Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks

Deep learning models have achieved excellent recognition results on large-scale video benchmarks. However, they perform poorly when applied to videos with rare scenes or objects, primarily due to the bias of existing video datasets. We tackle this problem from two different angles: algorithm and dataset. From the perspective of algorithms, we propose Spatial-aware Multi-Aspect Debiasing (SMAD), which incorporates both explicit debiasing with multi-aspect adversarial training and implicit debiasing with the spatial actionness reweighting module, to learn a more generic representation invariant to non-action aspects. To neutralize the intrinsic dataset bias, we propose OmniDebias to leverage web data for joint training selectively, which can achieve higher performance with far fewer web data. To verify the effectiveness, we establish evaluation protocols and perform extensive experiments on both re-distributed splits of existing datasets and a new evaluation dataset focusing on the action with rare scenes. We also show that the debiased representation can generalize better when transferred to other datasets and tasks.Comment: ECCVW 202

Laser-driven lepton polarization in the quantum radiation-dominated reflection regime

Generation of ultrarelativistic polarized leptons during interaction of an ultrarelativistic electron beam with a counterpropagating ultraintense laser pulse is investigated in the quantum radiation-dominated domain. While the symmetry of the laser field tends to average the radiative polarization of leptons to zero, we demonstrate the feasibility of sizable radiative polarization through breaking the symmetry of the process in the reflection regime. After the reflection, the off-axis particles escape the tightly focused beam with polarization correlated to the emission angle, while the particles at the beam center are more likely to be captured in the laser field with unmatched polarization and kinetic motion. Meanwhile, polarization along the electric field emerges due to the spin rotation in the transverse plane via precession. In this way, the combined effects of radiative polarization, spin precession and the laser field focusing are shaping the angle-dependent polarization for outgoing leptons. Our spin-resolved Monte Carlo simulations demonstrate an angle-dependent polarization degree up to $\sim20\%$ for both electrons and positrons, with a yield of one pair per seed electron. It provides a new approach for producing polarized high density electron and positron jets at ultraintense laser facilities

Interferences effects in polarized nonlinear Breit-Wheeler process

The creation of polarized electron-positron pairs by the nonlinear Breit-Wheeler process in short laser pulses is investigated using the Baier-Katkov semiclassical method beyond local-constant-field approximation (LCFA), which allows for identifying the interferences effects in the positron polarization. When the laser intensity is in the intermediate %multiphoton regime, the interferences of pair production in different formation lengths induce an enhancement of pair production probability for spin-down positrons, which significantly affects the polarization of created positrons. The polarization features are distinct from that obtained with LCFA, revealing the invalidity of LCFA in this regime. Meanwhile, the angular distribution for different spin states varies, resulting in an angular-dependent polarization of positrons. The average polarization of positrons at beam center is highly sensitive to the laser's carrier-envelope phase (CEP), which provides a potential alternative way of determining the CEP of strong lasers. The verification of the observed interference phenomenon is possible for the upcoming experiments

PointGPT: Auto-regressively Generative Pre-training from Point Clouds

Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks. Inspired by the advancements of the GPT, we present PointGPT, a novel approach that extends the concept of GPT to point clouds, addressing the challenges associated with disorder properties, low information density, and task gaps. Specifically, a point cloud auto-regressive generation task is proposed to pre-train transformer models. Our method partitions the input point cloud into multiple point patches and arranges them in an ordered sequence based on their spatial proximity. Then, an extractor-generator based transformer decoder, with a dual masking strategy, learns latent representations conditioned on the preceding point patches, aiming to predict the next one in an auto-regressive manner. Our scalable approach allows for learning high-capacity models that generalize well, achieving state-of-the-art performance on various downstream tasks. In particular, our approach achieves classification accuracies of 94.9% on the ModelNet40 dataset and 93.4% on the ScanObjectNN dataset, outperforming all other transformer models. Furthermore, our method also attains new state-of-the-art accuracies on all four few-shot learning benchmarks.Comment: 9 pages, 2 figure

rac-Ethyl 2-amino-3-hy­droxy-3-[4-(methyl­sulfon­yl)phen­yl]propano­ate

In the title compound, C12H17NO5S, the orientations of the 2-ethyl-2-amino-3-hy­droxy­propano­ate group and the 4-methyl­sulfonyl moiety towards the aromatic ring are periplanar and (−)-anti­clinal, respectively. In the crystal packing, the dominant inter­action is O—H⋯N hydrogen bonding, which generates a chain running along [100]. N—H⋯O and C—H⋯O interactions are also observed

Spatio-Temporal Change of LakeWater Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015

Urban lakes play an important role in urban development and environmental protection for the Wuhan urban agglomeration. Under the impacts of urbanization and climate change, understanding urban lake-water extent dynamics is significant. However, few studies on the lake-water extent changes for the Wuhan urban agglomeration exist. This research employed 1375 seasonally continuous Landsat TM/ETM+/OLI data scenes to evaluate the lake-water extent changes from 1987 to 2015. The random forest model was used to extract water bodies based on eleven feature variables, including six remote-sensing spectral bands and five spectral indices. An accuracy assessment yielded a mean classification accuracy of 93.11%, with a standard deviation of 2.26%. The calculated results revealed the following: (1) The average maximum lake-water area of the Wuhan urban agglomeration was 2262.17 km2 from 1987 to 2002, and it decreased to 2020.78 km2 from 2005 to 2015, with a loss of 241.39 km2 (10.67%). (2) The lake-water areas of loss of Wuhan, Huanggang, Xianning, and Xiaogan cities, were 114.83 km2, 44.40 km2, 45.39 km2, and 31.18 km2, respectively, with percentages of loss of 14.30%, 11.83%, 13.16%, and 23.05%, respectively. (3) The lake-water areas in the Wuhan urban agglomeration were 226.29 km2, 322.71 km2, 460.35 km2, 400.79 km2, 535.51 km2, and 635.42 km2 under water inundation frequencies of 5%–10%, 10%–20%, 20%–40%, 40%–60%, 60%–80%, and 80%–100%, respectively. The Wuhan urban agglomeration was approved as the pilot area for national comprehensive reform, for promoting resource-saving and environmentally friendly developments. This study could be used as guidance for lake protection and water resource management