GenAI4ELab/papercli-papers huggingface dataset:
Long-Range Grouping Transformer for Multi-View 3D Reconstruction
Authors: Liying Yang; Zhenwei Zhu; Xuxin Lin; Jian Nong; Yanyan Liang
Venue: ICCV 2023 (main)

Abstract: Nowadays, transformer networks have demonstrated superior performance in many computer vision tasks. In a multi-view 3D reconstruction algorithm following this paradigm, self-attention processing has to deal with intricate image tokens including massive information when facing heavy amounts of view input. The curse of information content leads to the extreme difficulty of model learning. To alleviate this problem, recent methods compress the token number representing each view or discard the attention operations between the tokens from different views. Obviously, they give a negative impact on performance. Therefore, we propose long-range grouping attention (LGA) based on the divide-and-conquer principle. Tokens from all views are grouped for separate attention operations. The tokens in each group are sampled from all views and can provide macro representation for the resided view. The richness of feature learning is guaranteed by the diversity among different groups. An effective and efficient encoder can be established which connects inter-view features using LGA and extract intra-view features using the standard self-attention layer. Moreover, a novel progressive upsampling decoder is also designed for voxel generation with relatively high resolution. Hinging on the above, we construct a powerful transformer-based network, called LRGT. Experimental results on ShapeNet verify our method achieves SOTA accuracy in multi-view reconstruction. Code is available at https://github.com/LiyingCV/Long-Range-Grouping-Transformer.
Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression Recognition
Authors: Isack Lee; Eungi Lee; Seok Bong Yoo
Venue: ICCV 2023 (main)

Abstract: Most research on facial expression recognition (FER) is conducted in highly controlled environments, but its performance is often unacceptable when applied to real-world situations. This is because when unexpected objects occlude the face, the FER network faces difficulties extracting facial features and accurately predicting facial expressions. Therefore, occluded FER (OFER) is a challenging problem. Previous studies on occlusion-aware FER have typically required fully annotated facial images for training. However, collecting facial images with various occlusions and expression annotations is time-consuming and expensive. Latent-OFER, the proposed method, can detect occlusions, restore occluded parts of the face as if they were unoccluded, and recognize them, improving FER accuracy. This approach involves three steps: First, the vision transformer (ViT)-based occlusion patch detector masks the occluded position by training only latent vectors from the unoccluded patches using the support vector data description algorithm. Second, the hybrid reconstruction network generates the masking position as a complete image using the ViT and convolutional neural network (CNN). Last, the expression-relevant latent vector extractor retrieves and uses expression-related information from all latent vectors by applying a CNN-based class activation map. This mechanism has a significant advantage in preventing performance degradation from occlusion by unseen objects. The experimental results on several databases demonstrate the superiority of the proposed method over state-of-the-art methods.
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization
Authors: Xinlin Li; Bang Liu; Rui Heng Yang; Vanessa Courville; Chao Xing; Vahid Partovi Nia
Venue: ICCV 2023 (main)

Abstract: Efficiently deploying deep neural networks on low-resource edge devices is challenging due to their ever-increasing resource requirements. To address this issue, researchers have proposed multiplication-free neural networks, such as Power-of-Two quantization, or also known as Shift networks, which aim to reduce memory usage and simplify computation. However, existing low-bit Shift networks are not as accurate as their full-precision counterparts, typically suffering from limited weight range encoding schemes and quantization loss. 
 In this paper, we propose the DenseShift network, which significantly improves the accuracy of Shift networks, achieving competitive performance to full-precision networks for vision and speech applications. In addition, we introduce a method to deploy an efficient DenseShift network using non-quantized floating-point activations, while obtaining 1.6X speed-up over existing methods. 
 To achieve this, we demonstrate that zero-weight values in low-bit Shift networks do not contribute to model capacity and negatively impact inference computation. To address this issue, we propose a zero-free shifting mechanism that simplifies inference and increases model capacity. We further propose a sign-scale decomposition design to enhance training efficiency and a low-variance random initialization strategy to improve the model's transfer learning performance. Our extensive experiments on various computer vision and speech tasks demonstrate that DenseShift outperforms existing low-bit multiplication-free networks and achieves competitive performance compared to full-precision networks. Furthermore, our proposed approach exhibits strong transfer learning performance without a drop in accuracy. Our code was released on GitHub.
Preparing the Future for Continual Semantic Segmentation
Authors: Zihan Lin; Zilei Wang; Yixin Zhang
Venue: ICCV 2023 (main)

Abstract: In this study, we focus on Continual Semantic Segmentation (CSS) and present a novel approach to tackle the issue of existing methods struggling to learn new classes. The primary challenge of CSS is to learn new knowledge while retaining old knowledge, which is commonly known as the rigidity-plasticity dilemma. Existing approaches strive to address this by carefully balancing the learning of new and old classes during training on new data. Differently, this work aims to avoid this dilemma fundamentally rather than handling the difficulties involved in it. Specifically, we reveal that this dilemma mainly arises from the greater fluctuation of knowledge for new classes because they have never been learned before the current step. Additionally, the data available in incremental steps are usually inadequate, which can impede the model's ability to learn discriminative features for both new and old classes. To address these challenges, we introduce a novel concept of pre-learning for future knowledge. Our approach entails optimizing the feature space and output space for unlabeled data, which thus enables the model to acquire knowledge for future classes. With this approach, updating the model for new classes becomes as smooth as for old classes, effectively avoiding the rigidity-plasticity dilemma. We conducted extensive experiments and the results demonstrate a significant improvement in the learning of new classes compared to previous state-of-the-art methods.
Efficient Computation Sharing for Multi-Task Visual Scene Understanding
Authors: Sara Shoouri; Mingyu Yang; Zichen Fan; Hun-Seok Kim
Venue: ICCV 2023 (main)

Abstract: Solving multiple visual tasks using individual models can be resource-intensive, while multi-task learning can conserve resources by sharing knowledge across different tasks. Despite the benefits of multi-task learning, such techniques can struggle with balancing the loss for each task, leading to potential performance degradation. We present a novel computation- and parameter-sharing framework that balances efficiency and accuracy to perform multiple visual tasks utilizing individually-trained single-task transformers. Our method is motivated by transfer learning schemes to reduce computational and parameter storage costs while maintaining the desired performance. Our approach involves splitting the tasks into a base task and the other sub-tasks, and sharing a significant portion of activations and parameters/weights between the base and sub-tasks to decrease inter-task redundancies and enhance knowledge sharing. The evaluation conducted on NYUD-v2 and PASCAL-context datasets shows that our method is superior to the state-of-the-art transformer-based multi-task learning techniques with higher accuracy and reduced computational resources. Moreover, our method is extended to video stream inputs, further reducing computational costs by efficiently sharing information across the temporal domain as well as the task domain. Our codes are available at https://github.com/sarashoouri/EfficientMTL.
Self-supervised Cross-view Representation Reconstruction for Change Captioning
Authors: Yunbin Tu; Liang Li; Li Su; Zheng-Jun Zha; Chenggang Yan; Qingming Huang
Venue: ICCV 2023 (main)

Abstract: Change captioning aims to describe the difference between a pair of similar images. Its key challenge is how to learn a stable difference representation under pseudo changes caused by viewpoint change. In this paper, we address this by proposing a self-supervised cross-view representation reconstruction (SCORER) network. Concretely, we first design a multi-head token-wise matching to model relationships between cross-view features from similar/dissimilar images. Then, by maximizing cross-view contrastive alignment of two similar images, SCORER learns two view-invariant image representations in a self-supervised way. Based on these, we reconstruct the representations of unchanged objects by cross-attention, thus learning a stable difference representation for caption generation. Further, we devise a cross-modal backward reasoning to improve the quality of caption. This module reversely models a "hallucination" representation with the caption and "before" representation. By pushing it closer to the "after" representation, we enforce the caption to be informative about the difference in a self-supervised manner. Extensive experiments show our method achieves the state-of-the-art results on four datasets. The code is available at https://github.com/tuyunbin/SCORER.
Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generation
Authors: Yaowei Li; Bang Yang; Xuxin Cheng; Zhihong Zhu; Hongxiang Li; Yuexian Zou
Venue: ICCV 2023 (main)

Abstract: Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists. However, simultaneously establishing global correspondences between the image (e.g., Chest X-ray) and its related report and local alignments between image patches and keywords remains challenging. To this end, we propose an Unify, Align and then Refine (UAR) approach to learn multi-level cross-modal alignments and introduce three novel modules: Latent Space Unifier (LSU), Cross-modal Representation Aligner (CRA) and Text-to-Image Refiner (TIR). Specifically, LSU unifies multimodal data into discrete tokens, making it flexible to learn common knowledge among modalities with a shared network. The modality-agnostic CRA learns discriminative features via a set of orthonormal basis and a dual-gate mechanism first and then globally aligns visual and textual representations under a triplet contrastive loss. TIR boosts token-level local alignment via calibrating text-to-image attention with a learnable mask. Additionally, we design a two-stage training procedure to make UAR gradually grasp cross-modal alignments at different levels, which imitates radiologists' workflow: writing sentence by sentence first and then checking word by word. Extensive experiments and analyses on IU-Xray and MIMIC-CXR benchmark datasets demonstrate the superiority of our UAR against varied state-of-the-art methods.
Synthesizing Diverse Human Motions in 3D Indoor Scenes
Authors: Kaifeng Zhao; Yan Zhang; Shaofei Wang; Thabo Beeler; Siyu Tang
Venue: ICCV 2023 (main)

Abstract: We present a novel method for populating 3D indoor scenes with virtual humans that can navigate in the environment and interact with objects in a realistic manner. Existing approaches rely on high-quality training sequences that contain captured human motions and the 3D scenes they interact with. However, such interaction data are costly, difficult to capture, and can hardly cover the full range of plausible human-scene interactions in complex indoor environments. To address these challenges, we propose a reinforcement learning-based approach that enables virtual humans to navigate in 3D scenes and interact with objects realistically and autonomously, driven by learned motion control policies. The motion control policies employ latent motion action spaces, which correspond to realistic motion primitives and are learned from large-scale motion capture data using a powerful generative motion model. For navigation in a 3D environment, we propose a scene-aware policy with novel state and reward designs for collision avoidance. Combined with navigation mesh-based path-finding algorithms to generate intermediate waypoints, our approach enables the synthesis of diverse human motions navigating in 3D indoor scenes and avoiding obstacles. To generate fine-grained human-object interactions, we carefully curate interaction goal guidance using a marker-based body representation and leverage features based on the signed distance field (SDF) to encode human-scene proximity relations. Our method can synthesize realistic and diverse human-object interactions (e.g., sitting on a chair and then getting up) even for out-of-distribution test scenarios with different object shapes, orientations, starting body positions, and poses. Experimental results demonstrate that our approach outperforms state-of-the-art human-scene interaction synthesis methods in terms of both motion naturalness and diversity. Code, models, and demonstrative video results are publicly available at: https://zkf1997.github.io/DIMOS.
Deep Optics for Video Snapshot Compressive Imaging
Authors: Ping Wang; Lishun Wang; Xin Yuan
Venue: ICCV 2023 (main)

Abstract: Video snapshot compressive imaging (SCI) aims to capture a sequence of video frames with only a single shot of a 2D detector, whose backbones rest in optical modulation patterns (also known as masks) and a computational reconstruction algorithm. Advanced deep learning algorithms and mature hardware are putting video SCI into practical applications. Yet, there are two clouds in the sunshine of SCI: i) low dynamic range as a victim of high temporal multiplexing, and ii) existing deep learning algorithms' degradation on real system. To address these challenges, this paper presents a deep optics framework to jointly optimize masks and a reconstruction network. Specifically, we first propose a new type of structural mask to realize motionaware and full-dynamic-range measurement. Considering the motion awareness property in measurement domain, we develop an efficient network for video SCI reconstruction using Transformer to capture long-term temporal dependencies, dubbed Res2former. Moreover, sensor response is introduced into the forward model of video SCI to guarantee end-to-end model training close to real system. Finally, we implement the learned structural masks on a digital micro-mirror device. Experimental results on synthetic and real data validate the effectiveness of the proposed framework. We believe this is a miestone for real-world video SCI. The source code and data are available at https://github.com/pwangcs/DeepOpticsSCI.
DDIT: Semantic Scene Completion via Deformable Deep Implicit Templates
Authors: Haoang Li; Jinhu Dong; Binghui Wen; Ming Gao; Tianyu Huang; Yun-Hui Liu; Daniel Cremers
Venue: ICCV 2023 (main)

Abstract: Scene reconstructions are often incomplete due to occlusions and limited viewpoints. There have been efforts to use semantic information for scene completion. However, the completed shapes may be rough and imprecise since respective methods rely on 3D convolution and/or lack effective shape constraints. To overcome these limitations, we propose a semantic scene completion method based on deformable deep implicit templates (DDIT). Specifically, we complete each segmented instance in a scene by deforming a template with a latent code. Such a template is expressed by a deep implicit function in the canonical frame. It abstracts the shape prior of a category, and thus can provide constraints on the overall shape of an instance. Latent code controls the deformation of template to guarantee fine details of an instance. For code prediction, we design a neural network that leverages both intra- and inter-instance information. We also introduce an algorithm to transform instances between the world and canonical frames based on geometric constraints and a hierarchical tree. To further improve accuracy, we jointly optimize the latent code and transformation by enforcing the zero-valued isosurface constraint. In addition, we establish a new dataset to solve different problems of existing datasets. Experiments showed that our DDIT outperforms state-of-the-art approaches.
Joint Demosaicing and Deghosting of Time-Varying Exposures for Single-Shot HDR Imaging
Authors: Jungwoo Kim; Min H. Kim
Venue: ICCV 2023 (main)

Abstract: The quad-Bayer patterned image sensor has made significant improvements in spatial resolution over recent years due to advancements in image sensor technology. This has enabled single-shot high-dynamic-range (HDR) imaging using spatially varying multiple exposures. Popular methods for multi-exposure array sensors involve varying the gain of each exposure, but this does not effectively change the photoelectronic energy in each exposure. Consequently, HDR images produced using gain-based exposure variation may suffer from noise and details being saturated. To address this problem, we intend to use time-varying exposures in quad-Bayer patterned sensors. This approach allows long-exposure pixels to receive more photon energy than short- or middle-exposure pixels, resulting in higher-quality HDR images. However, time-varying exposures are not ideal for dynamic scenes and require an additional deghosting method. To tackle this issue, we propose a single-shot HDR demosaicing method that takes time-varying multiple exposures as input and jointly solves both the demosaicing and deghosting problems. Our method uses a feature-extraction module to handle mosaiced multiple exposures and a multiscale transformer module to register spatial displacements of multiple exposures and colors. We also created a dataset of quad-Bayer sensor input with time-varying exposures and trained our network using this dataset. Results demonstrate that our method outperforms baseline HDR reconstruction methods with both synthetic and real datasets. With our method, we can achieve high-quality HDR images in challenging lighting conditions.
Scene-Aware Feature Matching
Authors: Xiaoyong Lu; Yaping Yan; Tong Wei; Songlin Du
Venue: ICCV 2023 (main)

Abstract: Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This results in significant performance degradation when handling challenging scenes such as scenes with large viewpoint and illumination changes. To tackle this problem, we propose a novel model named SAM, which applies attentional grouping to guide Scene-Aware feature Matching. SAM handles multi-level features, i.e., image tokens and group tokens, with attention layers, and groups the image tokens with the proposed token grouping module. Our model can be trained by ground-truth matches only and produce reasonable grouping results. With the sense-aware grouping guidance, SAM is not only more accurate and robust but also more interpretable than conventional feature matching models. Sufficient experiments on various applications, including homography estimation, pose estimation, and image matching, demonstrate that our model achieves state-of-the-art performance.
FDViT: Improve the Hierarchical Architecture of Vision Transformer
Authors: Yixing Xu; Chao Li; Dong Li; Xiao Sheng; Fan Jiang; Lu Tian; Ashish Sirasao
Venue: ICCV 2023 (main)

Abstract: Despite the fact that transformer-based models have yielded great success in computer vision tasks, they suffer from the challenge of high computational costs that limits their use on resource-constrained devices. One major reason is that vision transformers have redundant calculations since the self-attention operation generates patches with high similarity at a later stage in the network. Hierarchical architectures have been proposed for vision transformers to alleviate this challenge. However, by shrinking the spatial dimensions to half of the originals with downsampling layers, the challenge is actually overcompensated, as too much information is lost. In this paper, we propose FDViT to improve the hierarchical architecture of the vision transformer by using a flexible downsampling layer that is not limited to integer stride to smoothly reduce the sizes of the middle feature maps. Furthermore, a masked auto-encoder architecture is used to facilitate the training of the proposed flexible downsampling layer and produces informative outputs. Experimental results on benchmark datasets demonstrate that the proposed method can reduce computational costs while increasing classification performance and achieving state-of-the-art results. For example, the proposed FDViT-S model achieves a top-1 accuracy of 81.5%, which is 1.7 percent points higher than the ViT-S model and reduces 39% FLOPs.
Tuning Pre-trained Model via Moment Probing
Authors: Mingze Gao; Qilong Wang; Zhenyi Lin; Pengfei Zhu; Qinghua Hu; Jingbo Zhou
Venue: ICCV 2023 (main)

Abstract: Recently, efficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in exploiting the final representations for task-dependent classification. However, most of the existing methods focus on how to effectively introduce a few of learnable parameters, and little work pays attention to the commonly used LP module. In this paper, we propose a novel Moment Probing (MP) method to further explore the potential of LP. Distinguished from LP which builds a linear classification head based on the mean of final features (e.g., word tokens for ViT) or classification tokens, our MP performs a linear classifier on feature distribution, which provides a stronger representation ability by exploiting richer statistical information inherent in features. Specifically, we represent feature distribution by its characteristic function, which is efficiently approximated by using first- and second-order moments of features. Furthermore, we propose a multi-head convolutional cross-covariance to compute second-order moments in an efficient and effective manner. By considering that MP could affect feature learning, we introduce a partially shared module to learn two recalibrating parameters (PSRP) for backbones based on MP, namely MP+. Extensive experiments on ten benchmarks using various models show that our MP significantly outperforms LP and is competitive with counterparts at less training cost, while our MP+ achieves state-of-the-art performance.