraft, Cars196, and CUB200) and demonstrate its superior performance.

This paper investigates data-free class-incremental learning (DFCIL) for hand gesture recognition from 3D skeleton sequences. In this class-incremental learning (CIL) setting, while incrementally registering the new classes, we do not have access to the training samples (i.e. data-free) of the already known classes due to privacy. Existing DFCIL methods primarily focus on various forms of knowledge distillation for model inversion to mitigate catastrophic forgetting. Unlike SOTA methods, we delve deeper into the choice of the best samples for inversion. Inspired by the well-grounded theory of max-margin classification, we find that the best samples tend to lie close to the approximate decision boundary within a reasonable margin. To this end, we propose BOAT-MI -- a simple and effective boundary-aware prototypical sampling mechanism for model inversion for DFCIL. Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D -- which we extract from a publicly available RGBD dataset. Both our codebase and the EgoGesture3D skeleton dataset are publicly available: https://github.com/humansensinglab/dfcil-hgr

The goal of continual learning (CL) is to continuously learn new tasks without forgetting previously learned old tasks. To alleviate catastrophic forgetting, gradient projection based CL methods require that the gradient updates of new tasks are orthogonal to the subspace spanned by old tasks. This limits the learning process and leads to poor performance on the new task due to the projection constraint being too strong. In this paper, we first revisit the gradient projection method from the perspective of flatness of loss surface, and find that unflatness of the loss surface leads to catastrophic forgetting of the old tasks when the projection constraint is reduced to improve the performance of new tasks. Based on our findings, we propose a Data Augmented Flatness-aware Gradient Projection (DFGP) method to solve the problem, which consists of three modules: data and weight perturbation, flatness-aware optimization, and gradient projection. Specifically, we first perform a flatness-aware perturbation on the task data and current weights to find the case that makes the task loss worst. Next, flatness-aware optimization optimizes both the loss and the flatness of the loss surface on raw and worst-case perturbed data to obtain a flatness-aware gradient. Finally, gradient projection updates the network with the flatness-aware gradient along directions orthogonal to the subspace of the old tasks. Extensive experiments on four datasets show that our method improves the flatness of loss surface and the performance of new tasks, and achieves state-of-the-art (SOTA) performance in the average accuracy of all tasks.

Image retrieval methods based on CNN descriptors rely on metric learning from a large number of diverse examples of positive and negative image pairs. Domains, such as night-time images, with limited availability and variability of training data suffer from poor retrieval performance even with methods performing well on standard benchmarks. We propose to train a GAN-based synthetic-image generator, translating available day-time image examples into night images. Such a generator is used in metric learning as a form of augmentation, supplying training data to the scarce domain. Various types of generators are evaluated and analyzed. We contribute with a novel light-weight GAN architecture that enforces the consistency between the original and translated image through edge consistency. The proposed architecture also allows a simultaneous training of an edge detector that operates on both night and day images. To further increase the variability in the training examples and to maximize the generalization of the trained model, we propose a novel method of diverse anchor mining. The proposed method improves over the state-of-the-art results on a standard Tokyo 24/7 day-night retrieval benchmark while preserving the performance on Oxford and Paris datasets. This is achieved without the need of training image pairs of matching day and night images. The source code is available at https://github.com/mohwald/gandtr .

Wide-angle lenses are commonly used in perception tasks requiring a large field of view. Unfortunately, these lenses produce significant distortions making conventional models that ignore the distortion effects unable to adapt to wide-angle images. In this paper, we present a novel transformer-based model that automatically adapts to the distortion produced by wide-angle lenses. We leverage the physical characteristics of such lenses, which are analytically defined by the radial distortion profile (assumed to be known), to develop a distortion aware radial swin transformer (DarSwin). In contrast to conventional transformer-based architectures, DarSwin comprises a radial patch partitioning, a distortion-based sampling technique for creating token embeddings, and an angular position encoding for radial patch merging. We validate our method on classification tasks using synthetically distorted ImageNet data and show through extensive experiments that DarSwin can perform zero-shot adaptation to unseen distortions of different wide-angle lenses. Compared to other baselines, DarSwin achieves the best results (in terms of Top-1 accuracy) with significant gains when trained on bounded levels of distortions (very-low, low, medium, and high) and tested on all including out-of-distribution distortions. The code and models are publicly available at https://lvsn.github.io/darswin/

Domain generalization (DG) attempts to learn a model on source domains that can well generalize to unseen but different domains. The multiple source domains are innately different in distribution but intrinsically related to each other, e.g., from the same label space. To achieve a generalizable feature, most existing methods attempt to reduce the domain discrepancy by either learning domain-invariant feature, or additionally mining domain-specific feature. In the space of these features, the multiple source domains are either tightly aligned or not aligned at all, which both cannot fully take the advantage of complementary information from multiple domains. In order to preserve more complementary information from multiple domains at the meantime of reducing their domain gap, we propose that the multiple domains should not be tightly aligned but composite together, where all domains are pulled closer but still preserve their individuality respectively. This is achieved by using instance-adaptive classifier specified for each instance's classification, where the instance-adaptive classifier is slightly deviated from a universal classifier shared by samples from all domains. This adaptive classifier deviation allows all instances from the same category but different domains to be dispersed around the class center rather than squeezed tightly, leading to better generalization for unseen domain samples. In result, the multiple domains are harmoniously composite centered on a universal core, like a dandelion, so this work is referred to as DandelionNet. Experiments on multiple DG benchmarks demonstrate that the proposed method can learn a model with better generalization and experiments on source free domain adaption also indicate the versatility.

Low-light video enhancement is a challenging task with broad applications. However, current research in this area is limited by the lack of high-quality benchmark datasets. To address this issue, we design a camera system and collect a high-quality low-light video dataset with multiple exposures and cameras. Our dataset provides dynamic video pairs with pronounced camera motion and strict spatial alignment. To achieve general low-light video enhancement, we also propose a novel Retinex-based method named Light Adjustable Network (LAN). LAN iteratively refines the illumination and adaptively adjusts it under varying lighting conditions, leading to visually appealing results even in diverse real-world scenarios. The extensive experiments demonstrate the superiority of our low-light video dataset and enhancement method. Our dataset and code will be publicly available.