GenAI4ELab/papercli-papers huggingface dataset:
Boosting Single Image Super-Resolution via Partial Channel Shifting
Authors: Xiaoming Zhang; Tianrui Li; Xiaole Zhao
Venue: ICCV 2023 (main)

Abstract: Although deep learning has significantly facilitated the progress of single image super-resolution (SISR) in recent years, it still hits bottlenecks to further improve SR performance with the continuous growth of model scale. Therefore, one of the hotspots in the field is to construct efficient SISR models by elevating the effectiveness of feature representation. In this work, we present a straightforward and generic approach for feature enhancement that can effectively promote the performance of SR models, dubbed partial channel shifting (PCS). Specifically, it is inspired by the temporal shifting in video understanding and displaces part of the channels along the spatial dimensions, thus allowing the effective receptive field to be amplified and the feature diversity to be augmented at almost zero cost. Also, it can be assembled into off-the-shelf models as a plug-and-play component for performance boosting without extra network parameters and computational overhead. However, regulating the features with PCS encounters some issues, like shifting directions and amplitudes, proportions, and patterns of shifted channels, etc. We impose some technical constraints on the issues to simplify the general channel shifting. Extensive and throughout experiments illustrate that the PCS indeed enlarges the effective receptive field, augments the feature diversity for efficiently enhancing SR recovery, and can endow obvious performance gains to existing models.
Learning to Upsample by Learning to Sample
Authors: Wenze Liu; Hao Lu; Hongtao Fu; Zhiguo Cao
Venue: ICCV 2023 (main)

Abstract: We present DySample, an ultra-lightweight and effective dynamic upsampler. While impressive performance gains have been witnessed from recent kernel-based dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce much workload, mostly due to the time-consuming dynamic convolution and the additional sub-network used to generate dynamic kernels. Further, the need for high-res feature guidance of FADE and SAPA somehow limits their application scenarios. To address these concerns, we bypass dynamic convolution and formulate upsampling from the perspective of point sampling, which is more resource-efficient and can be easily implemented with the standard built-in function in PyTorch. We first showcase a naive design, and then demonstrate how to strengthen its upsampling behavior step by step towards our new upsampler, DySample. Compared with former kernel-based dynamic upsamplers, DySample requires no customized CUDA package and has much fewer parameters, FLOPs, GPU memory, and latency. Besides the light-weight characteristics, DySample outperforms other upsamplers across five dense prediction tasks, including semantic segmentation, object detection, instance segmentation, panoptic segmentation, and monocular depth estimation. Code is available at https://github.com/tiny-smart/dysample.
LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models
Authors: Junyi Zhang; Jiaqi Guo; Shizhao Sun; Jian-Guang Lou; Dongmei Zhang
Venue: ICCV 2023 (main)

Abstract: Creating graphic layouts is a fundamental step in graphic designs. In this work, we present a novel generative model named LayoutDiffusion for automatic layout generation. As layout is typically represented as a sequence of discrete tokens, LayoutDiffusion models layout generation as a discrete denoising diffusion process. It learns to reverse a mild forward process, in which layouts become increasingly chaotic with the growth of forward steps and layouts in the neighboring steps do not differ too much. Designing such a mild forward process is however very challenging as layout has both categorical attributes and ordinal attributes. To tackle the challenge, we summarize three critical factors for achieving a mild forward process for the layout, i.e., legality, coordinate proximity and type disruption. Based on the factors, we propose a block-wise transition matrix coupled with a piece-wise linear noise schedule. Experiments on RICO and PubLayNet datasets show that LayoutDiffusion outperforms state-of-the-art approaches significantly. Moreover, it enables two conditional layout generation tasks in a plug-and-play manner without re-training and achieves better performance than existing methods. Project page: https://layoutdiffusion.github.io.
Efficiently Robustify Pre-Trained Models
Authors: Nishant Jain; Harkirat Behl; Yogesh Singh Rawat; Vibhav Vineet
Venue: ICCV 2023 (main)

Abstract: A recent trend in deep learning algorithms has been towards training large scale models, having high parameter count and trained on big dataset. However, robustness of such large scale models towards real-world settings is still a less-explored topic. In this work, we first benchmark the performance of these models under different perturbations and datasets thereby representing real-world shifts, and highlight their degrading performance under these shifts. We then discuss on how complete model fine-tuning based existing robustification schemes might not be a scalable option given very large scale networks and can also lead them to forget some of the desired characterstics. Finally, we propose a simple and cost-effective method to solve this problem, inspired by knowledge transfer literature. It involves robustifying smaller models, at a lower computation cost, and then use them as teachers to tune a fraction of these large scale networks, reducing the overall computational overhead. We evaluate our proposed method under various vision perturbations including ImageNet-C,R,S,A datasets and also for transfer learning, zero-shot evaluation setups on different datasets. Benchmark results show that our method is able to induce robustness to these large scale models efficiently, requiring significantly lower time and also preserves the transfer learning, zero-shot properties of the original model which none of the existing methods are able to achieve.
Efficient Video Prediction via Sparsely Conditioned Flow Matching
Authors: Aram Davtyan; Sepehr Sameni; Paolo Favaro
Venue: ICCV 2023 (main)

Abstract: We introduce a novel generative model for video prediction based on latent flow matching, an efficient alternative to diffusion-based models. In contrast to prior work, we keep the high costs of modeling the past during training and inference at bay by conditioning only on a small random set of past frames at each integration step of the image generation process. Moreover, to enable the generation of high-resolution videos and to speed up the training, we work in the latent space of a pretrained VQGAN. Finally, we propose to approximate the initial condition of the flow ODE with the previous noisy frame. This allows to reduce the number of integration steps and hence, speed up the sampling at inference time. We call our model Random frame conditioned flow Integration for VidEo pRediction, or, in short, RIVER. We show that RIVER achieves superior or on par performance compared to prior work on common video prediction benchmarks, while requiring an order of magnitude fewer computational resources. Project website: https://araachie.github.io/river.
Surface Normal Clustering for Implicit Representation of Manhattan Scenes
Authors: Nikola Popovic; Danda Pani Paudel; Luc Van Gool
Venue: ICCV 2023 (main)

Abstract: Novel view synthesis and 3D modeling using implicit neural field representation are shown to be very effective for calibrated multi-view cameras. Such representations are known to benefit from additional geometric and semantic supervision. Most existing methods that exploit additional supervision require dense pixel-wise labels or localized scene priors. These methods cannot benefit from high-level vague scene priors provided in terms of scenes' descriptions. In this work, we aim to leverage the geometric prior of Manhattan scenes to improve the implicit neural radiance field representations. More precisely, we assume that only the knowledge of the indoor scene (under investigation) being Manhattan is known -- with no additional information whatsoever -- with an unknown Manhattan coordinate frame. Such high-level prior is used to self-supervise the surface normals derived explicitly in the implicit neural fields. Our modeling allows us to cluster the derived normals and exploit their orthogonality constraints for self-supervision. Our exhaustive experiments on datasets of diverse indoor scenes demonstrate the significant benefit of the proposed method over the established baselines. The source code will be available at https://github.com/nikola3794/normal-clustering-nerf.
Distracting Downpour: Adversarial Weather Attacks for Motion Estimation
Authors: Jenny Schmalfuss; Lukas Mehl; Andrés Bruhn
Venue: ICCV 2023 (main)

Abstract: Current adversarial attacks on motion estimation, or optical flow, optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, adverse weather conditions constitute a much more realistic threat scenario. Hence, in this work, we present a novel attack on motion estimation that exploits adversarially optimized particles to mimic weather effects like snowflakes, rain streaks or fog clouds. At the core of our attack framework is a differentiable particle rendering system that integrates particles (i) consistently over multiple time steps (ii) into the 3D space (iii) with a photo-realistic appearance. Through optimization, we obtain adversarial weather that significantly impacts the motion estimation. Surprisingly, methods that previously showed good robustness towards small per-pixel perturbations are particularly vulnerable to adversarial weather. At the same time, augmenting the training with non-optimized weather increases a method's robustness towards weather effects and improves generalizability at almost no additional cost. Our code is available at https://github.com/cv-stuttgart/DistractingDownpour.
Adaptive Similarity Bootstrapping for Self-Distillation Based Representation Learning
Authors: Tim Lebailly; Thomas Stegmüller; Behzad Bozorgtabar; Jean-Philippe Thiran; Tinne Tuytelaars
Venue: ICCV 2023 (main)

Abstract: Most self-supervised methods for representation learning leverage a cross-view consistency objective i.e., they maximize the representation similarity of a given image's augmented views. Recent work NNCLR goes beyond the cross-view paradigm and uses positive pairs from different images obtained via nearest neighbor bootstrapping in a contrastive setting. We empirically show that as opposed to the contrastive learning setting which relies on negative samples, incorporating nearest neighbor bootstrapping in a self-distillation scheme can lead to a performance drop or even collapse. We scrutinize the reason for this unexpected behavior and provide a solution. We propose to adaptively bootstrap neighbors based on the estimated quality of the latent space. We report consistent improvements compared to the naive bootstrapping approach and the original baselines. Our approach leads to performance improvements for various self-distillation method/backbone combinations and standard downstream tasks. Our code is publicly available at https://github.com/tileb1/AdaSim.
Generalized Differentiable RANSAC
Authors: Tong Wei; Yash Patel; Alexander Shekhovtsov; Jiri Matas; Daniel Barath
Venue: ICCV 2023 (main)

Abstract: We propose -RANSAC, a generalized differentiable RANSAC that allows learning the entire randomized robust estimation pipeline. The proposed approach enables the use of relaxation techniques for estimating the gradients in the sampling distribution, which are then propagated through a differentiable solver. The trainable quality function marginalizes over the scores from all the models estimated within -RANSAC to guide the network learning accurate and useful inlier probabilities or to train feature detection and matching networks. Our method directly maximizes the probability of drawing a good hypothesis, allowing us to learn better sampling distributions. We test -RANSAC on various real-world scenarios on fundamental and essential matrix estimation, and 3D point cloud registration, outdoors and indoors, with handcrafted and learning-based features. It is superior to the state-of-the-art in terms of accuracy while running at a similar speed to its less accurate alternatives. The code and trained models are available at https://github.com/weitong8591/differentiable_ransac.
Unfolding Framework with Prior of Convolution-Transformer Mixture and Uncertainty Estimation for Video Snapshot Compressive Imaging
Authors: Siming Zheng; Xin Yuan
Venue: ICCV 2023 (main)

Abstract: We consider the problem of video snapshot compressive imaging (SCI), where sequential high-speed frames are modulated by different masks and captured by a single measurement. The underlying principle of reconstructing multi-frame images from only one single measurement is to solve an ill-posed problem. By combining optimization algorithms and neural networks, deep unfolding networks (DUNs) score tremendous achievements in solving inverse problems. In this paper, our proposed model is under the DUN framework and we propose a 3D Convolution-Transformer Mixture (CTM) module with a 3D efficient and scalable attention model plugged in, which helps fully learn the correlation between temporal and spatial dimensions by virtue of 
 Transformer. To our best knowledge, this is the first time that Transformer is employed to video SCI reconstruction. Besides, to further investigate the high-frequency information during the reconstruction process which are neglected in previous studies, we introduce variance estimation characterizing the uncertainty on a pixel-by-pixel basis. Extensive experimental results demonstrate that our proposed method achieves state-of-the-art (SOTA) results. Code can be found on https://github.com/zsm1211/CTM-SCI.
Non-Semantics Suppressed Mask Learning for Unsupervised Video Semantic Compression
Authors: Yuan Tian; Guo Lu; Guangtao Zhai; Zhiyong Gao
Venue: ICCV 2023 (main)

Abstract: Most video compression methods aim to improve the decoded video visual quality, instead of particularly guaranteeing the semantic-completeness, which deteriorates downstream video analysis tasks, e.g., action recognition. In this paper, we focus on a novel unsupervised video semantic compression problem, where video semantics is compressed in a downstream task-agnostic manner. To tackle this problem, we first propose a Semantic-Mining-then-Compensation (SMC) framework to enhance the plain video codec with powerful semantic coding capability. Then, we optimize the framework with only unlabeled video data, by masking out a proportion of the compressed video and reconstructing the masked regions of the original video, which is inspired by recent masked image modeling (MIM) methods. Although the MIM scheme learns generalizable semantic features, its inner generative learning paradigm may also facilitate the coding framework memorizing non-semantic information with extra bitcosts. To suppress this deficiency, we explicitly decrease the non-semantic information entropy of the decoded video features, by formulating it as a parametrized Gaussian Mixture Model conditioned on the mined video semantics. Comprehensive experimental results demonstrate the proposed approach shows remarkable superiority over previous traditional, learnable and perceptual-quality-oriented video codecs, on three video analysis tasks and seven datasets.
ResQ: Residual Quantization for Video Perception
Authors: Davide Abati; Haitam Ben Yahia; Markus Nagel; Amirhossein Habibian
Venue: ICCV 2023 (main)

Abstract: This paper accelerates video perception, such as semantic segmentation and human pose estimation, by levering cross-frame redundancies. Unlike the existing approaches, which avoid redundant computations by warping the past features using optical-flow or by performing sparse convolutions on frame differences, we approach the problem from a new perspective: low-bit quantization. We observe that residuals, as the difference in network activations between two neighboring frames, exhibit properties that make them highly quantizable. Based on this observation, we propose a novel quantization scheme for video networks coined as Residual Quantization. ResQ extends the standard, frame-by-frame, quantization scheme by incorporating temporal dependencies that lead to better performance in terms of accuracy vs. bit-width. Furthermore, we extend our model to dynamically adjust the bit-width proportional to the amount of changes in the video. We demonstrate the superiority of our model, against the standard quantization and existing efficient video perception models, using various architectures on semantic segmentation and human pose estimation benchmarks.
Inverse Compositional Learning for Weakly-supervised Relation Grounding
Authors: Huan Li; Ping Wei; Zeyu Ma; Nanning Zheng
Venue: ICCV 2023 (main)

Abstract: Video relation grounding (VRG) is a significant and challenging problem in the domains of cross-modal learning and video understanding. In this study, we introduce a novel approach called inverse compositional learning (ICL) for weakly-supervised video relation grounding. Our approach represents relations at both the holistic and partial levels, formulating VRG as a joint optimization problem that encompasses reasoning at both levels.
 For holistic-level reasoning, we propose an inverse attention mechanism and a compositional encoder to generate compositional relevance features. Additionally, we introduce an inverse loss to evaluate and learn the relevance between visual features and relation features.
 At the partial-level reasoning, we introduce a grounding by classification scheme. By leveraging the learned holistic-level features and partial-level features, we train the entire model in an end-to-end manner.
 We conduct evaluations on two challenging datasets and demonstrate the substantial superiority of our proposed method over state-of-the-art methods. Extensive ablation studies confirm the effectiveness of our approach.
XMem++: Production-level Video Segmentation From Few Annotated Frames
Authors: Maksym Bekuzarov; Ariana Bermudez; Joon-Young Lee; Hao Li
Venue: ICCV 2023 (main)

Abstract: Despite advancements in user-guided video segmentation, extracting complex objects consistently for highly complex scenes is still a labor-intensive task, especially for production. It is not uncommon that a majority of frames need to be annotated. We introduce a novel semi-supervised video object segmentation (SSVOS) model, XMem++, that improves existing memory-based models, with a permanent memory module. Most existing methods focus on single frame annotations, while our approach can effectively handle multiple user-selected frames with varying appearances of the same object or region. Our method can extract highly consistent results while keeping the required number of frame annotations low. We further introduce an iterative and attention-based frame suggestion mechanism, which computes the next best frame for annotation. Our method is real-time and does not require retraining after each user input. We also introduce a new dataset, PUMaVOS, which covers new challenging use cases not found in previous benchmarks. We demonstrate SOTA performance on challenging (partial and multi-class) segmentation scenarios as well as long videos, while ensuring significantly fewer frame annotations than any existing method. Project page: https://max810.github.io/xmem2-project-page/
MHCN: A Hyperbolic Neural Network Model for Multi-view Hierarchical Clustering
Authors: Fangfei Lin; Bing Bai; Yiwen Guo; Hao Chen; Yazhou Ren; Zenglin Xu
Venue: ICCV 2023 (main)

Abstract: Multi-view hierarchical clustering (MCHC) plays a pivotal role in comprehending the structures within multi-view data, which hinges on the skillful interaction between hierarchical feature learning and comprehensive representation learning across multiple views. However, existing methods often overlook this interplay due to the simple heuristic agglomerative strategies or the decoupling of multi-view representation learning and hierarchical modeling, thus leading to insufficient representation learning. To address these issues, this paper proposes a novel Multi-view Hierarchical Clustering Network (MHCN) model by performing simultaneous multi-view learning and hierarchy modeling. Specifically, to uncover efficient tree-like structures among all views, we derive multiple hyperbolic autoencoders with latent space mapped onto the Poincare ball. Then, the corresponding hyperbolic embeddings are further regularized to achieve the multi-view representation learning principles for both view-common and view-private information, and to ensure hyperbolic uniformity with a well-balanced hierarchy for better interpretability. Extensive experiments on real-world and synthetic multi-view datasets have demonstrated that our method can achieve state-of-the-art hierarchical clustering performance, and empower the clustering results with good interpretability.
End-to-End Diffusion Latent Optimization Improves Classifier Guidance
Authors: Bram Wallace; Akash Gokul; Stefano Ermon; Nikhil Naik
Venue: ICCV 2023 (main)

Abstract: Classifier guidance---using the gradients of an image classifier to steer the generations of a diffusion model---has the potential to dramatically expand the creative control over image generation and editing. However, currently classifier guidance requires either training new noise-aware models to obtain accurate gradients or using a one-step denoising approximation of the final generation, which leads to misaligned gradients and sub-optimal control. We highlight this approximation's shortcomings and propose a novel guidance method: Direct Optimization of Diffusion Latents (DOODL), which enables plug-and-play guidance by optimizing diffusion latents w.r.t. the gradients of a pre-trained classifier on the true generated pixels, using an invertible diffusion process to achieve memory-efficient backpropagation. Showcasing the potential of more precise guidance, DOODL outperforms one-step classifier guidance on computational and human evaluation metrics across different forms of guidance: using CLIP guidance to improve generations of complex prompts from DrawBench, using fine-grained visual classifiers to expand the vocabulary of Stable Diffusion, enabling image-conditioned generation with a CLIP visual encoder, and improving image aesthetics using an aesthetic scoring network.
FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction
Authors: Noah Stier; Anurag Ranjan; Alex Colburn; Yajie Yan; Liang Yang; Fangchang Ma; Baptiste Angles
Venue: ICCV 2023 (main)

Abstract: Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency. However, the reconstructed geometry, typically represented as a 3D truncated signed distance function (TSDF), is often coarse without fine geometric details. To address this problem, we propose three effective solutions for improving the fidelity of inference-based 3D reconstructions. We first present a resolution-agnostic TSDF supervision strategy to provide the network with a more accurate learning signal during training, avoiding the pitfalls of TSDF interpolation seen in previous work. We then introduce a depth guidance strategy using multi-view depth estimates to enhance the scene representation and recover more accurate surfaces. Finally, we develop a novel architecture for the final layers of the network, conditioning the output TSDF prediction on high-resolution image features in addition to coarse voxel features, enabling sharper reconstruction of fine details. Our method, FineRecon, produces smooth and highly accurate reconstructions, showing significant improvements across multiple depth and 3D reconstruction metrics.