Datasets:
mteb
/

image
imagewidth (px)
44
1.64k
file_name_index
stringlengths
26
33
text
stringlengths
16
2.81k
class
stringclasses
5 values
super_class
stringclasses
2 values
sub_class
stringclasses
5 values
split
stringclasses
1 value
$2310.12978v1-Figure5-1.png
Figure 5: Comparison with existing metrics on Motion-X. Existing evaluation metrics (Guo et al., 2022) are illustrated in red, and ours are in green. The B = 32 and B = 256 settings for retrieval are denoted as “ •−−” and “ ▲−−” respectively.
fig_result
fig
result_fig
train
$2310.12978v1-Figure6-1.png
Figure 6: Sentences similarity comparison between the sBERT and MPNet .
fig_result
fig
result_fig
train
$2310.12978v1-Figure7-1.png
Figure 7: Visualization of MotionCLIP generated results. The first frame and the final frame of motions are shown in the figure.
fig_result
fig
result_fig
train
$2310.12978v1-Figure8-1.png
Figure 8: Qualitative comparison with T2M-GPT.
fig_result
fig
result_fig
train
$2310.12978v1-Figure9-1.png
Figure 9: Visualization of the whole-body motions generated by HumanTOMATO (1).
fig_result
fig
result_fig
train
$2310.12978v1-Table1-1.png
Table 1: Main results of motion generation on Motion-X dataset.
table_result
table
result_tab
train
$2310.12978v1-Table10-1.png
Table 10: Different vector quantization methods on Motion-X.
table_result
table
result_tab
train
$2310.12978v1-Table11-1.png
Table 11: Different vector quantization methods on GRAB.
table_result
table
result_tab
train
$2310.12978v1-Table12-1.png
Table 12: Different vector quantization methods on HumanML3D.
table_result
table
result_tab
train
$2310.12978v1-Table13-1.png
Table 13: The ablation on how can H2VQ help the whole-body motion generation on T2M-GPT.
table_result
table
result_tab
train
$2310.12978v1-Table14-1.png
Table 14: Abaltion on how pre-trained text-motion aligned model helps to generate the text-aligned body-only motion (on HumanML3D).
table_result
table
result_tab
train
$2310.12978v1-Table15-1.png
Table 15: R-Precision of GT motions and texts on the Motion-X dataset.
table_parameter
table
parameter
train
$2310.12978v1-Table16-1.png
Table 16: R-Precision of GT motions and texts on the HumanML3D dataset.
table_result
table
result_tab
train
$2310.12978v1-Table2-1.png
Table 2: Comparison of the motion reconstruction errors (MPJPE in mm) of different quantization methods on Motion-X, GRAB, and HumanML3D. Our H2VQ shows significant improvements.
table_result
table
result_tab
train
$2310.12978v1-Table3-1.png
Table 3: Abaltion on a pre-trained text-motion-aligned model for motion generation on Motion-X. Both TMR embedding and text-motion alignment supervision help generate text-aligned motions.
table_result
table
result_tab
train
$2310.12978v1-Table4-1.png
Table 4: Ablation study of different motion representations on the Humanml3D dataset.
table_result
table
result_tab
train
$2310.12978v1-Table5-1.png
Table 5: Recall@K (T2M and M2T) of GT motions and texts on the Motion-X dataset.
table_result
table
result_tab
train
$2310.12978v1-Table6-1.png
Table 6: Recall@K (T2M and M2T) of GT motions and texts on the HumanML3D dataset.
table_result
table
result_tab
train
$2310.12978v1-Table7-1.png
Table 7: Quantitative Comparison on the Motion-X dataset (FID, TMR-R-Precision(256), and RPrecision(32) metrics).
table_result
table
result_tab
train
$2310.12978v1-Table8-1.png
Table 8: Quantitative Comparison on the Motion-X dataset (TMR-Matching Score, Matching Score, and MModality metrics).
table_result
table
result_tab
train
$2310.12978v1-Table9-1.png
Table 9: Facial motion generation results on Motion-X dataset.
table_result
table
result_tab
train
$2310.12980v1-Figure1-1.png
Figure 1: Standard SymTFT setup. Topological symmetry operators (green, U) link heavy defect operators (grey, D) in the (D + 1)-dimensional slab. The defects stretch from the topological boundary (blue, Btop) to the physical boundary (red, Bphys).
fig_result
fig
result_fig
train
$2310.12980v1-Figure10-1.png
Figure 10: Sketch of double throat internal geometry with two sets of localized degrees of freedom (red). Horizontal slices of constant radius are initially disjoint and then combine resulting in a connected asymptotic boundary.
fig_result
fig
result_fig
train
$2310.12980v1-Figure12-1.png
Figure 12: (i): String / brane running between two local models. Such an object admits a partition into three pieces, two of which are contained in a local model, and one which connect the two via the bulk of X. In the QFT spacetime the result is a pair of defects E(12). (ii): Deforming the configuration into a spaceti...
fig_result
fig
result_fig
train
$2310.12980v1-Figure15-1.png
Figure 15: Pair of pants describing the uplift of a SymTree junction.
fig_illustration
fig
illustration
train
$2310.12980v1-Figure17-1.png
Figure 17: We sketch the partially smoothed geometry X ′ as a fibration over a Y-shaped base. This picture presents a horizontal slice of figures similar to figure 2.
fig_illustration
fig
illustration
train
$2310.12980v1-Figure18-1.png
Figure 18: Depiction of line operators after adjoint Higgsing of 4D N = 4 SYM with gauge group SU(N) = SU(N1 +N2) to S(U(N1)× U(N2)). A non-genuine ’t Hooft line (0, 1) and dyonic line (1,−1) of two sectors of the multi-sector theory can fuse at the junction with an electric Wilson line (1, 0). These are realized by (p...
fig_illustration
fig
illustration
train
$2310.12980v1-Figure2-1.png
Figure 2: We depict a trivalent junction J of symmetry TFTs. The junctions supports the D-dimensional theory GJ ⊗ TFTJ . Color conventions: Junctions are purple.
fig_result
fig
result_fig
train
$2310.12980v1-Figure20-1.png
Figure 20: SymTree with topological boundary condition for SU(N1 + N2 + · · · + NK) global form, and junction and tree Υ describing the Higgsing SU(N1 + N2 + · · · + NK) ⊃ S(U(N1)×U(N2)×· · ·×U(NK)). Node theories are 4D SYM with indicated gauge algebra.
fig_illustration
fig
illustration
train
$2310.12980v1-Figure22-1.png
Figure 22: SymTrees for brane probes of singularities. (i): stack of K D3-branes probing an C2/ZN singularity. In the extra dimensional geometry the D3-branes and the singularity are separated. (ii): cross-section of this configuration. (iii): we contract a branch / center the filtration on the D3-brane stack. Moving r...
fig_result
fig
result_fig
train
$2310.12980v1-Figure23-1.png
Figure 23: We depict the base of the local K3 π : X → B = C. The preimage under π of a base filtration FB gives a filtration FX . The critical slice projects to a figure eight. The filtration for the base lifts to the full geometry, as determined by the SL(2,Z) monodromy matrices M1,M2.
fig_illustration
fig
illustration
train
$2310.12980v1-Figure24-1.png
Figure 24: SymTree derived from the filtration FX of the orbifold X = (T 2 × C2)/Z3. We give the topological models for the radial shells at the legs and junctions. The junction valency is 4.
fig_illustration
fig
illustration
train
$2310.12980v1-Figure25-1.png
Figure 25: On the left, we depict the collection of separated 4D N = 4 suNi SYM theories that we label by Ti where Ni = M + ϵi and a sum of local operators in each of these sectors. The wedges represent the AdS5 × S5 dual spacetimes for each sector. On the right, we illustrate the averaged operator O in the ensemble av...
fig_result
fig
result_fig
train
$2310.12980v1-Figure26-1.png
Figure 26: In the SymTFT frame work changing from SU(2) to SU(2)/Z2 = SO(3)+ gauge theory can be formulated as a change in topological boundary condition. Equivalently, we can realize this as an insertion of the Fourier operator P . This operator can the be collided with the physical boundary condition giving a notion ...
fig_illustration
fig
illustration
train
$2310.12980v1-Figure27-1.png
Figure 27: SymTree with two topological boundary conditions realizing the polarization change SU(2) to SO(3)+.
fig_illustration
fig
illustration
train
$2310.12980v1-Figure28-1.png
Figure 28: Sketch of the Hopf-fibration S3/ZNi → S2 and the bounding chain Σi within it. The Euler class NivolS2 of this circle fibration characterizes the obstruction to the existence of a section. Consider attempting to construct such a section, as depicted, by starting at the south pole of S2 and growing a disk insi...
fig_illustration
fig
illustration
train
$2310.12980v1-Figure29-1.png
Figure 29: Depict the covering of the large radius Mayer-Vietoris sequence with respect to the M-theory circle fibration. In R3, the IIA dual to X ′, we have two spheres S2 i touching along a two-disk D2 marked blue. The preimage of this disk and its complement are the large radius covering. The boundary of the preimag...
fig_illustration
fig
illustration
train
$2310.12980v1-Figure3-1.png
Figure 3: Junctions can be assembled into trees (i). The tree Υ can be visualized as a horizontal cross-section. Junctions can have arbitrary valency (ii).
fig_result
fig
result_fig
train
$2310.12980v1-Figure30-1.png
Figure 30: Sketch of the geometry X◦.
fig_illustration
fig
illustration
train
$2310.12980v1-Figure4-1.png
Figure 4: Depiction of retracting a SymTree to produce the corresponding SymTFT Sfull for the multi-sector QFT with topological couplings between the different sectors. In terms of the SymTree, this amounts to pulling in the different branches into the physical boundaries.
fig_result
fig
result_fig
train
$2310.12980v1-Figure6-1.png
Figure 6: We depict two SymTrees related by an associator move Υ ↔ Υ′. There is an anomaly whenever fusion of trivalent junctions produces distinct tetravalent junctions. Generalizations are immediate. This can be accompanied by a non-zero obstruction class / anomaly αΥ,Υ′ , although the examples considered in this pap...
fig_result
fig
result_fig
train
$2310.12980v1-Figure7-1.png
Figure 7: Sketch of possible defect configurations for a trivalent junction of symmetry TFTs. The red and purple dots denote the spacetime defects, the brown line marks the defect within the SymTree. The purple junction defect is said to dress the red boundary defects.
fig_illustration
fig
illustration
train
$2310.12980v1-Figure8-1.png
Figure 8: We sketch a deformation of the defect configuration depicted in (i). In (i) the purple and red defect are coincident in spacetime, as shown on the lefthand side in (iii). In (ii) we displace these along a spacetime direction x⊥ and deforming the resulting defect configuration into a horizontal and vertical pi...
fig_result
fig
result_fig
train
$2310.12980v1-Figure9-1.png
Figure 9: Sketch of different topological symmetry operator configurations for a trivalent junction of symmetry TFTs. We consider the initial configuration depicted in (i). Deforming it (partially) across/into the junction gives various equivalent configurations of topological operators presented in subfigures (ii) - (...
fig_result
fig
result_fig
train
$2310.12981v1-Figure1-1.png
FIG. 1. A rectangular lattice of qubits used for a pairwise measurement-based realization of the surface code. Data qubits are shown as open dots and auxiliary qubits are shown as solid dots. The blue and red squares will correspond to Z-type and X-type plaquettes (4-gons), respectively. Each plaquette exclusively util...
fig_illustration
fig
illustration
train
$2310.12981v1-Figure10-1.png
FIG. 10. The possible reductions of Z-type n-gons for step 1, where dead data qubits (denoted by starbursts) are removed from the code operation. (Reductions related to these by rotations and reflections are not shown separately.) The reductions for X-type n-gons may be obtained from these by 90 degree rotations. Even ...
fig_result
fig
result_fig
train
$2310.12981v1-Figure11-1.png
FIG. 11. The possible splittings of Z-type n-gons for step 2, where dead auxiliary qubits (denoted by starbursts) are removed from the code operation. (Splittings related to these by rotations and reflections are not shown separately.) The splittings for X-type n-gons may be obtained from these by 90 degree rotations. ...
fig_result
fig
result_fig
train
$2310.12981v1-Figure12-1.png
FIG. 12. The possible splittings of Z-type n-gons for step 3, where dead connections (denoted by starbursts) are removed from the code operation. (Splittings related to these by rotations and reflections are not shown separately.) The splittings for X-type n-gons may be obtained from these by 90 degree rotations. In so...
fig_result
fig
result_fig
train
$2310.12981v1-Figure13-1.png
FIG. 13. Configuration for one dead data qubit.
fig_result
fig
result_fig
train
$2310.12981v1-Figure18-1.png
FIG. 18. Measurement loops in Majorana hardware corresponding to the measurements needed for our code. MZMs (red circles) are located at the end points of topological wires (gray lines). Two topological wires connected by a trivial superconducting spine (dark gray) form a tetron. Tetrons can be connected to each other ...
fig_illustration
fig
illustration
train
$2310.12981v1-Figure19-1.png
FIG. 19. Interference loops of measurements for the implementation of our measurement-based surface code realization in Majorana hardware with double-rail semiconductor layouts. This layout avoids loop conflicts, allowing for the (minimal) period 4 operation schedule.
fig_illustration
fig
illustration
train
$2310.12981v1-Figure2-1.png
FIG. 2. The MZZZZ circuit for measuring ZZZZ on four data qubits using three auxiliary qubits. The data qubits are labeled 1-4 in the order that they are addressed in this circuit. The auxiliary qubits are labeled A-C. The measurement schedule shown here is useful for repeatedly applying the measurement circuit with a ...
fig_result
fig
result_fig
train
$2310.12981v1-Figure20-1.png
FIG. 20. The implementation of our measurement-based surface code realization in Majorana hardware with single-rail semiconductor layouts requires resolution of loop conflicts in two steps. A resolution with period 5 operation schedule is shown here.
fig_illustration
fig
illustration
train
$2310.12981v1-Figure21-1.png
FIG. 21. (left) Performance results for our pairwise measurement-based surface code realization described in Sec. II on a rotated surface code patch using boundary conditions that make the hook errors benign (see Sec. IV). (right) Performance results for the 4.8.8 Floquet code on a planar patch with rectangular boundar...
fig_result
fig
result_fig
train
$2310.12981v1-Figure22-1.png
FIG. 22. Resource requirements to reach target logical error rates of ptargetlogical = 10−8, 10−12, and 10−15, comparing our realization of the surface code and the 4.8.8 planar Floquet code. The respective target logical error rates correspond to columns from left to right, and the resource quantities being considered...
fig_result
fig
result_fig
train
$2310.12981v1-Figure23-1.png
FIG. 23. (left) Performance results for the single-rail variant of our pairwise measurement-based surface code realization described in Sec. VI on a rotated surface code patch using boundary conditions that make the hook errors benign. (right) Performance results for the single-rail variant of 4.8.8 Floquet code on a p...
fig_result
fig
result_fig
train
$2310.12981v1-Figure24-1.png
FIG. 24. Resource requirements to reach target logical error rates of ptargetlogical = 10−8, 10−12, and 10−15, comparing single-rail variants of our realization of the surface code and the 4.8.8 planar Floquet code.
fig_result
fig
result_fig
train
$2310.12981v1-Figure25-1.png
FIG. 25. (left) Performance results for our pairwise measurement-based surface code realization on a rotated surface code patch using boundary conditions that make the hook errors malignant (see Sec. IV). We note that the d = 3 curve is not expected to intersect with the other curves at (or near) threshold, as the code...
fig_result
fig
result_fig
train
$2310.12981v1-Figure26-1.png
FIG. 26. Performance results for the hook-preventing variant of our pairwise measurement-based surface code realization described in Sec. III on a torus for noise model without (left) and with (right) idle noise. The full code distance is recovered and performance is improved with respect to the code using the original...
fig_result
fig
result_fig
train
$2310.12981v1-Figure27-1.png
FIG. 27. The stabilizer measurement circuits can be interleaved and run on a synchronous schedule, i.e. (0Z, 0X), (1Z, 1X), . . ., by addressing the data qubits in a different order than the pipelined measurement schedule presented in Sec. II. This interleaved measurement schedule has significant disadvantages compared...
fig_result
fig
result_fig
train
$2310.12981v1-Figure28-1.png
FIG. 28. The MZZZZ circuit from Ref. 9 for the pentagonal tiling realization of the surface code.
fig_illustration
fig
illustration
train
$2310.12981v1-Figure29-1.png
FIG. 29. A modification of the MZZZZ circuit from Ref. 9 that prevents the problematic hook errors associated with readout error at the MXAXB measurement. Incorporating this and a similar modification of the MXXXX circuit reduces the problem of bidirectional hook errors to unidirectional hook errors in the pentagonal t...
fig_result
fig
result_fig
train
$2310.12981v1-Figure30-1.png
FIG. 30. The possible splittings of Z-type n-gons for step 2, where dead auxiliary qubits are removed from the code operation for the measurement-based pentagonal tiling realization of the surface code. (Splittings related to these by rotations and reflections are not shown separately.) The splittings for X-type n-gons...
fig_result
fig
result_fig
train
$2310.12981v1-Figure31-1.png
FIG. 31. The possible splittings of Z-type n-gons for step 3, where dead connections are removed from the code operation for the measurement-based pentagonal tiling realization of the surface code. (Splittings related to these by rotations and reflections are not shown separately.) The splittings for X-type n-gons may ...
fig_result
fig
result_fig
train
$2310.12981v1-Figure32-1.png
FIG. 32. The splittings of Z-type or X-type plaquettes for steps 2 and 3, where dead auxiliary qubits and connections are removed from the code operation for the CNOT gate-based realization of the surface code. All possible n-gons splittings are not shown because they all follow the same pattern: for step 2, a dead aux...
fig_result
fig
result_fig
train
$2310.12981v1-Figure4-1.png
FIG. 4. The MZZZZ and MXXXX measurement circuit steps for Z-type and X-type plaquettes. Labels indicate the operators measured on the respective qubits in the given step, with connecting lines indicating pairwise measurement. These circuits can be applied in parallel with a relative shift in their operation schedules. ...
fig_result
fig
result_fig
train
$2310.12981v1-Figure5-1.png
FIG. 5. An example of a hook error in the MZZZZ measurement circuit is given by a Z error on auxiliary qubit B that occurs between steps 2 and 3, as shown in the circuit on the left. This error is equivalent to a ZZ error on data qubits 1 and 3, as shown in the circuit on the right. (Errors are marked in yellow.)
fig_result
fig
result_fig
train
$2310.12981v1-Figure6-1.png
FIG. 6. The modified circuit for MZZZZ in which there are no hook errors. Steps 3, 4, and 7 shown here are the additional steps that have been inserted into the original circuit from Fig. 2. (The MZB auxiliary qubit measurement only needs to occur twice per cycle, but we have shown it occurring three times.) Here, we o...
fig_result
fig
result_fig
train
$2310.12981v1-Figure7-1.png
FIG. 7. The hook-preventing MZZZZ and MXXXX measurement circuit steps for Z-type and X-type plaquettes. These can be pipelined as in Eq. (4) to achieve a 7 step period.
fig_architecture
fig
architecture
train
$2310.12981v1-Figure8-1.png
FIG. 8. Z-type (left) and X-type (right) measurement circuits for 1-gons, 2-gons, and 3-gons.
fig_result
fig
result_fig
train
$2310.12981v1-Figure9-1.png
FIG. 9. Different boundary conditions for a square patch of surface code, all shown for fault distance df = 3. (a) The rotated surface code with a good choice of boundary conditions aligns the logical strings perpendicular to the corresponding hook errors of the same type, so df = d. The relation between number of phys...
fig_result
fig
result_fig
train
$2310.12981v1-TableI-1.png
TABLE I. Comparison between key properties of pairwise measurement-based codes, including our realization of the surface code (denoted “3aux”), the double ancilla and windmill realizations of Ref. 8, the pentagonal tiling realization of Ref. 9, and the Floquet code on the 4.8.8 and honeycomb lattices [5, 10–12]. The te...
table_result
table
result_tab
train
$2310.12981v1-TableII-1.png
TABLE II. Fault distance df required to reach a target logical error rate ptargetlogical of 10 −8, 10−12 and 10−15, respectively, comparing our realization of the surface code and the 4.8.8 planar Floquet code. The determination of the required df is the same as in Fig. 22.
fig_result
fig
result_fig
train
$2310.12981v1-TableIII-1.png
TABLE III. Fault distance df required to reach a target logical error rate ptargetlogical of 10 −8, 10−12 and 10−15, respectively, comparing single-rail variants of our realization of the surface code and the 4.8.8 planar Floquet code.
table_result
table
result_tab
train
$2310.12982v1-Figure2-1.png
Figure 2. Overview of Cutie. We store pixel memory F and object memory S representations from past segmented (memory) frames. Pixel memory is retrieved for the query frame as pixel readout R0, which bidirectionally interacts with object queries X and object memory S in the object transformer. The L object transformer b...
fig_result
fig
result_fig
train
$2310.12982v1-Figure3-1.png
Figure 3. Visualization of cross-attention weights (rows of AL) in the object transformer. The middle cat is the target object. Top: without foreground-background masking – some queries mix semantics from foreground and background (framed in red). Bottom: with foreground-background masking. The leftmost three are foreg...
fig_result
fig
result_fig
train
$2310.12982v1-Table1-1.png
Table 1. Quantitative comparison on video object segmentation benchmarks. All algorithms with available code are re-run on our hardware for a fair comparison. We could not obtain the code for [67, 68, 75] at the time of writing, and thus they cannot be reproduced on datasets that they do not report results on. For a fa...
table_result
table
result_tab
train
$2310.12982v1-Table2-1.png
Table 2. Comparisons of performance on long videos on the BURST dataset [3]. Mem.: maximum GPU memory usage. FIFO: first-in-first-out memory bank; INF: unbounded memory; LT: longterm memory [9]. DeAOT [66] is not compatible with long-term memory. All methods are trained with the MOSE [12] dataset.
table_result
table
result_tab
train
$2310.12982v1-Table5-1.png
Table 5. Ablations on positional embeddings in the object transformer.
table_result
table
result_tab
train